5840503
Silicon Retina with Adaptive Filtering Properties
This paper describes a small, compact circuit, the retino-laminar (RL) circuit, that captures the temporal and adaptation properties both of the photoreceptor and of the laminar layers of the fly. The RL circuit uses only six transistors and two capacitors. The circuit is operated in the subthreshold domain; it has a low DC gain and a high transient gain. The adaptation time constant of the RL circuit can be controlled via an external bias. Its temporal filtering properties change with the background intensity and with the signal-to-noise ratio. The frequency response of the circuit shows that, in the frequency range of 1 to 100 Hz, the circuit response goes from highpass filtering under high light levels to lowpass filtering under low light levels (i.e., when the signal-to-noise ratio is low).
18592302
A Winner-Take-All Circuit with Controllable Soft Max Property
I describe a silicon network consisting of a group of excitatory neurons and a global inhibitory neuron. The output of the inhibitory neuron is normalized with respect to the input strengths. This output models the normalization property of the wide-field direction-selective cells in the fly visual system. This normalizing property is also useful in any system where we wish the output signal to code only the strength of the inputs, and not be dependent on the number of inputs. The circuitry in each neuron is equivalent to that in Lazzaro's winner-take-all (WTA) circuit with one additional transistor and a voltage reference. Just as in Lazzaro's circuit, the outputs of the excitatory neurons code the neuron with the largest input. The difference here is that multiple winners can be chosen. By varying the voltage reference of the neuron, the network can transition between a soft-max behavior and a hard WTA behavior. I show results from a fabricated chip of 20 neurons in a 1.2 µm CMOS technology.
34951314
Spike synchronization in a network of silicon integrate-and-fire neurons
Spike-event based computational processing by neuronal networks depends on a variety of spatio-temporal mechanisms that are distributed across neuronal synapses, dendrites, and somata. The overall processing of spatio-temporal patterns of spike requires global temporal coherence between all these processes both within a neuron, and across all neurons of the network. We are evaluating the use of networks of hybrid analog-digital integrate-and-fire neurons, fabricated using VLSI, for studying real-time event-based processing. These hardware networks have the advantages of real-time operation, and inherent global synchronization by their analog implementation. However, one of the technical challenges in the operation of such networks is the unavoidable fabrication related variance between the different neurons of the network. Here we show that despite this variance, an important behavior of neuronal networks, spike synchronization, can be obtained from a simple network of VLSI neurons interconnected by lateral excitation and global inhibition.
1845062
Adaptive Retina with Center-Surround Receptive Field
Both vertebrate and invertebrate retinas are highly efficient in extracting contrast independent of the background intensity over five or more decades. This efficiency has been rendered possible by the adaptation of the DC operating point to the background intensity while maintaining high gain transient responses. The center-surround properties of the retina allows the system to extract information at the edges in the image. This silicon retina models the adaptation properties of the receptors and the antagonistic center-surround properties of the laminar cells of the invertebrate retina and the outer-plexiform layer of the vertebrate retina. We also illustrate the spatio-temporal responses of the silicon retina on moving bars. The chip has 59×64 pixels on a 6.9×6.8mm2 die and it is fabricated in 2 µm n-well technology.
6979172
Encoding the Temporal Statistics of Markovian Sequences of Stimuli in Recurrent Neuronal Networks
Encoding, storing, and recalling a temporal sequence of stimuli in a neuronal network can be achieved by creating associations between pairs of stimuli that are contiguous in time. This idea is illustrated by studying the behavior of a neural network model with binary neurons and binary stochastic synapses. The network extracts in an unsupervised manner the temporal statistics of the sequence of input stimuli. When a stimulus triggers the recalling process, the statistics of the output patterns reflects those of the input. If the sequence of stimuli is generated through a Markov process, then the network dynamics faithfully reproduces all the transition probabilities.
12793172
Continuous-time adaptive delay system
We have developed an adaptive delay system that adjusts the delay of a delay element so that it matches the temporal disparity between the onset of two input signals. The delay is controlled either by an external bias voltage, or by an intrinsic signal derived from an adaptive block. The operation of the adaptive delay system is similar to that of a charge-pump phase-lock loop, with an extended lock-in range of more than 5 decades. Standard CMOS transistors are used in their subthreshold region. Experimental results from circuits fabricated in 2 /spl mu/m CMOS technology are in agreement with the analysis.<<ETX>>
14314870
Formalization, Testing and Execution of a Use Case Diagram
Errors in a requirements model have prolonged detrimental effects on reliability, cost, and safety of a software system. It is very costly to fix these errors in later phases of software development if they cannot be corrected during requirements analysis and design. A use case diagram, as a requirements model, plays an important role in giving requirements for a software system. It provides a communication tool between software requirements developers and prospective users to understand what requirements of a software system are. However most descriptions of a use case diagram are written in some informal language, leading to possible misunderstanding between developers and users. In this paper, we propose a new rigorous review technique which can be applied to software requirements models. Using this new technique before a software system is fully designed will help us find some potential errors in a requirements model, resulting in reduced time, labor and expenditure in software development.
