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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets"

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signal and data processing of small targets 2012

Signal and Data Processing of Small Targets 2012

引用

signal and data processing of small targets 2012

ISBN: (纸本)9780819490711

The proceedings contain 24 papers. The topics discussed include: detecting clustered chem/bio signals in noisy sensor feeds using adaptive fusion;investigation of kinematic features for dismount detection and tracking;multichannel adaptive generalized detector based on parametric Rao test;a mathematical model for MIMO imaging;space-time signal processing for distributed pattern detection in sensor networks;small curvature particle flow for nonlinear filters;Lagrangian relaxation approaches to closed loop scheduling of track updates;particle filter tracking for long range radars;ambiguous data association and entangled attribute estimation;measurement level AIS/radar fusion for maritime surveillance;exploratory joint and separate tracking of geographically related time series;prediction, tracking, and retrodiction for path-constrained targets;and data modeling for nonlinear track prediction of targets through obscurations.

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Exploring Artificial Intelligence Techniques for Enhanced Accuracy in Medical Imaging and Early Disease Diagnosis

Exploring Artificial Intelligence Techniques for Enhanced Ac...

引用

International conference on signal processing and Communication (ICSC)

作者： Sandeep Kumar Pratik Agrawal Nitin Rakesh Monali Gulhane Saurabh Bhattacharya School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Nagpur India School of Computer Science & Engg. Galgotias University Greater Noida (UP)

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Utilizing Artificial Intelligence (AI) methods in medical imaging has completely changed how early disease screening is done, leading to big gains in accuracy, speed, and patient results. This essay looks at how AI can improve medical images. It focuses on deep learning algorithms, computer vision, and machine learning models that can make complicated diagnosis tasks easier to do automatically. These technologies make it possible to find and study small trends in medical pictures that a person might miss. This helps doctors make faster and more accurate diagnoses, especially in cancer, heart, and neurology. AI can find problems in X-rays, CT scans, and MRIs using convolutional neural networks (CNNs), and its performance is on par with or even better than that of expert doctors. The paper also looks at how AI can be used to predict diseases by looking at old image data along with clinical factors. Researchers are looking at techniques like reinforcement learning and generative adversarial networks (GANs) to see if they can improve picture quality and help make fake data for rare diseases. This will help make diagnosis tools that are more accurate. The study also shows how AI is becoming more and more important for solving problems like inconsistent data, picture noise, and wrong interpretations. As healthcare continues to go digital, adding AI to medical imaging systems could help with personalized treatment plans, lower the number of mistakes made during diagnosis, and speed up work processes.

关键词： Deep learning Accuracy Medical services Generative adversarial networks data models Medical diagnosis Convolutional neural networks Artificial intelligence Medical diagnostic imaging Cancer

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20.4 MEMS-Free 4096-Pixel CMOS E-Nose Gas-Sensor Array with Molecular-Selective Metal-Organic-Framework Sensing and In-Pixel Thermodynamic Modulation for Fast Sensor Regeneration

20.4 MEMS-Free 4096-Pixel CMOS E-Nose Gas-Sensor Array with ...

引用

IEEE International conference on Solid-State Circuits (ISSCC)

作者： Marco Saif Fuze Jiang Shao–Wei Lo Adam Wang Zhikai Huang Jiachen Wang Hangxing Liu Chih–Jen Shih Thomas Burger Hua Wang ETH Zürich Zürich Switzerland

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

Accurate and real-time sensing of gas molecule species and concentrations is critical for a myriad of applications, including environmental protection, industry automation, public health monitoring, and bio-/chemical-security surveillance. For example, gas sensing in large chemical plants is crucial for safety and production efficiency, with ammonia (NH3) being particularly important due to its extensive use [1]. Electronic noses (e-noses) consist of a chemical sensor array with surface recognition modules for capturing specific or multi-species gas molecules followed by electronic signal acquisition and processing [2]. Despite decades of exploration [3]–[5], the development of e-noses is still in its early stages, and existing e-noses continue to face major challenges as: (1) limited selectivity on target molecules (2) poor sensor regeneration due to slow and incomplete gas desorption process, which can result in sensing memory effect, i.e., affecting low-trace gas detection due to prior exposure to high concentrations, (3) a small number of sensing nodes instead of a large array, also lacking in-pixel signal processing and data digitization [6], [7]. To address these issues, we present a monolithically integrated molecular specific metal-organic-frameworks (MOFs) CMOS e-nose sensor array with in-pixel fast capacitance-gas interfacing, readout, and data digitization with local temperature regulation and sensing. For demonstration, NiNi-Pyz MOFs have been employed to achieve NH3 specificity. In particular, the in-pixel temperature regulation can modulate the thermodynamic of NH 3 adsorption and desorption at the sensor interfaces, significantly improving the sensitivity and allowing rapid sensor regeneration.

