scholarly journals A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3558 ◽  
Author(s):  
Miroslav Schneider ◽  
Zdenek Machacek ◽  
Radek Martinek ◽  
Jiri Koziorek ◽  
Rene Jaros
Keyword(s):  
Detection System ◽  
Low Cost ◽  
Low Complexity ◽  
Recognition System ◽  
Detection Methods ◽  
Intelligent Building ◽  
Dynamic Background ◽  
Image Field ◽  
Camera Systems ◽  
Background Image

This article deals with the design and implementation of a prototype of an efficient Low-Cost, Low-Power, Low Complexity–hereinafter (L-CPC) an image recognition system for person detection. The developed and presented methods for processing, analyzing and recognition are designed exactly for inbuilt devices (e.g., motion sensor, identification of property and other specific applications), which will comply with the requirements of intelligent building technologies. The paper describes detection methods using a static background, where, during the search for people, the background image field being compared does not change, and a dynamic background, where the background image field is continually adjusted or complemented by objects merging into the background. The results are compared with the output of the Horn-Schunck algorithm applied using the principle of optical flow. The possible objects detected are subsequently stored and evaluated in the actual algorithm described. The detection results, using the change detection methods, are then evaluated using the Saaty method in order to determine the most successful configuration of the entire detection system. Each of the configurations used was also tested on a video sequence divided into a total of 12 story sections, in which the normal activities of people inside the intelligent building were simulated.

A Tracking and Location Detection System Using ZigBee Wireless Network

2012 ◽  
Vol 546-547 ◽  
pp. 1223-1228
Author(s):  
Jiong Shi ◽  
Hong Dao Song ◽  
Zhao Xi Fang
Keyword(s):  
Embedded Systems ◽  
System Architecture ◽  
Detection System ◽  
Low Cost ◽  
Low Complexity ◽  
Commercial Application ◽  
Positioning System ◽  
Wireless Technology ◽  
System Components ◽  
Location Detection

With the development of wireless networks and embedded systems, it is increasingly popular to make use of these technologies to tracking and location detection applications. ZigBee is a low power, low cost, low data rate and low complexity wireless technology for personal-area and device-to-device networking. In this paper, a practical ZigBee based tracking and positioning system for objects and personnel is proposed. Besides, the system architecture, system components as well as their functionalities are presented. Furthermore, the commercial application results have shown that the proposed system is suitable for tracking and location detection.

scholarly journals An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Francy Shu ◽  
Jeff Shu
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Low Cost ◽  
Fall Detection ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Nursing Facilities ◽  
Risk Zones ◽  
Set Up ◽  
Current Detection

AbstractFalls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system’s flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.

scholarly journals Pitot Tube-Based Icing Detection: Effect of Ice Blocking on Pressure

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xianglian Lv ◽  
Jie Guan ◽  
Shengkun Wang ◽  
Haiyang Zhang ◽  
Shijie Xue ◽  
...  
Keyword(s):  
Total Pressure ◽  
Static Pressure ◽  
Detection System ◽  
Low Cost ◽  
Pitot Tube ◽  
Detection Methods ◽  
Pressure Drops ◽  
Aerial Vehicle ◽  
Pressure Output ◽  
Detection Effect

This study aims at addressing a problem on icing detection for Unmanned Aerial Vehicle (UAV for short) because traditional icing detection methods are costly and bulky. Toward this end, a pitot-based icing detection method is proposed, and the effect of different types of icing blocking on pressure is firstly reported. An icing detection system based on the pitot tube is designed and fabricated. Icing wind tunnel results indicate that if the pitot tube is blocked by glaze ice, then the total pressure of the pitot tube decreases gradually and remains unchanged and less than static pressure. However, if the pitot tube is blocked by rime ice, then the total pressure drops to the same level as the static pressure. If the pitot tube is blocked by non-ice organic materials, then the total pressure suddenly drops to the same level as the static pressure and remains unchanged. Furthermore, if the pitot tube contacts the water droplets but does not freeze, the total pressure output value fluctuates slightly. The effect of icing on pressure is caused by differences in ice microstructure, temperature, and flow velocity. At the same time, the proposed method offers a facile and low-cost approach for UAV icing detection.

scholarly journals Environment Monitoring for Anomaly Detection System Using Smartphones

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3834 ◽  
Author(s):  
Van Khang Nguyen ◽  
Éric Renault ◽  
Ruben Milocco
Keyword(s):  
Anomaly Detection ◽  
Clustering Algorithm ◽  
Detection System ◽  
Low Cost ◽  
Mean Shift ◽  
Detection Methods ◽  
The Road ◽  
Current Threshold ◽  
Grubbs Test ◽  
Outlier Tests

Currently, the popularity of smartphones with networking capabilities equipped with various sensors and the low cost of the Internet have opened up great opportunities for the use of smartphones for sensing systems. One of the most popular applications is the monitoring and the detection of anomalies in the environment. In this article, we propose to enhance classic road anomaly detection methods using the Grubbs test on a sliding window to make it adaptive to the local characteristics of the road. This allows more precision in the detection of potholes and also building algorithms that consume less resources on smartphones and adapt better to real conditions by applying statistical outlier tests on current threshold-based anomaly detection methods. We also include a clustering algorithm and a mean shift-based algorithm to aggregate reported anomalies on data to the server. Experiments and simulations allow us to confirm the effectiveness of the proposed methods.

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

scholarly journals Rapid and Sensitive Detection of miRNA Based on AC Electrokinetic Capacitive Sensing for Point-of-Care Applications

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3985
Author(s):  
Nan Wan ◽  
Yu Jiang ◽  
Jiamei Huang ◽  
Rania Oueslati ◽  
Shigetoshi Eda ◽  
...  
Keyword(s):  
Limit Of Detection ◽  
Point Of Care ◽  
Low Cost ◽  
Clinical Diagnostics ◽  
Detection Methods ◽  
Capacitive Sensing ◽  
Application Potential ◽  
Mirna Detection ◽  
Mirna 25 ◽  
Low Sensitivity

A sensitive and efficient method for microRNAs (miRNAs) detection is strongly desired by clinicians and, in recent years, the search for such a method has drawn much attention. There has been significant interest in using miRNA as biomarkers for multiple diseases and conditions in clinical diagnostics. Presently, most miRNA detection methods suffer from drawbacks, e.g., low sensitivity, long assay time, expensive equipment, trained personnel, or unsuitability for point-of-care. New methodologies are needed to overcome these limitations to allow rapid, sensitive, low-cost, easy-to-use, and portable methods for miRNA detection at the point of care. In this work, to overcome these shortcomings, we integrated capacitive sensing and alternating current electrokinetic effects to detect specific miRNA-16b molecules, as a model, with the limit of detection reaching 1.0 femto molar (fM) levels. The specificity of the sensor was verified by testing miRNA-25, which has the same length as miRNA-16b. The sensor we developed demonstrated significant improvements in sensitivity, response time and cost over other miRNA detection methods, and has application potential at point-of-care.

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