Positioning has been a driving factor in the development of ubiquitous computing applications throughout the past two decades. Numerous devices and techniques have been developed—few of them are actually used commercially.
The precision is limited to specific applications, the availability limited to the provider of specific services. Occasionally, few methods have been combined to recalibrate each other by means of data fusion.
We present a novel architecture for processing the vast amount of data from pervasive devices penetrating everyday objects to the cheapest level. Location information can be inferred from infrastructure deployed for different purposes, only partly designed for positioning in the first place.
The massive redundancy of such nodes and the synergetic heterogeneity of completely different recognition principles allows to tailor the perceived positioning probability to the specific requirements of the target application. A self-learning and self-healing approach to misleading, wrong and outdated pieces of information provides a new quality of interworking position and context aware systems.
Keywords: Positioning; Location awareness; Context awareness; Distributed objects
Location Awareness in general describes applications in computing and telecommunication, which alter their behaviour in dependence on the location of an entity. The latter might be the user of the application, a person the user of the application wants to communicate with, or an object capable of changing location. Such location represents a major category of context , and is derived by various methods of positioning.
To achieve tradeability of contextual information among different organisational domains, it requires qualitative parameters regarding precision, trustworthiness and timeliness, recently phrased as Quality of Context .
Authentication is an issue implicitly related to certain methods of positioning, employing the same technology.
With Ubiquitous Computing thriving recently, dozens of applications demand such location information. It has sufficiently been discussed that localisation indoors has different technological and topological requirements than outdoors. Even if we could derive a probable geographical positioning of one meter indoors, this left us uncertain if we were at the one or the other side of the wall between two rooms. Thus, a different approach is required in local environments.
Our novel architecture massively combines numerous individual positioning technologies to obtain more precise and more reliable results according to the various needs of the whole range of location based services.
1.1. Categories of localisation technology
In the relation of localisation and communication , we can distinguish two major categories, seen from the perspective of the mobile device:
The first category is receptive localisation. The position information is distributed ubiquitously, and the mobile device can derive its own location from this information. Satellite distributed GPS is a typical example. The mobile device can either relate the derived location to a map independently and provide a local service (without revealing its own position to any third party), or purposely transmit this location in order to obtain a value-added service.
The second category is transmissive localisation. The position is derived by a fixed station which either sees the mobile device or receives a beacon from it. The station can then either transmit the derived location information to the mobile device, or use it to generate the value-added service for the mobile user. Sub-cell GSM positioning is the example, where the beacon of the mobile communication channel is also used for positioning. At cell level, receptive location is possible in this example also, when the mobile device can identify the very cell it is in.
Hybrid techniques combining the principles above are possible.
However, despite years of experimentation in the labs, very few positioning technologies have currently significant economical impact—most prominently GPS. Most other technologies, for indoor as well as outdoor, have niche markets only, if they are commercialised at all.
Reasons include high costs compared to the added value achieved, or immature precision and reliability.
1.2. Interworking localisation
Progress in the latter issues is expected in most recent proposals to combine heterogeneous positioning data from different technological sources, in order to obtain a higher probability for a certain position scan by principles of data fusion . The number of different parameters overlapping into a final position assessment is still small.
However, an increasing number of technologies suitable for positioning are becoming available. We are at the advent of the penetration of everyday objects with pervasive devices to the cheapest level. Reliable, camera-based visual tracking and shape/face recognition becomes very feasible with falling costs of the imaging components. Biometric devices might be available in every office. Wireless and wired sensor networks of a variety of categories can detect presence and proximity of people and objects. Position information can be derived from sources not previously designed for this purpose—such diverse as triangulations in wireless communication networks (Wifi, GSM), sightings of campus cards at cash registers, usage of IP or MAC addresses at certain wired network patches, etc.
The interworking of all these systems will provide a massively redundant approach of positioning and a new quality of context awareness. An architecture harnessing the parallel data streams from all these sources for positioning is presented in this paper, leading to a unprecedented level of precision and well-structured confidence in the data presented to the respective applications.
The heterogeneity of the recognition principles being included, used in a synergetic manner, allows to tailor the perceived positioning probability to the specific requirements of the target application. This means in an example, that for setting some room or music preferences in a smart environment, a recognition probability in the upper 80% would be sufficient, while paying money from an ATM would require much higher confidence in the authentication.
The massive redundancy of such nodes implies that not every piece of information might be correct, it might be wrong in the first place, temporarily misleading or be out of date. This aspect has been considered in our architecture, and self-learning, self-adapting and self-healing approaches keep it alive. Support for manageability is provided.
Within this paper, we briefly discuss in Section 2 some existing technology and current approaches of fusion, with regards to the above goal in mind. Section 3 introduces our redundant positioning architecture. In Section 4, the required research and implementation is outlined and Section 5 summarises the paper.
2. Related work
2.1. Impact of individual technologies
Research into positioning systems has focused around leveraging location information from various media such as sound, infrared, radio and vision. Each system has addressed the aggregation of sensor data into location estimations via their own specific methods.
The following collection of related localisation technology is not meant to provide an exhaustive analysis, but merely a collection of examples in order to discuss the pros and cons of each approach, and in particular to categorise them in regards of their current economical impact.
2.1.1. Widespread commercial systems
The list of widely deployed positioning services with everyday visibility is very short.
Radar for locating ships and aircrafts is the longest used positioning system, however, with no impact expectable for ubiquitous computing.
The Global Positioning System (GPS) , as receptive radio localisation, is currently the most reputable location system, widely used in navigation and open-area logistics. GPS receivers can compute their location anywhere on the planet to a high degree of accuracy. Well defined protocols for transferring location information from receivers to other devices have been established, thus making it easy to integrate with other systems.
Contactless inductive transponder based positioning and visual on-the-fly bar-code recognition are widely used in factory hall logistics processes in industry (e.g. tracking items on, or delivered to, an assembly belt), delivery services (parcel carrier, etc.), and road toll collection.
Cell based GSM location is explicitly used for its primary purpose of enabling communication.
2.1.2. Location inference
Another class of possible positioning are technologies not primarily developed for this purpose, but bearing implicit information which can be derived by inference of proximity.
Examples are readers (at fixed positions) for magnetic swipe cards or chip cards, used for access control and time registration, for money-related functions (credit cards), or combined functionality in a certain area (campus cards).
BlueTooth devices, can be recognised in proximity to each other.
Further, network addresses (IP, MAC) can be mapped to positions. On a global scale, the mapping of blocks of addresses assigned to countries and institutions is already used for countrywise or citywise localisation of web-services. In terms of a company, the internal patch-database provides office-level granularity.
2.1.3. Laboratories and niche markets
Lots of positioning technologies have been developed in the labs, and to some extent commercialised for a number of niche markets.
Passive RFID transponders becoming cheaper. E.g. wrist-worn devices are available for child-location and payment functions in theme parks . Extremely cheap RFID tags (few cents per item) are expected in particular from the AutoID initiative of the Automatic Identification and Data Collection industry , significantly driven by global consumer product manufacturers and retailers targeting to individually serial-number any item in the supply chain with the Electronic Product Code (EPC) .
Sub-cell GSM positioning —as an example for transmissive radio localisation and as demanded in the ‘E-911 mandate’  and similar European requirements—is currently too immature, requires still relatively expensive mobile devices and/or hardware investment on the provider side, and cannot distinguish room boundaries. The diversity of network providers—binding services to this specific provider—hinders, e.g. the company-wide usage of location services where employees have different mobile contracts.
Indoor room-level positioning, requiring a different kind of granularity than geographical coordinates out-door, lead to a number of approaches exploiting Infrared, Ultrasonic sound, and local-range Radio Frequency (RF).
RF localisation in general, for a long-time, could not provide the dynamic granularity to know on which side of the wall the object is (10 cm), or the proximity (e.g. 1 m) to a particular Point of Interest (PoI). Bahl et al. (Microsoft Research)  describe the ‘RADAR’ approach based on existing WLAN infrastructure, analysing the signal strength already measured in any WLAN card (RSSI: Receiver Signal Strength Indication). Within the coverage of multiple base stations for triangulation, they achieve a resolution of 2–3 m, without requiring extra hardware beyond the WLAN, derived from either a mathematical or an empirical radio-mapping of the building. Weaknesses of this method derive from the propagation characteristics of its radio frequency. A privacy bonus is the fact that the location is known to the receiving mobile only, leaving the dissemination to the discretion of the user.
AeroScout is a commercial WLAN based location system from Bluesoft. It employs additional custom hardware to remotely detect and locate WLAN enabled devices . The system uses a central server to collect, process and expose location data.
Ultra-wideband (UWB) radio technology  currently becomes a promising technology for RF positioning, as the extremely short pulses can be easily distinguished from multi-path receptions. Most recently, UWB has been demonstrated in positioning approaches with centimeter-wide precision by UbiSense .
Modulated Infrared (IR) light based technology provides advantages such as the restriction of signals within rooms (IR does not pass walls) and absence of electromagnetic interference. The signal power can easily be adjusted to cover small areas only.
Olivetti Research (ORL) and Cambridge University, UK, pioneered with the nowadays ‘classical’ approach of the (transmissive) Active Badge  and  infrared sensor system. Designed, prototyped and trademarked by ORL between 1989 and 1992, a small device worn by personnel, transmits a unique IR signal every 10 s.
Commercial follow-ups, like the EIRIS Infrared Localisation System from ELPAS Electro-optic Systems Ltd, Raanana, Israel , provide more reliable sightings due to advanced use of diffuse infrared transmissions. The sensitivity of the IR receiver had been pushed to the limit by carefully shielding the diode and amplifier with meshed copper and complex signal regeneration—thereby also increasing the price, significantly.
The general problem consists of battery power being the scarce resource in the badge, allowing limited power of the IR emission only. Sensitivity of the wall-mounted receiver is crucial—but expensive.
An approach to overcome these limitations has been reported from Fraunhofer FOKUS , converting from transmissive to receptive badges. Wire-powered, arbitrarily strong, stationary infrared beacons blast their ID in the room, which is received by the badge and, more battery-efficiently, communicated via RF signals to a small number of base stations in the building.
All these badges are currently used in niche markets and labs only, also suffering (to a different degree) from their intentional or accidental concealability, sometimes considered as a privacy advantage, but unsuitable for security applications. Environmental conditions, such as direct sunlight, often interfere with the signals.
The transmission of ultrasonic sound within rooms led to a number of interesting prototypes. The basic idea is, compared to GPS, to move to a slower carrier (speed of sound instead of speed of light), to make the received timing differences more measurable in a room environment. Strong dampening effects of walls again limit the signal to the desired room-coverage. Again both, transmissive and receptive approaches are found.
Randell  compares the timing of four ultrasonic signals received from four transmitters within a room, while the timing of the signals has been transmitted via RF, and calculates the relative position. This approach is two-way receptive, in the ultrasonic band as well as in the RF. The localisation is very precise (10–25 cm). However, the cost of setting up the ultrasonic transmitters, which must be networked for synchronisation, allows the deployment in selected rooms only.
The Cricket Compass  also receives ultrasonic signals accompanied with RF location IDs. Five receivers in the mobile device form a dedicatedly shaped layout. It then can triangulate from ceiling beacons and derive location as well as orientation (‘compass’) information.
In conversion of the principle, the Active Bat (AT&T labs, the former ORL)  transmits ultrasonic pulses from the mobile device, received from a matrix of ceiling microphones. It obtains a resolution of 8–10 cm.
From the commercial aspect, the price of such ultrasonic installations would limit application to a small number of mobiles attached with the technology.
Weight or force measuring on the floor is another laboratory approach. It starts with the ORL Active Floor , analysing the movement of people and carried objects by Markov Chains, and Georgia Tech's Smart Floor —still requiring expensive installations.
Currently, semiconductor manufacturer Infineon prototypes, in cooperation with the Vorwerk Teppichwerke carpet plant in Hameln, Germany, a ‘smart carpet’, within their ‘intelligent textile materials’ programme . A self-organising network of robust chips allows to embed sensor functions, such as monitors for pressure, temperature or vibration, directly into woven fabric materials. Linked by a wired grid it allows to cut the material arbitrarily.
Wireless sensor networks—similarly to the wired carpet above—are a hot research topic currently, and might be used to sense the proximity of people and objects (e.g. by temperature or chemical impacts), as their main purpose or as a side effect. While from the current perspective they might not be deployed as primary means of positioning, they could contribute to the redundancy approach discussed later in this paper.
The visual channel is the most developed one for human orientation—and most challenging for technology.
Visual localisation and tracking based on advanced stereoscopic image processing is a very promising approach  and . Falling costs for small cameras and increasing processing power make it feasible that such systems might take the place in the ceiling corners of the room, where nowadays the motion sensors of the burglar alarm reside. Privacy issues are of course most debatable within this area.
