Binary Sensors Research Articles

Asymptotic locally optimal detector for large-scale sensor networks under the Poisson regime

We consider distributed detection with a large number of identical binary sensors deployed over a region where the phenomenon of interest (POI) has spatially varying signal strength. Each sensor makes a binary decision based on its own measurement, and the local decision of each sensor is sent to a fusion center using a random access protocol. The fusion center decides whether the event has occurred under a global size constraint in the Neyman-Pearson formulation. Assuming homogeneous Poisson distributed sensors, we show that the distribution of alarmed sensors satisfies the local asymptotic normality (LAN). We then derive an asymptotically locally most powerful (ALMP) detector optimized jointly over the fusion form and the local sensor threshold under the Poisson regime. We establish conditions on the spatial signal shape that ensure the existence of the ALMP detector. We show that the ALMP test statistic is a weighted sum of local decisions, the optimal weights being the shape of the spatial signal; the exact value of the signal strength is not required. We also derive the optimal threshold for each sensor. For the case of independent, identically distributed (iid) sensor observations, we show that the counting-based detector is also ALMP under the Poisson regime. The performance of the proposed detector is evaluated through analytic results and Monte Carlo simulations and compared with that of the counting-based detector. The effect of mismatched signal shapes is also investigated.

A line in the sand: a wireless sensor network for target detection, classification, and tracking

Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called “A Line in the Sand” and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensor nodes at MacDill Air Force Base in Tampa, FL, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.

Stochastic maps for autonomous plume tracing under turbulent flow

In this paper we address the problem of chemical plume tracing using an autonomous underwater vehicle. We assume that turbulent water flow induces a random pattern of the readings of a binary (on off) sensor used to detect the chemical being dispersed in the water. Our algorithm is completely sensor driven, not requiring idependent measures of the water flow direction and intensity. Instead, it relies on a stochastic model of the plume dispersion which is parametrised by the chemical source position, which we want to estimate, and by other nuisance parameters - comprising water flow - which are jointly estimated along with the desired source location. Instead of being based on a discrete controller that chains a series of perception driven behaviors, the mapping trajectory is chosen, at each step, as the direction along which the expected probability (given all data observed so far) of detecting the chemical is maximal.

System identification using binary sensors

System identification is investigated for plants that are equipped with only binary-valued sensors. Optimal identification errors, time complexity, optimal input design, and impact of disturbances and unmodeled dynamics on identification accuracy and complexity are examined in both stochastic and deterministic information frameworks. It is revealed that binary sensors impose fundamental limitations on identification accuracy and time complexity, and carry distinct features beyond identification with regular sensors. Comparisons between the stochastic and deterministic frameworks indicate a complementary nature in their utility in binary-sensor identification.

Decentralized detection in sensor networks

In this paper, we investigate a binary decentralized detection problem in which a network of wireless sensors provides relevant information about the state of nature to a fusion center. Each sensor transmits its data over a multiple access channel. Upon reception of the information, the fusion center attempts to accurately reconstruct the state of nature. We consider the scenario where the sensor network is constrained by the capacity of the wireless channel over which the sensors are transmitting, and we study the structure of an optimal sensor configuration. For the problem of detecting deterministic signals in additive Gaussian noise, we show that having a set of identical binary sensors is asymptotically optimal, as the number of observations per sensor goes to infinity. Thus, the gain offered by having more sensors exceeds the benefits of getting detailed information from each sensor. A thorough analysis of the Gaussian case is presented along with some extensions to other observation distributions.

Quasi-convexity and optimal binary fusion for distributed detection with identical sensors in generalized Gaussian noise

We present a technique to find the optimal threshold /spl tau/ for the binary hypothesis detection problem with n identical and independent sensors. The sensors all use an identical and single threshold /spl tau/ to make local decisions, and the fusion center makes a global decision based on the n local binary decisions. For generalized Gaussian noise and some non-Gaussian noise distributions, we show that for any admissible fusion rule, the probability of error is a quasi-convex function of threshold /spl tau/. Hence, the problem decomposes into a series of n quasi-convex optimization problems that may be solved using well-known techniques. Assuming equal a priori probability, we give a sufficient condition of the non-Gaussian noise distribution g(x) for the probability of error to be quasi-convex. Furthermore, this technique is extended to Bayes risk and Neyman-Pearson criteria. We also demonstrate that, in practice, it takes fewer than twice as many binary sensors to give the performance of infinite precision sensors in our scenario.

