Mammography screening also leads to a high rate of false positive results. This may lead to unnecessary worry, inconvenient follow-up care, additional imaging studies, and sometimes the need for tissue. blood draws (o...
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ISBN:
(纸本)9781510679344;9781510679351
Mammography screening also leads to a high rate of false positive results. This may lead to unnecessary worry, inconvenient follow-up care, additional imaging studies, and sometimes the need for tissue. blood draws (often a needle biopsy). Convolutional neuralnetworks (CNN) are one of the most important networks in the field of deep learning. The neuralnetworks form some feature vectors often contain weak features. There are known methods for eliminating weak features based on the mutual information. In this paper, we propose a convolutional neural network based to recognize local geometrical features. Computer simulation results are provided to illustrate the performance of the proposed method.
The proceedings contain 9 papers. The special focus in this conference is on Design and Architecture for Signal and imageprocessing. The topics include: sEMG-Based Gesture Recognition with Spiking neural Network...
ISBN:
(纸本)9783031628733
The proceedings contain 9 papers. The special focus in this conference is on Design and Architecture for Signal and imageprocessing. The topics include: sEMG-Based Gesture Recognition with Spiking neuralnetworks on Low-Power FPGA;A Highly Configurable Platform for Advanced PPG Analysis;preface;Standalone Nested Loop Acceleration on CGRAs for Signal processingapplications;optimising Graph Representation for Hardware Implementation of Graph Convolutional networks for Event-Based Vision;Improving the Energy Efficiency of CNN Inference on FPGA Using Partial Reconfiguration;scratchy: A Class of Adaptable Architectures with Software-Managed Communication for Edge Streaming applications.
This paper provides a comprehensive overview of artificialneuralnetworks (ANNs), exploring their theoretical foundations, practical applications, and recent advancements. I delve into the basic constructs of ANNs, d...
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In recent years, diffusion models (DMs) have drawn significant attention for their success in approximating data distributions, yielding state-of-the-art generative results. Nevertheless, the versatility of these mode...
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ISBN:
(纸本)9798350359329;9798350359312
In recent years, diffusion models (DMs) have drawn significant attention for their success in approximating data distributions, yielding state-of-the-art generative results. Nevertheless, the versatility of these models extends beyond their generative capabilities to encompass various vision applications, such as image inpainting, segmentation, adversarial robustness, among others. This study is dedicated to the investigation of adversarial attacks through the lens of diffusion models. However, our objective does not involve enhancing the adversarial robustness of image classifiers. Instead, our focus lies in utilizing the diffusion model to detect and analyze the anomalies introduced by these attacks on images. To that end, we systematically examine the alignment of the distributions of adversarial examples when subjected to the process of transformation using diffusion models. The efficacy of this approach is assessed across CIFAR-10 and imageNet datasets, including varying image sizes in the latter. The results demonstrate a notable capacity to discriminate effectively between benign and attacked images, providing compelling evidence that adversarial instances do not align with the learned manifold of the DMs.
DNN (deep neural network) and CNN (convolution neural network) have been widely used in real-time artificial intelligent (AI) applications, particularly image or video recognitions, because they have been proved physi...
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DNN (deep neural network) and CNN (convolution neural network) have been widely used in real-time artificial intelligent (AI) applications, particularly image or video recognitions, because they have been proved physically in many occasions. However, most prior AI hardware works either suffered from high on-silicon area cost or low usage thereof. This investigation presents a power efficient and high performance implementation of a digital logic accelerator (DLA) for the real-time underwater object recognition. The proposed DLA is also featured with 2-dimensional PE (processing element) array to increase the processing throughput by the enhancement of parallelism. The DLA design was realized and fabricated using TSMC 40-nm CMOS process. Not only the post-layout simulation results are shown, the on-silicon measurement outcome as well as the system validation in water are also demonstrated to prove the function correctness and the performance. The area efficiency (GOPS/mm(2)) is 4.562, and the power efficiency (TOPS/W) is 0.5668 on silicon, which both are the best to date.
This work analyzes the impact of the hyper-parameters of a weightless neural network based on Multi-valued Probabilistic Logic Neurons (MPLN) in order to design an efficient and concise network topology. The study is ...
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ISBN:
(纸本)9789819746767;9789819746774
This work analyzes the impact of the hyper-parameters of a weightless neural network based on Multi-valued Probabilistic Logic Neurons (MPLN) in order to design an efficient and concise network topology. The study is done based on the implementation of several MPLN architectures for the handwritten digit identification application. The analysis is performed by varying one given parameter while the others are kept unchanged. This allows the impact evaluation of such parameter on the classification accuracy, necessary epoch number to train the network and required processing time. The present work further proposes a modification in the MPLN network for multi-class problems, termed the Mod-MPLN network. The Mod-MPLN network is defined by a change in the network training algorithm and by the inclusion of a specific discriminator at the network output, without changing the intrinsic characteristics of the MPLN-based topology.
Deep learning networks have been widely used in remote sensing imageprocessing, playing a significant role in ground-based intelligent processing, on-board intelligent processing, and terminal-based intelligent proce...
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Over the past few years, artificial Intelligence has achieved significant performance in many fields. In artificial intelligence techniques, deep neuralnetworks have experienced rapid development recently. They have ...
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Traffic sign recognition is crucial for the safe and efficient operation of autonomous vehicles. While previous research has primarily focused on traffic sign recognition in foreign countries, these studies often face...
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ISBN:
(纸本)9798350352368
Traffic sign recognition is crucial for the safe and efficient operation of autonomous vehicles. While previous research has primarily focused on traffic sign recognition in foreign countries, these studies often face limitations such as differing traffic sign designs, language barriers in textual information, and varying environmental conditions. In this paper, we propose a traffic sign detection and recognition system tailored for Malaysia, utilizing Convolutional neuralnetworks (CNNs) and Optical Character Recognition (OCR). In this paper, we propose a traffic sign detection and recognition system utilizing You Only Look Once (YOLO) V8 for object detection and EasyOCR to process textual information on selected traffic signs. Our system achieves a mean Average Precision (mAP) of 0.824 and an average processing time of 1.2 seconds per frame, which is comparable to existing literature. Furthermore, the complexity of our method is significantly reduced, enhancing its potential for real-time processingapplications, as evidenced by its efficient processing time.
The ability to visualize the semantic connections between relationships and entities is a powerful feature of knowledge graphs. Unfortunately, it is typically challenging to extract the multi-level information of thes...
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