Binary Sensors Research Articles

An Air-to-Fuel ratio estimation strategy for turbocharged spark-ignition engines based on sparse binary HEGO sensor measures and hybrid linear observers

An effective Air-to-Fuel Ratio (AFR) control is paramount to ensure a good combustion and high catalyst efficiency. This work addresses the problem of determining continuous-time estimates of AFR in turbocharged Spark Ignition (SI) engines on the basis of binary sparse measurements of the exhaust gas Oxygen. The latter are provided by a HEGO (Heated Exhaust Gas Oxygen) sensor installed at the catalytic converter input in place of a more expensive linear UEGO (Universal Exhaust Gas Oxygen) sensor, as nowadays common in commercial cars. The HEGO sensor outputs two voltage values only, corresponding respectively to low or high concentration of the residual Oxygen in the exhaust gas (on/off behavior). In view of this, it can be classified as a binary sensor generating irregular and sparse measurements in that the useful information is only present at the instants of the on/off and off/on transitions. An estimation scheme based on the use of a recursive least-squares algorithm has been designed by resorting to the theory of linear hybrid observers with quantized inputs. A detailed convergence analysis of the state reconstruction error is also provided. The proposed hybrid observer scheme is employed in a PI control-loop designed to maintain the AFR close to a desired value. The effectiveness of the proposed method is demonstrated by several numerical simulations based on both synthetic and real data.

Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining.

Ambient Assisted Living (AAL) is becoming crucial to help governments face the consequences of the emerging ageing population. It aims to motivate independent living of older adults at their place of residence by monitoring their activities in an unobtrusive way. However, challenges are still faced to develop a practical AAL system. One of those challenges is detecting failures in non-intrusive sensors in the presence of the non-deterministic human behaviour. This paper proposes sensor failure detection and isolation system in the AAL environments equipped with event-driven, ambient binary sensors. Association Rule mining is used to extract fault-free correlations between sensors during the nominal behaviour of the resident. Pruning is then applied to obtain a non-redundant set of rules that captures the strongest correlations between sensors. The pruned rules are then monitored in real-time to update the health status of each sensor according to the satisfaction and/or unsatisfaction of rules. A sensor is flagged as faulty when its health status falls below a certain threshold. The results show that detection and isolation of sensors using the proposed method could be achieved using unlabelled datasets and without prior knowledge of the sensors’ topology.

Exploiting convexification for Bayesian optimal sensor placement by maximization of mutual information

Bayesian optimal sensor placement, in its full generality, seeks to maximize the mutual information between uncertain model parameters and the predicted data to be collected from the sensors for the purpose of performing Bayesian inference. Equivalently, the expected information entropy of the posterior of the model parameters is minimized over all possible sensor configurations for a given sensor budget. In the context of structural dynamical systems, this minimization is computationally expensive because of the large number of possible sensor configurations. Here, a very efficient convex relaxation scheme is presented to determine informative and possibly optimal solutions to the problem, thereby bypassing the necessity for an exhaustive, and often infeasible, combinatorial search. The key idea is to relax the binary sensor location vector so that its components corresponding to all possible sensor locations lie in the unit interval. Then, the optimization over this vector is a convex problem that can be efficiently solved. This method always yields a unique solution for the relaxed problem, which is often binary and therefore the optimal solution to the original problem. When not binary, the relaxed solution is often suggestive of what the optimal solution for the original problem is. An illustrative example using a 50‐story shear building model subject to sinusoidal ground motion is presented, including a case where there are over 47 trillion possible sensor configurations. The solutions and computational effort are compared with greedy and heuristic methods.

Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition

Human activity recognition has become essential to a wide range of applications, such as smart home monitoring, health-care, surveillance. However, it is challenging to deliver a sufficiently robust human activity recognition system from raw sensor data with noise in a smart environment setting. Moreover, imbalanced human activity datasets with less frequent activities create extra challenges for accurate activity recognition. Deep learning algorithms have achieved promising results on balanced datasets, but their performance on imbalanced datasets without explicit algorithm design cannot be promised. Therefore, we aim to realise an activity recognition system using multi-modal sensors to address the issue of class imbalance in deep learning and improve recognition accuracy. This paper proposes a joint diverse temporal learning framework using Long Short Term Memory and one-dimensional Convolutional Neural Network models to improve human activity recognition, especially for less represented activities. We extensively evaluate the proposed method for Activities of Daily Living recognition using binary sensors dataset. A comparative study on five smart home datasets demonstrate that our proposed approach outperforms the existing individual temporal models and their hybridization. Furthermore, this is particularly the case for minority classes in addition to reasonable improvement on the majority classes of human activities.