Concluding this section, none of the developments alone as discussed above would fulfil requirements such as affordability and precision targeted to specific applications. To provide a solution for a situation where the number of localisation areas (office/meeting rooms, multiple PoIs in exhibitions, campus) has the same magnitude than the number of persons or objects to be localised, further approaches have to be considered.
2.2. Location fusion approaches
Many researches in the area of location sensing have come to the conclusion that location information needs to be centrally managed. Some of these researchers have come to the added conclusion that one location system is not enough for reliable positioning and that the aggregation of data from multiple location systems is necessary for robust person and device positioning .
A common approach to this issue is the use of a layered model to transform specific data from individual location systems into generic location information. Part of this transformation is the use of Sensor Fusion techniques to combine information form multiple heterogeneous sensor networks.
Sensor Fusion is defined by Hightower/Borriello  as ‘the use of multiple location systems simultaneously to form hierarchical and overlapping levels of sensing … to increase accuracy beyond what is possible using any individual system’.
This group at Intel Research has proposed the standardisation of location systems for ubiquitous computing through a six layered model that incorporates location sensor fusion as well as context information fusion ,  and . The system is proposed as a replacement to ‘monolithic’, specific location systems.
Similar work by Leonhardt/Magee from the Imperial College, London  results into a three layer stack that can then be layered on to itself. This recursive approach presents a way of mapping the stack to all aspects of a location information system from sensors to clients.
Enhancement of location estimations by evaluating previous, historical data have rarely been discussed. In RADAR, Bahl  reports improvements by using short-term, history-based user-tracking to follow a user by evaluating the likelihood of a new position depending on the immediate previous ones.
A completely different perspective is described by Ferscha  in terms of the Digital Aura of objects, allowing for spontaneous interaction, based on detected experience of the same context—and certainly people can develop such aura as well. Inherently, such aura considers historical data for its self-awareness.
Many approaches discuss the individual error, precision or confidence level of the respective technology, but wrong and misleading information is not being considered. Typically in the fusion approaches, an overall confidence level is calculated—abstracting from the underlying input technology.
3. Redundant positioning architecture
We assume that the whole avalanche of ubiquitous computing devices of most different kinds that is set to arrive in our fives can be exploited for positioning purposes—even if not originally built for this—and supported by a number of dedicated positioning systems. It is the combination of all of them, delivering small pieces of information into a mosaic representation of the real life—Fig. 1 gives some illustration.
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Fig. 1. Positioning across domains.
Naturally, not all of this information will be correct, as different classes of errors occur, leading to different degrees of imperfection:
• Uncertainties in the actual position are mostly discussed in the literature, they are caused by the limited precision of the respective measurement, and can vary within the same technology, depending on the actual Dilution of Precision (DoP), e.g. depending on satellite constellations, RF multi-path reflections, etc.
• Expectations of how long an object is still in proximity after a position scan might be based on typical assumptions. On a footpath, the typically expected speed would be pedestrian; or a location scan at the cash register of the cafeteria would assume a typical eating time of 20–30 min. But as exceptions, objects can be faster, and the lunch could have been interrupted.
• Relations between objects change, incidentally or intentionally. Labels fall off, the RFID tagged sweatshirt—so far a typical characterisation of a certain person—has been handed to the sister, or the cash card has been borrowed from a colleague to buy some coffee in the canteen.
If we compare these issues with ordinary human life, then we have learnt to recognise people by a large number of individual characteristics. Facial properties, size, typical glasses worn, the style of clothing, voice, and many more. Even if one of these properties does not match, we recognise due to the redundancy of the other, compensating misleading information, and we adjust in a learning process some properties to new values, e.g. the new hair cut or other clothing.
When we meet new people, this learning process is obvious. First, we lay out the business cards in front of us in the seating order, but soon we hear the people speaking, and individualise them by behavioural characteristics—sooner for strong characters, after some reinforcing cycles of further meetings for the other.
3.1. Characteristics of redundant positioning
Applying this behaviour to a technical system leads to the proposed approach of redundant positioning with a vast amount of sensing nodes contributing to the whole image, including the consideration of imperfect, misleading and wrong information from individual nodes, and the accumulation of history data from previous sightings for the learning process.
As discussed in Section 2.1 there is a large number of positioning systems and methods available. Each system has established characteristics including the overhead involved in installing its infrastructure support (hardware and/or software) and expense to procure the system. Extensive physical installation or high costs may limit large-scale deployment to key areas. Other systems, such as those based on Wifi, may be easily deployed on large-scale, but are limited in accuracy, and may be able to locate a sub-set of devices only. When every possible source of location data is pooled, greater coverage of an area can be achieved. As well as coverage issues, multiple sightings from different location systems can be used to reinforce or discount a user's estimation.
3.1.2. Temporal validity
It can be argued that every sighting has a temporal validity attached to it. For example, a device sighted by a WiFi location system will be valid for a small period of time (1 or 2 s). A user sighted using his debit card to buy a cup of coffee from a till in a coffee shop could be valid for a number of minutes (the time taken to drink the coffee). By attaching temporal validity data to a sighting the system can maintain location estimations between sparse sightings.
The confidence is a measure of the potential error in a location estimation. It is not just a distance measuring error—it could also be an error in the perceived carrier of a located device. In other words, it is geometrical precision vs. detecting the wrong person. Confidence is important as applications potentially using the location information may reject it if the confidence is not high enough. As there are different classes of confidence (or errors, as discussed above), giving an application just a numeric value of confidence, e.g. in percent, is not sufficient when the type of possible error is relevant.
3.2. Input channels
No possible input channel should be excluded from the system approach, even if implementation will be limited by cost and pragmatic reasons. Naturally, different combinations of such channels will be exploited at different locations. Modularity is the key issue in this part of the architecture. Listing a number of possible channels below, therefore, provides examples in the context of redundancy, not completeness.
The satellite based positioning is embedded in an increasing number of devices such as PDAs and phones. It provides an interesting relevance for indoor positioning when the sudden absence of a GPS signal coincides with the last reception at the entrance of a building—this a valuable piece of information triggering the transition from the outdoor model into indoors.
Readers for RFID in gate situations like doors detect objects and persons passing, RFID readers in container objects (desks, briefcases, bags) detect their content, RFID readers at furniture and desktop inventory detect proximity of people and associated objects.
3.2.3. Visual tracking
Cameras in corners of the room track recognised contours (which are then personalised, if not by the visual technology itself, by mapping to read RFIDs at specific points).
Weight-measuring floors, or the more economical Infineon carpet, qualify the trajectory observed by other positioning.
3.2.5. Wireless sensor networks (WSN)
Measuring proximity of people (e.g. temperature) by wireless sensor networks might be used alternatively to the floors. Alternatively, RFID readers might become so small and inexpensive that they can be deployed as self-organising WSN.
3.2.6. Active badges
The badges are an option for places where the obtrusiveness of these devices counts as positive argument (e.g. factory visitors), and the maintenance cycles can be fulfilled.
3.2.7. Biometric sensors
Providing a high (certainly not absolute) level of confidence, biometrics can be used punctually, when positioning requires that level of authentication.
3.2.8. Ultrasonic and ultra-wideband positioning
As long as the installation cost per room can be kept reasonable, the high precision obtained from this principles can be helpful in calibrating other channels in the redundancy approach, as discussed in Section 3.4.
3.2.9. Wifi positioning
Using additional hardware provides more precision, but already the software-based approach alone is of major relevance for the redundant positioning approach, given the rapidly increasing installation base and coverage of wireless LAN.
Already the cellular nature of an installation (supported by some more sophisticated approaches discussed in Section 2.1) provides good coarse positioning, well suitable to be complemented by other technologies.
3.2.10. GSM and beyond
Cell and sub-cell positioning is an inherent feature of mobile communication, however, the large business models behind the competing telecoms demand for exclusive exploitation of value-added location based services. This makes a cooperative approach difficult and requires a change of thinking.
3.2.11. Location inference
All software approaches based on already existing systems might be considered as location inference. Typically, these methods depend on the cooperation of the owner/operator of the specific system. Typical examples are: recognising devices at wired ethernet patches, the proximity of Bluetooth enabled devices, the Wifi positioning already discussed, and the usage of swipe or smart cards at stationary stations. While usage at bank ATMs should be withheld for security reasons, a more relaxed policy might be acceptable for multi-purpose campus cards (library, access control, petty cash).
Obviously, the best effects are expected from the most different kinds of principles of positioning, thereby being most complementary.
In Fig. 2, an architecture for the approach of redundant positioning is presented, and discussed from bottom to top below. It features an arbitrary number of different input channels, each representing a large number of sensing devices of a common positioning technology, as discussed above.
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Fig. 2. Architecture for redundant positioning.
The heterogeneity of these input channels requires preprocessing tailored to the specific technology. E.g. a technology generating large numbers of sightings might apply an early aggregation of data, outputting the counting of same or similar scans. The video tracking or face recognition might have the tracking and recognition process encapsulated there, outputting room vectors of sightings, or extracted standardised visual features.
In the preprocessing, privacy filters can be implemented. So I might switch a video tracking camera in a mode prohibiting face recognition or apply temporal restraints. RFID readers could contain a blacklist of items not to be scanned (or, more restrictively, a positive list of allowed items, like office property). The Wifi positioning software might respect non-sighting escape zones, e.g. the canteen.
The goal of that early filtering is to avoid protected data to accumulate in history buffers appearing in upstream modules.
The pre-processed data are forwarded to the collection module. Its most important task is to provide a ‘plug-in’ socket for each input channel. Two basic types of modules can be distinguished:
• a module that uses a specific location systems API to collect data from it,
• a module that has an API that a location system uses to pass in data.
Scans will be time stamped here, if they have not been stamped in their originating positioning system. This time stamp is crucial to compare data from different sources, and for applications to determine if there is a location determination lag.
The format of the data is not unified at this point—it would be normal to expect multiple, incomparable formats.
On the collection level, a history of object positions is important to be kept. This preserves valuable data coming from individual location systems for later comparisons, detection of typical patterns and routines. Beyond this point data can/will be modified.
The abstraction module changes the location data (if necessary) into a site specific form as a default operation. The primary goal is to present the data in a unified, object-oriented view. The abstraction module consists of elements that transform between data types. A limited number of transformers can be chained together to perform any transformation, avoiding the need for n:n individual transformers.
The fusion module combines data from multiple sightings on multiple location systems. Employing the provided redundancy at this level leads to potentially improved accuracy. The fusion should take into account that some location systems have a built-in level of accuracy. Potentially, the fusion module could have different fusion algorithms—and could chain them to perform multiple fusions—in order to provide different perspectives to the presentation layer, which then can tailor the perspective to the heterogeneous applications.
At the fusion level, it is possible to store a history of object interactions, i.e. preliminary assignments of objects to other objects and objects to people, to observe regular patterns and routines, e.g. what items a person regularly wears, what valuables typically go with which owner.
This history is the prerequisite for the learning process, which then can lead to predictions of probable interactions in the future. Prediction is a supplement to fusion. It uses history information to make predictions of where a user should be or is going. People tend to retrace their steps due to routines they may have or sheer habit. Prediction could aid accuracy and provide for interesting applications such as predicting that a doorway or hall will be very busy in the near future and, therefore, should be avoided.
The presentation level must provide everything an application could possibly need. In coding location based services it is often equally important to locate a device as well as the person using it. Previously proposed approaches of fusion, as discussed in Section 2.2, obscure/abstract location data to a level where useful information is lost (e.g. the type of devices that have been located and have been part of the fusion process, or the reason for assigning a certain confidence level).
This hinders the application to ask different questions, e.g. the question “Where is Tom?” might be answered by an aggregation of sightings of items he regularly wears. Having such sightings fused with details lost would make it impossible to answer Tom's question “Where is my PDA?”. Avoiding this problem, our approach allows the presentation level to communicate with pre-fusion levels, such as collection and abstraction.
Patterns of joint movement are part of the object interaction history, and can be employed by applications where the authorisation of movement can be observed—e.g. a car key moving with somebody else than the owner, or appearingy alone, could raise attention.
On the output side, beside the location itself, the confidence level, and the type of confidence expressed in the type of the providing device are presented.
So far, the system has been restricted to (timed) location as a subclass of context. Whether introducing other subclasses and background knowledge of the surrounding world—as indicated with the Ontology module—improves the positioning, has to be investigated.
