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Proceedings of the 2020 4th International conference on Vision, image and signal processing

作者： Poommetee Ketson Patiwet Wuttisarnwattana Department of Computer Engineering Chiang Mai University Chiang Mai Thailand Graduate School Chiang Mai University Chiang Mai Thailand Department of Computer Engineering Chiang Mai University Chiang Mai Thailand Biomedical Engineering Institute Chiang Mai University Chiang Mai Thailand

ISBN: (纸本)9781450389532

Spleen is one of the important organs for studying graft-versus-host disease development using cryo-imaging modality. Up to this point, there is no algorithm developed to segment spleen tissues in the cryo-imaging data. In this study, we developed a new, automatic segmentation algorithm for spleen tissues in the cryo-images for the first time. the algorithm consisted of (1) pre-processing, (2) predicting, and (3) post-processing. the predicting model consisted of two U-Nets to separate spleen from background and white pulp from red pulp. the results generated by the algorithm were compared against the ground truth generated by cryo-imaging experts. the Dice similarity coefficients were about 86% for red pulp class and 87% for white pulp class. We also used the algorithm to measure spleen volumes and performed the T-cells proliferation assays on the results. We found that there was a significant difference between ratios of white pulp volume to spleen volume between allogeneic spleen and syngeneic spleen. the results were consistent with the previous biological reports. In conclusion, the algorithm accurately and objectively segmented the spleen tissues with similar quality to the experts. By incorporating the algorithm into the T-cell proliferation assay workflow, it should greatly increase throughput of the process as compared to manual segmentation by humans. We expect that this algorithm will help accelerate the research for further understanding of graft-versus-host disease and development of the cure.

关键词： Medical image Segmentation Cryo-imaging U-Net Fully Convolutional Network Spleen Graft-versus-host Disease T-cell Proliferation Assay

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Clinical Breast Imaging and Deep Learning Model for image Malignancy Prediction Using Resource-Constrained Embedded Devices

Clinical Breast Imaging and Deep Learning Model for Image Ma...

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Artificial Intelligence and signal processing (AISP), INDIA, International conference on

作者： Sumedha R. Chokhandre Pranjali Jadhav Nilesh Gole Amit N. thakare Akansha R Bondade Priyanka Singh Dept. of Information Technology YCCE Nagpur AI & DS Department Vishwakarma Institute of Information Technology Pune Department of CSE Vidharbha Institute of Technology Nagpur Department of CSE Cummins College of Engineering for Women Nagpur Department of CSE G.H. Raisoni University Saikheda School of Computer Science VIT-AP University Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Computer assisted diagnosis(CAD) of diseases provides more accurate and precise diagnostic reports towards better information regarding the medical condition of patients. A clinician can minimize the error by applying his experience acquired by practice, cognitive intuition or scientific research backed by laboratory reports and computer assisted medical image analysis. the findings by the experts based on the analysis of such data are crucial as the suggested treatment is dependent on evaluation at this stage. Machine learning techniques while applied in the medical field performs decision making by mimicking the steps performed by a medical expert in diagnosing the disease, but using algorithms rather intuitive. It brings out accurate medical data through analysis of images performed by computing devices that can reveal valuable information regarding the disease prognosis. Computer aided disease diagnosis with state-of-the-art machine learning and deep learning offers seamless assistance in medical care with near human accuracy. Technology integrated medical support systems combined with sophisticated algorithms can reduce the number of false positive incidents as well as false negative cases. Convolutional neural network (CNN) models can be trained using handcrafted features to derive conclusive inferences for binary class as well as multi-class classification. Artificial Intelligence (AI) supported techniques in disease diagnosis provide assistance to medical experts in decision making by virtue of cloud based data analytics tools for storage and computing.

关键词： Deep learning Performance evaluation Accuracy Machine learning algorithms Computational modeling Decision making Predictive models Medical diagnosis Medical diagnostic imaging Diseases

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EDRNet: A Lightweight Underwater Sonar Target Detection Model Based on an Efficient De-Redundancy Network

EDRNet: A Lightweight Underwater Sonar Target Detection Mode...

