A Low-Cost and Fast Vehicle Detection Algorithm With a Monocular Camera for Adaptive Driving Beam Systems

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

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Vehicle detection algorithm based on LW-SSD

Journal of Physics Conference Series ◽

10.1088/1742-6596/1748/3/032042 ◽

2021 ◽

Vol 1748 ◽

pp. 032042

Author(s):

Yunxiang Liu ◽

Guoqing Zhang

Keyword(s):

Vehicle Detection ◽

Detection Algorithm

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A new vehicle detection algorithm using gray-level features

2013 International Conference on Computational Problem-Solving (ICCP) ◽

10.1109/iccps.2013.6893537 ◽

2013 ◽

Author(s):

Qin Gu ◽

Liangchao Li ◽

Jianyu Yang

Keyword(s):

Vehicle Detection ◽

Detection Algorithm ◽

Gray Level

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MEMS Based SINS/OD Filter for Land Vehicles’ Applications

Mathematical Problems in Engineering ◽

10.1155/2017/1691320 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Huisheng Liu ◽

Zengcai Wang ◽

Susu Fang ◽

Chao Li

Keyword(s):

Navigation System ◽

Velocity Measurement ◽

Microelectromechanical Systems ◽

Low Cost ◽

Detection Algorithm ◽

Measurement Unit ◽

Integrated Navigation ◽

Integrated Navigation System ◽

Land Vehicles ◽

Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

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Absence Seizure Detection Algorithm for Portable EEG Devices

Frontiers in Neurology ◽

10.3389/fneur.2021.685814 ◽

2021 ◽

Vol 12 ◽

Author(s):

Pawel Glaba ◽

Miroslaw Latka ◽

Małgorzata J. Krause ◽

Sławomir Kroczka ◽

Marta Kuryło ◽

...

Keyword(s):

Detection Rate ◽

Low Cost ◽

Clinical Manifestations ◽

Epileptic Seizures ◽

Seizure Detection ◽

Detection Algorithm ◽

Absence Epilepsy ◽

Absence Seizure ◽

Epileptiform Discharges ◽

Morlet Continuous Wavelet Transform

Absence seizures are generalized nonmotor epileptic seizures with abrupt onset and termination. Transient impairment of consciousness and spike-slow wave discharges (SWDs) in EEG are their characteristic manifestations. This type of seizure is severe in two common pediatric syndromes: childhood (CAE) and juvenile (JAE) absence epilepsy. The appearance of low-cost, portable EEG devices has paved the way for long-term, remote monitoring of CAE and JAE patients. The potential benefits of this kind of monitoring include facilitating diagnosis, personalized drug titration, and determining the duration of pharmacotherapy. Herein, we present a novel absence detection algorithm based on the properties of the complex Morlet continuous wavelet transform of SWDs. We used a dataset containing EEGs from 64 patients (37 h of recordings with almost 400 seizures) and 30 age and sex-matched controls (9 h of recordings) for development and testing. For seizures lasting longer than 2 s, the detector, which analyzed two bipolar EEG channels (Fp1-T3 and Fp2-T4), achieved a sensitivity of 97.6% with 0.7/h detection rate. In the patients, all false detections were associated with epileptiform discharges, which did not yield clinical manifestations. When the duration threshold was raised to 3 s, the false detection rate fell to 0.5/h. The overlap of automatically detected seizures with the actual seizures was equal to ~96%. For EEG recordings sampled at 250 Hz, the one-channel processing speed for midrange smartphones running Android 10 (about 0.2 s per 1 min of EEG) was high enough for real-time seizure detection.

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Intelligent Highway Traffic Detection Algorithm based on Deep Learning

10.21203/rs.3.rs-805608/v1 ◽

2021 ◽

Author(s):

ming ji ◽

Chuanxia Sun ◽

Yinglei Hu

Keyword(s):

Deep Learning ◽

Real Time ◽

Traffic Congestion ◽

Road Network ◽

Vehicle Detection ◽

Detection Algorithm ◽

Recognition Algorithm ◽

Traffic Data ◽

Vehicle Recognition ◽

Environment Recognition

Abstract In order to solve the increasingly serious traffic congestion problem, an intelligent transportation system is widely used in dynamic traffic management, which effectively alleviates traffic congestion and improves road traffic efficiency. With the continuous development of traffic data acquisition technology, it is possible to obtain real-time traffic data in the road network in time. A large amount of traffic information provides a data guarantee for the analysis and prediction of road network traffic state. Based on the deep learning framework, this paper studies the vehicle recognition algorithm and road environment discrimination algorithm, which greatly improves the accuracy of highway vehicle recognition. Collect highway video surveillance images in different environments, establish a complete original database, build a deep learning model of environment discrimination, and train the classification model to realize real-time environment recognition of highway, as the basic condition of vehicle recognition and traffic event discrimination, and provide basic information for vehicle detection model selection. To improve the accuracy of road vehicle detection, the vehicle target labeling and sample preprocessing of different environment samples are carried out. On this basis, the vehicle recognition algorithm is studied, and the vehicle detection algorithm based on weather environment recognition and fast RCNN model is proposed. Then, the performance of the vehicle detection algorithm described in this paper is verified by comparing the detection accuracy differences between different environment dataset models and overall dataset models, different network structures and deep learning methods, and other methods.

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Low-Cost Smart Heads-Up Display Assist System for Automobiles

Journal of VLSI Design and Signal Processing ◽

10.46610/jovdsp.2021.v07i02.004 ◽

2021 ◽

Vol 7 (2) ◽

Author(s):

Gautham G ◽

Deepika Venkatesh ◽

A. Kalaiselvi

Keyword(s):

Fuel Economy ◽

Low Cost ◽

Detection Algorithm ◽

Lane Detection ◽

Raspberry Pi ◽

Worst Case ◽

The Road ◽

Traffic Conditions ◽

Metropolitan Cities ◽

Ingenious Method

In recent years, due to the increasing density of traffic every year, it is been a hassle for drivers in metropolitan cities to maintain lane and speeds on road. The drivers usually waste time and effort in idling their cars to maintain in traffic conditions. The drivers get easily frustrated when they tried to maintain the path because of the havoc created. Transportation Institute found that the odds of a crash(or near crash) more than doubled when the driver took his or her eyes off the road formore than two seconds. This tends to cause about 23% of accidents when not following their lane paths. In worst case the fuel economy often drops and tends to cause increase in pollution about 28% to 36% per vehicle annually. This corresponds to the wastage of fuel. Owing to this problem, we proposed an ingenious method by which the lane detection can be made affordable and applicable to existing automobiles. The proposed prototype of lane detection is carried over with a temporary autonomous bot which is interfaced with Raspberry pi processor, loaded with the lane detection algorithm. This prototype bot is made to get live video which is then processed by the algorithm. Also, the preliminary setups are carried over in such a way that it is easily implemented and accessible at low cost with better efficiency, providing a better impact on future automobiles.

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