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A Statistical Approach for Improving Image Quality and Cell Quantification from Imaging Mass Cytometry

A Statistical Approach for Improving Image Quality and Cell ...

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Communication, Image and signal processing (CCISP), International Conference on

作者： Xu Xiao Yating Lin Lei Zhang Wenxian Yang Rongshan Yu School of Informatics Xiamen University Xiamen China Aginome Scientific Xiamen China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

Imaging Mass Cytometry (IMC), a multiplexed imaging technology, has become a valuable tool in biomedical research due to its capability to measure over 100 markers theoretically. However, the presence of noise in IMC images can affect cell phenotyping and the precision of clinical analysis. To address this challenge, we propose IMCell, a highly effective and generalizable method that enhances image quality and cell quantification by leveraging biological priors. IMCell begins by generating decoy cells using Monte Carlo sampling and incorporates cell shape information for each protein channel. Subsequently, it estimates background noise from the expression of decoy cells by fitting Gaussian mixture models with the EM algorithm. Additionally, it identifies positive cell expressions by comparing distribution patterns between real cells and decoy cells. Based on the analysis results from IMCell, we further introduce an image quality index for comprehensive image quality assessment by combining noise and positive cell expressions. Experiment results demonstrate the efficacy of our proposed approach in enhancing image quality and facilitating downstream analysis. By improving the quality of IMC images, IMCell holds the potential to enhance our understanding of complex biological systems and disease mechanisms.

关键词： Image quality Proteins Multiplexing Protein engineering Monte Carlo methods Shape Noise signal processing algorithms Clustering algorithms Background noise

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Automatic Early Detection of Potato Blight Disease Using Deep Neural Networks

Automatic Early Detection of Potato Blight Disease Using Dee...

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Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Shibdas Dutta Subhrendu Guha Neogi Anangsha Halder Department of Computer Science and Engineering Amity University Madhya Pradesh Gwalior Madhya Pradesh India Department of Computer Science and Engineering Brainware University Kolkata West Bengal India Department of Computer Science and Engineering The Neotia University Sarisha West Bengal India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Effective disease control in crops is essential for ensuring food security, as numerous diseases can significantly reduce agricultural productivity. Leaf disease is a crucial determinant of crop yield and quality. Potatoes are widely regarded as a vital staple in diets around the world. Potato blight, often known as late blight, is a severe disease impacting Solanaceae plants, particularly the potato. The oomycete Phytophthora infestans serves as the causal agent, posing a significant threat to potato crops by reducing yields and leading to financial losses. Identifying potato diseases is essential for ensuring food security and minimizing economic losses in agriculture. Numerous studies have explored the applications of machine learning and deep learning technologies for detecting blight diseases in affected crops. The proposed methodology outlined in our study offers a promising and effective means to enhance the accuracy of potato late blight classification. Advancements in technology can significantly improve the identification and treatment of diseases. Deep learning algorithms have been effectively employed in a range of practical applications. In our investigation, convolutional neural network models are used to analyze images of leaves. The primary aim of this initiative is to gain insights into and enhance the capacity to recognize diseases in their early phases. After the input images undergo preprocessing, the target region is distinctly isolated. CNN examines images to identify unique features, including edges, colours, and textures. This study evaluates the effectiveness of the proposed model through a diverse dataset of potato leaf images, making essential contributions to the domain of automated disease detection systems. The results of this study could enhance techniques for agricultural management and strengthen the sustainability of agriculture. This study introduces an improved CNN model designed to identify the blight disease state in potato leaf

关键词： Deep learning Biological system modeling signal processing algorithms Food security signal processing Agriculture Classification algorithms Convolutional neural networks Sustainable development Diseases

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Robust Wakeup, Acquisition, and Alignment for Low Cost, Battery-Limited ACOMMS Modems Operating with High Speed Platforms

Robust Wakeup, Acquisition, and Alignment for Low Cost, Batt...

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Underwater Communications and Networking (UComms)

作者： Dale Green Mark Baumgartner Jim Partan Sandip Singh Woods Hole Oceanographic Insti. Woods Hole MA USA Biology Department Woods Hole Oceanographic Insti. Woods Hole MA USA Applied Ocean Physics and Engineering Department Woods Hole Oceanographic Insti. Woods Hole MA USA

ISBN: (数字)9798331542511

ISBN: (纸本)9798331542528

Protection of marine mammals, especially “right” whales along the Western Atlantic Ocean, has become a critical imperative for US and Canadian Regulatory agencies and the lobster and crab fishing industries. The historical and continuing practice of deploying sea floor traps connected to surface floats via long rope is one of two primary causes of whale injury and depth. The other is ship strike from speeding vessels. A consensus has been reached within these communities that the ropes must be replaced, but no consensus has yet been reached on precisely how this can be achieved. The Woods Hole Oceanographic Institution (WHOI), has informally developed a concept for the combined use of acoustic communications (acomms) between surface boats and sea floor traps, combined with “cloud-based” connectivity to mitigate mutual interference and noise. The acomms system is based loosely on JANUS [1] in that the modulation follows the precise description developed at CMRE. However, at a center frequency of 25 kHz the wakeup and acquisition in the presence of very high-speed and noisy platforms (up to +/- 7 m/s, or +/-15 kts) requires a significantly different approach.

