Sensor Channels Research Articles

Recognition of Subtle Gestures by 2-Channel sEMG Using Parameter Estimation Classifiers Based on Probability Density

The abundant movement information carried by the surface electromyography (sEMG) signal can comprehensively reflect the muscle movement patterns on the surface of the human body, and the characteristics of physiological signals involving movement information have promoted the vigorous development in the field of human-computer interaction in machine learning. This paper takes subtle gestures as the research objects, and proposes a subtle gestures recognition system that uses two-channel sEMG signal and utilizes Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) which are parameter estimation classifiers based on probability density. First, combining the raw sEMG signal and the envelope sEMG signal, the number of channels for sensors and corresponding position of the muscle groups are preferably selected. Then, we propose to use the Pearson correlation coefficient to optimize the four types of features extracted in the time domain, frequency domain, time-frequency domain and AR model parameters, which effectively ensures the uniqueness of each gesture and eliminates individual differences. Finally, this paper compares four classifiers of QDA, LDA, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), and explores the pattern recognition classifier suitable for subtle actions. The experiment collects 9 kinds of subtle gestures from 8 subjects. The results show that the system can use only two channels of sEMG signal to recognize 9 kinds of subtle gestures which include multi-finger clicking or pinching or holding objects, and the most suitable classifiers QDA and LDA have the average recognition rates of 95.79% and 95.01%, respectively.

A 3D nyomtatás lehetőségei a repülőgépiparban

A technológiai fejlődés területén az innováció egy új arculata mutatta meg magát. A 3D nyomtatás korunk egyik legforradalmibb gyártástechnológiái közé sorolható, mindazonáltal története már az 1980-as évekre is visszavezethető. Olyan események eredményezték a fellendülést, mint a 3D nyomtatáshoz kapcsolódó szabadalmak lejárata, illetve az Ipar 4.0-nak nevezett ipari forradalom jelensége. A technika által kínált lehetőségeket nemzetközi szinten több különböző repülőgépipari vállalat is kihasználta. Elterjedését követően olyan cégek és vállalatok kezdték el nagymértékben alkalmazni a 3D nyomtatást, mint az Airbus vagy a Boeing. Cikkünkben a technológia nemzetközi felhasználásán túl, az általunk készített pilóta nélküli légi járműbe integrált szenzorcsatorna elkészítését szeretnénk bemutatni, amely egy igazán érdekes példája a 3D nyomtatási technológia hazai szintű felhasználásának.

River water classification pattern in Malang city based on electronic tongue for identification of environmental pollution

The e-tongue in this study was developed in 16 lipid membrane-based sensor channels. Each membrane lipid arrange by different polymer membranes. Membranes are made by combining polyvinyl chloride (PVC) as a matrix. Four different types of plasticizers (2-NPOE, bis (2-Ethylhexyl) Pthalate, bis (2-Ethylhexyl) phosphate, bis (1-butyl pentyl) adipate). Four types of lipids (octadecylamine, oleyl alcohol, methyltrioctylammonium chloride, and oleic acid) for active ingredients. This electronic tongue is used to classify river water pollution in Malang City from various sources. The data obtained were analysed using the PCA (Principal Component Analysis) method. Based on the PCA score plot shows the value of PC1 = 49.18%, and PC2 = 20.09%. Based on the loading plot shows the sensors that have the greatest contribution in classifying river water pollution are sensor 5, sensor 6. The PCA pattern shows that the river in front of UIN Malang, the river around Sumbersari and the river around Landungsari have a very close pattern. The data obtained show that the source of pollution of the three rivers is almost the same. The river around Soekarno Hatta and the river around Merjosari have a different pattern from the other three rivers. It can be concluded that using the PCA method can distinguish the classification of river water in different places.

Stealthy Sensor Attacks for Plants Modeled by Labeled Petri Nets

Abstract The problem of stealthy sensor attacks for labeled Petri nets is considered. An operator observes the plant to establish if a set of critical markings has been reached. The attacker can corrupt the sensor channels that transmit the sensor readings, making the operator incapable to establish when a critical marking is reached. We first construct the stealthy attack Petri net that keeps into account the real plant evolutions observed by the attacker and the corrupted plant evolutions observed by the operator. Starting from the reachability graph of the stealthy attack Petri net, an attack structure is defined: it describes all possible attacks. The supremal stealthy attack substructure can be obtained by appropriately trimming the attack structure. An attack function is effective if the supremal stealthy attack substructure contains a state whose first element is a critical marking and the second element is a noncritical marking.