Efficacy of Imbalanced Data Handling Methods on Deep Learning for Smart Homes Environments

Human activity recognition as an engineering tool as well as an active research field has become fundamental to many applications in various fields such as health care, smart home monitoring and surveillance. However, delivering sufficiently robust activity recognition systems from sensor data recorded in a smart home setting is a challenging task. Moreover, human activity datasets are typically highly imbalanced because generally certain activities occur more frequently than others. Consequently, it is challenging to train classifiers from imbalanced human activity datasets. Deep learning algorithms perform well on balanced datasets, yet their performance cannot be promised on imbalanced datasets. Therefore, we aim to address the problem of class imbalance in deep learning for smart home data. We assess it with Activities of Daily Living recognition using binary sensors dataset. This paper proposes a data level perspective combined with a temporal window technique to handle imbalanced human activities from smart homes in order to make the learning algorithms more sensitive to the minority class. The experimental results indicate that handling imbalanced human activities from the data-level outperforms algorithms level and improved the classification performance.

Mean Shift-Based Multisource Localization Method in Wireless Binary Sensor Network

Source localization is one of the major research contents in the localization research of wireless sensor networks, which has attracted considerable attention for a long period. In recent years, the wireless binary sensor network (WBSN) has been widely used for source localization due to its high energy efficiency. A novel method which is based on WBSN for multiple source localization is presented in this paper. Firstly, the Neyman-Pearson criterion-based sensing model which takes into account the false alarms is utilized to identify the alarmed nodes. Secondly, the mean shift and hierarchical clustering method are performed on the alarmed nodes to obtain the cluster centers as the initial locations of signal sources. Finally, some voting matrices which can improve the localization accuracy are constructed to decide the location of each acoustic source. The simulation results demonstrate that the proposed method can provide a desirable performance superior to some traditional methods in accuracy and efficiency.

The Complexity of Analytical Tasks: A Driving Force for Sensor Array Design

Introduction Analytical tasks, the detection goals and scoping of quantitative measurements, are a foundational consideration for the design of chemical sensor arrays. Often sensor arrays are expected to support a variety of possible tasks which are only vaguely defined by both the end user and array designer. Nonetheless, the expedient choices made by or forced upon array designers and fabricators can significantly impact the analytical tasks that could be supported by an array. Analytical tasks may be defined as boundaries in an abstract space defining chemically and practically relevant quantities that are to be measured; chemical sensor and instrument responses may be defined in related spaces specific to their physics. Intelligently finding a mapping between these two abstract spaces defines our problem and informs the impact of analytical tasks upon sensor array design while avoiding the “black box” problems of popular machine learning approaches. This presentation details recent work at NRL that probes the meaning and impact of analytical tasks on chemical sensor array design through general, device agnostic figures of merit developed via techniques originating in statistical mechanics, information theory, information retrieval, and theoretical computer science. Approaches Multiple theoretical approaches are explored to determine and measure the impact of analytical task on chemical sensor array design. To begin, we use Ising model-like descriptions of binary sensor responses first described in a biological context by Murugan et. al. [1-3] and subsequently developed by us for chemical sensing with environmental interferents. Analytical tasks are then scoped as molecular and concentration identification against an unknown chemical background. On this framework, we explore the capabilities of a variety of approaches towards assessing the impact of analytical task on sensor array design.The first set of approaches originate in information theory and we consider the mutual information and Kullback-Leibler divergences between different sensor array configurations considered over probability distributions defining the likelihood of that sensor array needing to perform different analytical tasks. Platform-agnostic figures of merit are derived and used to show the impact of analytical tasks on sensor array design.Next, Van Riijsbergen and Melucci [4,5] have developed and elaborated upon an approach to representing and searching information in Hilbert spaces using operator theoretic methods reminiscent to those used in quantum mechanical calculations. By transferring our probabilistic sensor array models to a Hilbert space, we are able to perform similar searches against sensor arrays and by operator methods to ask logical questions of the sensor array using operators that encode analytical tasks and specific exigencies like comparisons to the effectiveness of the array for other analytical tasks.Finally, Almagor et. al. [6-9] have recently developed a finite state machine approach for formally describing the complexity of sensing tasks within formal languages. We attempt to map our binary chemical sensor array model to this methodology and to describe the formal complexity of this toy model for chemical sensing. Analytical tasks are input as restrictions on available formal words in the finite state language. Conclusions Using a simple statistical mechanical model for a chemical sensor array with background interferents, we have been able to probe multiple approaches to quantifying the impact of analytical tasks on the design and relevance of a sensor array for a set of chemical detection problems. Importantly, these approaches avoid drawbacks associated with more empirical, machine learning-based approaches, enabling a more clear understanding of the chemical landscape and how chemical sensor array design interacts with it. Our investigation has explored ways to construct figures of merit for specific families of analytical tasks using methodologies as diverse as information theory, Hilbert space search methods, and complexity techniques for automata-defined sensing.

