Abstract— Embedded systems often implement behavior for common application domains, such as the control systems domain or the signal processing domain. An increasingly common domain is pattern recognition, such as determining which kind of fruit is passing on a conveyor belt. Embedded system students and designers typically are not experts in such domains and could benefit from simpler platforms to help them gain insight into the problem of pattern recognition and help them develop such algorithms rapidly. Generic frameworks, such as PID (proportional- integral-derivative) for control, or FIR (finite impulse response) for signal filtering, empower non-expert embedded system designers to quickly build robust systems in those domains. We introduce a generic pattern recognition framework, useful for education as well as for various real systems. The framework divides the task into three phases: feature extraction, classification, and actuation (FCA). We provide template code (in C) that a student or designer can modify for their own specific application. We show that the FCA pattern recognition framework can readily be adapted for various pattern recognition applications, like recognizing box sizes, fruit type, mug type, or detecting vending machine vandalism, requiring only 2-3 hours to create each new application. We report results of a randomized controlled study with 66 students in an intermediate embedded systems class, showing that the framework could be learned in tens of minutes and yielding applications with higher recognition accuracy of 71% for pattern recognition vs. 57% without the framework (p-value=0.03).
Index Terms—Embedded Systems, Pattern Recognition, Teaching framework, Computer Science Education
While many embedded system applications are unique, others implement behavior of well-known domains, illustrated in Figure 1.
For example, heating an oven to a target temperature, or propelling a small robotic car at a target speed, both represent instances of control systems. A control system  strives to control a physical system to match an actual physical feature (temperature, speed) to a target value. A rich discipline of control theory exists, with various mathematical techniques for modeling and controlling physical systems aiming to best match the target (minimizing overshoot, oscillations, and steady-state error) and ensuring stability. However, such theory requires extensive knowledge and training, which many embedded systems students lack early in their careers. Thus, a generic framework for control has been developed, known as PID (proportional-integral- derivative) control  to empower non-experts and students to build good quality control systems. The simple concept of PID, plus template code, and techniques for tuning P, I, and D variables in such code, quickly yield systems of sufficient quality for various applications. Without domain experts or knowledge, embedded system students and designers might otherwise develop low-quality control solutions; PID enables such designers to build quality solutions, in the same or even less time as they would otherwise.
Similarly, removing noise from a digital signal, or letting only a certain frequency band pass, both represent instances of digital signal filtering. An FIR (finite impulse response) filter  is an easy-to-understand generic framework for filtering in software, allowing non-experts and students to modify variables in template code to implement specific filtering applications.
PID and FIR frameworks each build on existing domain theory to provide simple but powerful methods for embedded system designers and students. Meanwhile, embedded systems are increasingly used to recognize specific patterns. In general, a pattern recognition system takes as input data for an object, and outputs a category for that object. For example, a common such system takes a face photograph as input, and outputs a known person’s name. Most work on pattern recognition systems have a desktop computing model, where input and output are via a file or database. In contrast, a pattern recognizing embedded system, illustrated in Figure 2, gets input from sensors, like weight or color sensors, and generates output by controlling actuators, like changing a directional gate on a conveyor belt, all in real- time. For example, a warehouse may use a pattern recognition system to recognize whether a box on a conveyor belt is one of three sizes, keeping count for inventory purposes. A grocery system may recognize the kind of fruit on a scale (apple, pear, banana) as in Figure 2 and output a total price based on kind and weight.
A front-door camera system may detect whether a person is a known person from a database and generate a different doorbell ring for known persons. A wrist-worn device may recognize that an elderly person has fallen and alert caregivers . An embedded camera may be used to detect a red peach  on a tree for harvesting. A package express center may use an embedded magnetic matrix and outside sensors to monitor conveyor belt parameters such as tension, load, and position of objects . A system may detect the surface defects on fruits to separate bad fruit from good fruit . A system may be used for detecting the size and grading a fruit to determine its quality  for pricing purposes. Governmental organizations may predict floods using pressure and ultrasound flow sensors attached to embedded computer nodes to reduce damages caused by flooding . A traffic system may choose the proper light at intersections to tackle the traffic congestion problem using controllers in traffic lights and sensors on the road . As can be seen, pattern recognition is becoming another common application domain in embedded systems.
Like control systems and signal filtering, pattern recognition is an established domain with extensive theory and techniques. However, many (if not most) embedded systems students and designers do not have that domain knowledge. The pattern recognition domain has dozens of techniques that can overwhelm a student or an embedded system designer who tries to quickly learn about the domain to improve their embedded application. Without access to domain experts, designers may build low-quality pattern recognition systems.
This paper introduces the FCA (feature extraction, classification, actuation) framework for pattern recognition in embedded systems, describes the template code, and summarizes results of using the FCA framework in a classroom setting.
II. FCA FRAMEWORK
Years of engaging with commercial embedded systems applications through various consulting and other arrangements has led to our observation that many embedded applications handle pattern recognition poorly. Some other researchers similarly state the issue , with some pointing to issues like limited resources making the pattern recognition problem harder , and to the lack of good embedded systems curriculum . In some cases, the developers are not aware of the pattern recognition field or do not realize that their application is performing pattern recognition, and thus the developers do not attempt to draw upon established pattern recognition techniques. Instead, their software may simply use a series of conditions implemented via if-else statements, or a finite state machine, to categorize data coming from sensors. Or, developers may choose a pattern recognition technique but use it poorly. Furthermore, developers may attempt such categorization on raw sensor data. Such software may be making decisions based on too-detailed highly varying input data and may also have actuation distributed throughout the code. The result is a complex piece of software that is hard to update (such as if new sensors are introduced) and that may have low accuracy.
Thus, our first step was to define a modular process specifically for pattern recognition in embedded systems. The process divides pattern recognition into three phases —feature extraction, classification, and actuation, or FCA —shown in Figure 3, whose parts are summarized below.
Sensors: The input consists of a combination of various sensors, differing depending on the application. One application may have weight and color sensors. Another application may have color and infrared (IR) break beam sensors. Another may have accelerometers. A designer determines which sensors to use to help with pattern recognition tasks.
Feature extraction: The first phase in pattern recognition is to convert raw sensor data into features that will guide classification. For example, 3-dimensional accelerometer data may be converted into a velocity vector or an acceleration magnitude. Data from several IR sensors may be combined to estimate length, width, and height features, or converted into a volume feature. This phase prevents the common situation of embedded systems designers trying to use raw sensor data directly to make classification decisions. Feature extraction simplifies classification by pre- shaping the data to represent information most useful to classification. For example, in package sorting, box height is more directly useful than data on how many of four vertical IR sensors currently have broken beams (assume multiple IR sensors are stacked at increasing height and a moving box will break a number of the beams depending on its height). The output of feature extraction consists of values for a set of features, such as “height = 3.5 in” and “red = 200” (indicating the amount of red on a scale of 0-255).
Classification: Given values for a set of features of an object, classification determines to which category the object most likely belongs. The output is a single category, such as “apple”, or “small box”.
Actuation: Given a category, actuation takes an appropriate action by setting values of an actuator. If a “small box” is detected in a warehouse system, a stepper motor (a kind of actuator) may be set to guide the box on a conveyor belt to a particular bin. If “apple” is detected in a grocery system, a variable may be set to an appropriate price based on the fruit’s weight.
While seemingly simple, this division into three phases enforces a modularity that developers otherwise might bypass. The division disallows using any raw sensor data in classification, requiring instead that such data be pre-shaped into well-defined features. The division also disallows setting actuators throughout the code; instead, all actuator setting is done after classification is completed. An analog might be made with programming, wherein a function should only read/write its parameters and not global variables – a concept that was not well-understood in the early days of programming, whereas today the importance of modularity with respect to functions is well understood. Using raw sensor data or setting an actuator in the classification code is analogous to reading or writing a global variable in a function, both of which can have unintended side effects and may result in complex hard-to- maintain code. Establishing such modularity is one goal of the FCA framework.
Pattern recognition is a widely studied domain, stemming from the fields of computer science and electrical engineering. The domain goes by various names, including pattern recognition, machine learning, data mining, and knowledge discovery, with papers dating back to the 1960s. As a result, a multitude of techniques exist.
The techniques of most interest to embedded system designers are those known as supervised learning , in particular classification. In classification, a set of training objects is provided, with each object labeled as being in a certain category. Then, given a new object, a classification technique, based on the training set, strives to determine in which category the new object most likely belongs, such as: apple, pear, or banana.
For an embedded system designer, trying to determine which classification technique to apply can be overwhelming. For example, the Wikipedia  entry on supervised learning states that no technique is best and lists many, each technique having extensive literature:
- Support vector machines
- Linear regression
- Logistic regression
- Naive Bayes
- Linear discriminant analysis
- Decision trees
- K-nearest neighbors
- Neural networks
An embedded system designer can be quickly overwhelmed by all the options. Thus, we strove to determine if one technique could serve as a good basis for a generic framework in embedded systems. Our criteria included:
- Simple to learn by embedded system designers
- Good classification accuracy across various common embedded system applications
- Efficiently implemented on resource- constrained embedded platforms
Below, we review K-nearest neighbors, logistic regression, naive Bayes, decision trees, support vector machines, and neural networks along with their advantages and disadvantages based on our criteria.
K-nearest neighbor (KNN)  is a simple, easily understandable classification algorithm. KNN places known objects in an N- dimensional space, where each dimension is a feature (weight, height, color, etc.). Then, to classify a new object, KNN determines the K closest neighbors in the space; the most common category among those K neighbors wins. Figure 4 illustrates. KNN is powerful and can handle non-linearity and multiclass classification. Moreover, by having a large enough training set, the error rate of KNN would be less than twice the minimum achievable error rate . But, in case of having a large training set, this algorithm can be inefficient in terms of processing time/memory due to the need for storing and searching the training set.
Logistic regression  is a linear binary classification algorithm that statistically predicts the odds of an object being in a class based on the features. Logistic regression is fast and efficient in production. However, in this algorithm, objects of different classes should be linearly separable. Therefore, to make the algorithm work well in various embedded systems applications, an additional complex feature representation step is required before classification.
Naive Bayes  is a classification algorithm designed based on Bayes’ theorem, and learns the distribution of the input data to predict the probability that an input object belongs to a particular class. Naive Bayes is simple both in concept and implementation and is efficient in production. However, it makes a strong assumption of independence between features of the input data and it suffers in performance when data is sparse in some classes or features, which makes it incompatible with many real- world embedded system applications.
Decision trees  are a nonlinear classification technique, which learns simple decision rules from the training set to classify a new input object. Decision trees are easy to interpret and understand. However, decision trees are prone to overfitting (matching particular data too closely, thus being not general) and lack robustness due to high variance in classification accuracy. To increase robustness, one can use an ensemble of decision trees, but that makes the technique more complex for embedded system development.
Support vector machines  are a binary classification algorithm, which classifies objects by finding the hyperplane that represents the largest separation between two classes and maximizing their margin. The technique is robust and demonstrated good quality in various embedded systems applications [19, 20, 21]. Moreover, the technique is efficient in production in terms of memory and processing speed. However, the technique’s performance heavily relies on a kernel function, which is not easy to choose especially for non-expert embedded systems developers. Given that support vector machines are mainly binary classifiers and their extension to ternary and higher classification is not very effective, they have limited applicability in the embedded applications.
Neural networks  are inspired by the human brain and nervous system. The technique is powerful and has achieved much of the state of the art in the image processing and natural language processing fields. However, the technique is complicated to understand and implement. Moreover, training requires a large amount of data, which often is not available in embedded systems applications.
After some investigation, discussions, and many recommendations from various classification researchers, we chose KNN as the most appropriate general classification technique for embedded systems developers. KNN is highly intuitive: most people can understand the algorithm just by looking at Figure 4. The algorithm is straightforward to code and has small code size and is efficient for moderately-sized training sets as is common in embedded systems. For larger training sets, techniques exist to improve efficiency, such as aggregating objects, caching previous results, etc. The algorithm has demonstrated high accuracy. These features make it a popular choice in general pattern recognition as well but are especially useful in embedded systems due to limitations on code resources (speed, size) and due to ease of understanding and modification by embedded developers.
IV. TEMPLATE CODE
A simple but powerful software productivity improvement techniques is to provide working template code (called “reference code” in many domains) that a developer can modify for his/her particular application. We thus developed template code in C to support the FCA framework. A designer can adjust the code to carry out their own pattern recognition application. Highlights of the code are shown below (the appendix has more complete code); various helper functions and other items have been omitted. A key aspect to notice is the code’s simple organization, enabling a non- expert embedded developer to readily understand, navigate, and modify the code.
The template code shown in Figure 5 begins by defining a categories array. It then defines a structure for an object (either in the training set, or the new object to be classified), with fields being the object’s category and the various features of the object. It next defines a training set, with functions for populating the set.
Figure 5. Template Code
Next, the FeatureExtraction function samples input sensor values and converts those values into desired features. The FeatureExtraction function performs a simple unit conversion. Often, the conversion may involve combining values from multiple sensors, such as converting three x, y, and z accelerometer sensor values into the two features of acceleration direction and acceleration magnitude. FeatureExtraction ends by calling the RescaleObject function to scale all features to values between 0 and 1, so that “distances” make sense in the subsequent KNN algorithm. The RescaleObject function is omitted.
Next, the ComputeDistanceOfObjects function computes the Euclidean distance of any two objects, based on their N (scaled) features in an N-dimensional space. That function is used by the subsequent Classification function, which computes the distance between a new object and every object in the training set, to determine the K nearest training objects, and then returning the most frequent category for those K objects.
Next, the Actuation function sets outputs (actuators) in response to a given category.
Finally, the main function populates the training set, sets up a sampling rate, and then repeatedly calls FeatureExtraction, Classification, and Actuation at that rate (using a microcontroller’s built-in timer or whatever timing services are provided).
V. ADAPTING THE TEMPLATE FOR VARIOUS APPLICATIONS
We carried out various adaptations of the applications, to gauge the effort required and effectiveness of the resulting system.
The first was an application from a factory warehouse. The system was required to detect which of three sized boxes were on a conveyor belt and direct each box to a different bin for each size. We initially considered a video-based system but decided a simpler approach would be preferable. We used a break beam sensor to detect a box’s presence, and five distance sensors: one to measure height, one to measure width, and two to measure length. The feature extractor included a simple state machine: When the beam was broken, a new box was arriving. When that beam was no longer broken, we considered all the distance sensor values before that moment. With those values, we computed height, width, and length features of the box. For training, we ran the three-sized boxes past the sensors in various orientations and recorded the height, width and length values, yielding 9 training data values. We entered those values into the array of known objects in the template. The entire process of modifying the template code and populating the known objects array with training data required 3 hours. We tested the system with 9 new boxes and obtained accuracy of 100% (9/9 correct classifications).
The second was a grocery store application to detect which types of fruits were passing through on a conveyor belt. The fruits were apples, oranges, lemons, and pears. We used a break beam sensor to detect a fruit’s presence, plus a weight sensor and color sensor. For training, we ran 4 of each fruit through the system to record the fruits’ weights and colors, giving us 16 training values. These values were then entered into the array of known objects in the template. This process took 2.5 hours: one hour to modify the template and the rest spent running and entering the training values. We tested the system with 20 fruits and obtained 19/20 or 95% accuracy – one pear was marked as an apple when its bruised side faced the color sensor.
The third application involved putting a mug on a scale to detect if the cup was an 11 oz mug, a 16 oz mug, or a travel mug. A break beam sensor started the system. We used three distance sensors, two to measure the width of the mug and one to measure the height, and a weight sensor. We trained by sending nine mugs through the system, three of each type. These values were entered into the array of known objects in the template. The process of modifying this code took just under two hours due to us already having the code for the distance sensors and the weight sensor. We tested the system with 6 new mugs and obtained 6/6 or 100% accuracy.
Another application was detecting whether or not a vending machine was being tampered with. In contrast to the above applications that used a break beam sensor to start the pattern recognition process, this application ran the feature extraction part every one second. We used an accelerometer as the only sensor. The sensor provided three values: change in the x axis, change in the y axis, and change in the z axis. Every second, one hundred values were measured with a delay of 10 milliseconds between each measurement. We used a formula to measure the largest change in each axis and these changes were added to the training data, ending with 8 training sets in all, categorized as “normal shaking” or “abnormal shaking”. This application took 4 hours to modify due to the feature extraction portion being different from the other applications. We then gave the device different shakes and obtained 17/20 or 85% accuracy. Here, “accuracy” is less well defined, because humans are the ones defining and judging normal and abnormal shaking, both during training and testing.
It should be noted that, ultimately, the quality of a recognizer’s robustness and accuracy relies on having robust training datasets.
VI. RANDOMIZED CONTROLLED STUDY
To further evaluate the framework’s usability, we conducted an experiment with all 66 students in the Intermediate Embedded and Real-Time Systems class at the (university name withheld for blind review). We randomly divided participants into two groups, A and B.
Both groups started at an “Instructions” web page indicating that they would be participating in an embedded systems coding experiment, and that they were required to work independently and not refer to any outside resources; teaching assistants were present to ensure those rules were met. The instructions indicated that they had 45 minutes, that they were afterwards be given a survey, and that their submissions were anonymized (i.e., no impact on their course grade). Group A had one additional instruction, asking students to complete the tutorial available on the next page, before attempting to code.
Upon clicking “Start experiment”, all students were taken to an “Experiment” page. At the top was a problem description asking to solve a problem of classifying a person to a man, woman, or child category based on the height and weight of the person using training data for 18 people. Beneath the problem statement was an embedded systems simulator where students could write C code to read from microcontroller inputs and write to outputs; students had been using this simulator throughout the quarter already. The simulator showed its standard code for both groups, consisting of required includes at the top, and a main function consisting of a timer initialization followed by an infinite while(1) loop.
The only difference between the groups here was that Group A saw a button at the top labeled “Tutorial”, while that button was not present for Group B. The button for Group A led to a brief discussion on pattern recognition along with FCA template code and an example. Note that the instructions did not tell Group A students that they were required to use the template code.
Upon reaching that experiments page, a timer at the top of the page immediately started counting up from 0 minutes. The teaching assistants ensured all students stopped before reaching 45 minutes.
We evaluated the submitted models on one hundred test cases. As we assumed (and hoped), all Group A students chose to use the FCA template code in their solutions. Table 1 shows the average accuracy and average time spent to solve the problem for each group. The comparison verifies that access to the classification resources in group A led to the development of significantly more accurate models (p=0.03 using an independent t-test), and that the provided template was quickly learnable.
Figure 6 provides a detailed view of the accuracies obtained by each group; Group A had many more applications with 80-100% accuracy, and none with 0-20% accuracy that occurred for some in Group B.
Analysis of the submission time information shows that participants in group A spent slightly higher time to solve the problem compared to group B. This is mostly due to group A needing to take extra time to read the tutorial and review and understand the reference code, which is a one-time overhead that would be reduced if the individual used the framework again.
Also, a more detailed study of submitted models shows that similar to the sample model displayed in Figure 7, all participants in group B used a series of branch statements to solve the problem (as we had observed in various commercial applications, mentioned in Section 2). In contrast, all participants in group A copy- pasted and modified the template code, as in Figure 8, even though the default code provided to group A was only a basic main() function with a timer and infinite loop, identical to group B.
Discussion: Some might say that Group A of course would do better because they received a tutorial and template code. However, such better performance was not a forgone conclusion. If FCA was not easy to understand, and if the code template was large or confusing,
Group A students might have chosen to ignore the FCA code and write their own code, as Group B did. Or, Group A students might have tried to use the FCA template code but failed to complete the task or created inferior solutions. Indeed, many of the pattern recognition techniques would have been almost impossible to teach and gain success in such a short period of time. We intended to show that FCA is quickly learnable and effective, and the results seem to support that hypothesis.
After submitting the code, participants took a survey with seven questions regarding solving the problem and the tool. Each question in the survey had six options representing the level of agreement with the question statement. We converted those options into numbers from 1 to 6, where 1 corresponds to the strongly disagree option, and 6 corresponds to the strongly agree option. Table 2 shows the weighted average responses of each group. The results show a slight improvement across 4 of the 5 questions, though not statistically significant except for the last question. The conclusion here is that the extra work of the FCA tutorial in the short available time did not create any extra stress or confusion.
VII. RANDOMIZED CONTROLLED STUDY
Pattern recognition applications continue to grow in embedded systems. We developed a framework to enable embedded systems students and designers to readily build robust pattern recognition applications, without having pattern recognition domain expertise. The framework divides pattern recognition into three phases: feature extraction, classification, and actuation (FCA). We chose K-nearest neighbors for classification due to simplicity and robustness. We developed template code in C for the FCA framework with great emphasis on simplicity.
We found that we could adapt the template code to four different applications in just a few hours for each, with highly accurate classification. We conducted an experiment showing students could readily understand the code and adapt it for a given application, yielding significantly improved recognition accuracy.
Embedded system students typically are not experts in such domains and could benefit from simpler platforms to help them gain insight into the problem of pattern recognition and help them develop such algorithms rapidly. This work contributes to this educational mission. Furthermore, many embedded systems engineers are non-experts as well. This work enables engineers to build robust pattern recognition systems without being an expert in the pattern recognition field, akin to how PID control and FIR filters enable engineers to build robust control or filtering systems without being experts in those fields.
This appendix provides a nearly complete listing of the template code, as shown in Figure 9. Much attention was paid to keeping the code as short and simple as possible, to enable rapid understanding by embedded developers.
Figure 9. Nearly complete code listing.
This work was supported in part by the National Science Foundation (NSF grant number 1563652). We thank Shayan Salehian and Bailey Herms, whose masters projects contributed to this work.
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I don't know whether your phone would be affected by it, but apparently for a time "too flat to charge" was a real thing. Apparently on some phones the power required to kick off a charging cycle was more than the threshold at which it would shut off.
You might want to double-check that it is actually not accepting a charge by leaving the charger plugged in for an extended period (e.g. overnight), because it may be that while it's not displaying stuff on the screen it's still charging anyway. If that still fails, then it's probably an issue with the handset. If you have access to another phone that takes the same battery, try sticking your battery in there and see if it charges, or try and charge the battery from the other phone in your own.


