Vector quantization and multi class support vector machines based fingerprint classification

Choudhury, Sabarna; Bandyopadhyay, Shreyasi; Mukhopadhyay, Sayan; Mukherjee, Subhajit

doi:10.1109/inventive.2016.7824828

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…It is also possible to use data compression strategies to reduce the amount of information to be sent and then decompress and process it in the cloud. Usual techniques for accomplishing data compression are Principal Component Analysis (PCA) [31][32][33][34], Sequential Forward Selection (SFS) [35], Random Subset Feature selection (RSFS) [35], Independent Component Analysis (ICA) [34], I-PCA [34], Vector Quantization (VQ) [36], and Frequency Sensitive Competitive Learning (FSCL) [37,38].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure

et al. 2021

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Extensive unsupervised livestock farming is a habitual technique in many places around the globe. Animal release can be done for months, in large areas and with different species packing and behaving very differently. Nevertheless, the farmer’s needs are similar: where livestock is (and where has been) and how healthy they are. The geographical areas involved usually have difficult access with harsh orography and lack of communications infrastructure. This paper presents the design of a solution for extensive livestock monitoring in these areas. Our proposal is based in a wearable equipped with inertial sensors, global positioning system and wireless communications; and a Low-Power Wide Area Network infrastructure that can run with and without internet connection. Using adaptive analysis and data compression, we provide real-time monitoring and logging of cattle’s position and activities. Hardware and firmware design achieve very low energy consumption allowing months of battery life. We have thoroughly tested the devices in different laboratory setups and evaluated the system performance in real scenarios in the mountains and in the forest.

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Section: Introductionmentioning

confidence: 99%

Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure

et al. 2021

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“…Vector quantization models [22][23][24][25][26], mentioned above, present higher compression ranges that exceed a hundred compression ratio [22] and are commonly used for image compression. Typical PSNR values in these models range from 25 to 60 dBs [22,23].…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction for Smart IoT Sensors

et al. 2020

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Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the same raw data. This increasing intelligence can occur at various levels, including in the sensor itself, at the edge, and in the cloud. As sending one byte of data is several orders of magnitude more energy-expensive than processing it, data must be handled as near as possible to its generation. Thus, the intelligence should be located in the sensor; nevertheless, it is not always possible to do so because real data is not always available for designing the algorithms or the hardware capacity is limited. Smart devices detecting data coming from inertial sensors are a good example of this. They generate hundreds of bytes per second (100 Hz, 12-bit sampling of a triaxial accelerometer) but useful information comes out in just a few bytes per minute (number of steps, type of activity, and so forth). We propose a lossy compression method to reduce the dimensionality of raw data from accelerometers, gyroscopes, and magnetometers, while maintaining a high quality of information in the reconstructed signal coming from an embedded device. The implemented method uses an adaptive vector-quantisation algorithm that represents the input data with a limited set of codewords. The adaptive process generates a codebook that evolves to become highly specific for the input data, while providing high compression rates. The codebook’s reconstruction quality is measured with a peak signal-to-noise ratio (PSNR) above 40 dB for a 12-bit representation.

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Application of Generalized Relevance Linear Vector Quantization for Diabetes Diagnosis

Hameed

Jamıl

Orman

et al. 2021

2021 International Conference on Innovative Computing (ICIC)

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Vector quantization and multi class support vector machines based fingerprint classification

Cited by 3 publications

References 7 publications

Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure

Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure

Dimensionality Reduction for Smart IoT Sensors

Application of Generalized Relevance Linear Vector Quantization for Diabetes Diagnosis

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