A Clustering Method Based on K-Means Algorithm

The use of counterfeit integrated circuits (ICs) in electronic products decreases its quality and lifetime. Recycled ICs can be detected by the method of aging analysis. Aging is carried out through reliability analysis with the effect of hot carrier injection and bias temperature instability (BTI). In this work, three machine learning methods, namely Kmeans clustering, back propagation neural network (BPNN) and support vector machines (SVMs), are used to detect the recycled IC aged for a shorter period (1 day) with minimum data size. This work also distinguishes the effects of degradation due to process variations and reliability effects. The reliability and Monte Carlo simulation are performed on benchmark circuits such as c17, s27, b02 and fully differential folded-cascode amplifier using the Cadence Virtuoso tool, and the parameters such as minimum voltage, delay value, supply current, gain, phase margin and bandwidth are measured. Machine learning methods are developed using MATLAB to train and classify the parameters. From the results obtained, it is observed that the classification rate for the benchmark circuits is 100%, and using BPNN, K-means clustering and SVM and the proposed method, recycled IC or used IC is detected even if it was used for 1 day.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…K‐means clustering [33, 34] is one of the most widely used clustering algorithms. It starts with the random initialization of the centroids.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…K‐means clustering [33, 34, 36] is the fastest and efficient way of clustering data. It is used in the detection of counterfeit IC.…”

Section: Proposed Methods To Detect Aged Integrated Circuitsmentioning

confidence: 99%

Recycled integrated circuit detection using reliability analysis and machine learning algorithms

Krishnan

Palanisamy

2020

IET Computers & Digital Tech

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“…In order to e ectively extract the visual information, a Fuzzy c-Means (FCM) [36] algorithm is used. e FCM algorithm can be thought of as a soft k-means algorithm [37]. In the k-means algorithm, the data are clustered into k clusters, and a single sample can only belong to one cluster, whereas in the c-means algorithm, each input sample has a degree of belonging to each and every cluster, in true fuzzy manner.…”

Section: Visual Feature Extractionmentioning

confidence: 99%

Emotional Video to Audio Transformation Using Deep Recurrent Neural Networks and a Neuro-Fuzzy System

Sergio

Lee

2020

Mathematical Problems in Engineering

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Generating music with emotion similar to that of an input video is a very relevant issue nowadays. Video content creators and automatic movie directors bene t from maintaining their viewers engaged, which can be facilitated by producing novel material eliciting stronger emotions in them. Moreover, there is currently a demand for more empathetic computers to aid humans in applications such as augmenting the perception ability of visually-and/or hearing-impaired people. Current approaches overlook the video's emotional characteristics in the music generation step, only consider static images instead of videos, are unable to generate novel music, and require a high level of human e ort and skills. In this study, we propose a novel hybrid deep neural network that uses an Adaptive Neuro-Fuzzy Inference System to predict a video's emotion from its visual features and a deep Long Short-Term Memory Recurrent Neural Network to generate its corresponding audio signals with similar emotional inkling. e former is able to appropriately model emotions due to its fuzzy properties, and the latter is able to model data with dynamic time properties well due to the availability of the previous hidden state information. e novelty of our proposed method lies in the extraction of visual emotional features in order to transform them into audio signals with corresponding emotional aspects for users. Quantitative experiments show low mean absolute errors of 0.217 and 0.255 in the Lindsey and DEAP datasets, respectively, and similar global features in the spectrograms.is indicates that our model is able to appropriately perform domain transformation between visual and audio features. Based on experimental results, our model can e ectively generate an audio that matches the scene eliciting a similar emotion from the viewer in both datasets, and music generated by our model is also chosen more often (code available online at https://github.com/gcunhase/Emotional-Video-to-Audio-with-ANFIS-DeepRNN).Hinda i Ma hema ical P oblem in Enginee ing Vol me 2020, A icle ID 8478527, 15 age h ://doi.o g/

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“…Principal Component Analysis (see [17], [18], [19], [20], [21], [22]) and the t-sne algorithm, that is, a nonlinear algorithm for data size reduction (see [23], [24], [25], [26]) are used for data size reduction. Associated clustering methods are k-Means [27], [28] and Fuzzy C-Means [29], [30], [31], [32], [33], [34], [35]. The Dunn index evaluates each classification obtained [36], [37], [38], [39], [40], [41].…”

Section: Experimental Datamentioning

confidence: 99%

Untitled

2020

IJFLS

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A Clustering Method Based on K-Means Algorithm

Cited by 243 publications

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Recycled integrated circuit detection using reliability analysis and machine learning algorithms

Recycled integrated circuit detection using reliability analysis and machine learning algorithms

Emotional Video to Audio Transformation Using Deep Recurrent Neural Networks and a Neuro-Fuzzy System

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