The Alinco V17E is a superb new 2m handy that combines rugged polycarbonate materials, an ergonomic design and leading edge technology in an attractive, compact full-power VHF/UHF HT that is compatible to IPX7 so it’s submersible 1m/3ft. for 30 minutes!
Unlike water-resistant radios you may have looked at before, the DJ-V17E has a large 40mm internal speaker so its audio is clean and crisp, not muffled. The superior speaker design provides 500mW max audio-output for high quality audio.
With a full range of features and choice of power options, this tough talker resists rain and moisture. Take it with you when you enjoy outdoor activities such as fishing, camping, snow-sports or volunteer public radio services.
The exciting new V17E features a rugged water resistant case for outstanding performance when operating from outdoor locations .
Operates on the 2m 144Mhz Band, and features 2 touch instant repeater access and a host of user friendly features. Has optional coverage 137-174Mhz, and also has up to 5 watts output power.
- Alinco DJ-V17E VHF 2m handheld transceiver
- High Grade body compatible to IPX7 (submersible 1m/3feet for 30min) and rugged body.
- 39 CTCSS tone squelch
- 104 DCS digital code squelch
- Time out Timer
- Alphanumeric Display 4 User selectable tonebursts ( 1750, 2100,1000, 1450Hz)
- Direct frequency entry from the DTMF keypad
- 9 Auto dial memories easily accessed from the DTMF keypad
- Ni-Mh Battery 7.2v 700mah fitted as standard
- EDC-148 AC adaptor / charger supplied
- Refresh function for rechargeable battery reconditioning
- Cable Cloning
- Frequency range: DJ-V17E
E: TX/RX 144 – 145.995MHz
TX :RX :130 – 173.995MHz
- 136 – 173.995MHz
- Modulation: F3E (FM)
- Frequency step: 5,10, 12.5, 15, 20, 25, 30kHz
- Memory channel: 200 channels + 1 call channel + 1 Repeater-Access parameter memory
- Antenna impedance: 50ohm unbalanced
- Frequency stability: DJ-V17 : +/- 5ppm
- Supply voltage: DC 7.0 – 16.0V (EXT DC-IN)
- Current consumption: 1.4A (typical) Transmit high at 5W / 250mA (typical) Receive at 500mW / 70mA (typical) Standby / 25mA (typical) Batt.save on
- Temperature range:Unit -10digC to 60digC (+14digF to +140digF) Battery packs: -10digC to +45digC (+14digF to +113digF)
- Dimensions: 58(W)x110(H)x36.4(D)mm (2.28″(W)x4.33″(H)x1.43″(D)) with EBP-65N w/o projection
- Weight: Approx.280g (9.9oz) with EBP-65N & antenna
- Power output: Approx. 5W (with EBP-65N) Approx. 5W (with DC 13.8V) Approx. 0.8W (LOW output)
- Modulation: Variable reactance
- Spurious emission: -60dB or less
- Max. deviation: +/- 5KHz
- Mic. impedance: 2Kohm
- System: Double-conversion super heterodyne
- Sensitivity: -14.0dBu (0.2uV) or less
- Intermediate frequency: DJ-V17 / S17: 1st IF 21.7MHz 2nd IF 450kHz
DJ-V47 / S47: 1st IF 38.85MHz 2nd IF 450kHz
- Selectivity: -6dB: 12kHz or more -60dB: 26kHz or less
- AF output: 500mW (MAX) / 400mW (8ohm, 10% distortion)
- Alinco EMS-59 Compact speaker microphoneA new style speaker microphone from Alinco, this twin plug fitting is very popular and will fit the following models.
- Fits Alinco models :-
- DJ-V17E/ V446 ( with optional EDS-10/14 adaptor cable, purchased separately.)
*Will also fit the newer DJ-V range when used with the optional EDS-10/14 adaptor lead.We have found this microphone also works very well on some of the older type Yaesu/Icom/Standard handies with the same twin plug fitting.
This Mic works well with Midland G7, G8, G9 PMR446 radios