Implementing a semi-causal domain-specific language for context detection over binary sensors

In spite of the fact that many sensors in use today are binary (i.e. produce only values of 0 and 1), and that useful context-aware applications are built exclusively on top of them, there is currently no development approach specifically targeted to binary sensors. Dealing with notions of state and state combinators, central to binary sensors, is tedious and error-prone in current approaches. For instance, developing such applications in a general programming language requires writing code to process events, maintain state and perform state transitions on events, manage timers and/or event histories. In another paper, we introduced a domain specific language (DSL) called Allen, specifically targeted to binary sensors. Allen natively expresses states and state combinations, and detects contexts on line, on incoming streams of binary events. Expressing state combinations in Allen is natural and intuitive due to a key ingredient: semi-causal operators. That paper focused on the concept of the language and its main operators, but did not address its implementation challenges. Indeed, online evaluation of expressions containing semi-causal operators is difficult, because semi-causal sub-expressions may block waiting for future events, thus generating unknown values, besides 0 and 1. These unknown values may or may not propagate to the containing expressions, depending on the current value of the other arguments. This paper presents a compiler and runtime for the Allen language, and shows how they implement its state combining operators, based on reducing complex expressions to a core subset of operators, which are implemented natively. We define several assisted living applications both in Allen and in a general scripting language. We show that the former are much more concise in Allen, achieve more effective code reuse, and ease the checking of some domain properties.

Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition.

Over the past few years, the Internet of Things (IoT) has been greatly developed with one instance being smart home devices gradually entering into people’s lives. To maximize the impact of such deployments, home-based activity recognition is required to initially recognize behaviors within smart home environments and to use this information to provide better health and social care services. Activity recognition has the ability to recognize people’s activities from the information about their interaction with the environment collected by sensors embedded within the home. In this paper, binary data collected by anonymous binary sensors such as pressure sensors, contact sensors, passive infrared sensors etc. are used to recognize activities. A radial basis function neural network (RBFNN) with localized stochastic-sensitive autoencoder (LiSSA) method is proposed for the purposes of home-based activity recognition. An autoencoder (AE) is introduced to extract useful features from the binary sensor data by converting binary inputs into continuous inputs to extract increased levels of hidden information. The generalization capability of the proposed method is enhanced by minimizing both the training error and the stochastic sensitivity measure in an attempt to improve the ability of the classifier to tolerate uncertainties in the sensor data. Four binary home-based activity recognition datasets including OrdonezA, OrdonezB, Ulster, and activities of daily living data from van Kasteren (vanKasterenADL) are used to evaluate the effectiveness of the proposed method. Compared with well-known benchmarking approaches including support vector machine (SVM), multilayer perceptron neural network (MLPNN), random forest and an RBFNN-based method, the proposed method yielded the best performance with 98.35%, 86.26%, 96.31%, 92.31% accuracy on four datasets, respectively.

