A Modular and Reconfigurable Pipeline Architecture for Learning Vector Quantization

Zhang, Xiangyu; An, Fengwei; Chen, Lei; Ishii, Idaku; Mattausch, Hans Jürgen

doi:10.1109/tcsi.2018.2804946

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…The Industrial Internet of Things (IIoT) is the primary impetus behind the development of the 6th generation (6G) mobile communications infrastructure [1]. A new paradigm will be presented by an artificial intelligence (AI) supported 6G network, which will promote the inclusion of vertical industry in various scenarios such as industrial digital transformation and smart manufacturing [2]. The evolutionary 6G network needs to offer ultra-high data rate with ultra-low latency, reliability, ubiquitous intelligence, and massive connections [3], which is supporting the intelligent production scheduling, prediction maintenance of equipment, high-precision product quality control, and other value-added services in a variety of industrial IoT systems, which is giving rise to the fourth industrial revolution [4].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Kumar¹,

Srinivasan²

2023

Intelligent Automation &Amp; Soft Computing

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At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image compression in the sensor, on the other hand, expedites the transfer of data while simultaneously reducing bandwidth use. The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties. The image encryption model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system. This helps to ensure that image datasets are kept safe. The Linde-Buzo-Gray (LBG) methodology is an example of a vector quantization algorithm that is extensively used and a relatively new form of picture reduction known as vector quantization (VQ). As a result, the purpose of this research is to create an artificial humming bird optimization approach that combines LBG-enabled codebook creation and encryption (AHBO-LBGCCE) for use in an IIoT setting. In the beginning, the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ. The Burrows-Wheeler Transform (BWT) model is used in order to accomplish codebook compression. In addition, the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained. A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms. The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.

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Section: Introductionmentioning

confidence: 99%

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Kumar¹,

Srinivasan²

2023

Intelligent Automation &Amp; Soft Computing

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“…The first, third quartile, and median values were higher for the proposed classifier [ 14 ]. Zhang et al proposed a LVQ with a modular reconfigurable pipelining architecture for object recognition and image compression [ 15 ]. The method was verified in a 65 nm CMOS prototype chip.…”

Section: Introductionmentioning

confidence: 99%

Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images

Latha

Muthu

Dhanalakshmi

et al. 2022

Computational Intelligence and Neuroscience

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Plaque deposits in the carotid artery are the major cause of stroke and atherosclerosis. Ultrasound imaging is used as an early indicator of disease progression. Classification of the images to identify plaque presence and intima-media thickness (IMT) by machine learning algorithms requires features extracted from the images. A total of 361 images were used for feature extraction, which will assist in further classification of the carotid artery. This study presents the extraction of 65 features, which constitute of shape, texture, histogram, correlogram, and morphology features. Principal component analysis (PCA)-based feature selection is performed, and the 22 most significant features, which will improve the classification accuracy, are selected. Naive Bayes algorithm and dynamic learning vector quantization (DLVQ)-based machine learning classifications are performed with the extracted and selected features, and analysis is performed.

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“…Backpropagation Network and Learning Vector Quantization (LVQ) are the most commonly used neural network algorithms to solve pattern recognition problems. Backpropagation has the advantage of finding optimal results and on the other hand, LVQ classifies based on sample vectors from semioptimal training data [9]. In terms of performance, LVQ and Backpropagation have similar performances [10].…”

Section: Introductionmentioning

confidence: 99%

Identification of Formaldehyde Bananas using Learning Vector Quantization

Musa

Akbar

2021

Indonesian J. of Inf. Syst.

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Bananas that ripen with chemical process or do not ripen naturally usually, this can be recognized by the presence of blackish patches on the surface of the skin. But visual recognition has its drawbacks, which is that it is difficult to recognize similarities between formalin bananas and natural bananas, resulting in a lack of accurate identification. In this study, a system was built that can determined formalin bananas and natural bananas through digital image identification using supervised classification. The image to be identification previously goes through the process of transforming RGB (Red Green Blue) color to Grayscale, and the process of extracting texture features using statically recognizable features through histograms, in the form of average, standard deviation, skewness, kurtosis, energy, entropy and smoothness. The extraction of texture features is classified with LVQ (Learning Vector Quantization) to determine formalin or natural bananas. The test was conducted with 122 banana imagery sample data, 100 imagery as training data consisting of 50 imagery for natural bananas and 50 imagery for bananas formalin, 22 imagery as test data. The test results showed LVQ method has the best percentage at Learning Rate 0.1, Decreased Learning Rate 0.75 and maximum epoch of 1000 with the smallest epoch of 7, obtained accuracy 90.90%, precision 84.61% and recall 100%.

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A Modular and Reconfigurable Pipeline Architecture for Learning Vector Quantization

Cited by 10 publications

References 31 publications

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images

Identification of Formaldehyde Bananas using Learning Vector Quantization

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