Multiple Sensor Configurations Research Articles

Developing an IoT-enabled probabilistic model for quick identification of hidden radioactive materials in maritime port operations to strengthen global supply chain security

Uncovering hidden radioactive materials continues to be a major hurdle in worldwide supply chains. Recent research has not adequately investigated practical Internet of Things (IoT)-based approaches for improving and implementing efficient data fusion techniques. Current systems often misuse resources, leading to security vulnerabilities in typical settings. Our research delves into the fundamental principles of detection using both single and multiple sensor configurations, adopting a probabilistic method for merging data. We introduce a model aimed at accelerating the detection of radiation emissions in actual port operations. The results highlight the model’s effectiveness in rapid identification and determine the best conditions for its application in scenarios involving stacked containers, whether they are on ships or positioned in storage areas.

Comparison of state-of-the-art deep learning architectures for detection of freezing of gait in Parkinson's disease.

Freezing of gait (FOG) is one of the most debilitating motor symptoms experienced by patients with Parkinson's disease (PD). FOG detection is possible using acceleration data from wearable sensors, and a convolutional neural network (CNN) is often used to determine the presence of FOG epochs. We compared the performance of a standard CNN for the detection of FOG with two more complex networks, which are well suited for time series data, the MiniRocket and the InceptionTime. We combined acceleration data of people with PD across four studies. The final data set was split into a training (80%) and hold-out test (20%) set. A fifth study was included as an unseen test set. The data were windowed (2 s) and five-fold cross-validation was applied. The CNN, MiniRocket, and InceptionTime models were evaluated using a receiver operating characteristic (ROC) curve and its area under the curve (AUC). Multiple sensor configurations were evaluated for the best model. The geometric mean was subsequently calculated to select the optimal threshold. The selected model and threshold were evaluated on the hold-out and unseen test set. A total of 70 participants (23.7 h, 9% FOG) were included in this study for training and testing, and in addition, 10 participants provided an unseen test set (2.4 h, 11% FOG). The CNN performed best (AUC = 0.86) in comparison to the InceptionTime (AUC = 0.82) and MiniRocket (AUC = 0.76) models. For the CNN, we found a similar performance for a seven-sensor configuration (lumbar, upper and lower legs and feet; AUC = 0.86), six-sensor configuration (upper and lower legs and feet; AUC = 0.87), and two-sensor configuration (lower legs; AUC = 0.86). The optimal threshold of 0.45 resulted in a sensitivity of 77% and a specificity of 58% for the hold-out set (AUC = 0.72), and a sensitivity of 85% and a specificity of 68% for the unseen test set (AUC = 0.90). We confirmed that deep learning can be used to detect FOG in a large, heterogeneous dataset. The CNN model outperformed more complex networks. This model could be employed in future personalized interventions, with the ultimate goal of using automated FOG detection to trigger real-time cues to alleviate FOG in daily life.

Tracking Displacement of a Worm-Like Robot With Multiple Sensor Configurations

Tracking Displacement of a Worm-Like Robot With Multiple Sensor Configurations

Simultaneous atrial and ventricular restitution and fibrillation analyses using a low-cost scalable panoramic whole-heart optical mapping configuration for whole-heart imaging of translational models

Abstract Introduction Optical mapping (OM) is the primary method for imaging electrophysiologically relevant parameters from the outer surface of Langendorff-perfused hearts. However, current panoramic OM systems are financially challenging, require multiple sensors to map wide areas of the surface with overlapping regions and missing areas of interest due to the intrinsic shape of the heart. We proposed different panoramic configurations to dynamically characterize atrial and ventricular function during arrhythmia and, in presence of a coronary occlusion. Methods We devised both a multi-sensor and a single sensor system integrated with the perfusion circuit comprising sensor elements, a control system for led illumination, electrical stimulation and sensing of physiological variables. Two configurations were tested: A multi-sensor panoramic approach including different camera views and, a precision near-ellipsoidal curved mirror (highly concentrated reflectance in the range 380nm-800nm), with exponential profile and diameter to harbor small to large hearts was used. Twelve-rabbits and two pig hearts were included in the study. OM was performed using di-4 ANDBQPQ near-infrared dye and 5–10uM blebbistatine. Initial camera calibration was done using a fishnet-like pattern and nylon landmarks. We characterized signal-tonoise ratios (SNR) of reconstructed action potentials (AP) and obtained parametric maps of the activation process. A custom-built computational pipeline for accurate motion compensation and AP reconstructions was used. Hearts were stimulated until ventricular fibrillation (VF) was induced with and without presence of a coronary occlusion in the LADartery (LADCO). Results High SNRs spatial maps were obtained after AP reconstruction (42.24±4.23dB,stimulated; 23.31±5.24dB,VF) in the most restrictive set-up, the single-sensor near-ellipsoidal configuration. Quantified activation and repolarization dynamic restitution properties were assigned to anatomically labelled scale-invariant areas in the atrium and ventricles during the induction process. Atrial and ventricular restitution dynamics could be reconstructed and analyzed simultaneously. Slower conduction (CT) and higher dispersion in repolarization (DoR) was observed after LADCO in LV (CT: 13.4±5.31ms-vs-22.42±6.82ms,DoR:23.24±4.23ms-vs-38.03±9.17ms,p<0.05). During VF, more heterogeneous activation patterns, enhanced left-to-right VV spectral gradient profiles, and spatially constraint shorter-lived filaments, partially explained the disturbances observed during LADCO. Results obtained with a direct multiple sensor configuration allowed for complete tracking of dominant sources, yet presenting overlapped information. Conclusion Hereby results comparing two low-cost configurations for panoramic imaging are presented and validated for the study of cardiac propagation throughout the whole epicardial surface for the study of arrhythmia, drug interventions and cardiovascular disease. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): CIBERcv

Evaluation of Murrell's EKF-Based Attitude Estimation Algorithm for Exploiting Multiple Attitude Sensor Configurations.