A Binary Image Sensor for Motion Detection

This Paper proposes a binary image sensor for motion detection. The sensor detects motion vectors using block-matching method. Each pixel of the image sensor has a shift register. Exclusive OR(XOR) circuits in each pixel compare the shifted image of the previous frame and the image of the current frame. The outputs of the XOR circuits are converted to current value. The current-sum circuits calculate the current-sum of pixel outputs. The current-sum of a matching block represents the sum of absolute difference. The motion vector is obtained by routine of shifting image of the previous frame and comparing the sum of absolute difference. The chip was fabricated using 1.2um 2-Metal 2-Poly-Si CMOS technology and composed of 32 × 32 pixel array and peripheral circuits in a 7.3mm × 7.3mm die.

Actuator-sensor-interface interconnectivity

The paper deals with low-level fieldbus interconnections intended for use in simple digital measurement and control applications employing ASI (Actuator–Sensor–Interface), which specifies an industrial communication connection between the foremost binary sensors and actuators and a controlling site. The kernel of the paper stems from two case studies that offer a more detailed insight into system design employing ASI interconnections. The first case deals with a regular implementation of the gateway between ASI and NBIP (nine-bit inter-processor protocol), which exemplifies a microcontroller-dedicated protocol, whereas the second case introduces an ASI–ASI coupler enabling the realization of hierarchical ASI systems.

Object Recognition and Localization from Scanning Beam Sensors

Model-based object recognition and object localization are fundamental tasks in industrial automation. In this article, we present a system that quickly recognizes (5 μs) and accurately localizes (0.025 mm) objects using a scanning beam sensor that consists of an array of binary light-beam sensors. Our scanning beam sensor is robust, inexpensive, compact, precise, and insensitive to ambient light, which are all prerequisites for industrial manufacturing applications. Scanning beam sensing involves moving objects with respect to the sensor and record ing the manipulator positions when the sensor outputs change. The recognition problem and the localization problem share the correspondence subproblem, the task of interpreting the sensed data in terms of model features. In this article, we present a constant-time indexing correspondence algorithm. Indexing in volves discretizing the sensed data to achieve integral indices, and using these indices to look up a table entry containing the correspondence information (the consistent model feature inter pretations). Complete indexing tables are crucial for scanning beam sensing, and for sparse sensing strategies in general, because each experiment only produces a handful of indexing coordinates. Constructing complete indexing tables has previ ously been an open problem (Clemens and Jacobs 1991). In this article, we describe a method for constructing complete indexing tables by enumerating the cells in an arrangement in configuration space.

Generalized Polyhedral Object Recognition and Localization Using Crossbeam Sensing

Object localization is a fundamental task in industrial automa tion. In this article, we present a recognition and localization technique based upon binary beam sensors. Binary beam sen sors, which consist of modulated LED light sources and de tectors, are appropriate for manufacturing applications due to their reliability, high accuracy, robustness, inexpensiveness, and ease of calibration. By extracting sufficient information, recognition and localization can be performed as fast (~ 0.1 s) and as accurately (~ 25 microns) as high-speed manufactur ing requires. Generalized polyhedral objects are recognized and localized by being passed through a crossbeam sensor which is a set of coplanar oriented binary light-beam sensors; robot positions are recorded when the beam sensors' outputs change. Recognition and localization share two subproblems: the correspondence problem (the problem of interpreting the sensed features in terms of the model features), and the pose- estimation problem (the problem of estimating the object's pose from the sensed data and an interpretation of the sensed fea tures in terms of model features). In Wallack and Manocha (1994), we described a pose-estimation technique for crossbeam sensor data. In this article, we present two methods for solving the correspondence problem for crossbeam sensor data: a lin ear time completely on-line method, and a constant-time on-line method that utilizes preprocessing.