Leak detection in a gas pipeline using spectral portrait of acoustic emission signals

This paper proposes a reliable method to detect leaks and recognize their various sizes in a gas pipeline based on the spectral portrait of acoustic emission (AE) signals. Through analyzing the relation of leak syndromes and AE signals in the frequency domain, this paper demonstrates that leak identification only by signatures obtained from measured individual signals might be unreliable due to strong attenuation in wave propagation along pipelines. The proposed method constructs a vector quantity in the frequency domain from two AE signals simultaneously acquired by two sensor channels, combined with a wave propagation model. This quantity characterizes the spectral portrait of AE signals emitted from a leak, its signatures therefore reflect leakage syndromes the same as leak signals, whereas those of individual sensor-measurements involve considerable distortions. Hence, the accuracy and reliability of leak detection and recognition are improved by the approach using the spectral portrait-related quantity. This paper also describes bank filters designed to encode the spectral envelope of AE signals instead of observing their total continuous frequency span. Finally, a trained multi-class support vector machine (SVM) classifier is responsible for the leak categorization, exploiting a set of the most discriminative encoded spectral envelope (ESE) features. The experimental results show that the proposed method outperforms conventional algorithms in pattern separability quantified by Kullback-Leibler distance and classification accuracy.

Model-Based Event-Triggered Tracking Control of Underactuated Surface Vessels With Minimum Learning Parameters.

This article studies the model-based event-triggered control (ETC) for the tracking activity of the underactuated surface vessel (USV). Following this ideology, the continuous acquisition of states is no longer needed, and the communication traffic is reduced in the channel of sensor to controller. The control laws are fabricated in the frame of an adaptive model, which is renewed with the states of the original system whenever the triggering condition is violated. In the scheme, both internal and external uncertainties are approximated by the neural networks (NNs). To decrease the computing complexity, the minimum learning parameters (MLPs) are involved both in the adaptive model and the derived controller. The adaptive laws of only two MLPs are devised, and their updating only happens at triggering instants. Using the MLPs, an adaptive triggering condition is further derived. To avoid the "Zeno" phenomenon in small tracking errors, a dead-zone operator is designed for the triggering condition. Furthermore, we incorporate the dynamic surface control (DSC) into the controller design, such that the jumping of virtual control laws at triggering instants is smoothed and the problem of "complexity explosion" is circumvented. Through the techniques of the impulsive dynamic system and the direct Lyapunov function, the parameter setting for the DSC is derived to guarantee the semiglobal uniformly ultimate boundedness (SGUUB) of all the error signals in the closed-loop system. Finally, the effectiveness of the proposed scheme is validated through the simulation.

Sensor Placement for Outage Identifiability in Power Distribution Networks

Accurate topology information is critical for effective operation of power distribution networks. Line outages change the operational topology of a distribution network. Hence, outage detection is an important task. Power distribution networks are operated as radial trees and are recently adopting the integration of advanced sensors to monitor the network in real time. In this paper, a dynamic-programming-based minimum cost sensor placement solution is proposed for outage identifiability. A novel formulation of the sensor placement as a cost optimization problem involving binary placement decisions is proposed, along with a dynamic programming algorithm to solve it in polynomial time. Our sensor placement solution provides a set of locations in a distribution network where sensor placement is mandatory for outage identifiability. We also extend our sensor placement optimization problem and dynamic programming algorithm to the case in which the number of measurement channels of sensors are limited. The advantage of placement strategies are that they incorporate various types of sensors, have a polynomial execution time, are cost effective and ensure outage identifiability for any arbitrary values of loads. Numerical results illustrating the proposed sensor placement solutions are presented under different scenarios for multiple feeder models including standard IEEE test feeders.

Construction of multi-channel fluorescence sensor array and its application for accurate identification and sensitive quantification of multiple metal ions

Herein, a small molecule based multi-channel fluorescent sensor array (SA) for accurate discrimination and sensitive quantitation of multiple heavy metal ions was developed, with its detection mechanism simulates an electronic tongue. The synergistic effects of the differentiated affinity of chelator for metal ions and multiple emission channels of sensors led to the diverse emission intensity change and wavelength switch, displaying obvious cross-reactivity and diversified signal output. Treated with linear discriminant analysis (LDA), this diversified multi-channel output allowed correct and high-resolution discrimination of 11 heavy metal ions at different concentration as well as multivariate mixture of hard-distinguish metal ions. Further titration experiment exhibited the regular arrangement of each metal ions in different extension directions at the range of 0–20 μM in 3D LDA plot, endowing SA array with the ability to identify and quantify each heavy metal ion. The feasibility of the SA array was also verified by the accurate detection of a large number of actual water samples. This strategy enables simple, efficient, sensitive identification and quantification of multiple metal ions in real water, demonstrating its potential for simplification of water quality analysis process.

Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh

We present DR-Train, the first long-term open-access dataset recording dynamic responses from in-service light rail vehicles. Specifically, the dataset contains measurements from multiple sensor channels mounted on two in-service light rail vehicles that run on a 42.2-km light rail network in the city of Pittsburgh, Pennsylvania. This dataset provides dynamic responses of in-service trains via vibration data collected by accelerometers, which enables a low-cost way of monitoring rail tracks more frequently. Such an approach will result in more reliable and economical ways to monitor rail infrastructure. The dataset also includes corresponding GPS positions of the trains, environmental conditions (including temperature, wind, weather, and precipitation), and track maintenance logs. The data, which is stored in a MAT-file format, can be conveniently loaded for various potential uses, such as validating anomaly detection and data fusion as well as investigating environmental influences on train responses.

MIMO Over-the-Air Computation for High-Mobility Multimodal Sensing

In future Internet-of-Things networks, sensors or even access points can be mounted on ground/aerial vehicles for smart-city surveillance or environment monitoring. For such high-mobility sensing , it is impractical to collect data from a large population of sensors using any traditional orthogonal multi-access scheme due to the excessive latency. To tackle the challenge, a technique called over-the-air computation (AirComp) was recently developed to enable a data-fusion center to receive a desired function of sensing data from concurrent sensor transmissions, by exploiting the superposition property of a multi-access channel. This paper aims at further developing multiple-input-multiple output (MIMO) AirComp for enabling high-mobility multimodal sensing . Specifically, we design MIMO-AirComp equalization and channel feedback techniques for spatially multiplexing multifunction computation. Given the objective of minimizing the computation error, a close-to-optimal equalizer is derived in closed-form using differential geometry. The solution can be computed as the weighted centroid of points on a Grassmann manifold, where each point represents the subspace spanned by the channel matrix of a sensor. As a by-product, the problem of MIMO-AirComp equalization is proved to have the same form as the classic problem of multicast beamforming, establishing the AirComp-multicasting duality . Its significance lies in making the said Grassmannian-centroid solution transferable to the latter problem which otherwise is solved using the computation-intensive semidefinite relaxation method. Last, building on the AirComp architecture, an efficient channel-feedback technique is designed for direct acquisition of the equalizer at the access point from simultaneous feedback by all sensors. This overcomes the difficulty of provisioning orthogonal feedback channels for many sensors.

Cyber-physical cooperative response strategy for consensus-based hierarchical control in micro-grid facing with communication interruption

Recently, more and more consensus-based hierarchical control methods are proposed in micro-grid. The main components of these methods are primary and secondary controllers. And they are usually used to adjust the output frequency and voltages of distributed energy resources. But the implementation of these methods is highly dependent on the communication network which is consisted by sensors and communicators. Once the data transmit of some sensors or communicators is interrupted, it will directly affect the control effect of hierarchical control. To solve the above problems, a cyber-physical cooperative response strategy is proposed in this paper. The main designs are as follows: (1) To solve the interruption problem among communicators, there are two defense methods proposed in the cyber layer: path planning and path reconstruction. Both methods are combined with cyber-physical hybrid vulnerability assessment and improved genetic algorithm; (2) To solve the interruption problem in the output channels of sensors, a distributed predictive compensation method combined with extreme learning machine and model predictive control is proposed in physical layer; (3) Based on the designs in (1) and (2), a novel secondary controller is proposed in this paper. Finally, the simulation results confirm the effectiveness of the proposed cooperative response strategy.

Blip glitches in Advanced LIGO data

Blip glitches are short noise transients present in data from ground-based gravitational-wave observatories. These glitches resemble the gravitational-wave signature of massive binary black hole mergers. Hence, the sensitivity of transient gravitational-wave searches to such high-mass systems and other potential short duration sources is degraded by the presence of blip glitches. The origin and rate of occurrence of this type of glitch have been largely unknown. In this paper we explore the population of blip glitches in Advanced LIGO during its first and second observing runs. On average, we find that Advanced LIGO data contains approximately two blip glitches per hour of data. We identify four subsets of blip glitches correlated with detector auxiliary or environmental sensor channels, however the physical causes of the majority of blips remain unclear.