Atmos has a huge variety of distinct varieties of Vaporizers for different forms of Vape-users. Atmos has a number of the best vaporizers under its belt. The Atmos Jump vaporizer is certainly the value for the money. Atmos vaporizers are starting to be a significant craze among vaporizer users. Altogether the Atmos Raw vaporizer is a remarkable vaporizer. Not that the Raw isn’t great, but it isn’t a power bank and you need ton’t get confused.
What You Should Do to Find Out About Atmos Vape Before You’re Left Behind
Because of the use dispersion, difficult to handle recovery, waste battery regeneration price is large. Inadequate battery life for the iPhone can result in a wide scope of issues. Battery ride on toys have been quite popular with kids over the past few years. The Atmos Jump is created in a distinctive design as it was constructed with an effective carbon fiber housing that’s much safer than steel as you won’t ever risk getting any varieties of burns or the exterior of the vaporizer to ever become hot.
The Key to Successful Atmos Vape
Switch off features you don’t require. Together, the features enable the devices to heat each one of the herbs in the chamber evenly and to create a steady stream of vapor. An innovative feature is the fact that it may also be eliminated and replaced in the event the battery is ever used up, even though that isn’t likely. There are various choices when it comes to your power supply for the charger. Depending on what you need and the quantity of space you’ve got available, there are many choices when it comes to batteries. With this kind of a wide range of fashions and designs there are lots of diverse uses for the twelve volt battery.
The kind of device can often determine which kinds of batteries you’ll be able to use. If you don’t intend not using your unit for a significant lengthy time it’s probably best to prevent turning your device off completely. To begin with, as stated earlier, the unit is not a real vaporizer till you learn how to utilize it properly with a glass screen filter. There’s also an Atmos device called the Raw or RX. Battery technology has made advancements over time, when it comes to power, longevity and endurance.
Ideally, batteries will be saved in specially designed cases that are simple to transport. Waste batteries isn’t only bad for humans, in addition, it comprises lots of resources. After a time the battery will forget it has unused capacity and it’ll discharge much faster each time that it is used. Thus, it’s important to purchase the very best battery for your vape device.
The battery lasts for a very long time and can outlast many different sessions. If your laptop battery gets hot to the touch you’ll need to eliminate the battery and enable it to cool off. Some laptop batteries, like the Ni-Cad, will suffer from a problem which is ordinarily known as the memory effect. Whenever your laptop isn’t in use for an elongated time period it is better to take out the battery.
Some chargers incorporate international converter plugs. Following that, the charger ought to be unplugged battery ought to be disconnected. A lot of the advanced fully automatic chargers come at very affordable rates.
You need to check the battery while it in storage so that it is not going to be allowed to fully discharge. At some point, you should alter the battery at right moment. So, one ought to be exceedingly careful whilst recharging the battery. Batteries should be removed while the device won’t be used for long stretches of time. Reconditioning an aged battery is quite simple. If you’ve got old rechargeable batteries that will need to get restored then the refresh mode is the best way to go.
New Ideas Into Atmos Vape Never Before Revealed
Periodically testing your battery can help you stay away from potential trouble down the street and deliver you warning when your battery is beginning to lose or is unable to hold a charge. For instance, if you are purchasing a battery for a mod, then the battery should have the most suitable power configuration to stop problems. Frequently the battery is easily the most expensive portion of a power tool. A dead or weak battery could be the reason for your car failing to get started. Supposedly dead batteries are thrown away, so even when you can just rejuvenate one in three, you’re making very good money at no cost, and with at least effort. Inside you’ll be confronted with different batteries and a range of wires.
The charger ought to be run for a couple hours or overnight. Solar chargers are usually employed for outdoor lighting. A solar battery charger may also be utilised in other remote places and supply the power for a lot of the buoys used for maritime navigation. Battery chargers have several capabilities. You always should make sure you’ve got a battery charger with the vital qualities you want. Be certain to read the package to understand precisely what you’re getting with your battery charger. It is crucial to know the ideal battery charger for your vehicle.