Experimental modeling of radio information systems for solving the problem of failures forecasting

We report a simulation model of technical condition data of radio information systems at solving the problem of failures forecasting. Time cost analysis on sampling for high-accuracy forecasting of radio information systems failures showed that the performance of samples accumulation by testing the equipment at the stands does not allows us to create radio information systems with automated operation management system on time. The existing level of product unification allows the use of both data from previous generation stations and current data from of the built-in control of new generation radar stations, due to their correlation connections. These features create the conditions for operative formation of a large amount of data on the technical states of the stations due to the development of a simulation data model of the built-in control. A feature of such a model concludes in its versatility, i.e. the formation of the data stream on both binary sensors (operational or not) and sensors of physical parameters. An analysis of the temperature recorded during the development process of the components was carried out to determine the minimum sample size required. The temperature prediction model described above was trained on samples of various volumes for determination of the influence of the training sample volume on forecast accuracy. The developed methodological apparatus for formation of data from the station’s built-in control provides a solution of the problem of high-precision failure prediction.

A Cell Reconstruction Tool to Deploy Binary Pyroelectric Sensor Arrays

Due to the advantages of being low cost and low power consumption, pyroelectric infrared sensors are widely employed in many applications, such as target location and tracking. In such applications, reference structure which is used to modulate views of binary sensors can help improve spatial resolutions by segmenting monitoring spaces into many cells which are identified by states (called signatures). A spatial resolution determines localization or tracking accuracy, which depends on the sizes, the shapes, and the signatures of the cells, and is drastically impacted by deployment of sensors and reference structure. However, in order to obtain a better deployment formation and a better spatial resolution, researchers have to spend lots of time to deploy and measure the deployment results of cells (such as their locations, sizes, shapes, and signatures), i.e., a huge amount of measurements which cost time and money in practice. Hence, in this paper, we propose a tool to visually and efficiently represent and analyze the deployment formation and spatial resolution. Generally, the location of a cell is represented by a list of vertices scattered in a cartesian coordinate system and can help researchers get its size, shape, and signature. A cell reconstruction algorithm is proposed to reconstruct cells generated by the deployment formation in which lists of vertices are generated by regarding cells as shortest path rings. We further provide some theoretical analysis and computational complexity analysis of the proposed algorithm. We conduct experiments using our tool to study sensor efficiency and spatial resolution easily and effectively.

Fusion estimation under binary sensors

Binary sensors compress the measurement information into two possible values as output and have found applications in many fields such as medical monitoring, environment monitoring and localization. This paper is concerned with the fusion estimation problem for a class of binary sensor systems with bounded noises, where the monitored physical process is described by time-varying state-space models. Since binary sensors only provide the valid information at the switching instant, a novel uncertain measurement model is proposed to characterize this feature. Then, by constructing an upper bound of fusion/estimation error at each time, convex optimization problems are established in terms of linear matrix inequalities to design stable centralized and distributed fusion estimators. An illustrative example is given to show the effectiveness of the proposed methods.

Development of an Underground Through-Soil Wireless Acoustic Communication System

The practical development of WUSN would benefit numerous applications, including online infrastructure monitoring, earthquake warning systems, and agricultural automation. However, stateof- the-art RF electromagnetic wireless systems are ineffective for communicating through-soil, due to extreme signal path losses from absorption and scattering in soil, even though the typical data requirements for such applications are modest, ranging from tens to hundreds of bps. In contrast, elephants, rodents and a wide variety of insects use acoustic waves for communicating through-soil, suggesting the viability of soil as a communication channel, and of acoustic waves as carriers. In this article, we demonstrate how soil can be used as a communication medium for wireless acoustic digital communication over distances up to 50 m at 20 bps data rates. By leveraging a physical model of the soil channel derived from its identified characteristics, then employing QPSK and OOK modulation and sparse decision feedback equalization schemes, and exploiting well-coupled and matched acoustic sources, wireless digital communication was successful in both laboratory and field experiments, illustrating the viability of sending application- specific data, ranging from binary sensor readings to low-resolution images.

Distributed fusion Kalman filtering under binary sensors

SummaryBinary sensors are special sensors that only transmit one‐bit information at each time and have been widely applied to environmental awareness and medical monitoring. This paper is concerned with the distributed fusion Kalman filtering problem for a class of binary sensor systems. A novel uncertainty approach is proposed to better extract valid information from binary sensors at switching instant. By minimizing a local estimation error covariance, the local robust Kalman estimates are firstly obtained. Then, the distributed fusion Kalman filter is designed by resorting to the covariance intersection fusion criterion. Finally, a blood oxygen content model is employed to show the effectiveness of the proposed methods.