For any external access, a filter and firewall module restrict access to an allowed amount of information, which can be adjustable to the degree of authentication the querying party provides. Further details about security and privacy are discussed in Section 3.6.
3.4. Location calibration
When positions are derived by inference from more or less stationary equipment, it has to be inventarised in respect of its precise location in the room. This would be a tedious process when done manually and raises questions of manageability: nobody would like to type lots of positions in databases. A location aware registration tool, e.g. in the form of an RFID label dispenser, would be very helpful.
Combining a precise indoor positioning device, e.g. Ultrasonic/UWB positioning (such as Cricket compass  or Active Bat , UbiSense ) or a laser range-finder, with a near-range RFID reader results in a position inventory tool, as depicted in Fig. 3.
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Fig. 3. Mobile Position Inventory Device: location aware RFID dispenser.
The RFID, already on the object and thereby assigned to the name of the item, or freshly dispensed on location, is read, while simultaneously the precise position within the room is determined. Via the Wifi interface, the location and if necessary the just inventarised item description is communicated towards a database.
Re-calibration of the positions of equipment after rearrangements is just a short walk through the office, touching each item with the tool.
3.5. Inter-domain communication
It is logical to split geographic regions up for the purposes of ease of management, for example, local government or councils. The same logic applies to location information systems. By making a location information system responsible for a specific area it limits the number of devices it has to constantly locate, thus reducing the stress on the system. Other location information systems can then be established to cover other geographic locations, or other property domains. If the systems are aware of each other and can query device locations from one another, a peer to peer location information network forms, as shown in Fig. 4. Thereby, they operate as a group of ‘loosely coupled cooperating domains’.
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Fig. 4. Inter-Domain location Information exchange.
The information exchange requires a message format and protocol, which considers the heterogeneity of the data being exchanged. Examples for such data possibly exchanged are:
• Geographical, room-relative or map-specific positions,
• characteristics of objects (RFID, visual image suitable for recognition, visual sub-image, e.g. logo, extracted visual features in the recognition process, measurements, voice pattern, acoustical features of the voice in the recognition process, etc.)
• biometric data sets,
• meta-information about the objects or object interactions (e.g. by whom typically carried).
As the communication might comprise different administrative domains, different degrees of private or public services have to be considered. Therefore, a filtering firewall allows to tailor the data to the authorisation level of the other party.
3.6. Security and privacy
Security issues arise in particular when location information is exchanged between independent location domains. Privacy and ethical implications have become a topic of hot discussions  with few solutions so far. Location information firewalling and filtering, as well as early avoidance of collecting certain data are used to solve these issues.
The vast amount of information being processed for the redundant positioning might be acceptable within an environment of open communication, i.e. within a department, a small company, a family, or among friends. For any external questions, this information needs to be filtered, and only explicitly specified details, or highly aggregated data would be provided.
The home domain (home office or residential home server) of a user might, e.g. publish some identity information about the user on the level of an ID card, comprising a public photograph, some biometrics, a cryptographic hash of his RFID number, and a digital signature. Further information, e.g. about objects typically worn/carried might be limited to certain trusted domains, or withhold completely in respect to relevance and privacy. Aggregated position information might be externally provided, e.g. during business hours. The filter rules can be adjustable for differently authenticated users (friends, cooperating partners, strangers).
The filtering and blocking algorithm built into the pre-processing before data collection avoids unwanted data to accumulate in the system.
4. Research and implementation
Section 3 has described principles of a new quality of highly redundant fusion for positioning and authentication in location aware systems. This approach is being followed from the theoretical as well as the practical side.
4.1. Data model
A location is much more than a set of geographic coordinates. Coordinates can only contribute as a method to uniquely identify locations . Each location usually has a role such as representing a position in a specific street, being the entrance to a building or a meeting room. In indoor positioning any location can be defined by more than its coordinates as room, floor and building, giving a complete address, or relative positions within the room.
The semantic of location information depends on the application domain. The area of interest determines in which form the final position is being expected. A number of geographical data models—as illustrated in Fig. 5—have been discussed in literature. For example,  breaks ‘Location’ into four levels of granularity, namely Position (a single fixed place of an object, measured in relative or absolute coordinates), Address (a structured, textual description how to find a Location), GeographicArea and Structure (representing buildings, floors, rooms, and other entities).
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Fig. 5. Area of Interest and Position data modelling illustration .
In any location system, regardless of the type of coordinates used, there is likely to be a set of impossible coordinates. These represent areas where it would—be physically impossible for a device to be. Knowing that certain coordinates can be discounted will aid accuracy and influence decisions.
The data model to be developed needs to consider the possible requirements of the application as well as the heterogeneous sources of positions from the redundant supply.
4.2. Testbeds and experiments
As part of the NOMAD project , a wide scale infrastructure to support mobile applications has been deployed on the WIT campus. This infrastructure includes Wifi positioning technology that is both software and hardware based. So far it has been used for psychological experiments of group interaction among mobile device users, equipped with augmented PDAs.
Each access point in this network is being polled for the list of devices currently associated to it. This gives very general device sightings for general areas within the campus. At certain spots of interest, the more accurate hardware based Wifi triangulation provides higher precision.
Based on a existing partnership with the mobile GSM/GPRS operator O2 Ltd in Ireland, sub-cell location data can be obtained for an area of the city including the campus. Given the nearly complete penetration of GSM/GPRS handsets among the student body, a significant amount of data contributes to the system.
RFID reader technology is being installed at numerous points around the campus, RFID tags are being dispensed in a large number (to handsets of participating students, to office property, to teaching material, etc.).
Visual tracking cameras are being deployed in selected labs only for current privacy concerns.
A dual Magnetic Swipe and Smart Card card of the WIT campus is currently being used for access control, library check-out and petty cash card. The debiting is processed in a central database, which will deliver data about the specific cash terminal used as positioning information.
The patch-panel database where fixed private IP addresses are assigned to room locations is being used for login-based positioning at workstations.
Exploiting all sources discussed above will provide the critical mass of redundant data to implement and test the system architecture as described in Section 3, and preliminary experiment with the data flow, while more theoretical aspects of the approach are being investigated.
4.3. Analysis, modelling and simulation
The redundant positioning approach processes a vast amount of data. For the algorithms in fusion and decision, for the continuity and the timeliness of the arriving data, appropriate models are being designed and evaluated in a mathematical analysis, and tested in simulations, before they can finally be implemented. Some approaches of handling the data stream can also been derived from the Auto-ID consortium, e.g. the Savant software .
The modelling also needs to determine how other context classes can contribute to the improvement of positioning.
Analysing technology for positioning available commercially and in the laboratories from a usability perspective, an architecture has been designed that allows to combine an arbitrary number of dedicated positioning systems and location inference methods to contribute in a massively redundant way. This allows the reliable determination of positions of objects and persons, at the time present and in predictable near future, as well as their interactions and the elimination of wrong and misleading information by self-learning, self-adapting and self-healing.
The presented architecture has purposely been restricted to positioning (including aspects of recognition and authentication), as a subclass of context. To what extent the interaction with the other subclasses of context would increase the usability of the system has to be investigated.
 Alois Ferscha, Coordination in pervasive computing environments, Proceedings of the 12th IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE'03) (2003).
 T. Buchholz, A. Küpper and M. Schiffers, Quality of context: what it is and why we need it, Proceedings of the 10th HP-Open View University Association Workshop, Geneva, Switzerland, July (2003).
 Jürgen Bohn, Vlad Coroama, Marc Langheinrich, Friedemann Mattern and Michael Rohs, Social, economic, and ethical implications of ambient intelligence and ubiquitous computing. In: W. Weber, J. Rabaey and E. Aarts, Editors, Ambient Intelligence, Springer, Berlin (2004).
 Jeffrey Hightower, Gaetano Borriello, A survey and taxonomy of location sensing systems for ubiquitous computing. Technical Report UW-CSE 01-08-03, University of Washington, Department of Computer Science and Engineering, Seattle, WA, Aug. 2001.
 Jeffery Hightower, Gaetano Borriello and Dieter Fox, The location stack. White Paper IRS-TR-03-008, Intel Research, Seattle (2003).
 David Graumann, Walter Lara, Jeffrey Hightower and Gaetano Borriello, Real-world Implementation of the Location Stack: The Universal Location Framework, Proceedings of the 5th IEEE Workshop on Mobile Computing Systems and Applications (WMCSA 2003) (2003).
 Ulf Leonhardt and Jeff Magee, Multi-Sensor Location Tracking, Proceedings of the 4th ACM/IEEE International Conference on Mobile Computing and Networking, Dallas, TX (1998) pp. 203–214.
 Jeffrey Hightower, Barry Brumitt and Gaetano Borriello, The Location Stack: A Layered Model for Location in Ubiquitous Computing, Proceedings of the 4th IEEE Workshop on Mobile Computing Systems and Applications (WMSCA 2002), Callicoon, NY (2002) pp. 22–28.
 Albrecht Schmidt, Ubiquitous Computing—Computing in Context. PhD thesis. Schöneiche, Germany: Multicon, 2003, ISBN: 3-930736-04-7.
 P. Bahl and V. Padmananbhan, RADAR: An In-Building RF-based User Location and Tracking Systems, Proceedings of IEEE Infocom Tel-Aviv, Israel (2000).
 C. Randell and H. Muller, Low Cost Indoor Positioning System, UbiComp 2001: International Conference on Ubiquitous Computing, Atlanta, Georgia, USA; Springer LNCS 2201 (2001).
 Steve Poizner and Karissa Todd, Extending GPS capabilities, Wireless Review 16 (1999), p. 9 Overland Park, KS, 1 May 1999.
 W. Hirt, Ultra-wideband Radio Technology: Overview And Future Research, Computer Communications vol. 26, Elsevier, Amsterdam (2003) 1; pp. 46–52.
 Ubisense Ultra-wideband (UWB) positioning technology, http://www.ubisense.net/ http://www.ubisense.net/, 2004.
 A. Harter and A. Hopper, Distributed location system for the active office, IEEE Network 8 (1994), p. 1 Jan/Feb. 1994, IEEE Computer Society, pp. 62–70.
 R. Want, Andy Hopper, V. Falcao and J. Gibbons, The Active Badge Location System, ACM Transactions on Information Systems 10 (1992) (Jan), p. 1.
 EIRIS Infrared Localization System, System Manual, ELPAS Electro-optic Systems Ltd, Raanana, Israel, Jan 1999, see also: http://www.elpas.com/ http://www.elpas.com/.
 T. Pfeifer and D. Elias, Commercial Hybrid IR/RF Local Positioning System,, KiVS'2003, Kommunikation in Verteilten Systemen, Leipzig, Germany, Feb 2003. In: K. Irmscher and K-P. Fähnrich, Editors, KiVS 2003. Kurzbeiträge, Praxisberichte, VDE Verlag, Berlin, Offenbach (2003), pp. 119–127 (ISBN 3-8007-2753-6).
 M.D. Addlesee and A. Jones et al., The ORL active floor, IEEE Personal Communications 4 (1997), p. 5 IEEE, New York, Oct. 1997, pp. 35–41.
 A. Ward, A. Jones and A. Hopper, A new location technique for the active office, IEEE Personal Communications 4 (1997), p. 5 IEEE, New York, Oct. 1997, pp. 42–47.
 Nissanka Priyantha, Allen Miu, Hari Balakrishnan and Seth Teller, The Cricket Compass for Context-Aware Mobile Applications, Proceedings of 7th ACM MOBICOM, Rome, Italy, July (2001).
 Trevor Darrell, Vision-based Perceptive User interfaces. Presentation on September 30, 2001, Workshop on Sensing and Perception for Ubiquitous Computing at UbiComp 2001, International Conference on Ubiquitous Computing, Atlanta, Georgia, USA (2001).
 P. Bahl, V. Padmananbhan, A Software System for Locating Mobile Users: Design, Evaluation, Lessons, Microsoft Research Technical Report TR-2000-12, Feb. 2000.
 Gregory D. Abowd, Robert J. Orr, The smart floor: a mechanism for natural user identification and tracking. Extended Abstracts of the 2000 Conference on Human Factors in Computing Systems, The Hague, The Netherlands, ISBN 1-58113-248-4, pp. 275–276.
 Infineon: News.-http://www.infineon.com/cn/news/20030715-01.htm http://www.infineon.com/cn/news/20030715-01.htm (Jul. 2003) and http://www.infineon.com/cn/news/20040114-01.htm http://www.infineon.com/cn/news/20040114-01.htm,Jan.2004.
 Ryan Cassel, Christophe Collet, Tracking of Real Time Acrobatic Movements by Image Processing, Gesture-Based Communication in Human–Computer Interaction: 5th International Gesture Workshop, GW 2003, Genova, Italy, April 15–17, 2003, Springer, LNCS 2915/2004, ISBN 3-540-21072-5.
 SafeTzone Child locating system. http://www.safetzone.com/ http://www.safetzone.com/.
 AeroScout Location system.-http://www.aeroscout.com/ http://www.aeroscout.com/, 2003.
 AutoID standards summary, http://www.autoid.org/standards/project_Summary.htm http://www.autoid.org/standards/project_Summary.htm, March 2004.
 Electronic Product Code (EPC) Network: EPCglobal, http://www.epcglobalinc.org/ http://www.epcglobalinc.org/, 2004.
 S. Long, R. Kooper, G.D. Abdowed and C.G. Atkeson, Rapid Prototyping of Mobile Context-Aware Applications: The Cyberguide Case Study, Proceedings of the 2nd ACM International Conference on Mobile Computing and Networking, MobiCom (1996).
 3GPP Technical Specification Group Radio Access Network. Stage 2: Functional Specification of Location Services in UTRAN, Technical Specification 3G TS 25.305 V3.1.0, 3rd Generation Partnership Project (3GPP/TM), March 2000, http://www.3gpp.org http://www.3gpp.org.
 Tim Kridel, E-911, mandate of opportunity, Wireless Review 15 (1998), p. 23 Overland Park, KS, 15 Nov. 1998.
 Radu Popescu-Zeletin and Tom Pfeifer, A Modular Location-Aware Service and Application Platform, Proceedings of The Fourth IEEE Symposium on Computers and Communications, ISCC'99, Red Sea, Egypt (1999).
 Mario Schuster, An Open Service Platform for Context and Location Aware Applications, Diplomarbeit (MSc Thesis), Technical University of Berlin, 2001.
 Shared Information/Data (SID) Model, Addendum 5PR-Physical Resource, Business Entity Definitions, GB922-Addendum-5PR, Member Evaluation Version 3.0, NGOSS Release 3.5, June, 2003.
 OpenGIS, Location Services (OpenLS): Core Services, Open GIS Consortium, 2004, http://portal.opengis.org/files/?artifact_id=3418 http://portal.opengis.org/files/?artifact_id=3418, http://xml.coverpages.org/OpenGIS-LS03006r3.pdf http://xml.coverpages.org/OpenGIS-LS03006r3.pdf.
 NOMAD project: SToRC (Dundalk IT), CCTA (Dun Laoghaire IADT-DL), TSSG (Waterford IT), Ireland, http://www.nomadireland.org/