The Instrument Development Group (IDG) is a recharge facility of the University of California at San Diego. Since establishment in 1972, IDG continues to play a major role in developing a number of instruments for oceanographic use.
The Vector Measuring Current Meter (VMCM) was developed and transitioned to commercial manufacture by IDG. IDG collaborated with Amatek-Straza in developing the first commercially available shipboard Acoustic Doppler Current Profiler (ADCP) and with RD Instruments to develop the first self-contained ADCP for use on moorings; both these were immediately commercialized.
The CODE drifter has been used in large numbers for coastal observations. The Autonomous Lagrangian Circulation Explorer (ALACE) was developed in collaboration with Webb Research Corp. that now provides a modernized float commercially.
IDG also developed the Profiling ALACE and transitioned that development to a commercial product sold by Webb Research. IDG developed a second-generation autonomous profiling float, the Sounding Oceanographic Lagrangian Observer (SOLO), a major contributor to the U.S. component of Argo and which is also manufactured at Woods Hole and in Germany.
The Instrument Development Group (IDG) develops autonomous instrument platforms and sensors for them, builds instrumented vehicles for University of California projects (including collaborations outside UC), and operates floats and gliders for such projects. IDG's primary projects are now developing the SOLO float and Spray glider as instrument platforms. In addition to constructing floats for the Argo program, IDG is adapting the SOLO platform for specialized projects, and developing an entirely new 6000-m Deep SOLO to extend Argo coverage. IDG is also adding instruments to the Spray glider, improving its basic operation, and studying how a new glider design might expand long-term ocean observations. Mark Ohman, for example, is having a high-resolution camera added to Spray to observe zooplanton continuously over weeks.
Currently, IDG primarily supports global and regional time series using small autonomous instrument platforms, floats and gliders, and sensors that can be used from them. IDG provides the improved SOLO II profiling floats to Dean Roemmich's project in Argo. A sustained regional time series, primarily of physical variability but including indicators of low trophic levels, has been maintained in the California Current under direction of Dan Rudnick. The focus is on how ecosystem indicators and physical measurements track coupled physical-biological interannual variability. Sustained measurements across the Solomon Sea track volume and heat transport through the tropical western boundary current that feeds the western-Pacific warm pool and the Equatorial Undercurrent.
Additionally, exploration and process experiments are supported by IDG, which provides instrumentation, equipment preparation, testing, and operational abilities. One example is Jim Moum's Chi-Pod integrated to a SOLO2 for process studies in Sep2015 in the Bay of Bengal.


Android PDA devices
Introduction Method 1: Keystroke output Method 2: Intent Broadcast Zebra Devices Common issues
Devices with an integrated laser scanner are more accurate and faster than smartphone cameras.
Such devices are supported by Barcode to PC, with one of the following methods:
- Keystroke output
- Intent Broadcast
If you have a Zebra device you can skip directly to the
Method 1: Keystroke output
The laser scanner can be configured to type the barcodes inside the Barcode to PC Input field.
To enable this feature, you need to open the system app to manage the scanner settings (Usually called DataWedge or keyboardemulator) and enable the Keystroke Output option and the ENTER suffix (someties is also called [LF]), like this:
Method 2: Intent Broadcast
By default the app is configured to receive barcodes using the following Intent:
- Intent action:
- Intent category:
- Intent extra:
datafield is checked)
- Intent type: Broadcast Intent
If you have a Zebra device, you don't need to configure anything, since we created a DataWedge profile that will apply the correct Intent configuration for you.
Download the Barcode to PC DataWedge Profile: dwprofile_Barcode to PC.db
Transfer it to the smartphone in the
You can use a USB cable, like this:
Enable File transfer:
Open the device storage:
Open the folder
- Import the profile to the DataWedge app:
From the 3 dots menu, select Settings:
Select Import profile:
Select the file
dwprofile_Barcode to PC.db:
At this point can open the Barcode to PC app and start scanning barcodes using the laser scanner.
The barcode gets typed in the Keyboard input field but it isn’t automatically sent
Probably the “Send ENTER key” option of your device is disabled. The “Send ENTER key” is required to be turned on, you can also find it called as “Suffix” option, and in that case, you should set it to [LF] or alternatively to [CR][LF] (to emulate an ENTER keypress). By enabling this option the barcode will be automatically sent and you won’t have to press the send button on the app.
This option may vary on your device brand, and you should be able to find it in your device scanner settings app, for example:


The TMT sector touches all our lives in the 21st century, and its importance is certain to grow over the coming years. TMT is directly impacted by changing demographics as a growing population, combined with the pervasiveness of connected platforms as well as changing patterns of consumption, will result in sharply higher demand for connectivity. Connectivity is also a critical enabler of new technologies, including the Internet of Things (IOT) and wearable devices.
“There is a significant shift in our use of connectivity which is prevalent across various age groups, affluence levels and geographies,” notes Katarzyna Sek, director in the Structured Finance Telecom, Media and Technology team at ING Wholesale Banking. “For example, we are departing from a traditional linear media model and embracing on-demand consumption and connectivity at all times. We are moving away from ownership to access, with connectivity as a critical facilitator. These changes are propelling telecom operators to redesign their business models and redefine sources of their competitive advantage.”
Changes span the generations
While millennials tend to embrace new trends faster than other demographic groups, all age groups are becoming increasingly aware of the benefits that the connected world can offer. “Penetration of smartphones, which is an important facilitator for connectivity, is growing across all age groups,” says Sek. “Almost all mobile users in developed countries now own smartphones. Despite the high penetration, sales of smartphones continue to be strong, driven by the continuing demand for innovation and declining prices.”
In order to ensure that telecoms infrastructure can meet customers’ growing demand for data connectivity, network operators are increasing capital expenditure to upgrade networks. This facilitates the exponential growth in the demand for connectivity. “The rate of fibre deployments is accelerating while there is also increasing investment in mobile in order to make more efficient use of the scarce spectrum.” In the coming years, many telecom operators will migrate from 4G to 5G; as a result, power antennas and other infrastructure will need to be replaced or upgraded.
The ubiquitous use of video is one of the key drivers of the need for increased network capacity. Multi-screen and mobile consumption of content is becoming more prevalent, while at the same time picture quality is continuously improving, with HD now the norm and 4K expected to become more common. All of these lead to new consumer habits, requiring high speed connectivity anytime, anywhere.
Meanwhile, technology is also developing new ecosystems, such as wearable devices, which lie outside of the mainstream products, like video. “For instance, innovative healthcare solutions targeted towards seniors are emerging, allowing increased peace of mind for families. All of this is enabled by innovation”, says Sek. “Trials are currently underway to assess the potential of such services.” Though there are often legal and regulatory challenges to turn these innovations into viable business models, the potential is there. The key question for telecom operators is to define how much of a role they can play in the value chain. Will the tech giants (such as Google and Amazon) dominate this market or will telecom operators be able to take a piece of it?”
Evolving business models
Although millennials’ high consumption of data should favour telecom operators, the generational shift is also challenging their traditional business models. “Millennials have no appetite for long-term locked-in contracts,” says Sek. “They want flexible, tailor-made solutions. They also don’t think about connectivity as a location-defined service (i.e. a cable operator providing home-connectivity and a separate mobile contract for out-of-home use) but as part of a package. That includes access to relevant content, without any caps on data consumption and through a range of devices.”
These market developments are prompting network operators to enter into strategic partnerships with content providers, as demonstrated by Liberty Global entering into a global partnership with Netflix in 2016. Alternatively, some telecom operators are directly acquiring media assets to increase their competitive advantage. For example, BT recently secured the TV rights for the Champions League and Europa Leagues in the UK until 2021 for £1.2 billion.
While the developed world is embracing the internet and mobile connectivity, there remain large numbers of people who are not part of the digital world. “There are concerns about people being left behind,” says Sek. “More than 4 billion people still don’t have internet access, largely because they live in countries with poor existing infrastructure and the return on network investment remains limited by the lower disposable incomes. But we know that access to the internet is a driver of economic growth so it is important to broaden access in order to create opportunities and empower locals to participate in the global economy.”
There are currently several innovative projects underway that seek to decrease the cost of connectivity and bring internet access to the world’s poor. “Many of these projects – such as Google’s connected balloons, OneWeb’s and Space X’s low level satellites or the use of drones to deliver internet to remote parts of the world – are deploying largely untested technology,” says Sek.
Nevertheless, there are already success stories, such as O3b, which launched in 2010 as a start-up satellite venture aiming to bring affordable internet to the unconnected parts of the world. “Today it is an established satellite communication services provider (acquired by Luxembourgian satellite operator SES) operating a fleet of 12 satellites with four under construction,” notes Sek. ING’s TMT team supported the company since its inception. “The challenge remains, but the level of ambition reflects the optimism of the technology sector and the potential role it has in addressing the global demographic and social challenges.”


The AIDA-SHM solution is supported by the European Space Agency (https://business.esa.int/projects/aida-shm).
AIDA-SHM is an artificial intelligence software solution for monitoring bridges and viaducts structural integrity and, more generally, all reinforced concrete infrastructures.
The highly innovative elements of AIDA-SHM are:
Passive acoustic emission is scientifically recognized as a non-invasive technique (it does not involve any input of energy into the materials), that allows to identify and locate the weak points of a structure, and therefore it is useful for real time monitoring of metal and composite structures. In fact, in the solid materials, the lesions (even if microscopic) expanding spontaneously release energy in the form of particular ultrasonic signals, called acoustic emissions. Several studies establish that the acoustic emission phenomenon depends on the damage type in the material. In general, during the evolution of damage, there is an increase in acoustic emissions both in number and in amplitude and a reduction in frequencies in the advanced stages of the process. The latter phenomenon suggests the transition from a micro-cracking regime to one in which macro-cracks are formed and at the end there is a structural collapse.
The acoustic emission is based on the acquisition of ultrasonic signals, usually with a frequency between 20 kHz and 1 MHz, spontaneously emitted by a material under stress due to irreversible effects, such as damage, micro-cracking, degradation, corrosion. The interested acoustic signals, releasing during the check, are due to the presence of structural defects or damage to the structure. If a component is subjected to stress, the pressure is spontaneously generated in the weak point, producing an acoustic impulse. All solids have a certain elasticity: they deform under the action of external loads and return to the initial configuration when the external loads are removed. The maximum tolerable effort and the consequent elastic deformation depend on the capability to store elastic energy. Overcoming the elastic limit, in fragile materials, the fracture occurs immediately. However, in materials with high plasticity, the fracture occurs only after deformation. If the material, subjected to a pulling load, has a defect (e.g. in welded joints, a non-metallic inclusion, a gas bubble), the damage will most likely occur around these native defects since they are likely to be the points most strongly stressed. In fact, near the defects, the stress field is further amplified. In heterogeneous materials (rocks, concrete, composites) the fracture occurs at the peak of progressive damage due to the applied loads or the severity of the environmental conditions (corrosion, degradation, …). In particular, the micro fracturing process occurs together with a fast dislocation motion which is associated with a spontaneous rapid release of energy in the form of transient elastic waves, or acoustic emissions (AE). The acoustic emission event manifests itself as an elastic wave that propagates through the material towards the surface of the element and can be detected and recorded by the sensors (AE sensors) by applying to the structure surface.
The artificial intelligence algorithms with automatic learning, applied to the integrated processing of the signals coming from the AE sensors and from the other typical sensors of the structural monitoring (accelerometers, strength gauge, temperature, humidity, …), learn to “know” the specific physical features of the product, to recognize the type and severity of the signals collected by the sensors and therefore they are able to carry out an early diagnosis of incipient structural damage by capturing precursor signs of fractures. Furthermore, measuring the arrival distribution of the acoustic signals to the various sensors overtime and using mathematical algorithms, it is possible to locate the source of the acoustic emission. Therefore, the combination of these techniques allows to predict (and so prevent) catastrophic events, to optimize and improve the effectiveness of maintenance activities, providing information on the location of the damage and consequently reducing maintenance costs.
The AI engine of AIDA-SHM uses the knowledge acquisition paradigms, which are able to:
- Extract information and identify associations and correlations between data and events
- Define representative models of the degree of evolution of the structures
- Calculate the deterioration of structures using models that are profoundly different from those obtainable with statistical analysis, based exclusively on the probabilities deriving from the frequency of events
The transfer of the data collected by the AIDA-SHM sensors is obtained using an edge computing – IoT satellite architecture, in order to extend the monitoring and prediction capabilities to large structures (bridge, viaduct) for which the monitoring requires a sensors extensive network.
The satellite connection is currently the only wireless connection able to guarantee the service levels and bandwidth necessary for the collection and real-time transfer of data coming from the sensors, positioned in strategic points of the structures.
The AE sensor network is strategically placed along the structure, e.g. a viaduct. This network can be integrated with an existing network of other types of sensors, such as temperature, position, inclination, accelerometer, etc. AE sensors generally require a wired connection with the multichannel DAQ device, which provides the correct power to the preamplifiers built into the sensors through the same coaxial cable.
The analog data produced by the sensors are acquired and digitized through a multi-channel system. Thus, the obtained historical series are locally pre-processed in the time and frequency domains (edge module of AIDA-SHM), ready for the next analysis carried out by the artificial intelligence engine. The pre-processing phase carried out by the “edge computing” module is necessary to avoid transferring the large amount of data coming directly from the high frequency sampling (500kH) of the AE sensors, which have to be able to detect the signals in the ultrasonic band.
The pre-processed data is transmitted to the “Central” module of AIDA-SHM, via satellite connection.
The “Central” module of AIDA-SHM (which can be on premise in the customer’s control room, or on a Cloud server), processes all the received data, both those collected by the sensors and relating to the manufact, and the control data of the satellite connection and the “edge-computing” infrastructure module, to guarantee the reliability and continuity of the system.
The information provided by AIDA-SHM can be viewed in a Dashboard accessible via a multi-channel WEB interface. Despite the technological complexity of the solution, the information is summarized in high-level functions that are logically consistent with those coming from conventional structural monitoring campaigns. For the use of the system, therefore, users are not required to have specific skills in addition to those typical of structural monitoring.
The output data of the artificial intelligence engine can be classified into the following types of information:
Every time the system detects the presence of a signal related to the occurrence of a possible damage (formation of a crack, detachment of the steel from the concrete, presence of unexpected structural discontinuities) an alarm signal is generated which is stored and subjected to the attention of the user in the preferred mode (console, SMS notification, email) and it is also possible to activate M2M actions (e.g. open a ticket, create a task, etc.).
When the “Training” phase is completed (during which AIDA-SHM “learns” the dynamics of the signals coming from the installed sensors on the structure and builds the interpretative models), every time it receives a signal that it is different from its “normal” interval, an anomaly message will be generated. Unlike sentinel signals, which require push notification in a short time, anomalies have a historical value, they are therefore recorded and communicated periodically to the customer, with a periodicity that can be configured according to user requirements.
The system evaluates the content of the signals coming from the AE sensors and compares them with the historical ones acquired in a “long” window which is sufficient to identify the structural changes. The analysis carried out here is different from the previous two because in this case slow changes, mainly related to the aging of the structure, have to be evaluated. AIDA-SHM generates and updates on a monthly scale a “health index” which takes into account the occurrence of “anomalies” on a very long time scale.scheda-AIDA-SHM_v4-en