18135743
A clustering entropy-driven approach for exploring and exploiting noisy functions
Linear, Gaussian, fitness proportional, clustering, and Rosca entropies are succinct measures of diversity that have been applied to balance exploration and exploitation in evolutionary algorithms. In previous studies, an entropy-driven approach using linear entropy explicitly balances and/or searches optimal solutions for the selected unimodal and multimodal functions excluding noisy functions. This paper investigates the reasons for such an exception and introduces a clustering entropy-driven approach to solve the problem. Such an approach provides a coarse-grained diversity measure that filters the noise of functions, varies cluster size and categorizes individuals at the genotype level. The experimental results show that the clustering entropy-driven approach further improves the searching results of noisy functions by one more degree.
1275297
Bridging Physical and Virtual Worlds: Complex Event Processing for RFID Data Streams
Advances of sensor and RFID technology provide significant new power for humans to sense, understand and manage the world. RFID provides fast data collection with precise identification of objects with unique IDs without line of sight, thus it can be used for identifying, locating, tracking and monitoring physical objects. Despite these benefits, RFID poses many challenges for data processing and management: i) RFID observations contain duplicates, which have to be filtered; ii) RFID observations have implicit meanings, which have to be transformed and aggregated into semantic data represented in their data models; and iii) RFID data are temporal, streaming, and in high volume, and have to be processed on the fly. Thus, a general RFID data processing framework is needed to automate the transformation of physical RFID observations into the virtual counterparts in the virtual world linked to business applications. In this paper, we take an event-oriented approach to process RFID data, by devising RFID application logic into complex events. We then formalize the specification and semantics of RFID events and rules. We demonstrate that traditional ECA event engine cannot be used to support highly temporally constrained RFID events, and develop an RFID event detection engine that can effectively process complex RFID events. The declarative event-based approach greatly simplifies the work of RFID data processing, and significantly reduces the cost of RFID data integration.
5842197
Configurable indexing and ranking for XML information retrieval
Indexing and ranking are two key factors for efficient and effective XML information retrieval. Inappropriate indexing may result in false negatives and false positives, and improper ranking may lead to low precisions. In this paper, we propose a configurable XML information retrieval system, in which users can configure appropriate index types for XML tags and text contents. Based on users' index configurations, the system transforms XML structures into a compact tree representation, Ctree, and indexes XML text contents. To support XML ranking, we propose the concepts of "weighted term frequency" and "inverted element frequency," where the weight of a term depends on its frequency and location within an XML element as well as its popularity among similar elements in an XML dataset. We evaluate the effectiveness of our system through extensive experiments on the INEX 03 dataset and 30 content and structure (CAS) topics. The experimental results reveal that our system has significantly high precision at low recall regions and achieves the highest average precision (0.3309) as compared with 38 official INEX 03 submissions using the strict evaluation metric.
1673600
Inferring Data Transformation Rules to Integrate Semantic Web Services
OWL-S allows selecting, composing and invoking Web Services at different levels of abstraction: selection uses high level abstract descriptions, invocation uses low level grounding ones, while composition needs to consider both high and low level descriptions. In our setting, two Web Services are to be composed so that output from the upstream one is used to create input for the downstream one. These Web Services may have different data models but are related to each other through high and low level descriptions. 
 
Correspondences must be found between components of the upstream data type and the downstream ones. Low level data transformation functions may be required (e.g. unit conversions, data type conversions). The components may be arranged in different XML tree structures. Thus, multiple data transformations are necessary: reshaping the message tree, matching leaves by corresponding types, translating through ontologies, and calling conversion functions. Our prototype compiles these transformations into a set of data transformation rules, using our tableau-based ALC Description Logic reasoner to reason over the given OWL-S and WSDL descriptions, as well as the related ontologies. A resolution-based inference mechanism for running these rules is embedded in an inference queue that conducts data from the upstream to the downstream service, running the rules to perform the data transformation in the process.