关键词： Sea surface Underwater communication Correlation Sea floor Whales signal processing algorithms Sea measurements Receivers Passband Protection

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Electromyography Controlled Smart Prosthetic Arm

Electromyography Controlled Smart Prosthetic Arm

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IEEE International Conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： Kalpana Murugan L.V.Naresh Reddy CH. Veerandra S.Sai Hussain N. Amarnadh Manoranjan Kumar Department of ECE Kalasalingam Academy of Research and Education Krishnankoil India Nanochip skills(Venkylabs) Bengaluru Karnataka India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Prosthetic technology has advanced to the point where intelligent prosthetic hands have been designed in an effort to restore functional capacities and improve quality of life for those who have undergone upper limb amputations. A novel electromyography (EMG)-based smart prosthetic hand is being built in this effort using deep learning techniques. Using non-invasive surface electromyography (EMG) data— the electrical impulses generated by muscles during contractions—the suggested smart prosthetic hand decodes the user's intended motions. To interpret and understand the complex EMG data associated with different hand motions, deep learning techniques called convolutional neural networks (CNNs) are employed. Deep learning made it possible through instruction allows for natural and real-time movement of the prosthetic hand. Smooth and instantaneous control over the prosthetic hand is made possible by the taught deep learning model, which accurately maps EMG signals to particular hand movements. Lastly, this demonstrates a deep learning algorithm-driven EMG-based smart prosthetic hand that may be able to enable people who have lost upper limb function regain dexterity when using prosthetic limbs.

关键词： Deep learning Prosthetic limbs signal processing algorithms Arms signal processing Electromyography Real-time systems

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Research on Indoor Localization Method Based on Smartphone WiFi-Sound Sensor Fusion

Research on Indoor Localization Method Based on Smartphone W...

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signal processing and Intelligent Computing (SPIC), International Conference on

作者： Zengting Zhang Zeyu Wang Jiaxuan Wu School of Information Science and Engineering Shenyang Ligong University Shenyang China Science and Technology Development Corporation Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Over the past few years, indoor positioning technology has advanced significantly, allowing researchers to determine a user's location using sensor data generated by smartphones. However, single-sensor localization techniques exhibit poor accuracy and stability. To address this issue, a fusion localization scheme based on the K-means clustering algorithm at the decision level has been employed. By utilizing the WiFi sensor and sound sensor within the smartphone, designed experiments are conducted and compared with a single algorithm to evaluate the localization performance of the fusion approach. The experimental results demonstrate that the fusion algorithm exhibits higher localization accuracy compared to single-sensor localization techniques.

关键词： Location awareness Accuracy signal processing algorithms Clustering algorithms Sensor fusion Stability analysis Error correction Smart phones Wireless fidelity

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Image edge detection based on improved ant colony algorithm

Image edge detection based on improved ant colony algorithm

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Yuehao Yan Juan Hui Gaohui Kong Shuaishuai Wang Liyi Feng Yujie Zhao State Grid Henan Electric Power Company Xingyang Power Supply Company Xingyang China Zhengzhou Xinda Advanced Technology Research Institute Zhengzhou China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Image edge detection is a necessary work in object recognition, feature extraction and structure analysis. Firstly, an improved ant colony algorithm is proposed to solve the problems of partial path selection, low accuracy and low efficiency of traditional ant colony algorithm. The initial pheromone, heuristic function, pheromone update rule and state transition parameters of ant colony algorithm were improved. Finally, the experimental simulation verifies that two classical images are selected to carry out adaptive edge extraction, and the edge detection effect is compared with the traditional detection algorithm. Experimental results show that the improved algorithm can achieve complete and continuous image edge extraction, and verify the effectiveness of the algorithm.

关键词： Accuracy Image edge detection Heuristic algorithms Noise signal processing algorithms Clustering algorithms Feature extraction Object recognition Detection algorithms Intelligent control

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An overview of parallel processing of rectangular determinant calculation

An overview of parallel processing of rectangular determinan...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Besnik Duriqi Halil Snopçe Armend Salihu Artan Luma Faculty of Computer Science South East European University - SEEU Republic of North Macedonia Faculty of Computer Science Republic of North Macedonia Department of Computer Science UNI Universum International College Prishtina Republic of Kosovo

ISBN: (数字)9798350387568

ISBN: (纸本)9798350387575

This paper focuses on developing algorithms for parallel determinant processing, a crucial task in linear algebra and computational mathematics. The aim is to improve efficiency in high-performance computing environments by designing and analyzing algorithms that use parallel processing to expedite determinant computation for various matrices range. The research explores methods like Laplace expansion, LU decomposition, eigenvalue decomposition, Gaussian elimination, and cofactor expansion, assessing their efficiency, scalability, and applicability in different computational environments. The study employs advanced parallel programming techniques and architectures, utilizing multi-core processors with the focus aim into utilization of Chio’s method of rectangular determinants processing in parallel etc. The research also investigates the mathematical underpinnings of parallel determinant algorithms, addressing challenges like load balancing, data distribution, and synchronization. The results show significant improvements in determinant calculations efficiency, reducing computation times for large matrices.