This is the extensively revised edition of the classic book that presented the author's discovery of an astronomical code in the organization of the Rgveda. This code has changed our understanding of the Vedic system of knowledge, rise of earliest astronomy, history of science, and the chronology of ancient India. The work was first reported in a series of journal articles; this book brings together these discoveries between the same two covers for the first time. Some comments on this work:
Subhash Kak is a widely known scientist and Indologist. Currently a professor of electrical and computer engineering at the Louisiana State University at Baton Rogue. He has authored ten books and more than 200 papers in various fields. Apart from his work on Indian astronomy, he has researched linguistics, Vedic science and history.
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Autonomous drone technology is becoming more valuable to the world of mining due to the latest technology developed by Emesent
Developed by former researchers from CSIRO’s Data61, autonomous drone company Emesent has developed world-leading ‘Hovermap’ technology that automates the collection of valuable data in underground areas that are too dangerous or difficult for people to survey or navigate, such as stopes or ore passes in mines.
Drones installed with Hovermap can be deployed in GPS-denied environments without a human controller to create 3D maps and record gas readings, videos and images.
Emesent recently raised $3.5 million in venture capital to commercialise Hovermap.
Hovermap in context: addressing problems for underground miners
Hovermap technology is a critical tool that improves the safety of mining professionals by removing the need to send people into potentially hazardous areas and generating awareness about the ground conditions to alert miners to potential hazards via 3D laser scans and images.
An example is stope mapping. Currently stopes are mapped using a CMS – a boom-mounted laser scanner. This requires the surveyors to operate close to the stope brow, one of the most hazardous areas to be underground. Using Hovermap, a drone can be launched from a safe distance and sent into the stope with no need for surveyors to be near the brow.
A second example is that it provides access into previously inaccessible areas. Drawpoint hangups rely on people working close to the brow to try and see the hangup. Hovermap removes people from near the brow for a remote inspection and the ability to specifically target the hangup, saving lost production time.
How the technology works
Hovermap includes a LiDAR (light detection and ranging) similar to those used on autonomous cars. This fires 300 000 laser pulses per second and measures the time taken for each pulse to reflect off the surroundings and return to the sensor. Based on the time taken and the speed of light, a range measure can be made for each pulse.
This range data is processed by a SLAM (simultaneous localisation and mapping) algorithm to create a 3D point cloud, a high resolution, high accuracy digital representation of the underground environment (stope, drive, chamber). The SLAM algorithm also estimates the motion of the drone and the location of the drone in the point cloud or map. This allows the drone to navigate without GPS.
The future of drones in mining
Early adopters are already using Hovermap underground and there is significant demand. For mines already adopting cutting edge technology and reaping the benefit, this technology will be standard in under two years. There are some very exciting advancements in the pipeline that will make it even easier, with analytics at the fingertips of the users.
The capability to make more definitive decisions, while reducing exposure to hazardous situations and improving efficiency and productivity is a very positive outcome. There are many challenges involved in getting information into the hands of decision makers. It is also these challenges that provide the greatest rewards.
Safe inspections of inaccessible areas
Hovermap is a payload that is mounted to a drone to provide advanced capabilities, including omni-directional collision avoidance and the ability to fly and navigate without GPS. This allows the drone to be flown safely underground into these inaccessible areas.
Inspections are often the task of the geotechnical engineers and are required to ensure a safe workplace. These inspections defining what is ‘safe’ and ‘unsafe’ are usually based on subjective observations and the experience and judgement of the geotechnical engineer. Remote inspections through the use of Hovermap can provide quantitative assessments without exposing anyone to hazards.
The technology can benefit many areas within an operation. For the application of flying into a stope, Hovermap is quicker to deploy and map the stope so there are time efficiencies for the survey department. Mining engineers can be provided with better stope reconciliations and a greater understanding of their blast practices to minimise dilution, improving the cost effectiveness of future stopes.
A history of innovative thinking
Before spinning out of CSIRO, Emesent established its leadership in automated industrial operations in the underground world. In 2017, Hovermap enabled the world’s first fully autonomous beyond line-of-sight drone flight in an underground mine, 600 metres below the surface in Western Australia.
The Hovermap system is already being used commercially for a variety of applications by early adopters in Australia, the United States, Canada, China and Japan. A new program targeting the underground mining sector has now been rolled out, providing early access to Emesent’s mining-specific autonomy functions to selected participants. The Queensland-based company has also received support from CSIRO’s ON Accelerator program. Both Main Sequence Ventures – CSIRO Innovation Fund and ON are supported by the Federal Government’s National Innovation and Science Agenda (NISA).
Businesses and applications like Emesent and Hovermap are a crucial step towards Australia realising its global digital innovation potential, and showcase the world-class work being done by Australian resources professionals.
For more information about Emesent and its new early adopter program, visit: www.emesent.io


