The proceedings contains 13 papers from the conference on applications of artificial neural networks in image processing vii. The topics discussed include: pornographic image detection with Gabor filters;classificatio...
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The proceedings contains 13 papers from the conference on applications of artificial neural networks in image processing vii. The topics discussed include: pornographic image detection with Gabor filters;classification of interframe difference image blocks for video compression;configuring artificialneuralnetworks to implement function optimization;fusion of ATR classifiers and dual-band FLIR fusion for target detection.
In the pursuit of high-performance designs for error-resilient applications, approximate computing emerges as a key strategy. This paper introduces an innovative approximate multiplier, leveraging two highly efficient...
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In the pursuit of high-performance designs for error-resilient applications, approximate computing emerges as a key strategy. This paper introduces an innovative approximate multiplier, leveraging two highly efficient compressors. These compressors operate in tandem across two stages, strategically compensating for errors and culminating in a multiplier that maintains accuracy and significantly reduces delay in the final stage. The proposed method is specifically tailored for applications reliant on multiplication, such as imageprocessing and neuralnetworks. HSPICE simulations were conducted using 7 nm FinFET technology to gauge its efficacy. Results indicate a remarkable 82% reduction in power-delay product (PDP) compared to traditional multipliers. Moreover, system-level simulations underscore the practicality of the proposed multiplier in real-world applications like imageprocessing and artificial intelligence, revealing minimal compromise in accuracy. This work contributes a nuanced perspective to approximate computing, presenting a multiplier poised to elevate efficiency without sacrificing precision in critical domains.
Satellites play a critical role in modern technology by providing images for various applications, such as detecting infrastructure and assessing environmental impacts. The author's work investigates the applicati...
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Satellites play a critical role in modern technology by providing images for various applications, such as detecting infrastructure and assessing environmental impacts. The author's work investigates the application of Fractal neuralnetworks (FractalNet) for automating the detection of specific objects in satellite images. The study aims to improve processing speed and accuracy compared to traditional Convolutional neuralnetworks (CNNs). The research involves developing and comparing FractalNet with CNNs, focusing on their effectiveness in image classification. The architecture of FractalNet, characterized by recursive structures and deep layers, is evaluated against CNNs like VGG16 and ResNet50. Data collection included manually gathering high-resolution satellite images of specific objects from Google Earth. The neural network models were trained and tested with varying hyperparameters, including learning rates and batch sizes. FractalNet demonstrated superior performance over CNNs, particularly in deep network configurations. The results improved significantly with data augmentation and optimized hyperparameters, achieving a test accuracy of up to 93.26% with a 32-layer model. Fractal neuralnetworks offer a promising approach for automating satellite image analysis, providing better accuracy and robustness compared to traditional CNNs architectures.
This research investigates the implementation of Kolmogorov-Arnold networks (KANs) for imageprocessing in resource-constrained IoTs devices. KANs represent a novel neural network architecture that offers significant ...
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This research investigates the implementation of Kolmogorov-Arnold networks (KANs) for imageprocessing in resource-constrained IoTs devices. KANs represent a novel neural network architecture that offers significant advantages over traditional deep learning approaches, particularly in applications where computational resources are limited. Our study demonstrates the efficiency of KAN-based solutions for image analysis tasks in IoTs environments, providing comparative performance metrics against conventional convolutional neuralnetworks. The experimental results indicate substantial improvements in processing speed and memory utilization while maintaining competitive accuracy. This work contributes to the advancement of AI-driven IoTs applications by proposing optimized KAN-based implementations suitable for edge computing scenarios. The findings have important implications for IoTs deployment in smart infrastructure, environmental monitoring, and industrial automation where efficient imageprocessing is critical.
artificialneuralnetworks have been one of the science's most influential and essential branches in the past decades. neuralnetworks have found applications in various fields including medical and pharmaceutical...
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artificialneuralnetworks have been one of the science's most influential and essential branches in the past decades. neuralnetworks have found applications in various fields including medical and pharmaceutical services, voice and speech recognition, computer vision, natural language processing, and video and imageprocessing. neuralnetworks have many layers and consume much energy. Approximate computing is a promising way to reduce energy consumption in applications that can tolerate a degree of accuracy reduction. This paper proposes an effective method to prevent accuracy reduction after using approximate computing methods in the CNNs. The method exploits the k-means clustering algorithm to label pixels in the first convolutional layer. Then, using one of the existing pruning methods, different pruning amounts have been applied to all layers. The experimental results on three CNNs and four different datasets show that the accuracy of the proposed method has significantly improved (by 17%) compared to the baseline network.
Foundation models prepare neuralnetworks for applications in specific domains, such as speech applications or image analysis, through self-supervised pretraining. These models can be adapted for specific applications...
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Foundation models prepare neuralnetworks for applications in specific domains, such as speech applications or image analysis, through self-supervised pretraining. These models can be adapted for specific applications, such as histopathological diagnostics. While adaptation still requires supervised training, AI applications based on foundation models achieve significantly better prediction accuracy with fewer training data compared to conventional approaches. This article introduces the topic and provides an overview of foundation models in pathology.
This paper presents cutting-edge advancements in exponential synchronization and encryption techniques, focusing on Quaternion-Valued artificialneuralnetworks (QVANNs) that incorporate two-sided coefficients. The st...
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This paper presents cutting-edge advancements in exponential synchronization and encryption techniques, focusing on Quaternion-Valued artificialneuralnetworks (QVANNs) that incorporate two-sided coefficients. The study introduces a novel approach that harnesses the Cayley-Dickson representation method to simplify the complex equations inherent in QVANNs, thereby enhancing computational efficiency by exploiting complex number properties. The study employs the Lyapunov theorem to craft a resilient control system, showcasing its exponential synchronization by skillfully regulating the Lyapunov function and its derivatives. This management ensures the stability and synchronization of the network, which is crucial for reliable performance in various applications. Extensive numerical simulations are conducted to substantiate the theoretical framework, providing empirical evidence supporting the presented design and proofs. Furthermore, the paper explores the practical application of QVANNs in the encryption and decryption of color images, showcasing the network's capability to handle complex data processing tasks efficiently. The findings of this research not only contribute significantly to the development of complex artificialneuralnetworks but pave the way for further exploration into systems with diverse delay types.
Numerous obstacles in enhancing the performance of computing systems have spurred the emergence of approximate computing. Extensive studies have been reported on approximate computing to develop high-performance, ener...
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Numerous obstacles in enhancing the performance of computing systems have spurred the emergence of approximate computing. Extensive studies have been reported on approximate computing to develop high-performance, energy-efficient hardware designs tailored to error-resilient applications. In this brief, we proposed 8-bit approximate multipliers with 15 levels of accuracy using three techniques: recursive, bit-wise, and hybrid approximation using partial bit OR (PBO). Compared to the existing multipliers, investigated designs have significantly improved the area, power, delay, Power Delay Product (PDP), and Power Area Delay Product (PADP) by 41.68%, 73.16%, 35.57%, 72.65%, and 75.42% respectively on average. On resemblance with the accurate multiplier, the area, power, delay, PDP, and PADP were enhanced by 54.41%, 57.57%, 25.73%, 60.14%, and 74.33% correspondingly on average. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values surpassing (30 dB, 94%), (31 dB, 96%), and (26 dB, 95%) by applying them to benchmarks in image smoothing, edge detection, and image sharpening successively. Moreover, upon scrutinizing the efficacy of multipliers in hardware implementations of deep neuralnetworks attaining the performance exceeding 95%. The obtained results confirm that suggested multipliers are well-suited for their widespread applications.
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