Research infrastructures are facilities, resources and services that are used by the research communities to conduct research and foster innovation in their fields. Where relevant, they may be used beyond research, e.g. for education or public services. They include: major scientific equipment (or sets of instruments); knowledge-based resources such as collections, archives or scientific data; e-infrastructures, such as data and computing systems and communication networks; and any other infrastructure of a unique nature essential to achieve excellence in research and innovation. Such infrastructures may be 'single-sited', ‘virtual’ or 'distributed'.
Research infrastructures play an increasing role in the advancement of knowledge and technology and their exploitation. By offering high quality research services to users from different countries, by attracting young people to science and by networking facilities, research infrastructures help structuring the scientific community and play a key role in the construction of an efficient research and innovation environment. Because of their ability to assemble a ‘critical mass’ of people, knowledge and investment, they contribute to national, regional and European economic development.
Activities funded under this Part foster the innovation potential of research infrastructures, for example by reinforcing partnerships with industry, transfer of knowledge and other dissemination activities, use of research infrastructures by industrial researchers, and involvement of industrial associations in consortia or in advisory bodies.
Research Infrastructure activities also contribute to widening participation to the programme by supporting the development of Regional Partner Facilities in ESFRI projects and integrating activities. The use of European Structural and Investment Funds to build capacities and infrastructures at national and regional level in line with the relevant smart specialisation strategy is encouraged .