Wirelessly Powered Data Aggregation for IoT via Over-the-Air Function Computation: Beamforming and Power Control

As a revolution in networking, the Internet of Things (IoT) aims at automating the operations of our societies by connecting and leveraging an enormous number of distributed devices (e.g., sensors and actuators). One design challenge is efficient wireless data aggregation (WDA) over the dense IoT devices. This can enable a series of the IoT applications ranging from latency-sensitive high-mobility sensing to data-intensive distributed machine learning. Over-the-air (function) computation (AirComp) has emerged to be a promising solution that merges computing and communication by exploiting analog-wave addition in the air. Another IoT design challenge is battery recharging for dense sensors which can be tackled by wireless power transfer (WPT). The coexisting of AirComp and WPT in the IoT system calls for their integration to enhance the performance and efficiency of WDA. This motivates the current work on developing the wirelessly powered AirComp (WP-AirComp) framework by jointly optimizing wireless power control, energy and (data) aggregation beamforming to minimize the AirComp error. To derive a practical solution, we recast the non-convex joint optimization problem into the equivalent outer and inner sub-problems for (inner) wireless power control and energy beamforming, and (outer) the efficient aggregation beamforming, respectively. The former is solved in closed form while the latter is efficiently solved using the semidefinite relaxation technique. The results reveal that the optimal energy beams point to the dominant Eigen-directions of the WPT channels, and the optimal power allocation tends to equalize the close-loop (down-link WPT and up-link AirComp) effective channels of different sensors. The simulation demonstrates that the controlling WPT provides additional design dimensions for substantially reducing the AirComp error.

Monitoring and diagnosis of multi-channel profile data based on uncorrelated multilinear discriminant analysis

Sensors are being widely used in many industrial practices and contain rich information which can be analyzed to detect system anomalies. The outputs of sensors are time-ordered data known as waveform signals, which are also called profiles. Many monitoring methods only focus on a single profile. However, multiple profiles are recorded by different sensor channels in many processes. It is crucial to study methods to analyze the multi-channel profiles. In this paper, uncorrelated multilinear discriminant analysis is suggested for fault detection and diagnosis. Then, the algorithm combined with tensor-to-tensor projection is proposed to make it get better performance in improving the accuracy of detection and reducing the fluctuation of the results in analyzing multi-channel profiles. The proposed method is applied directly to the multi-channel profiles. Discriminative and uncorrelated features are extracted, which are then fed into classifiers to identify different fault types. The effectiveness of the developed method is demonstrated by using both the simulation and a real-world case study. The real profiles in the case study are from a sensor fusion application in multiple forging operation processes, where multi-channel profiles are monitored to detect the faults of missing parts.

Sequential Sensor Selection and Access Decision for Spectrum Sharing

We develop an algorithm for sequential sensor selection and channel access decision in cognitive radio (CR) networks under shadowing with the objective of maximizing total number of correct decisions on channel availability over finite time periods. The problem is formulated as a multiarmed bandit (MAB) problem and our index-based heuristic algorithms is shown to achieve sublinear accumulated regret in time period $T$ . We also evaluate the performance of the proposed algorithms with numerical examples.

Fault Detection and Identification for Sensor Channels in Steam Generator Level Control Loops

Faults in sensor channels as a result of calibration errors or a bias in transmitters can lead to various safety and operational issues in nuclear power plants, such as reduced trip margins, jeopardized performance, or creating a false sense of security regarding the health of the plant operation. Unfortunately, such faults are difficult to detect, specifically when the sensors are part of a closed-loop system, which has a tendency to reduce or to mask the effects of the fault through feedback control actions during a dynamic transient process. Detecting these types of faults within a steam generator (SG) level control system is investigated in this paper. It is shown that the tools based on analytical redundancy can be very effective in this case. Using a typical SG level control system as a reference plant, specific analytical redundancy formulations are derived. Three sensor fault scenarios have been considered in detail. It has been shown, by simulation and analysis, that the proposed technique can effectively detect the sensor faults that are otherwise difficult to detect.

Performance Evaluation of Low-Cost Seismic Sensors for Dense Earthquake Early Warning: 2018⁻2019 Field Testing in Southwest China.

