An effective image watermarking method was created through the combination of the Discrete Wavelet Transform (DWT) with a chaotic map-based encryption technique. The algorithm utilizes a method that analyzes blocks of the high-frequency sub-band (HH) derived from applying the DWT to the blue channel of an input RGB image. It identifies the block with the highest energy to integrate a grayscale watermark image encrypted by the Henon map. Specifically, the algorithm applies alpha blending to incorporate the encrypted watermark into the high-energy block of the HH component that has been identified. The effectiveness of the watermarking scheme is assessed using various metrics, including the NCC, SSIM, and PSNR. The proposed method achieves an average PSNR of 43.7211 dB and SSIM of 0.9950. When not under attack, the NCC of the proposed methodology falls within the range of 0.991 to 0.993. To extract the watermark, the algorithm analyzes the patterns within the high-frequency component of the image, which has been subjected to various attacks after initial watermarking. The inverse DWT and the inverse Henon map are used for decryption during extraction. This energy-based block selection approach strengthens the watermarking scheme's ability to resist various image manipulation techniques

In the huge growth of semiconductor industry, it is noticed that the device simulation is a very sluggish process. It is very promising to use Machine Learning (ML) techniques in device modeling as their combination will create great results in semiconductor industry and reduce the computational time. Organic Thin Film Transistor (OTFT) is a promising alternative to amorphous silicon devices due to its flexibility, low cost, and can be manufactured at reduced temperatures. In traditional TCAD simulation, at once only a single simulation of OTFT for fixed length, width and dielectric thickness can be done, for change in any of the input parameter again simulation has to be done. To avoid this ML is used to predict drain current for simultaneous changes in input parameters. This introduces a machine learning based structure to model OTFT integrated with ML algorithm named Random Forest Regressor (RFR). ML based device model for p-type OTFT takes length, width and thickness of dielectric layer as input parameters and drain current as output parameter. Experimental results has shown that our ML-based model can predict drain current accurately. R2-value is found be around 0.997253. ML based performance optimization is a promising alternative to traditional technology computer aided design (TCAD) tools. The highest ION/IOFF ratio, very high ON current (ION), very low OFF current (IOFF) is achieved for OTFT. ION/IOFF ratio is obtained to be 1011. The trained RFR models can accelerate the optimization in terms of performance and serves as promising alternative.

With the rapid growth of the semiconductor industry, it is clear that device simulation has been considered as slow process. As a result of semiconductor device downscaling, obtaining the inevitable device simulation data is significantly more expensive because it requires complex analysis of multiple factors. Using Machine Learning (ML) techniques to device modeling is promising, as their combination will lead to great outcomes in the semiconductor industry. Nanosheet Field Effect Transistor (NSFET) is a promising device for high-performance integrated circuits due to their superior electrical control and reduced short-channel effects. This paper presents a ML based Nanosheet Field Effect Transistor modeling. In traditional Technology Computer-Aided Design (TCAD) simulation, at once only a single simulation of NSFET for fixed length, width and thickness can be done, for change in any of the input parameter again simulation has to be done. To overcome this, simultaneous changes in input parameters are predicted using machine learning. The length, width, and thickness of the dielectric layer are input parameters and the drain current is the output parameter for the ML-based device model for NSFET. Experimental results have shown that our ML-based model can predict drain current accurately. R2 -value is found be around 0.99832. The highest ION/IOFF ratio, very high ON current (ION), very low OFF current (IOFF) is achieved for NSFET. The primary goal of this work is to explore the possibility of ML model that can replace the device simulation to reduce the computational cost and drive energy-efficient devices.

Digital watermarking can be used to ensure the authenticity and copyright protection of images. In watermarking balancing the trade-offs between its features is an important issue. To address this issue, in this work an adaptive hybrid domain color image watermarking based on Discrete Wavelet Transform (DWT), Walsh Hadamard Transform (WHT), and Singular Value Decomposition (SVD) is proposed. Here, watermarking is carried on YCC color space. In this work, the embedding factor is calculated adaptively using the visual entropy and edge entropy. For better robustness the watermark is inserted into the Y component of YCC color Space. Further, Arnold Transform (AT) is used to secure the watermark. The average PSNR and SSIM of the proposed hybrid domain adaptive watermarking scheme is 40.0876 dB and 0.9883 respectively. The experimental results compared to the recent hybrid domain color watermarking, illustrate the superiority of the suggested approach.

Social media has become a vital platform for individuals, organizations, and governments worldwide to communicate and express their views. During the coronavirus disease 2019 (COVID-19) pandemic, social media sites play a crucial role in people communicating, sharing, and expressing their perceptions on various topics. Analyzing such textual data can improve the response time of governments and organizations to act on alarming issues. This study aims to perform sentiment analysis on the subject of COVID-19 vaccination, perform temporal and spatial analyses of the textual data, and find the most frequently discussed topics that may help organizations bring awareness to those topics. In this work, the sentiment analysis of tweets was performed using 14 different machine learning classifiers and natural language processing (NLP). Lexicon-based TextBlob and Vader are used for annotating the data. A natural language toolkit is used for preprocessing of textual data. Our analysis observed that unigram models outperform bigram and trigram models for all four datasets. Models using term frequency-inverse document frequency (TF-IDF) have higher accuracy than models using count vectorizer. In the count vectorizer class, logistic regression has the best average accuracy with 91.925%. In the TF-IDF class, logistic regression has the best average accuracy of 92%; logistic regression has the highest average recall, F1-score, and ten cross-validation scores, and a ridge classifier has the highest average precision. The unigram models show a standard deviation (SD) of less than 1 for all classifiers except for the Gaussian Naïve Bayes showing 1.18. The experimental results reveal the dates and times in which most positive, negative, and neutral tweets are posted.

Metamaterials have shown an enormous amount of success in the field of engineering as well as in physics and finds applications in various domains. One such important application of metamaterials i.e., Zeroth Order Resonator (ZOR) antennas is discussed in this article. Metamaterials are manmade materials having properties not found in nature occurring materials i.e., simultaneously negative permittivity (?) and permeability (?) over certain range of frequency. Due to these unique properties, metamaterials are used in various antennas to enhance the bandwidth, gain, polarization, radiation patterns etc. The omnidirection radiation pattern is obtained by using ZOR antennas, which is one of the important applications of Composite Right/Left-Handed Transmission Line (CRLH-TL). CRLH-TL uses the properties of metamaterial having exotic properties. This article presents a brief introduction to metamaterials followed by detailed discussion about CRLH-TL and various ZOR Antennas along with their properties. Eventually the applications and radiation patterns have also been studied in this report which will give the researchers an analysis of the research that has already been published.

Point clouds are a popular way to represent 3D data. Due to the sparsity and irregularity of the point cloud data, learning features directly from point clouds become complex and thus huge importance to methods that directly consume points. This paper focuses on interpreting the point cloud inputs using the graph convolutional networks (GCN). Further, we extend this model to detect the objects found in the autonomous driving datasets and the miscellaneous objects found in the non-autonomous driving datasets. We proposed to reduce the runtime of a GCN by allowing the GCN to stochastically sample fewer input points from point clouds to infer their larger structure while preserving its accuracy. Our proposed model offer improved accuracy while drastically decreasing graph building and prediction runtime.

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