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IEEE Joint International Information Technology and Artificial Intelligence conference (ITAIC)

作者： Qiuqin Fang Jianxun Tang Zhe Chen Mingsong Chen School of Information and Communication Guilin University of Electronic Technology Guilin China School of Ocean Engineering Guilin University of Electronic Technology Guilin China Cognitive Radio and Information Processing Key Laboratory Authorized by China's Ministry of Education Foundation Guilin University of Electronic Technology

Underwater object detection is one of the hot research topics in the field of acoustic signal processing. Using sonar to automatically detect seabed objects has significant importance for marine resource development and underwater military training. Traditional sonar image detection methods based on machine learning and clustering have problems such as large model parameters, redundant computation, low detection accuracy, and difficult deployment in practical engineering applications. To address these issues, this paper proposes a lightweight underwater sonar object detection model named EDRNet. First, it introduces the lightweight network EfficientFormerV2, which solves the problems of large model parameters and redundant computation. Second, it introduces the SimSPPF module, which enlarges the receptive field of the network. Finally, it adds the parameter-free attention mechanism SimAM, which enhances feature extraction and improves model computation efficiency and detection accuracy. Experiments on the SeabedObjects-KLSG dataset and the STCD dataset show that the proposed model achieves an average detection accuracy of 94.7%, which is 22.8%, 1.8%, 2.3%, and 0.4% higher than the existing mainstream models Faster R-CNN, YOLOv3, YOLOv4, and YOLOv5, respectively. the parameter size is 58.6% of YOLOv5s, and the computation amount is 51.25% of YOLOv5s. this verifies that the proposed model effectively reduces the model size while maintaining performance and has stronger practical significance for underwater sonar image detection.

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New Energy-Efficient 3-2 and 4-2 Approximate Adder Compressors Topologies

New Energy-Efficient 3-2 and 4-2 Approximate Adder Compresso...

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IEEE International conference on Electronics, Circuits and Systems (ICECS)

作者： Rodrigo Lopes Leonardo Antonietti Morgana M. A. Rosa Eduardo Costa Rafael Soares Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

Addition units are present across many computational kernels inherent in various error-tolerant applications, including machine learning, signal, image, and video processing. Notably, adder compressors are the target when high speed and low power are the main design concerns. this work introduces energy-efficient structures of 3-2 approximate adder compressors (Ax3-2) and three distinct versions of 4-2 approximate adder compressors (Ax4-2). We compared our proposed Ax3-2 and Ax4-2 compressors with state-of-the-art energy-efficient approximate adder compressors (AxACs). Both Ax3-2 and Ax4-2 models passed through rigorous tests as standalone units. Furthermore, we integrated the Ax4-2 models into an essential application kernel appearing in video coding, i.e., the Sum Squared Differences (SSD) video accelerator. We introduce Ax3-2 and Ax4-2 that yield a new Pareto front concerning both energy-quality and area-quality trade-offs, thereby demonstrating a marked improvement over the prevailing state-of-the-art energy-efficient AxACs.

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Densely-Accumulated Convolutional Network for Accurate LPI Radar Waveform Recognition

Densely-Accumulated Convolutional Network for Accurate LPI R...

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2021 IEEE Global Communications conference (GLOBECOM)

作者： thien Huynh-the Quoc-Viet Pham Toan-Van Nguyen Van-Sang Doan Nhan thanh Nguyen Daniel Benevides da Costa Dong-Seong Kim ICT Convergence Research Center Kumoh National Institute of Technology Korea Korean Southeast Center for the 4th Industrial Revolution Leader Education Pusan National University Korea Hongik University Korea Faculty of Communication and Radar Naval Academy Vietnam Centre for Wireless Communications University of Oulu Finland Future Technology Research Center National Yunlin University of Science and Technology Taiwan R.O.C. Federal University of Ceará Sobral CE Brazil

ISBN: (纸本)9781728181059

this paper presents a deep learning-based method to automatically recognize low probability of intercept (LPI) radar waveforms against diversified jamming attacks. Concretely, an efficient convolutional neural network (CNN) architecture, namely Densely-Accumulated Network (DANet), is introduced to learn the time-frequency representation transformed by the Wigner-Ville distribution. Such an architecture has several novel densely-accumulated connection modules specified by various symmetric and asymmetric convolutional layers to enrich diversified features at multiple representational maps. Besides, the skip-connection and dense-connection are leveraged to improve feature learning efficiency and prevent the vanishing gradient when the network goes deeper. Some image processing techniques (e.g., global thresholding and digital filtering) are adopted to enhance the quality of time-frequency image. Relying on simulations, we benchmark the proposed method on a synthetic 13-waveform dataset and also investigate the influence of hyper-parameters (such as image size, number of modules, training data size) on the overall recognition performance. Remarkably, with average accuracy of 98.2% at 0 dB signal-to-noise ratio (SNR), DANet outperforms several backbone CNNs and state-of-the-art networks of LPI waveform recognition while keeping a cost-efficient model.

