Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced...
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Entity matching is a crucial aspect of data management systems, requiring the identification of real-world entities from diverse expressions. Despite the human ability to recognize equivalences among entities, machine...
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This research is conducted to study a fuzzy system with an improved rule base. The rule base is an important part of any fuzzy inference system designed. The rules of a fuzzy system depend on the number of features se...
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Vertical Federated Learning (VFL) has emerged as a crucial privacy-preserving learning paradigm that involves training models using distributed features from shared samples. However, the performance of VFL can be hind...
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In engineering fields,time-varying matrix inversion(TVMI)issue is often *** neural network(ZNN)has been extensively employed to resolve the TVMI ***,the original ZNN(OZNN)and the integral-enhanced ZNN(IEZNN)usually fa...
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In engineering fields,time-varying matrix inversion(TVMI)issue is often *** neural network(ZNN)has been extensively employed to resolve the TVMI ***,the original ZNN(OZNN)and the integral-enhanced ZNN(IEZNN)usually fail to deal with the TVMI problem under unbounded noises,such as linear ***,a neural network model that can handle the TVMI under linear noise interference is urgently *** paper develops a double integral-enhanced ZNN(DIEZNN)model based on a novel integral-type design formula with inherent linear-noise ***,its convergence and robustness are verified by deriva-tion *** comparison and verification,the OZNN and the IEZNN models are adopted to resolve the TVMI under multiple identical noise *** experi-ments proved that the DIEZNN model has excellent advantages in solving TVMI problems under linear *** general,the DIEZNN model is an innovative work and is proposed for the first ***,the errors of DIEZNN are always less than 1�10−3 under linear noises,whereas the error norms of OZNN and IEZNN models are not convergent to *** addition,these models are applied to the control of the controllable permanent magnet synchronous motor chaotic system to indicate the superiority of the DIEZNN.
Lossless image compression techniques play a crucial role in preserving image quality while reducing storage space and transmission bandwidth. This paper proposes a novel hybrid integrated method for lossless image co...
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Lossless image compression techniques play a crucial role in preserving image quality while reducing storage space and transmission bandwidth. This paper proposes a novel hybrid integrated method for lossless image compression by combining Contrast Limited Adaptive Histogram Equalization (CLAHE), two-channel encoding, and adaptive arithmetic coding to achieve highly efficient compression without any loss of image information. The first step of the proposed approach involves applying Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the local contrast of the image. This pre-processing step aids in reducing the entropy and increasing the redundancy in the image, creating a more favourable environment for subsequent compression algorithms. Next, the image is divided into two channels: one channel focuses on encoding essential structural information, while the other channel handles the finer details. This segregation leverages the inherent properties of images to improve compression efficiency. To achieve further compression gains, an adaptive arithmetic coding algorithm for encoding the data in each channel is utilized. Adaptive arithmetic coding adapts its probability model during the encoding process, leading to improved compression performance compared to traditional static coding methods. The proposed method offers significant potential in various applications, it is especially crucial in medical imaging, where large volumes of high-resolution images are generated during procedures such as MRI, CT scans, or digital pathology, transmitting high-quality images in resource-constrained environments, and facilitating image processing tasks requiring precise data preservation. CLAHE can be a valuable tool in medical imaging to enhance essential diagnostic information in medical images before compression. By improving contrast and visibility of structures, CLAHE may aid in achieving better compression efficiency and reduce the risk of introducing compres
In this article, write-once-read-many-times (WORM) memory behavior of HfZrO (HZO) ferroelectric material is demonstrated. A stoichiometric Hf0.5Zr0.5O2 thin film prepared using a sol-gel process is used as a resistive...
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Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of th...
Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.
Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural network...
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Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least a...
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Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least amount of power from a large number of Reed-Muller(RM) logical expressions. The existing approach for optimizing the power of multi-output mixed polarity RM(MPRM) logic circuits suffer from poor optimization results. To solve this problem, a whale optimization algorithm with two-populations strategy and mutation strategy(TMWOA) is proposed in this paper. The two-populations strategy speeds up the convergence of the algorithm by exchanging information about the two-populations. The mutation strategy enhances the ability of the algorithm to jump out of the local optimal solutions by using the information of the current optimal solution. Based on the TMWOA, we propose a multi-output MPRM logic circuits power optimization approach(TMMPOA). Experiments based on the benchmark circuits of the Microelectronics Center of North Carolina(MCNC) validate the effectiveness and superiority of the proposed TMMPOA.
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