The data revolution, moreover known as precision ag, continues to introduce new innovations to the farming community. These educational videos have been designed to provide a foundational understanding of the science and on-farm uses of remote sensing.
Many farmers and ag service providers are interested in the benefits that can be derived from multispectral imagery. In this video, we introduce remote sensing and what to expect from the following lessons. [View Notes]
Using remote sensing technologies to monitor vegetative health is not a new practice. In this lesson, we begin to look at the history of remote sensing technologies. [View Notes]
Continuing with the history of remote sensing, this lesson turns to commercial interests and explains what hampered widespread adoption of the technology.
Utilizing multispectral imagery to measure vegetative health is similar in many ways to fertility management. One measures what is happening beneath the ground's surface while the other measures what happens above it. [View Notes]
As we begin to explore the nature of light, we will start with the visible light spectrum and the concept of refraction. [View Notes]
Remote sensing in farming replies a lot on invisible bands, such as infrared. In this video, we look at how the invisible portion of the light spectrum was discovered over 200 years ago. [View Notes]
Light energy travels in waves referred to as electromagnetic radiation. In this lesson, we get acquainted with the nature of light waves and the size of waves within the visible spectrum. [View Notes]
In this tutorial, we look at areas of radiation that are beyond the visible spectrum. You will be familiar with many of these waves from their utilization in the modern world. [View Notes]
Now that we have an idea of the nature of light, we will look at the spectral behavior of absorption and reflectance for leaf-light interactions. [View Notes]
In this tutorial, we explain how the different colors are mapped to create color infrared images, as well as maps derived from indices, such as NDVI. [View Notes]
When utilizing imagery products, look for large zones of stress and smaller abnormal patterns throughout the field. These often indicate pest infestations, irrigation problems, nutrient leaching, or compaction. [View Notes]
NDVI is not a crystal ball, but at times you will swear it's close to it. In this section, we look at some examples of NDVI maps that correlated with yield data.
Multispectral imagery is excellent for detecting irrigation issues and zones of leaching. In this video, we look at a few examples of each.
In this video, observe the life and death of a potato field throughout the season in CIR and NDVI. This study helped with storage bin placement for early harvesting.
In this video, we see how CIR and NDVI maps can be utilized in accessing zones of crop damage, such as green snap.
Multispectral imagery can be acquired in three basic ways - satellite, airplane, or UAV. In this video, we discuss the pros and cons of each acquisition method. [View Notes]
The problem with new technology is sometimes we get so caught up in gadgets and blinking lights that we lose sight of the true goal. In this video, we discuss factors to consider that will effect your business process and bottom line when utilizing remote sensing. [View Notes]
SST Summit serves as a seamless conduit for the ordering, processing, and delivery of quality imagery products via select imagery providers. In this video, we walk through the ordering process within SST Summit.
Once imagery has been acquired and processed, it is synced with SST Summit. In this video, we show examples of imagery and how to create management zones and printouts.
Imagery is an ideal product to guide crop scouting efforts. In this tutorial, we demonstrate how to create zones from imagery and sync them to Sirrus.
Interested in utilizing multispectral imagery? For more information, call SST at 888.377.5334.


JoinPad is an italian company active worldwide since 2010.
Today, it is one of the most advanced companies in the Augmented Reality field, mainly focused on Enterprise solutions, used by international companies such as Siemens and ABB.
Its purpose is to simplify complex processes by developing Context Computing industrial platforms with innovative user interfaces and interaction methods.
The proposed solutions are composed by several technologies patented by the company: object recognition algorithms; location-based services and machine learning applied to predictive maintenance.
JoinPad also developed Arrakis, its own software development kit, to create Augmented Reality applications for mobile devices and smartglasses.
With Arrakis SDK, customers can quickly build their own professional apps thanks to a unique dedicated toolset specific designed for smartglasses. JoinPad’s expertise, grown within more than five years in development on smart glasses, allowed the company to cover typical hardware specific issues such as physical keys mapping, interpupillary distance setup, hands-free interactions and true fullscreen mode.
Joinpad 自主研发了Arrakis软件开发工具包，可供手机，平板，智能眼镜的智能硬件设备方便快捷的创建AR应用。感谢Joinpad的 Arrakis的SDK，用户可以快速为自己的智能设备创建专业独特的AR App.


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|저자 : 한국천문학회
|등록일시 : 2024-02-29 12:20:50
Postdoctoral research position in gravitational dynamics of wide binary stars and/or galaxies: observation and/or data modeling
A postdoctoral research position is available immediately in the department of Physics and Astronomy at Sejong University in Seoul. The postdoc will join Prof. Kyu-Hyun Chae’s group to study gravitational dynamics in wide binary stars and/or galaxies (in particular, rotationally supported galaxies). In case the postdoc works on galaxies, the research will be carried out in collaboration with the SPARC-II project team (led by Dr. Federico Lelli at INAF in Italy).
The research work will be done through data modeling of the existing and upcoming data bases provided by Gaia and SPARC/SPARC-II and/or new photometric and/or spectroscopic observations of targeted binary stars. Any observer, computational or theoretical researcher is encouraged to apply. If you have any questions, feel free to contact Kyu-Hyun Chae via firstname.lastname@example.org.
Application materials (a CV, a publication list, research statement) can be sent to email@example.com. Please also arrange two letters of reference to be directly sent to Prof. Chae. Applications arriving before 31 March 2024 will receive a full consideration. The initial appointment will be for 1 year and extendable under mutual agreement for further years. The search may continue until the position is filled.
|2024년 제1차 한국천문연구원 계약직(연구직) 채용공고(재공고)
|[기초과학연구원 IBS] 위성 시스템 관리 감독 업...


An inside look at Justick’s electro-adhesion technology for use in space.
Houston, Texas — Justick attended and presented at the NASA In-Space Technology Workshop held in July to explore the use of electro-adhesion technology in space and zero-gravity (zero- G) environments.
Temporary adhesion remains a challenge in space. In most cases products such as Velcro and tapes are used to adhere objects and materials to be displayed and interacted with.
Justick Technology and products operate without the need for glue, pins, tape, magnets, or other traditional methods of holding objects in place. Instead, the products function via electro-adhesion technology, similar to the concept of static electricity, which can cause two surfaces to be attracted to one another. Simply place materials on the Justick patented surface and they will stick, just science…
Justick panels were primarily designed for adhesion of paper due to temporary adhesion requirements, typically used in display / notice boards environment. This technology can easily be configured to suit various applications in which variable adhesion strength and flexible surfaces are required. Dr Gerhard Ferreira, CEO of Justick indicated that research and development (R&D) has already commenced related to use of the technology in zero-G environments.
The good news for consumers is that a range of products is available for office and home use. Justick will revolutionize the way you present, teach, plan, advertise, display and communicate, making it suitable for nearly any environment. These display boards come in several different types and sizes to suit various needs, with more models coming in the near future.
Justick International is a South African company who invented the patented, award winning Justick electro-adhesion surface. Among its accolades are a gold medal in recognition of innovative excellence and the XEROX Innovation Award. Internationally known as a pioneer of quality technology, Justick creates products to simplify your world.


We all experience radiation in our daily lives, as visible light or the warmth generated by the absorption of sunlight. Radiation of higher energies, called X-rays and gamma rays, can penetrate deep into the human body. Their absorption in tissues is used in medicine for imaging and cancer therapy.
Some minerals and elements emit radiation through decay mechanisms and are therefore called radioactive. Imaging and modern radiation machines produce radiation for medical purposes. With advanced technology, it is possible to accurately focus the radiation to any point in the body. In this way, it can be used to target and destroy tumours.
Radiation is an energy that is transported in the form of electromagnetic waves or particles. Visible light and sunbeams are radiation forms in the micrometer and lower wavelength range. Energetically much stronger and invisible are radiation types like X-rays and gamma rays. These penetrate tissues and can, therefore, be used for imaging or cancer therapy purposes.
The decay of some minerals and elements, which are therefore called “radioactive”, can generate radiation that is used for medical purposes. In addition to radioactive sources, radiation in the form of x- and gamma rays are also produced by man in modern imaging and radiotherapy machines. One common way is to accelerate electrons and generate radiation through collision with certain materials. Radiation is categorised by the level of energy. The impact it has on cells is important and also depends on the nature and origin of the cells.
With respect to medical radiation, the radiation source can be placed inside the body (example of brachytherapy) or focused on the tumor from outside the body (external radiation therapy). Radiation emitted from radioactive pellets is limited in their penetration depth in tissue.
Consequently, the maximum damage in tumor cells is achieved if the pellets can be placed directly into or at least closely adjacent to the tumor. For external radiation sources, the maximum effect is achieved by directing a number of radiation beams from different angles directly into the tumor. This way the surrounding healthy tissue receives very little from the radiation.
The most commonly used radiation in modern radiation oncology consists of high energy photons. In addition to these photon beams radiotherapy also uses charged particle beams of electrons, protons or even heavier ions to destroy tumors. Common to these beams is their limited penetration depth in tissue which can be controlled by their initial kinetic energy.


ALT'2016 is the 6th international conference concerned with the latest scientific results and applications in reliability testing and analysis. The aim of the conference is to bring researchers and practitioners from universities, institutions and industries, together to present and discuss innovative methodologies and practical applications in the reliability field: assessment, modeling, testing, analysis, design and optimization. Theoretical issues and applied case studies will range from academic considerations to industrial, medical, and social applications. There will be invited talks, plenary and parallel sessions. Accepted papers will be published in the conference proceedings.
This edition will take place in Troyes, at the Troyes University of Technology (UTT). The event is organized by the Laboratory of Systems Modelling and Dependability (LM2S), team of the Institut Charles Delaunay (ICD).
Next edition will be the sixth one. Previous conferences were in Angers (2006), Bordeaux (2008), Clermont-Ferrand (2010), Rennes (2012), and Pau (2014).