Welcome to the website of the Cambridge-wide electron microscopy association, CamEM. It comprises a group of scientists and engineers from across the physical and life sciences, engineering and the pharmaceutical industry, representing many departments and institutes across Cambridge. It is entirely run by members of the electron microscopy community in Cambridge, UK. Here you will find details of the mailing list, the primary way of communicating with members, as well as details of current and past meetings and other information for electron microscopists in Cambridge.
Join the mailing list for updates.
Seminars hosted by the EM group in the Department of Materials Science and Metallurgy are announced here.
EM discussion group meetings at the MRC Laboratory of Molecular Biology which are open to the public are listed here on the LMB seminar list.


Lighted CW Straight Key Callsign Display LED
Bring your callsign to life with this LED light display that features 16 unique RGB colors. You have the choice to rotate through the different colors or only display one at a time.
Bases are powered by any standard USB Port or wall charger with the included cable.
These beautiful LED Edge Lighted displays are a great addition to any shack!
Dimensions: 6" High X 4" Wide
Please allow 2 business days for production.


Updating roof antenna in co ops
Under terms of the settlement, Continental must pay the co−op 6,000 this year in addition to an annual rent payment for the rooftop space.
The report from the co−op’s independent engineer found that the antennas were safe, according to the settlement.
Kymeta's technology "could solve the challenge of vehicle-based satellite communications," says Shigeki Tomoyama, who runs Toyota's intelligent transportation research efforts.
Because a passenger car needs less data than an airliner carrying hundreds of passengers, the automotive version of Kymeta's antenna will likely be six inches in diameter.
That's why Toyota's over the idea of 4G connections.
Continental Communications sued North Shore Towers and the co−op’s board of directors individually for 0 million after the co−op held off on endorsing the company’s permit for the antennas, which were up for renewal.
Herb Cooper, one of the Towers’ board of directors, was waiting for the co−op to hire an independent engineer to look into potential hazardous effects from the antennas before the co−op would act on the permit.
The Kymeta antenna is made up of a liquid crystal gap layer between two circuit boards or sheets of glass.
It can be electronically reconfigured to point right at the satellite, which means no moving parts and no heavy, very un-aerodynamic dish that needs to swing this way and that as the vehicle it's on moves around.That's why the Japanese automaker's working with Kymeta.