关键词： Time-frequency analysis thresholding (Imaging) Convolution Training data Radar Computer architecture Radar imaging

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image-Based Fashion Recommendation with Attention to Users' Interests 22

Image-Based Fashion Recommendation with Attention to Users' ...

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Proceedings of the 2022 4th International conference on Video, signal and image processing

作者： Wenxin Yao Kaoru Uchida Jianpeng Zou Xiaolin Zhong School of Software Engineering University of Science and Technology of China China and Graduate School of Computer and Information Sciences Hosei University Japan Graduate School of Computer and Information Sciences Hosei university Japan Meituan China

ISBN: (纸本)9781450397810

Building an effective fashion recommendation system is challenging due to the high level of subjectivity and the semantic complexity of the features involved. Users’ decision depends largely on their interest and the appearance of the product. Such information is often hidden in implicit feedback from users' purchase histories and product images. Most interest-based recommendation systems like Deep Interest Network (DIN) and Deep Interest Evolution Network (DIEN) only take advantage of product attributes and context review, which are all text information. there are also some studies focusing on the use of image features for a fashion product recommendation. they try to extract features from images and recommend products based on their similarity. However, for DIEN. It works not well when there are few interactions between users and items, the model can not find users' interests effectively. On the other hand, these image-based recommendation systems tend to ignore an important factor: the user's interest. We propose a new system trying to find users' interests by introducing the visual information of the product and our image-based deep interest attention model based on DIEN. Product attributes and user preference can both be represented by introducing visual information. We can model similar attribute items in the same place and find user interests more effectively. We conducted a series of experiments on our own dataset to compare user click-through rate (CTR) AUC and Accuracy using our model with DIEN and other existing *** experimental results demonstrate that visual information effectively aids CTR prediction and achieved better recommendation results.

关键词： Fashion recommendation User interest image feature Attention Mechanism

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QUALITY EVALUATION MODEL USING LOCAL FEATURES OF STILL PICTURE

QUALITY EVALUATION MODEL USING LOCAL FEATURES OF STILL PICTU...

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European signal processing conference

作者： Yuukou Horita Masaharu Sato Yoshikazu Kawayoke Z. M. Parvez Sazzad Keiji Shibata University of Pisa

the objective image quality evaluation model for coded image without using the reference is very useful for quality oriented image compression. In this paper, a new objective no-reference (NR) image quality evaluation model for JPEG coded image is presented, which is easy to calculate and applicable to various image processing applications. the proposed model is based on the local features information of the image such as edge, flat and texture area and also on the blockiness, activity measures, and zero crossing rate within block of the image. Our experiments on various image distortion types indicate that it performs significantly better than the conventional model.

关键词： image DISTORTION STILL zero crossings JPEG image compression Data compression image image coding picture image quality block effect

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Enabling Zero-Trust Architecture using a Federated Learning Framework with Differential Privacy and Blockchain Algorithms

Enabling Zero-Trust Architecture using a Federated Learning ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International conference on

