Hidden Markov models (HMMs) are a powerful class of dynamical models for representing complex systems that are partially observed through sensory data. Existing data collection methods for HMMs, typically based on act...
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Emotion recognition from speech is a significant research area in human–computer interaction and psychological assessments. This study proposes a novel three-stage process for emotion recognition from speech signals....
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The field of computer vision is predominantly driven by supervised models, which, despite their efficacy, are computationally expensive and often intractable for many applications. Recently, research has expedited alt...
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Silicon carbide (SiC) based inverters are widely used due to their fast switching and ability to withstand high temperatures and Voltage. This helps to increase efficiency and reduces switching losses compared to the ...
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We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with random dither levels. In particular, instead of observi...
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We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with random dither levels. In particular, instead of observing a subset of high-resolution entries of a low-rank matrix, we have access to a small number of one-bit samples, generated as a result of these comparisons. In order to recover the low-rank matrix using its coarsely quantized known entries, we begin by transforming the problem of one-bit matrix completion (one-bit MC) with random dithering into a nuclear norm minimization problem. The one-bit sampled information is represented as linear inequality feasibility constraints. We then develop the popular singular value thresholding (SVT) algorithm to accommodate these inequality constraints, resulting in the creation of the One-Bit SVT (OBSVT). Our findings demonstrate that incorporating multiple random dither sequences in one-bit MC can significantly improve the performance of the matrix completion algorithm. In pursuit of achieving this objective, we utilize diverse dithering schemes, namely uniform, Gaussian, and discrete dithers. To accelerate the convergence of our proposed algorithm, we introduce three variants of the OB-SVT algorithm. Among these variants is the randomized sketched OB-SVT, which departs from using the entire information at each iteration, opting instead to utilize sketched data. This approach effectively reduces the dimension of the operational space and accelerates the convergence. We perform numerical evaluations comparing our proposed algorithm with the maximum likelihood estimation method previously employed for one-bit MC, and demonstrate that our approach can achieve a better recovery performance. Authors
Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural net...
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Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.
The relay channel, consisting of a source-destination pair along with a relay, is a fundamental component of cooperative communications. While the capacity of a general relay channel remains unknown, various relaying ...
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Phishing is one of the most important security threats in modern information systems causing different levels of damages to end-users and service providers such as financial and reputational losses. State-of-the-art a...
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In thicker polymer active layers charge collection efficiency suffers due to low carrier mobility and increased recombination losses. In thin absorber polymer solar cell to increase absorption, light-trapping techniqu...
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In thicker polymer active layers charge collection efficiency suffers due to low carrier mobility and increased recombination losses. In thin absorber polymer solar cell to increase absorption, light-trapping techniques and plasmonic structures are essential. This study investigates the effect of shell thickness on the photocurrent density of a poly(3-hexylthiophene): phenyl-C61- butyric acid methyl ester (P3HT:PCBM) polymer based solar cell incorporating core–shell nanoparticles with configurations of Au–Ag and Ag-Au core–shell nanoparticles. Through a series of simulation, the photocurrent density was calculated as a function of shell thickness. The results demonstrate that, for both nanoparticle configurations, the photocurrent density generally increases with shell thickness, reaching an optimal point before stabilizing or slightly decreasing. Additionally, the effects of dielectric shells made of SiO₂ and Al₂O₃ on its performance parameters were analyzed. The study also found that the photocurrent decreases with increasing shell thickness for both SiO₂ and Al₂O₃ shells, with a more pronounced decrease for SiO₂ due to its smaller refractive index and greater change in shorter wavelengths. The photocurrent density of 13.74 mA/cm2 is achieved for a cell with a thickness of 80 nm without nanoparticles. This value increases to 16.62 mA/cm2 for a cell incorporating Ag nanoparticles and reaches 19.3 mA/cm2 for a cell with Au–Ag core–shell nanoparticles at the optimal shell thickness. The power conversion efficiency of the polymer solar cell increases from 7.02% without nanoparticles to 8.67% with Ag, 8.45% with Au, and reaches the highest value of 10.26% with Au–Ag core–shell nanoparticles, highlighting the superior performance of the core–shell configuration. This superior performance is attributed to the enhanced plasmonic effects of the Au–Ag combination, which facilitates better light trapping and absorption. These findings underscore the importance of optimizing
Community detection is a valuable tool for studying the function and dynamic structure of most real-world networks. Existing techniques either concentrate on the network's topological structure or node properties ...
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