关键词： Multicore processing Parallel programming Scalability signal processing algorithms Computer architecture Linear algebra Parallel processing

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Radar Working Mode Recognition Method Based on Contrastive Pulse Description Graph Learning

Radar Working Mode Recognition Method Based on Contrastive P...

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International Conference on signal processing Proceedings (ICSP)

作者： Wenbo Li Shuhan Yang Yang-Yang Dong Chunxi Dong Dept. School of Electronic Engineering Xidian University Xi’an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

With the development of radar technology, radar working modes are diverse and signal waveforms are complex and variable. However, existing radar working mode recognition methods heavily rely on prior knowledge and have poor generalization ability, making it difficult to adapt to increasingly complex real electromagnetic environment problems. In this paper, a contrastive pulse description graph learning (CPDGL) based approach for radar working mode recognition is proposed. The pulse description word (PDW) sequence is first intercepted by a certain sliding window length without the use of labels. Secondly, the radar PDW sequence that was intercepted is transformed into a pulse description graph (PDG), allowing for the successful conversion of working pattern recognition into picture categorization. Lastly, unsupervised learning of PDG data characteristics and working mode detection using contrastive learning method. According to simulation results, when complete sequence data is available, radar working mode detection accuracy can reach 96.71%. The approach suggested in this research can still achieve high accuracy in working mode recognition even in the presence of noisy pulses, poor interception conditions, and pulse loss. Simulating and comparing with common clustering techniques like Agglomerative clustering, DBSCAN, and K-means further highlights the superiority of the CPDGL-based radar working mode detection method.

关键词： Accuracy Simulation Radar detection signal processing algorithms Radar Contrastive learning Feature extraction Radar signal processing Radar countermeasures Unsupervised learning

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Indoor Human Detection Based on Convolutional Neural Network and Image-based processing

Indoor Human Detection Based on Convolutional Neural Network...

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Natural Language processing (ICNLP), International Conference on

作者： Junhua Yang Guodong Bai Jingyu Lu Xi’an University of Posts and Telecommunications School of Electronic Engineering Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Indoor human detection technology is a critical component in the fields of indoor security and smart furniture. The current technology, which is based on Wi-Fi Channel State Information (CSI), relies solely on a single CSI amplitude information, resulting in a less stable human body detection system. To overcome this limitation, this study introduces a novel method, CSI-Image processing (CSI-IP), which combines the lightweight convolutional neural network SqueezeNET with the traditional signal processing technology, and integrates CSI amplitude information and phase information. The method involves processing the original phase information and amplitude information using linear transform and wavelet transform, respectively, and constructing CSI amplitude image and phase image. These images are then fused using the Laplace pyramid fusion algorithm, and a convolutional neural network is used to classify and learn the fused image to achieve human body detection. Experimental results in four different environments (laboratory, laboratory corridor, library, and library corridor) show that the detection rate of the algorithm is 0.9264, 0.9416, 0.9068, 0.9464, respectively, which is significantly higher than that of traditional algorithms under the same conditions, demonstrating the effectiveness and stability of the method.

关键词： Wavelet transforms Training Laboratories signal processing algorithms signal processing Libraries Classification algorithms Convolutional neural networks Security Wireless fidelity

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Topology-Independent GEVD-Based Distributed Adaptive Node-Specific signal Estimation in Ad-Hoc Wireless Acoustic Sensor Networks

Topology-Independent GEVD-Based Distributed Adaptive Node-Sp...

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European signal processing Conference (EUSIPCO)

作者： Paul Didier Toon Van Waterschoot Marc Moonen Department of Electrical Engineering (ESAT) KU Leuven STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

A low-rank approximation-based version of the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm is introduced, using a generalized eigenvalue decomposition (GEVD) for application in ad-hoc wireless acoustic sensor networks. This TI-GEVD-DANSE algorithm as well as the original TI-DANSE algorithm exhibit a non-strict convergence, which can lead to numerical instability over time, particularly in scenarios where the estimation of accurate spatial covariance matrices is challenging. An adaptive filter coefficient normalization strategy is proposed to mitigate this issue and enable the stable performance of TI-(GEVD-)DANSE. The method is validated in numerical simulations including dynamic acoustic scenarios, demonstrating the importance of the additional normalization.

关键词： Wireless communication Wireless sensor networks Adaptive systems signal processing algorithms Estimation Adaptive filters Acoustic sensors Approximation algorithms Ad hoc networks Acoustics

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