作者： Prem Kumar M Raghavendra Hasthantram S. Vijayashaarathi Lithin Kumble P Raghavendra Nayaka Willron Electronics Bangalore India Sathyanarayana Setty Dayananda Sagar University Bangalore India Department of ECE Sona college of Technology Salem India School of Computing and IT REVA University Bangalore India School of Computing and Information Technology REVA University Bangalore India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In medical imaging, noise and artifacts can significantly degrade the quality and accuracy of diagnostic results. Traditional denoising techniques often struggle to balance noise reduction and preservation of critical details, leading to potential diagnostic errors. this paper proposes a novel deep learning-based framework, DL-DeNoiseNet, designed to effectively remove noise and reduce artifacts in medical images while preserving crucial anatomical and pathological information. the proposed framework leverages a combination of Convolutional Neural Networks (CNNs) and Residual Learning to model complex noise distributions and artifact patterns in medical images. A multi-scale feature extraction module is incorporated to capture fine-grained details across different resolutions, and a skip connection mechanism is employed to prevent loss of critical information during the denoising process. the performance of the proposed method is evaluated using standard medical imaging datasets, demonstrating superior results in terms of Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) compared to conventional and state-of-the-art denoising methods. the framework's robustness and efficiency make it a valuable tool for enhancing the quality of medical image diagnostics, ultimately contributing to improved clinical outcomes and decision-making. the proposed DL-DeNoiseNet framework was extensively evaluated on multiple publicly available medical imaging datasets, including MRI, CT, and ultrasound images, to benchmark its performance against existing state-of-the-art denoising methods. the evaluation was conducted using three primary metrics: Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Mean Absolute Error (MAE). On the MRI dataset, the DL-DeNoiseNet achieved an impressive PSNR of 38.57 dB, significantly outperforming the conventional method (34.20 dB) and the state-of-the-art approach (36.45 dB). Additionally, the framework

关键词： wavelet transforms PSNR Magnetic resonance imaging Computed tomography Noise reduction Rician channels Transforms Feature extraction Indexes Medical diagnostic imaging

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High Performance 32 bit Dadda Multiplier Using EDA

High Performance 32 bit Dadda Multiplier Using EDA

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International conference on Smart Structures and Systems (ICSSS)

作者： Madhav Venkata Srinivas Nandam Sudhakar Alluri CMRIT Hyderabad India

ISBN: (数字)9781728172231

ISBN: (纸本)9781728172286

the adder is the basic hardware unit of arithmetic operation. thus output adder influences the actual performance of the system CSA which is Carry Select Adder regularly used for high speeded implementations of many database processors, especially for arithmetic operations in virtual circuits. Multiplication is based almost entirely on product, adding modules, sampling and convolution which are widely used in image analysis applications. Typical Square Root (SQRT) CSA requires greater area because of the inclusion of multiple Ripple Carry Adders (RCAs) in the process. this similarity demonstrates because the theoretical SQRT CSLA is superior to the prevailing custom SQRT CSA and SQRT CSA utilizing RCA. It also improves the speed of the proposed model with a greater number of bits than the CSA with CBL. the intellect of this compressor in the 32-bit Dadda multiplier determinant is evaluated using Verilog HDL exploitation through the use of XILINX ISE design is simulated and synthesized in a constructive way and evaluated using adapted Carry swap for adder. Similarity of their criteria with the additional dadda multiplier is configured for compressors 4 to 2.

关键词： Adders conferences Delays Intelligent structures IEEE transactions signal processing Multiplexing

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conference RECORD - INTERNATIONAL conference ON COMMUNICATIONS, VOLUME 1, VOLUME 2, AND VOLUME 3, 1975.

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1975年

作者： Anon

Proceedings includes 265 papers, of which 13 are presented merely in the form of abstracts or summaries. Topics considered include: packet switches, facsimile, analog systems international understanding in communications, signal processing with charge-transfer devices, electromagnetic compatibility in communications, very wide band satellite communication systems, digital troposcatter communications, social implications of communication technology, effects of the scattering and reflection of radio waves, discrete time processes, error control, the ETS-4 system, modulation, coding, switching systems, single-channel-per-carrier satellite communications, data transmission through fading/dispersive communication channels, the future of world-wide communications, communication experiments using the applications technology satellite - 6 (ATS-6), digital microwave radio systems, digital encoding of television signals, high-speed tdm cable transmission, image coding, system economics, speech, communication theory, telephony modulation and coding techniques for space communications, digital communications in Europe, optic fiber communication systems, computer communication networks, timing effects in digital communication, microwave and millimeter-wave devices and techniques, paging and alarm systems, T1C - a new digital system for paired cable application, the telesat TDMA system, multipoint and point-to-point microwave systems, TSPS No. 1 - the remote trunk arrangement, network control and performance, and the T4 system - a T-type carrier for coaxial cable.

关键词： Telecommunication systems

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