Domain Adaptation of Binary Sensors in Smart Environments Through Activity Alignment

Activity Recognition is an active research topic focused on detecting human actions and behaviours in smart environments. In most cases, the use of data-driven models aim to relate data from sensors to an activity through a model developed by a supervised approach. In this work, we focus on the goal of domain adaptation between smart environments, which has required a novel approach to relate the concepts of domain adaptation using binary sensor and learning from daily imbalanced data. In this work, the sensor activation from a given context is translated to a different one, based on the temporal alignment from human activities. The domain adaptation of binary sensor is accomplished through a three step procedure: i) clustering of sensor activation, ii) activity based alignment of sensor data between the two environments, iii) an ensemble of classifiers used to mine a mapping function, translating sensor data between the two environments. The proposed method was evaluated over a publicly available dataset, and obtained preliminary results which were encouraging with an F1-Score of 87%.

Adaptive control for systems with two binary measurements

We propose a new type of binary controller for control loops with two binary measurements and an analog, continuous actuator. Unlike common binary controllers, the proposed adaptive controller requires no continued oscillation of the plant output. Instead, the proposed controller adapts an internal plant model to compute an optimal plant input so that the plant output settles on a constant value in between the two binary sensors. The parameter identification is fully automated and requires no user interaction, so that the proposed controller is as simple to implement as a conventional on-off controller. We evaluate the controller in a laboratory test setup and compare the energy comsumption to established control approaches.

SMRT: Multi-Resident Tracking in Smart Homes With Sensor Vectorization.

Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sensor events with the corresponding residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layouts, floor plans, and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data.

Exploiting Redundancy for Reliability Analysis of Sensor Perception in Automated Driving Vehicles

For automated driving, the perception provided by lidar, radar, and camera sensors is safety-critical. Validating sensor perception reliability with standard empirical tests is impractical, owing to the large required test effort and the need for a reference truth to identify sensor errors. To address these challenges, we investigate the possibility of estimating sensor perception reliability without a reference truth. In particular, we propose a framework to learn sensor perception reliability solely by exploiting sensor redundancies. We formulate a likelihood function for redundant binary sensor data without a reference truth and propose a Gaussian copula to model dependent sensor errors. Synthetic numerical experiments show that under an adequate dependence model, correct sensor perception reliabilities can be estimated without a reference truth. Because the selection of an adequate dependence model is challenging without a reference truth, we also investigate how inadequate dependence models influence the estimation. The proposed framework is a step toward the validation of sensor perception reliability because it could enable the learning of reliabilities from a fleet of driver-controlled vehicles equipped with series sensors.

Detection of a recurrent static target using binary sensor networks

The authors address the problem of decentralised detection of a recurrent event produced by a static uncooperative target in a wireless network of spatially distributed sensors. The presence or absence of the target is determined by applying fusion rules on local binary decisions of individual sensors. Optimal fusion rules require complete statistical knowledge about local detection and false alarm probabilities; hence, in practice they are infeasible. Previous works, such as the counting rule test, scan statistics and the generalised likelihood ratio test, have suggested sub-optimal fusion rules based on the total number of detecting sensors. Neither method takes full advantage of the spatial or temporal correlation between adjacent sensors. In practice, the decisions of adjacent sensors are dependent both in space (as the target is static) and in time (as the event reoccurs). Herein, the authors propose an approach that capitalises on the temporal and spatial arrangement of the local decisions. Simulation examples demonstrate that the detection probability of the proposed spatio-temporal rule is significantly higher than that of the common methods, and also achieves a lower probability of false alarm. Moreover, real data results show that the proposed approach is robust to strong environmental noises.

Activity Recognition Using Binary Sensors for Elderly People Living Alone: Scanpath Trend Analysis Approach

Unobtrusive activity recognition is known to be the most preferred solution for monitoring daily activities of elderly people. In this paper, Scanpath Trend Analysis (STA) is employed for unobtrusive activity recognition of elderly people living alone. Binary sensor data are used and each activity is considered as a sequence of sensor points for this purpose. The real-world long-term fully annotated Aruba open dataset collected by binary sensors is used for the verification of accuracy and the efficacy of the proposed approach. With the STA, the F1-score of 0.758 is obtained, and furthermore, by adding some extra semantic information through an activity transition matrix, it is possible to have F1-score as 0.863. This F1-score is superior to all the related works that use binary sensor data for activity prediction, while computationally the approach presented is advantageous since long periods of training process can be avoided.