Circuit tester meaning
A circuit tester is also known as an online circuit repair tester, and a circuit board fault detector is a general-purpose circuit board fault detection equipment. The voltage detectors have a series of indicators that show the wiring status in the socket, so the technician can verify that it is properly installed. The circuit tester has a universal digital/analog integrated IC device library for online functional testing of integrated IC.
Circuit tester recommended
The circuit tester has many choices on the market, each has its variety, characteristics are not the same, editor today recommended a AUTOOL BT260 automotive circuit tester.
It is appropriate for all 6V to 30V vehicle electrical system diagnoses. Ac voltage detection, DC voltage detection, resistance detection, diode testing, and current overload protection testing can be performed simply by connecting the tool to the vehicle battery. It has a long enough detection cable to cover from the front to the rear of the vehicle, making it easy to check the entire circuit system.
If you have this circuit tester, you can easily solve your automotive circuit detection problems, more product information can be found here.


Many consumers want to shift to more sustainable technology and reduce their carbon footprint, and electric vehicle manufacturers are capitalizing on this by creating newer and more affordable electric cars. If you’re in the market for an electric vehicle, you have many options at your fingertips. However, thanks to the abundance of options, navigating the electric vehicle landscape looks intimidating. We’re here to break down the process and benefits of buying an electric car, so you can decide if going electric is right for you.
Different Types of Electric Cars
There are currently three different types of electric vehicles on the market, and some of them combine a gas-powered engine with an electric battery.
Battery Electric Vehicles
Battery electric vehicles, or simply electric vehicles, are what most consumers think of when picturing a standard electric vehicle. Electric vehicles use battery-powered electric motors instead of the standard internal combustion engine. Electric vehicles don’t use fossil fuels, emit any exhaust, or use liquid fuel parts, such as a fuel pump, fuel line, or fuel tank. When an electric vehicle accelerates, electricity from the battery pack flows into the motor. The battery pack in an electric car needs to be plugged into a wall outlet or charging station to charge.
Plug-in Hybrid Electric Vehicles
Plug-in hybrid electric vehicles are a combination of an electric car and a traditional fuel-powered car. They use a traditional engine and a battery pack to generate power. Like a completely electric vehicle, the car’s battery is charged using an outlet or charging station. Once the battery is depleted, the car starts using its internal combustion engine and regular fuel. Plug-in hybrids use smaller batteries than a pure electric vehicle, but nightly charging will give you a good amount of range before the car switches to gas.
Hybrid Electric Vehicles
Like plug-in hybrids, hybrid electric vehicles contain a gasoline-powered engine and an electric battery. However, unlike plug-in hybrids, regular hybrid vehicles cannot be plugged in. The electric battery is charged via regenerative braking and the internal combustion engine. The addition of the electric battery gives hybrids a better fuel economy.
Electric Car Benefits
There are many benefits and incentives for drivers who choose to go electric, including:
The federal tax credit. Drivers who purchase an alternative-energy vehicle are eligible for a government-issued tax credit of up to $7,500. If you’re leasing the car, the manufacturer receives the credit but has the option of bundling it into your monthly payments.
State and local incentives. Depending on where you live, your state government might also offer incentives for electric car owners, including free parking, carpool access, and a tax credit for installing a home charging station.
Lower fuel and maintenance costs. Saving money on gas is one of the biggest perks of buying electric. In some states, charging an electric car is around 50% cheaper than fueling an equivalent gas-powered car. Electric cars also have fewer parts and require less maintenance overall.
Lower environmental impact. Powering vehicles using electricity instead of petroleum gasoline reduces the number of greenhouse gasses and other pollutants in the environment.
Is an Electric Vehicle Right for You?
While the above benefits are available to electric vehicle owners, electric vehicles aren’t suitable for every driver depending on their lifestyle. The following factors are worth taking into consideration before making your purchase.
Manufacturers are always improving upon their electric vehicles by extending their range, or the distance an electric vehicle can travel before running out of battery power. Most electric vehicles have a range of at least 200 miles, which is still considerably shorter than the typical 400-500 miles for gasoline-powered cars. However, this usually isn’t a problem for drivers who charge regularly and don’t drive long distances. If you’re planning to road trip in your electric vehicle, you might have to map out charging stations along your route. Since electric cars and hybrids are newer than traditional cars, charging stations aren’t as common as gas pumps.
While most drivers charge their vehicle overnight, this isn’t realistic for drivers who live in apartment buildings or lack off-street parking access. If you can’t charge overnight, you’ll need to charge at work or at a public charging station. Charging times vary depending on your vehicle’s battery size and range. Charging an electric vehicle at a public station usually takes between 25 to 60 minutes. This can be one of the most time-consuming aspects of owning an electric car, as refueling at a gas pump only takes a few minutes. Charging your vehicle overnight with a standard at-home charger takes roughly ten hours. Other factors, like inclement weather and rocky terrain, can affect your battery life and require additional charging.
Now that you know the benefits of buying an electric car, you can decide if going the electric or traditional route is right for you. If you’re thinking about buying an electric vehicle, you’re going to need quality insurance before you take that first ride. The General believes that everyone deserves quality coverage and five-star customer service. You can get a quote in two minutes or less, or read more articles about buying a car on our blog.


Intel Core i5-6300U 2.40GHZ? Win10 64 Bit Pro installed & Activated with Original Panasonic Drivers.(MK3 DOES NOT HAVE SERIAL PORT). Operating System Windows 10 Pro Graphics Intel®? HD graphics 520 Display 13.3?
Active Matrix colour IPS HD LCD (1366 x 768) with Touchscreen BluetoothT? 4.1 + EDR Class 1 WiFi Intel®?
Dual Band Wireless-AC 8260, IEEE 802.11 a/b/g/n/ac compliant LAN RJ-45 IEEE 802.3 10Base-T / IEEE 802.3u 100BASE-TX SD/SDXC Memory card slot x1 Serial Dsub, 9-pin USB 3.0 x1 USB 2.0 x2 VAS6160 7-PIN Fischer Port x2 Headphone Mini-jack, 3.5 DIA, stereo Microphone Mini-jack, 3.5 DIA, stereo Dimensions (W x H x D) 349mm x 46mm x 244mm 13.7? 4.96lb Battery Lithium-Ion (10.8V, 6.2Ah) Included Stylus pen, AC adaptor. This unit have fast 6th Generation Intel Core i5 Processors for fast performance.
Processor speed: 2.40 GHz. Ports, Slots & Chassis : USB 3.0 x1 USB 2.0: x1 (optional, configuration port) LAN Connector RJ-45 Serial Port : x1 Headset: x1 Port Replicator Connector Micro SD/SDXC Memory Card: x1 PC Card Slot-Type I or II: x1.Battery: These toughbooks are used, we do not give any guaranty of battery timing. It comes with a built-in. WiFi as it is ready to use.
Windows 10 Professional 64 Bits Installed and Activated. Google Chrome, VLC player, Media Player, Microsoft Security Essential, Open Office.1 x compatible charger and. 1 x Stylus pen (even though you can use it with finger touch). This means "You the Customer" are responsible for sending the item back to us. The item should be in original packaging.
We use 24 hours Fast Courier Service. Get the all latest Updates and Special Offers about OUR STORE. We aim high, set ambitious goals and deliver results, and we use customer feedback to re-calibrate when necessary. We move quickly and make timely, well-reasoned decisions because our future depends on them. Listing and template services provided by inkFrog.This item is in the category "Computers/Tablets & Networking\Laptops & Netbooks\PC Laptops & Netbooks". The seller is "mytek-uk" and is located in this country: GB. This item can be shipped to North, South, or Latin America, all countries in Europe, all countries in continental Asia, Australia, New Zealand, Egypt, Reunion, South Africa.


