Applications using a low-cost baseband pulsed microwave radar sensor

Duzdar, Ayman; Kompa, G.

doi:10.1109/imtc.2001.928819

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…Existing methods use various types of signal for vehicle detection and classification, including acoustic signal [2][3][4][5], radar signal [6,7], ultrasonic signal [8], infrared thermal signal [9], magnetic signal [10], 3D lidar signal [11] and image/video signal [12][13][14][15][16]. Furthermore, some of the methods can be combined with a variety of signals, such as radar&vision signal [7] and audio&vision signal [17].…”

Section: Related Workmentioning

confidence: 99%

Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification

Han

Jiang

et al. 2018

Advances in Multimedia

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Vehicle detection and classification are very important for analysis of vehicle behavior in intelligent transportation system, urban computing, etc. In this paper, an approach based on convolutional neural networks (CNNs) has been applied for vehicle classification. In order to achieve a more accurate classification, we removed the unrelated background as much as possible based on a trained object detection model. In addition, an unsupervised pretraining approach has been introduced to better initialize CNNs parameters to enhance the classification performance. Through the data enhancement on manual labeled images, we got 2000 labeled images in each category of motorcycle, transporter, passenger, and others, with 1400 samples for training and 600 samples for testing. Then, we got 17395 unlabeled images for layer-wise unsupervised pretraining convolutional layers. A remarkable accuracy of 93.50% is obtained, demonstrating the high classification potential of our approach.

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Section: Related Workmentioning

confidence: 99%

Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification

Han

Jiang

et al. 2018

Advances in Multimedia

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“…As pointed out previously, the performance of the UWB receiver is measured in terms of the quality of baseband signal reproduced by the designed receiver itself as compared with the original input signal directly measured by the commercial sampling oscilloscope [16]. As seen in Fig.…”

Section: B Balanced Sampling Headmentioning

confidence: 99%

Development of a Novel, Compact, Balanced, Micropower Impulse Radar for Nondestructive Applications

Liu

Xia

et al. 2015

IEEE Sensors J.

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In this paper, we present a novel development of compact micropower ultrawideband (UWB) radar by integrating ultrashort balanced transmitter, sampling receiver, and antennas into a single package. The developed transmitter produces a pair of Gaussian pulses with 1 MHz of PRF, 400 ps of duration, and 8.3 V for peak-to-peak amplitude. The receiver down-converts microwave signals with a conversion gain of 3 dB over a 4.5-GHz bandwidth. The integrated UWB micropower radar system allows for the short-range detection with high range resolution and accuracy, thus enhance effectively the capability of target detection and classification. The high performances of such radar system for detecting objects are demonstrated through the realfield tests of various target scenes.Index Terms-Micropower impulse radar (MIR), UWB transmitter/receiver, UWB antenna, pulse generation, sample and hold circuit, nondestructive test.

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“…Examples are acoustic [10][11][12], ultrasonic [13], magnetic [14], electromagnetic [15], infrared [16], and even video analysis systems [17][18][19][20]. Most of these techniques rely on a wireless sensor network (WSN) [21] or can be easily adapted to be used in a WSN approach.…”

Section: Introductionmentioning

confidence: 99%

Simple Design of Wireless Sensor Networks for Traffic Jams Avoidance

Jiménez

Garcia

2015

Journal of Sensors

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Intelligent transportation systems (ITS) are usually approached by exhaustive measuring and complex signal processing including medium-high cost hardware deployment. In this paper, a novel design of a wireless sensor network system using magnetometers and microphones for the detection and avoidance of traffic jams is described and analyzed. The system, which can also be used for traffic monitoring and surveillance, is simple, energy efficient, and accurate which allows to be implemented with a reduced hardware cost. In order to reduce the maintenance tasks, mini solar panels would also be installed for powering up the motes in the near future.

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Applications using a low-cost baseband pulsed microwave radar sensor

Cited by 11 publications

References 5 publications

Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification

Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification

Development of a Novel, Compact, Balanced, Micropower Impulse Radar for Nondestructive Applications

Simple Design of Wireless Sensor Networks for Traffic Jams Avoidance

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