We're back, with our second conversation on Artificial Intelligence. Last time we mostly talked about the past, while we tried to dissect what AI actually is. We mused on older technologies we dubbed Artificial Memory, like books, calculators, and hard drives. In this episode we discuss how video games (during humanity's never-ending quest to recreate reality) got us to the computational capacity for natural language processing, machine learning, and neural networks. Where is this technology now, and where are they in your life? Do you like that the machines are starting to make decisions for you? Do you even know they are there? This is the second of at least three episodes in our quest to figure out the five Ws of AI. Enjoy!
Support this podcast: https://anchor.fm/zengineering-podcast/support


Ergonomically designed pads in medical grade neoprene allowing for a confortable fit. The design allows flexibility to treat other areas of the body.
The LEDs deliver two wavelengths of light from one diode, which has enabled us to make the treatment heads smaller and able to fit onto the skin directly. The two wavelengths are optimised to deliver clinically proven results for wrinkles and hyperpigmentation. Easy to clean using alcohol wipes.
Easy to operate, simple control unit that launches into the full treatment cycle at the touch of a button, small, portable and lightweight. On board intelligence that monitors many safety features and fault diagnosis.
Easy to store


Systems for transport and processing of granular media are challenging to analyse, operate and optimise.
In the mining and mineral processing industries these systems are chains of processes with complex interplay between the equipment, control, and the processed material.
The material properties have natural variations that are usually only known at certain locations.
Therefore, we explore a material-oriented approach to digital twins with a particle representation of the granular media.
In digital form, the material is treated as pseudo-particles, each representing a large collection of real particles of various sizes, shapes and, mineral properties.
Movements and changes in the state of the material are determined by the combined data from control systems, sensors, vehicle telematics, and simulation models at locations where no real sensors can see.
The particle-based representation enables material tracking along the chain of processes.
Each digital particle can act as a carrier of observational data generated by the equipment as it interacts with the real material.
This makes it possible to better learn material properties from process observations, and to predict the effect on downstream processes.
We test the technique on a mining simulator and demonstrate analysis that can be performed using data from cross-system material tracking.
Supplementary video S1: Integration Test
Supplementary video S2: Simulator


Case Pile Wave Analysis Program (CAPWAP) Analysis
CAPWAP® is the most accurate analysis method to determine deep foundation capacity from pile top dynamic measurements. In pile driving analysis, there are three sets of unknowns: internal pile forces, pile motions, and external pile forces. CAPWAP is a signal matching software program that uses pile or shaft top force and velocity measurements collected by a Pile Driving Analyzer® (PDA) to extract the external deep foundation forces consisting of the static and dynamic soil resistance models.
CAPWAP results include the deep foundation’s mobilized total bearing capacity as well as the soil resistance distribution along the foundation length. CAPWAP graphical results include the measured force and velocity record, the best match, the resistance distribution versus depth, and a simulated static load test load-set curve.
CAPWAP numerical results include the CAPWAP summary results table detailing the resistance distribution, the dynamic soil models, match quality, and pile stress maxima. For driven piles, the CAPWAP determined soil resistance and dynamic soil models are often used to develop GRLWEAP refined wave equation input parameters and establish the pile installation criterion.
The founders of GRL Engineers, Inc., developed CAPWAP Signal Matching software. GRL Engineers are experts in the use of this program, allowing them to provide analysis results effectively and efficiently.


Dell Inspiron 15 3567 15.6" - i3 Laptop Computer - Black
- Intel Core i3-7130U Processor 2.7GHz
- 8GB RAM
- 1TB Hard Drive
- Intel HD Graphics 620
- Microsoft Windows 10 Home
- Media Card Reader
- 10/100 Network
- 802.11b/g/n Wireless
- Bluetooth 4.0
- 15.6" HD Widescreen LED-backlit Display
What you need. For all you do. The Inspiron 15 3567 laptop computer with essential performance, featuring an Intel Core i3-7130U processor, 8GB DDR4-2133 RAM, and a 1TB hard drive.
1 Year Warranty
This product is imported from the USA. Each American product imported by Gainmart is carefully selected for the local market maintaining the highest standards. We 100% guarantee the high quality and the authenticity of our products.


The proposed multichannel protocol guarantees an interference-free communication.
The proposed approach is simulated and compared against other similar MAC protocols, in terms of data-rate, control overhead and energy. Experimental results indicate that the multi-channel MAC gives better results when compared to single channel MAC protocols. Implementation of the proposed approach is tested using Berkley motes and by using custom designed mobile node. Mohamed Mubarak, T.
Di Francesco, M. Data collection in wireless sensor networks with mobile elements: A survey.
Caldeira, J. Rodrigues, and P. Tonneau, N. Mitton, and J. Vandaele, How to choose an experimentation platform for wireless sensor networks?
The Medium Access Sublayer (MAC)
Munir, C. Canfeng, J. Ma, and X. Namboodiri and L. A survey on routing techniques supporting mobility in sensor networks.
What is Media Access Control layer (MAC layer)? - Definition from alsiburtcimu.ml
In Mobile ad-hoc and sensor networks, Srikanth, M. Harish, and R. Sreejith, P. Khandelwal, K. Anupama, and L. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, Energy conservation in wireless sensor networks: A survey, Ad hoc networks, vol. Yick, B. Mukherjee, and D. Ghosal, Wireless sensor network survey, Computer networks, vol. Loo, J. Mauri, and J. Dong and W. Suri, A. Hansson, J. Nilsson, P. Lubkowski, K. Marcus, M.
- carpeta archivos temporales en mac;
- Table of Contents?
- Network Layer Protocols.
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Hauge, K. Lee, B. Standards for wireless communications exist for different applications: cellular telephony, satellite communications, broadcast radio, local area networks, and so forth. Three well-known standards for wireless data communication have been proposed for use in WSNs, each with certain advantages.
Introduction of MAC Address in Computer Network
However, WSNs do not have widely accepted standard communication protocols in any of the layers in the OSI model sense. The following subsections describe standardized protocols which may match WSN requirements. The protocols provide wireless data transmission with appropriate data rates for a wide range of applications, they can be implemented in battery-powered devices, and they do not require complicated planning and setup.
Several commercial products use these wireless standards, which could be an advantage for WSNs in cost and ease of implementation. The purpose of this section is to familiarize the reader with the standards, show their advantages and disadvantages, and discuss their use in WSNs. The MAC protocol has two modes [ 9 ]. Mode with no central device controlling the communication. Carrier sensing: a node senses the medium.
If it is idle, the node transmits the data frame.
If the medium is busy, the node waits until it becomes idle again, waits for a random time and transmits. Upon frame reception, the receiver node answers with an ACK acknowledgment control frame. If a collision occurs, transmitting nodes wait a random time and try again later.
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- Introduction of MAC Address in Computer Network?
Virtual carrier sensing: a node with a frame to transmit senses the medium. If it is idle, the node sends a control frame called RTS request to send , which contains the intended receiver address and the time required to send the information transmission delay. If the destination node agrees to communicate, it will answer with a CTS clear to send control frame which also contains the delay.
All nodes hearing RTS or CTS should refrain from transmission until the transmission delay has elapsed and the medium is idle again.
The receiver must respond with an ACK for each data frame received. A special node, the access point AP , polls every node and controls the communication process. An AP periodically broadcasts a beacon control frame with parameters and invitations to join the network [ 9 ]. Advantages of IEEE Data rates are high for wireless end user transmission and radio ranges can be hundreds of meters. Also, as IEEE Disadvantages include the large overhead in control and data packets. Another possibility is using UDP which employs less overhead, 8 bytes for the header.
Perhaps the most important problem for using Even though the standard has power saving mechanisms, according to Ferrari et al. In this standard, the physical layer uses 2. However, some Bluetooth devices have meter range [ 12 ]. An These networks are referred to as piconets and several piconets may communicate using a bridge node, forming a scatternet. In one time slot, the master will poll a single slave, inquiring if it has something to send. If the slave has data to transmit, it sends it to the master in the next time slot [ 13 ].
A master node must periodically transmit, even if there is no data to be exchanged, to keep slaves synchronized. Slaves cannot communicate directly; the information must go through the master node.


The Eco Freezer by industrial designer Ruben Iglesias is a concept portable freezer that is ideal for picnics and camping tours. The cover of the freezer features two LED lamps which are equipped with photovoltaic cells.
Generating solar energy, these lamps keep the contents of the freezer chilled and also provide illumination in times of an emergency. The lamps dispense current via magnetic induction. If the freezer is not completely full, the user can opt to use one of the lamps for the purpose of cooling, while the other lamp provides illumination.
After the picnic when the freezer is completely empty, both the lamps can be used for illumination in three different modes, including: completely on mode, SOS mode and flicker. However, if the sun isn’t shining that well, the freezer doubles as a thermal box, keeping the contents cold for a long time.