Earthquake Early Warning (EEW) was proved to be a potential means of disaster reduction. Unfortunately, the performance of the EEW system is largely determined by the density of EEW network. How to reduce the cost of sensors has become an urgent problem for building a dense EEW. A low-cost seismic sensor integrated with a Class C MEMS accelerometer was proposed in this paper. Based on minimal structure design, the sensor’s reliability was enhanced, while the costs were cut down as well. To fully reveal the performance, ten of the seismic sensors were installed and tested in Sichuan Province, southwest of China from May 2018 to February 2019. The seismic records obtained by the MNSMSs were compared with those by the traditional strong motion seismographs. The records obtained by the MNSMSs have good consistency with the data obtained by the Etnas. The MNSMSs can obtain clear seismic phases that are enough to trigger earthquake detections for EEW. By noise analysis, different channels of the same sensor and different sensors have good consistency. The tested dynamic range (over 87 dB) and useful resolution (over 14.5 bits) are completely in conformity with the designed parameters. Through real field testing, small earthquakes (M 3.1–3.6) can be detected by all three components E-W, N-S, and U-D within 50 km. In all, the low-cost seismic sensor proposed as a high-performance Class C MEMS sensor can meet the needs of dense EEW in terms of noise, dynamic range, useful resolution, reliability, and detecting capabilities.

On Multisensor Detection of Improper Signals

We consider the problem of detecting the presence of a complex-valued, possibly improper, but unknown signal, common among two or more sensors (channels) in the presence of spatially independent, unknown, possibly improper and colored, noise. Past work on this problem is limited to signals observed in proper noise. A source of improper noise is in-phase/quadrature-phase imbalance during down-conversion of bandpass noise to baseband. A binary hypothesis testing approach is formulated, and a generalized likelihood ratio test (GLRT) is derived using asymptotic distribution of a frequency-domain sufficient statistic, based on the discrete Fourier transform of an augmented measurement sequence. An asymptotic analytical solution for calculating the test threshold to yield a desired false alarm rate is provided. The performance of the GLRT is analyzed by deriving an approximate asymptotic distribution of the GLRT statistic under a sequence of local alternative hypotheses. These results (GLRT and test threshold calculation) are then modified to address two special cases: detection of improper signals in proper noise and detection of proper signals in proper noise. Simulation examples are presented in support of the proposed approaches.

Adaptive Channel Assignment With Predictions of Sensor Results and Channel Occupancy Ratio in PhyC-SN

Applications related to the Internet of Things are widely diversified and require both low latency and the access of massive sensors to wireless sensor networks. In physical wireless parameter conversion sensor networks (PhyC-SN), a fusion center (FC) can recognize information from all sensors on the frequency spectrum of the received signals via conversion from sensor information to signal frequency. The higher resolution of sensor information of PhyC-SNs requires securing more frequency bandwidth. Therefore, spectrum sharing between PhyC-SNs and other systems is essential. For this study, we assume that primary systems (PSs) refer to other wireless systems and that secondary systems (SSs) refer to the PhyC-SN. The SS detects any access from the PS and immediately stops access to an FC. This results in a loss of sensor information. Thus, the accuracy of the gathered sensor information by an FC is degraded. This paper proposes an adaptive channel assignment based on two predictions; sensor information and channel occupancy rate. In the proposed method, the predicted error caused by the cessation of channel access is calculated and the assignment is constructed by minimizing this predicted error. Since the sensor can select channels in accordance with the error of an instantaneous sensor result, the proposed channel assignment can utilize awareness of the instantaneous sensor result. The proposed method achieves high accuracy of gathered sensor information while delivering less frequent sensor information to the FC, thereby improving the utilization efficiency of frequency channels.

Energy-Efficient Optimization in Multi-Sensor WBAN With Multi-Antenna AP

This paper optimizes the energy efficiency performance by joint time and beamforming weights allocation in a wireless body area network (WBAN), which consists of a multi-antenna access point (AP), in particular, the AP intends to broadcast radio frequency (RF) power to several single-antenna on-body sensors and receives physiological information from them synchronously. Different path loss and shadowing values are considered in each sensor's channel model to be more practical. In this paper, maximal ratio transmission (MRT) technique and zero-forcing (ZF) technique are applied in RF energy transmission and information decoding, respectively. Considering the signal-to-inference-plus-noise ratio (SINR) threshold of each sensor, the total rate-energy ratio is formulated as a nonconvex optimization problem in each time block, we transform the nonconvex optimization problem into a convex optimization problem and solved it by convex optimization tools. We investigate the energy efficiency performance and reliability performance of the optimal solutions, and numerical results demonstrate the effectiveness of the optimal solutions.