There’s been a drought over the past forty-something years and the tech industry is quite thirsty. Virtual reality has been around for decades through different forms, but it hasn’t stuck on a commercial level until just a couple years ago. Why now? You ask? As the number of millennials and GEN X/Y gamers increase, so has the need for an evolution in the gaming industry. Dynamics have rapidly immersed a conundrum for game developers. They’ve caught on that they need to propel beyond the dimensions of basic gaming. Not only for the gaming population, multiple industries have tested the waters using VR hardware in their fields as well- to include real estate and the medical industry. But what’s more—the availability of the hardware is easier to come by than previous failed launches in past since the joke of a console Virtual Boy or the hard to acquire NASA headsets in the 80’s.
For the unaware, there are two key components for virtual reality—mobile and extreme gaming and both are responsible for the technology’s usage wow factor. Recently, Samsung has been able to market their Gear headsets at a reasonably fair price of $99 due to the rise of smart phone users in the market. Not for complex gaming, but these headsets are where other aspects of the virtual reality industry can be implemented, such as real estate where home buyers can travel through virtual tours of homes without the need to leave their current residence, or medical field and training purposes. Commercially, the entertainment industry has released short videos and fun exploratory worlds where the consumer can immerse themselves into a different environment with complete 360 degree manipulation.
Since the technology has evolved, so have the pricing points. For an Oculus Rift touch bundle coming in at less than $600 on Amazon, it’s fair to say that the competitiveness in pricing has improved since a couple years ago when virtual reality really made a bang in the world. Its competitor, the HTC Vive comes in at $800 on Amazon.
Another main important factor that virtual reality is making a stick this time is that of money. With capital venturists seeking new scopes of the tech industry, some are game enthusiasts by nature and want to help broaden the research/development of the field. Big juggernauts have backed the major platform developers such as Mark Zuckerberg, in the hopes of widening the virtual reality gap between hardware manufacturers and game developers.
With all the advancements, enthusiasm and hype, the virtual reality world is here to stay. There’s no telling to how far it can go from here.


baking for Raspberry Pi on Mac OSX
If you are on Mac there is an app that I recommend you for ‘baking’ the (micro)SD cards for Raspberry Pi: ApplePi-Baker. It allows you to prepare an SD-Card for use with Raspberry Pi’s NOOBS, and it allows you to “flash” and IMG file to an SD-Card. It’s also very suitable for creating or restoring card IMG backup and it also works with USB drives.
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The water circulates through our Delta UV sanitizer that uses the type C ultraviolet rays to completely purify water deactivating all the dangerous micro-organisms resulting in the cleanest and purest water possible for your pool. As small residual of chemicals are used in order to destroy any micro-organism that do not find their way into the UV sanitizer.
WHAT IS ULTRAVIOLET LIGHT?
Ultraviolet (UV) light is energy within the electromagnetic spectrum that has shorter wavelengths than that which are visible to the human eye. UV light is a range of electromagnetic waves from 100 to 400 nanometers (between x-ray and visible light). The division of UV light is classified as Vacuum UV (100-200 nm), UV-C (200-280 nm), UV-B (280-315 nm) and UV-A (315-400 nm). The energy waves provided in the UV-C spectrum demonstrate the germicidal efficiencies that provide highly effective disinfection.
For Cleaner, Healthier Pool Environment


Field Technician 41067
Company: Anistar Technologies
Posted on: September 24, 2022
Job Title: Field Technician
Location: Savannah, GA
Anistar Technologies is seeking a Field Technician to perform
maintenance/installation of two way radios, base stations,
repeaters, mobile devices, portable devices, wireless broadband
devices, and CCTV equipment.
- Familiarity with antennas, necessary cabling and grounding, and
- Demonstrate proficiency with installation of mobile and fixed
wireless radio equipment consistent with manufacturer and company
- Remove and re-install OEM vehicle interior trim
- Install electrical wiring, connect electrical fittings, drill
holes, soldering and perform all physical tasks associated with
modifying vehicles for the acceptance of emergency-related
- Develop wiring routes throughout the vehicle for the most
efficient wiring path and enable future replacement and/or
- Troubleshoot and diagnose problems with equipment as necessary
Required Knowledge/Skills, Education, and Experience:
- 2+ years' Service Technician experience in diagnosing,
repairing, programming, and installing Motorola equipment in the
2-way radio wireless and /or communication field.
- Knowledge/understanding of 2-way radio theory, RF propagation
characteristics at various frequency bands and communications
- P25 system and/or Motorola TRBO experience (preferred).
- Knowledge of battery backup systems, base stations, repeaters,
multicouplers and duplexers, basic TCP/IP principles and NIC
- Proficiency in setup and program routers and switches, set up
VLAN's and VPN's
- Operate various test equipment including but not limited to,
spectrum analyzers, system analyzers, watt meters, TDR's, FDR's,
- Knowledge of audio, RF, digital, and microprocessor electronic
circuits found in communications equipment at the component
- Ability to interpret schematics, blueprints, and diagrams.
- 2-year Associate degree in Electronics Technology or
- CET Certification Day to Day Responsibilities
- Install, maintain, and provide technical support and service of
wireless communications equipment.
- Provide hands-on and oversight of communications equipment and
hardware installation such as base stations, repeaters, mobile
devices, portable devices, wireless broadband devices, and CCTV
- Analyze and comprehend diagnostic test results in order to
repair and program radios and wireless equipment.
- Diagnose, troubleshoot, repair, and resolve wireless
communication equipment problems with customers in a positive and
- Service accounts by analyzing work orders; planning daily
- About Us: At Anistar Technologies, we connect individuals from
a variety of industries with respected companies, both nationally
and internationally. Each day, our Recruiters are presenting our
clients with talented industry professionals for their most
in-demand positions. To accomplish this, the Anistar staffing team
will invest the time to understand your goals, work experiences,
and career objectives.
To search for other exciting opportunities in your area, visit our
website at .
Anistar Technologies is an Equal Opportunity Employer and is
dedicated to fostering diversity in the workplace. Anistar utilizes
Interested Candidates, click "Apply", or contact the Anistar office
at the number listed below.
Anistar PAYS YOU for referrals!!!!!!!! If you know any qualified
job seekers looking for work, we want to talk with them! Call us to
find out more information regarding our referral placement
Keywords: Anistar Technologies, Savannah , Field Technician 41067, IT / Software / Systems , Savannah, Georgia
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I am a master student of artificial intelligence in Barcelona and currently focused on exploring (inverse) reinforcement learning and generative models such as GANs. With a background in software engineering, I have developed a passion for research in machine learning and artificial intelligence. I am leading the open source project IRL benchmark.
I believe that AI has huge potentials for the future of humanity, but that a lot of directed work and research will be necessary to align advanced AI systems with our values and objectives. To this end, I am exploring methods of applying machine learning to the problem of value learning: observing human behavior and deducing what their motivations were.
IRL benchmark: A open source framework for testing different inverse reinforcement learning algorithms on a variety of different problems to compare their performance and robustness IRL benchmark repository .
AI Safety Camp: I was main organizer of the second AI Safety Camp in Prague. We created a retreat for almost 30 aspiring researchers to work on topics related to AI alignment AISC website .
Generative Adversarial Networks: Experimenting on the capabilities of using GANs to generate additional data for supervised learning with few available training instances Report and Code on GitHub .
Automated Planning: Implementation of the Stanford Research Institute Problem Solver (STRIPS) Code on GitHub .
Bachelor Thesis: Anomaly Detection on Time Series Data for Predictive Maintenance. Using the restoration error of neural autoencoders to detect deviations from normal behavior of industrial time series data. Available upon request.
Have a look at my blog about topics related to AI alignment: thinking wires


A USB charger module for your legacy BK portable radio ClamShell.
The ClamPadre is a small circuit board about the size of an AA battery that is inserted into the empty slot of the BK ClamShell that provides a USB port for charging a mobile device. This is available as a DIY kit, or ship your ClamShell to us and we will install it for you. See below for purchase options:
- ClamPadre DIY self install kit $25.00 + $4.00 shipping.
- Ship us your ClamShell and we install the ClamPadre for you $30.00 + $4.00 shipping (We will email you our shipping address upon purchase).


