Reconfigurable High-Order Moving-Average Filter Using Inverter-Based Variable Transconductance Amplifiers

Kweon, Soon-Jae; Shin, Seung Heon; Jo, Sung-Hun; Yoo, Hyung-Joun

doi:10.1109/tcsii.2014.2362736

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…CSI amplitude changes caused by human bodies and their activities are mainly concentrated in the low-frequency part of CSI amplitude data, so CSI-based wireless sensing algorithms generally use low-pass filters to filter CSI amplitude data [20], such as moving average filter, Gaussian filter [8], and wavelet threshold method [21]. This paper focuses on reducing the time complexity of the algorithm, so we use the moving average filter with lower time complexity [22] to filter the high-frequency noise of CSI amplitude data. Moreover, this paper aims to use CSI amplitude for recognizing the number and states of people, which belongs to coarse-grained information recognition.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

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Texture features-based lightweight passive multi-state crowd counting algorithm

Tian,

Li,

et al. 2023

J Wireless Com Network

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Passive crowd counting using channel state information (CSI) is a promising technology for applications in fields such as smart cities and commerce. However, the most existing algorithms can only recognize the total number of people in the monitoring area and cannot simultaneously recognize the number and states of people and ignore the real-time performance of the algorithms. Therefore, they cannot be applied to the scenarios of multi-state crowd counting requiring high real-time performance. To address this issue, a lightweight passive multi-state crowd counting algorithm called TF-LPMCC is proposed. This algorithm constructs CSI amplitude data into amplitude and time–frequency images, extracts texture features using the gray-level co-occurrence matrix (GLCM) and gray-level difference statistic (GLDS) methods, and uses the linear discriminant analysis (LDA) algorithm to count the crowd in multi-states. Experiments show that the TF-LPMCC algorithm not only has low time complexity but also achieves an average recognition accuracy of 98.27% for crowd counting.

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Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…Seventy samples were collected for each category, with 40 randomly selected for training and the remaining 30 for testing during algorithm simulation. (22)…”

Section: Experimental Setup and Data Acquisitionmentioning

confidence: 99%

Texture features-based lightweight passive multi-state crowd counting algorithm

Tian,

Li,

et al. 2023

J Wireless Com Network

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show abstract

“…The table shows that, while the number of slices of the multi-filter designs is 4–6× higher than the single-filter design, the increase of power consumption is 24–29% since the static common power factor is dominant. The authors in [ 35 ] demonstrate that an application-specific integrated circuit (ASIC) implementation of a moving average filter using 65 nm CMOS technology consumes 5.4 mW on average.…”

Section: Performance Evaluationmentioning

confidence: 99%

Multi-Filter Decoding in WiFi Backscatter Communication

Nti

Yun

2021

Sensors

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WiFi backscatter communication has emerged as a promising enabler of ultralow-power connectivity for Internet of things, wireless sensor network and smart energy. In this paper, we propose a multi-filter design for effective decoding of WiFi backscattered signals. Backscattered signals are relatively weak compared to carrier WiFi signals and therefore require algorithms that filter out original WiFi signals without affecting the backscattered signals. Two multi-filter designs for WiFi backscatter decoding are presented: the summation and delimiter approaches. Both implementations employ the use of additional filters with different window sizes to efficiently cut off undesired noise/interference, thus enhancing frame detection and decoding performance, and can be coupled with a wide range of decoding algorithms. The designs are particularly productive in the frequency-shift WiFi backscatter communication. We demonstrate via prototyping and testbed experiments that the proposed design enhances the performance of various decoding algorithms in real environments.

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“…To avoid the effect of the external interference on the topography modeling, the median filtering [19] or moving average filtering [20] can be used before numerical analysis to remove noise. The smooth function is used to filter the 20 data specified around the data in this paper.…”

Section: Median Filtering Equal Length Processing and Secondary Concmentioning

confidence: 99%

Non-destructive 3D Topography Modeling Based on Surface Profile Measurement Data

Kang¹,

Tang²

2018

IJCEE

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Due to the limitations on the size and properties of samples, most of the surface topography tests are destructive and the contrast test of the surface morphology before and after friction and wear of the same workpiece cannot be carried out by the topography instruments. In addition, the destructive test increases the test period and the research cost, as well. In order to solve the above problems, a non-destructive surface topography measurement method is proposed in this paper. The 3D surface topography model of the workpiece surface can be established by the processing of the surface height distribution characteristics obtained by the surface profiler. It includes the discrete point reorganization, concentric processing, moving average filtering, equal length processing and other pre-processing and the post-processing like regression analysis, polynomial interpolation. Compared with the surface topography obtained by the topography instrument, it is found that the 3D surface topography model established not only characterize the surface features such as dents, bulges, and scratches accurately and effectively but also avoid the complex sample preparation processes. What is more, the surface topography of the workpiece can be acquired without destructing and the comparison test of the surface topography before and after friction and wear of the same workpiece can be conducted.Wencheng Tang received the B.S. degree from Nanjing Institute of Technology in 1982, the M.Sc. and the Ph.D. degrees from Southeast University in 1987. From 2008 to 2014, he was the dean of the School of Mechanical Engineering. His main research directions are CAD/CAE/CAM, computer integrated manufacturing system, researches on friction and wear of functional components and static and dynamic analysis and optimization design of structural parts.

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Reconfigurable High-Order Moving-Average Filter Using Inverter-Based Variable Transconductance Amplifiers

Cited by 13 publications

References 11 publications

Texture features-based lightweight passive multi-state crowd counting algorithm

Texture features-based lightweight passive multi-state crowd counting algorithm

Multi-Filter Decoding in WiFi Backscatter Communication

Non-destructive 3D Topography Modeling Based on Surface Profile Measurement Data

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