ASI Interconnectivity

The paper deals with low-level fieldbus interconnections aimed at simple digital measurement and control applications employing ASI (Actuator/Sensor Interface), which specifies an industrial communication connection of foremost binary sensors and actuators with controlling site. This contribution presents ASI-related results of the project focused on the fieldbus management and interconnection techniques using two interconnectivity case studies. Launching with the discussion of ASI concepts, the paper describes also flexible hardware and software architectures of the employed prototyping board. The kernel of the paper stems from two case studies that offer more detailed insight into the system design employing ASI interconnections. The first case deals with a regular implementation of the gateway between ASI and NBIP (Nine-Bit Inter-processor Protocol), which exemplifies a microcontroller-dedicated protocol, whereas the second case introduces the ASI-ASI coupler enabling to realize hierarchical ASI systems.

Balance Technique for Monitoring In Situ Structural Integrity of Prismatic Structures

A general method to monitor the structural integrity of prismatic structures composed of simple isotropic linear elastic materials is described and justified by a detailed mathematical model. This technique is distinct from usual approaches in that a small number of distributed sensors is shown to be sensitive to the presence of damage throughout the entire volume of a structure without determining its location. In addition, this approach inherently operates as a binary sensor, thus alleviating the need to collect a large amount of data. Potential extensions to more general structural geometries and materials also are described.

Operationally efficient architectures for fusion of binary-decision sensors in multidecision environments

Architectures necessary for fusion of decisions from sensors with limited decision capabilities, such as binary decision sensors oper- ating in multidecision problem environments, are conceived and ana- lyzed. The analysis is carried out from the perspective of efficiency of operations, as defined by the problem environment and its constraints. It takes into account the a priori probabilities or likelihoods of the different decision choices in the environment in developing estimates of the pro- cessing demands corresponding to these architectures. The analysis also brings out the feasibility of designing the architecture to be adaptive to take full advantage of the information about the relative likelihoods of the different decision choices facing the fusion processor as the informa- tion evolves during the operational phase. Architectures that are insen- sitive to the relative probabilities of the alternative decision choices are also brought out under this analysis. The relative merits of these alter- natives, their preferred domains in the class a priori probability space, and their relevance to different application environments are discussed to identify the natural domains of application of these architectures. © 1997 Society of Photo-Optical Instrumentation Engineers.

A Ferromagnetic Biosensor for Simple Assay of Organophosphate Pesticides

Abstract A new biospecific technique for organophosphate assay is described. The designed biosensor contains a ferromagnetic core passivated by an epoxide cover in a high-frequency electrical field. Complexing agent molecule with an 8-hydroxy-quinoline region is covalently bound to the cover and serves for strong fixation of aluminum hydroxide. This composition “Magnosorb” binds with the enzyme butyrylcholinesterase. Such a biosensor has high sensitivity to inhibition by organophosphate pesticides (up to 20–50 ng per ml). Various environment and food compounds can be tested without laborious preliminaries. However, the sensor's ability for inhibition is gradually decreased when the full component is stored, whereas esterase activity is more stable. This phenomenon is called “Functional Disadequacy”, and can be corrected with the help of a “Binary Sensor” technique, in which the sensor is made up of two parts (carrier “Magnosorb” and fresh dissolved enzyme) just before use. The proposed method can be used ...

The determining effect of ultralow metal addition on the response of semiconductor gas sensors

The gas sensitivity of ordinary metal oxides and some composites based on them has been studied with a view to the development of semiconductor sensors for determining the concentrations of minor gas constituents in the atmosphere of Mars (CO 2, H 2, O, O 2 in CO 2) and their diurinal variations under conditions of UV light and temperature extremes. The optimum sensitivity, the response time and operating temperature of the sensor in detecting CO have been obtained with TiO 2 specimens doped with ultraminor impurities of Pd (5 × 10 15 at cm −2). The introduction of niobium into TiO 2 considerably reduced specimen resistance (by a factor of 10 3) but did not worsen the characteristics of the sensor. For detecting O 2 and H 2O in the above-mentioned atmosphere, binary sensors based on ZnO and Ti-doped ZnO have been suggested. The coating of ZnO by atoms of Ti in the submonolayer results in a change in the response sign of the electrical condition of the specimens on adsorption of H 2O. If O 2 is being detected with H 2O present, the acceptor response of the Ti/ZnO specimen increases and that of the ZnO specimen decreases due to the above-mentioned effect. A computer analysis of the electrical conduction kinetics of the Ti/ZnO and ZnO specimens makes it possible to continuously determine the concentrations of O 2 and H 2O. This is possible with the sensor in a non-stationary condition which is important when it is necessary to maintain a constant operating temperature.