In today’s data-driven world, mastering big data technologies has become essential for individuals and businesses alike. Big data, characterized by its massive volume, velocity, and variety, presents a goldmine of insights and opportunities when harnessed effectively. This article will take you on a journey to explore the world of big data, from its fundamentals to its advanced applications.
Introduction to Big Data Technologies
Big data technologies encompass a wide array of tools and techniques that enable the collection, storage, processing, and analysis of vast amounts of data. With the advent of the digital age, data is generated at an unprecedented rate, making it crucial to have the right technology to manage it effectively.
The Significance of Big Data in Modern Business
In a highly competitive business landscape, data-driven decision-making has emerged as a game-changer. Discover how big data is transforming the way businesses operate, from customer insights to operational efficiency.
Understanding the Basics of Big Data
We’ll start by breaking down the core concepts of big data, including the three Vs: volume, velocity, and variety. You’ll gain a deeper understanding of what makes big data unique.
Storage Solutions for Big Data
Explore the storage options available for managing large datasets, such as Hadoop Distributed File System (HDFS) and cloud-based solutions.
Data Processing and Analysis
Learn how big data technologies enable the processing and analysis of data, uncovering valuable patterns and trends.
Distributed Computing in Big Data
Dive into the world of distributed computing, where data is processed across multiple machines, ensuring scalability and speed.
Big Data Frameworks
Discover popular frameworks like Apache Hadoop and Apache Spark, and how they play a crucial role in big data processing.
Real-time Processing with Big Data
Explore the real-time data processing capabilities of big data technologies and their applications in various industries.
Big Data in Machine Learning
Understand the synergy between big data and machine learning, and how this combination is driving innovation.
Security and Privacy Concerns in Big Data
Delve into the critical aspects of data security and privacy in the world of big data, and the measures to protect sensitive information.
Challenges of Big Data Management
Uncover the challenges organizations face when dealing with big data, from data quality issues to the cost of infrastructure.
The Role of Data Scientists
Learn about the crucial role of data scientists in extracting meaningful insights from big data and how to become one.
Big Data Success Stories
Explore real-world examples of companies that have harnessed big data technologies to achieve significant success.
The Future of Big Data Technologies
What lies ahead for big data technologies? Discover the emerging trends and technologies shaping the future of big data.
Key Concepts in Big Data Technologies
What is Big Data?
Big data refers to vast and complex datasets that cannot be easily managed or analyzed with traditional data processing tools. It is characterized by three main aspects: volume, velocity, and variety. Mastering big data technologies involves handling these massive datasets efficiently.
H2 Importance of Big Data
The significance of big data lies in its potential to uncover valuable insights, improve decision-making, enhance customer experiences, and drive innovation. Businesses across various industries rely on big data technologies to gain a competitive edge.
H3 Data Analytics
Data analytics is the process of examining and interpreting data to extract meaningful patterns and insights. It is a core component of big data technologies, enabling organizations to make informed decisions.
Machine Learning and AI
Machine learning and artificial intelligence play a pivotal role in big data technologies. They empower systems to learn from data, identify patterns, and make predictions, offering automation and efficiency.
Tools for Mastering Big Data Technologies
Hadoop is an open-source framework for distributed storage and processing of big data. It is designed to handle massive datasets across clusters of computers, making it a cornerstone of big data technology.
It excels at data processing, data streaming, and machine learning, offering speed and flexibility.
They provide scalability and flexibility in managing diverse data types.
Tableau is a popular data visualization tool that helps transform complex data into easily understandable visualizations. It aids in the presentation of insights derived from big data.
Apache Kafka is a distributed event streaming platform that enables real-time data streaming. It is vital for applications requiring continuous, real-time data feeds.
Python and R
Python and R are essential programming languages for data analysis and machine learning. They offer a wide range of libraries and tools that support big data technologies.
Mastering Big Data Technologies
H2 Data Quality and Cleansing
Ensuring data quality is crucial in mastering big data technologies. Implementing data cleansing processes helps remove inaccuracies and inconsistencies, ensuring reliable insights.
H3 Scalability and Performance
Scalability is a key consideration in big data technologies. Systems must be designed to handle increased workloads efficiently while maintaining performance.
H2 Security and Compliance
Protecting data and adhering to regulations is paramount. Big data technologies should incorporate robust security measures and compliance practices.
H3 Data Governance
Effective data governance establishes processes for data management, ensuring data is well-organized, accessible, and compliant with regulations.
Real-time analytics allow organizations to make decisions based on current data, enabling timely responses to changing circumstances.
Hybrid Cloud Solutions
Leveraging hybrid cloud solutions combines on-premises and cloud infrastructure, offering flexibility and cost-effectiveness in managing big data.
Q: What is the role of data lakes in big data technologies?
A: Data lakes are centralized repositories for storing raw data in its native format. They provide a flexible and cost-effective way to store and analyze large volumes of data.
Q: How can businesses ensure data privacy in big data technologies?
A: Businesses can employ encryption, access controls, and data anonymization techniques to protect sensitive data and ensure privacy.
Q: What are the challenges in data governance for big data technologies?
A: Challenges in data governance include data quality, compliance, and managing the complexity of diverse data sources.
Q: What is the future of big data technologies?
A: The future of big data technologies lies in continued innovation, increased automation, and broader integration with artificial intelligence.
Q: Can individuals learn big data technologies without a technical background?
A: Yes, individuals with non-technical backgrounds can learn big data technologies through online courses and resources designed for beginners.
Q: How can big data technologies benefit small businesses?
A: Big data technologies enable small businesses to gain insights into customer behavior, streamline operations, and make data-driven decisions.
Mastering big data technologies is an ongoing journey that offers boundless opportunities for professionals and businesses. As you delve into the world of big data, remember to stay updated with the latest tools and trends, and don’t hesitate to seek expert guidance when needed. With the right knowledge and resources, you can harness the power of big data and unlock its potential to transform your endeavors.
In today’s data-driven world, mastering big data technologies is not just an advantage; it’s a necessity. The ability to harness the power of vast datasets can provide businesses and professionals with valuable insights, improve decision-making, and unlock countless opportunities. In this comprehensive guide, we will take you on a journey through the realm of big data technologies, covering key concepts, tools, and insights. Whether you’re new to the field or looking to deepen your expertise, this article is your go-to resource.


If you have a home automation needs, you recognize that a reputable and economical battery is crucial. However what happens if you don’t have a lot of money to spend on a battery? That’s where a 48 volt 300ah forklift battery can be found in. This battery can be utilized in a range of different residence automation needs, from controlling lights and devices to charging gadgets. Not just is this battery inexpensive, however it is also trusted as well as will certainly last for a long period of time. So if you’re trying to find a battery that can be utilized for a range of house automation requires, be sure to have a look at our 48 volt 300ah forklift battery.
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What are the various usages for a house automation battery?
There are a lot of different usages for a home automation battery. Here are a few instances: -Residence automation can be used to power devices in your home such as lights, audio, as well as locks. -Residence automation can be utilized to power gadgets in your workplace such as computer systems, printers, and speakers. -Residence automation can be utilized to power devices in your house such as thermostats, air conditioning system, and also A/C units. -Residence automation can be used to power devices in your house such as safety systems as well as door locks. -Residence automation can be utilized to power tools in your house such as house cinemas and home automation systems.
Just how much does a 48 volt 300ah forklift battery price?
A 48 Volt 300ah Forklift Lithium Battery can be a wonderful choice for your residence automation requirements. This type of battery is ideal for those that require a trusted and also high-capacity battery. A 48 volt 300ah forklift battery can give you with around 24 volts. This is enough to power your residence automation gadgets and also provide you lots of power to do your work. And also, this type of battery is also really easy to make use of. You simply need to attach it to your home’s electrical energy and you prepare to go.
When it comes to house automation, a great battery is necessary. A lithium ion battery is a wonderful option for residence automation because it is dependable, sturdy, as well as has a lengthy life. A lithium ion battery can be used in a variety of house automation tools, such as house protection systems, smoke detectors, and thermostats. You can likewise utilize a lithium ion battery in a forklift to power up the lorry. This is a great option for people that want to run their home automation devices from a distance.


|Created||2018-02-02 00:04:27 GMT|
|Prod Updated||2018-10-26 08:16:37|
|Dev Updated||2018-02-02 00:04:27|
Provides new phone devices (regular, fax, mobile) and can be used by other modules to send alerts to as well receiving commands from. This module can be used to forward system alerts to phones as well as sending images.


Brimhall Equipment Checkout – This room stores audio and visual recording equipment while it is not in use. Communications students and faculty can checkout this equipment during normal hours. The room is open and available to communications students weekdays from 8-4. Their phone number is (801) 422-6178. Equipment includes:
- Audio recorders
- Video cameras
- Still photography cameras
Audio Recording – There are two audio recording studios on the first floor: Room 119 is configured for two microphones and features a telephone interface; Room 121 is configured for one microphone. Each of these rooms are equipped with audio editing software.
HBLL Equipment Checkout – The HBLL offers a variety of equipment and technology, including computers, computer accessories, multimedia production equipment, and multimedia viewing equipment.