Pico- and nano-satellites, due to their form factor and size, are limited in accommodating multiple or redundant attitude sensors. For such satellites, Murrell’s implementation of the extended Kalman filter (EKF) can be exploited to accommodate multiple sensor configurations from a set of non redundant attitude sensors. The paper describes such an implementation involving a sun sensor suite and a magnetometer as attitude sensors. The implementation exploits Murrell’s EKF to enable three sensor configurations, which can be operationally commanded, for satellite attitude estimation. Among the three attitude estimation schemes, (i) sun sensor suite and magnetometer, (ii) magnetic field vector and its time derivative and (iii) magnetic field vector, it is shown that the third configuration is better suited for attitude estimation in terms of precision and accuracy, but can consume more time to converge than the other two.

다중 자기센서를 이용한 실내 자기 지도 기반 보행자 위치 검출 정확도 향상 알고리즘

This research proposes a indoor magnetic map matching algorithm that improves the position accuracy by employing multiple magnetic sensors and probabilistic candidate weighting function. Since the magnetic field is easily distorted by the surrounding environment, the distorted magnetic field can be used for position mapping, and multiple sensor configuration is useful to improve mapping accuracy. Nevertheless, the position error is likely to increase because the external magnetic disturbances have repeated pattern in indoor environment and several points have similar magnetic field distortion characteristics. Those errors cause large position error, which reduces the accuracy of the position detection. In order to solve this problem, we propose a method to reduce the error using multiple sensors and likelihood boundaries that uses human walking characteristics. Also, to reduce the maximum position error, we propose an algorithm that weights according to their importance. We performed indoor walking tests to evaluate the performance of the algorithm and analyzed the position detection error rate and maximum distance error. From the results we can confirm that the accuracy of position detection is greatly improved.

Professor: A motorized field-based phenotyping cart

Abstract An easy-to-customize, low-cost, low disturbance, motorized, and adjustable proximal sensing cart for field-based high-throughput phenotyping is described. General dimensions, motor specifications, and a remote operation application are given. The cart, named Professor, supports mounting multiple proximal sensors and cameras for characterizing plant traits grown under field conditions. Professor easily adapts to multiple sensor configurations supporting detection of multiple target traits and has two axes of adjustable clearance by design. Professor is useful as a field-based phenotyping platform and offers a framework for customized development and application.

Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review.

Technical evaluation of swimming performance is an essential factor of elite athletic preparation. Novel methods of analysis, incorporating body worn inertial sensors (i.e., Microelectromechanical systems, or MEMS, accelerometers and gyroscopes), have received much attention recently from both research and commercial communities as an alternative to video-based approaches. This technology may allow for improved analysis of stroke mechanics, race performance and energy expenditure, as well as real-time feedback to the coach, potentially enabling more efficient, competitive and quantitative coaching. The aim of this paper is to provide a systematic review of the literature related to the use of inertial sensors for the technical analysis of swimming performance. This paper focuses on providing an evaluation of the accuracy of different feature detection algorithms described in the literature for the analysis of different phases of swimming, specifically starts, turns and free-swimming. The consequences associated with different sensor attachment locations are also considered for both single and multiple sensor configurations. Additional information such as this should help practitioners to select the most appropriate systems and methods for extracting the key performance related parameters that are important to them for analysing their swimmers’ performance and may serve to inform both applied and research practices.

Acoustic methods for tactical surveillance

Smart acoustic sensor systems can be deployed for the automatic detection, localization, classification and tracking of military activities, which are inherently noisy. Acoustic sensors are appealing because they are passive, affordable, robust, and compact. Also, the propagation of sound energy is not limited by obstacles that block or obscure the clear line of sight that is required for the effective operation of electromagnetic systems. Methods, with examples, for extracting tactical information from acoustic signals emitted by moving sources (air and ground vehicles) are provided for both single sensor and multiple sensor configurations. The methods are based on processing either the narrowband or broadband spectral components of the sources’ acoustic signature. Weapon firings generate acoustic impulses and supersonic projectiles generate shock waves enabling source localization and classification by processing the signals received by spatially-distributed sensors. The methods developed for land-based acoustic surveillance using microphone data are also applied to hydrophone data for passive acoustic surveillance of the underwater environment.

Sonar system for obstacle localization with insights from the echolocating bat

An active wide-beam sonar system for obstacle localization in 2 D is described. Efficient localization is achieved by mimicking the multiple sensor configuration of bats and employing an improved method for time-of-flight (TOF) estimation. Three transducers are employed, with a transmitter in the middle (the mouth) flanked by two receivers (the ears). After the transmitted pulse encounters an obstacle, the TOF information is extracted from the noisy echoes detected by each receiver, to estimate the range r and azimuth θ of an obstacle located within the active region of the sonar system. Simple thresholding does not produce accurate results due to the inevitable bias error in the TOF measurement. An unbiased estimate of TOF is developed by modeling the leading edge of the echo waveform by a parabola, whose vertex corresponds to the actual TOF. Based on this TOF information, unbiased range and azimuth estimates are derived for an isolated obstacle. Experimental results show that the estimates are most reliable if the obstacle is located along the system line-of-sight and that they improve with decreasing range, providing a novel interpretation for bat foraging. Experimental results indicate only a slight degradation compared to the Cramér-Rao lower bounds for estimator variances. [Work supported by NSF:ECS-8802627.]