Optical angular displacement sensor with high resolution integrated in silicon

A novel optical angular displacement sensor has been developed. It consists of two parts: a rotating element with reflective patterns fabricated onto it, and a stationary part with optical detectors and readout circuitry, integrated in a standard BiFET process. A high-resolution absolute angular position measurement is realized by combining the information of a high-resolution incremental analog sensor part with that of a low-resolution absolute binary sensor part. The high-resolution sensor part is implemented by a reflective zigzag pattern on the rotor and a position-sensitive device (PSD) on the stator. The low-resolution part comprises a binary reflective code pattern on the rotor and a photodiode array on the stator. An angular displacement will cause the code on the photodiode array to alternate and the reflected light spot on the PSD to move over the full length of the PSD in between each code transition. The sensor enables a contactless angular measurement to be made over the full range of 360 degrees, without blocking at full scale. The sensor is insensitive to electric and magnetic fields and can be placed radially at any position on a rotating shaft.

A computer assisted intelligent storm outage evaluator for power distribution systems

A knowledge-based approach for evaluating storm-related outages in distribution systems is proposed. At the outset, binary voltage sensors capable of transmitting the real-time voltage on/off symptoms are recommended to be installed at strategic locations in the distribution system. A semantic network model has been developed to represent the knowledge of the distribution system faults and their associated symptoms. The real-time voltage symptoms, transmitted by the sensors, serve as the basic input to the semantic network model in generating fault hypotheses. A rule base, incorporating temporal and topological knowledge of symptoms and system operators' heuristics, is used by the inference mechanism to determine the actual faults from the hypotheses set. The results suggest that the fault types and their exact sections can be diagnosed accurately. >

Automation Systems with Hierarchically Structured Communication Network: Aspects of Reliability and Low Cost Solution

In the future field area networks will replace the conventional star-shaped wiring of automation plants.Special attention is paid to the reliability of bus structures which is evaluated from the aspect of use in field area networks. Comparison of various structures has shown that contrarotating ring buses should be preferred with regard to reliability. The result is a special hierarchically structured field area network which consists of some ring buses connected via vertical and horizontal coupling units. On the first level of the hierarchically structured field area network special solutions for data transmission can be realized. These solutions are the so-called “feeders” used as low-cost buses for coupling binary sensors and actuators to the communication networks of automation plants.

A laboratory experiment to teach some concepts on sensor-based robot assembly systems

A laboratory experiment is described that can be performed by electrical engineering graduate students and senior undergraduates. The experiment involves the assembly of a diesel-engine oil pump with an IBM RS-1 robot. The robot is instrumented with a force-sensing gripper and an optical binary sensor to detect parts in the gripper. A GE Optomation binary vision system is also used to monitor the manipulator workspace through a charge-coupled device (CCD) camera mounted on the robot arm. The students are required to program the manipulator and to process the information from the various sensors, which are used to recognize assembly components and to verify assembly operations in real-time. This assembly experiment is designed to familiarize electrical engineering students with the problems which dominate robot-based assembly systems and to demonstrate a number of practical sensor-based motion strategies which can overcome part and robot positional uncertainty found in typical manufacturing environments. These sensor-based motion strategies improve the reliability of a robot assembly system. The sensor-based assembly experiment, the laboratory setup, and the sensor-based motion strategies are discussed in detail.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Automatic Programming of Controllers for Discrete Manufacturing Processes

This paper describes prototype software for automatically generating control programs for discrete manufacturing systems from a high-level description of the process. The control logic is synthesized from a specification of the physical resource states required to execute each operation. The software described in this paper allows the user to enter a functional description of the system into a database containing the operation sequencing conditions and related actuator and sensor data for each stage of the process. This information is then used to automatically generate programs for the on-line control computers. The current implementation supports binary sensor and actuator signals and generates control programs in Instruction List, a generic programming language for programmable logic controllers. The methodology is illustrated for the control of a conveyor system in an existing automated painting facility