关键词： Temperature sensors Thermodynamics Temperature distribution Array signal processing Surveillance Electronic noses Regulation Sensors Surface treatment Sensor arrays

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TROI: Cross-Subject Pretraining with Sparse Voxel Selection for Enhanced fMRI Visual Decoding

TROI: Cross-Subject Pretraining with Sparse Voxel Selection ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ziyu Wang Tengyu Pan Zhenyu Li Ji Wu Xiuxing Li Jianyong Wang Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

fMRI (functional Magnetic Resonance Imaging) visual decoding involves decoding the original image from brain signals elicited by visual stimuli. This often relies on manually labeled ROIs (Regions of Interest) to select brain voxels. However, these ROIs can contain redundant information and noise, reducing decoding performance. Additionally, the lack of automated ROI labeling methods hinders the practical application of fMRI visual decoding technology, especially for new subjects. This work presents TROI (Trainable Region of Interest), a novel two-stage, data-driven ROI labeling method for cross-subject fMRI decoding tasks, particularly when subject samples are limited. TROI leverages labeled ROIs in the dataset to pretrain an image decoding backbone on a cross-subject dataset, enabling efficient optimization of the input layer for new subjects without retraining the entire model from scratch. In the first stage, we introduce a voxel selection method that combines sparse mask training and low-pass filtering to quickly generate the voxel mask and determine input layer dimensions. In the second stage, we apply a learning rate rewinding strategy to fine-tune the input layer for downstream tasks. Experimental results on the same small sample dataset as the baseline method for brain visual retrieval and reconstruction tasks show that our voxel selection method surpasses the state-of-the-art method MindEye2 with an annotated ROI mask.

关键词： Training Visualization Noise Training data Functional magnetic resonance imaging Brain modeling Decoding Labeling Speech processing Optimization

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signal and data processing of small targets 2010

Signal and Data Processing of Small Targets 2010

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signal and data processing of small targets 2010

ISBN: (纸本)9780819481627

The proceedings contain 50 papers. The topics discussed include: a complex-domain adaptive order statistic filter and its application to signal detection in non-Gaussian noise and clutter;needle picking: a sampling based track-before-detection method for small targets;small moving targets detection using outlier detection algorithms;MIMO radar systems based on the generalized detector and space-time coding;algorithms for distributed chemical sensor fusion;identifying chemicals from their Raman spectra using minimum description length;nonlinear estimation for arrays of chemical sensors;comparative studies of Raman spectra estimation algorithms for single and multiple chemical substances;tracking interacting dust: comparison of tracking and state estimation techniques for dusty plasmas;track segment association for ground moving targets with evasive move-stop-move maneuvers;and temporal characterization of small arms muzzle flash in the broadband visible.

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Leveraging Deep Learning Technique with Meta-data-Featured Images Captured by Drone for Real-Time Victim Positioning in Flood-Affected Areas

Leveraging Deep Learning Technique with Meta-Data-Featured I...

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International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Trong Tuan Do Dinh Duy Nguyen Xuan Hai Ho Van Duc Nguyen School of Electrical and Electronic Hanoi University of Science and Technology Hanoi Vietnam Aerospace Electronics Laboratory School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Flooding has long been a major concern for nations frequently affected by natural disasters. The detection and localization of isolated victims for immediate temporary rescue have been a focal point for many researchers aiming to demonstrate the practical value of their solutions. However, accessing flood-affected areas poses significant challenges, as floods severely disrupt local transportation and damage infrastructure, rendering access nearly impossible. In such scenarios, the most feasible rescue approach is aerial intervention using Unmanned Aerial Vehicles (UAVs). Nevertheless, accurately detecting and localizing targets from a UAV's perspective is a significant challenge. UAVs operate at various altitudes and speeds, resulting in motion blur of targets against densely populated backgrounds. Additionally, target localization is made difficult due to the limited contextual understanding of the surroundings. To address these challenges, we propose training the YOLOv10 model to enhance the detection of small objects, integrated with a GPS-based target localization system. Furthermore, to enable the practical, real-world application of this solution, we introduce a communication infrastructure that facilitates the timely and effective deployment of both the detection and localization models. Field experiments have yielded promising results, with the artificial intelligence model achieving an Average Precision (AP) between 85–95%, and the localization model processing targets at an impressive speed of under 1 ms. The combined detection and localization approach demonstrated a positioning error of only 7–9 meters. Moreover, integrating the AI model and the localization algorithm into a low-latency communication system has shown promising results, providing near-instantaneous outputs with delays ranging from 0.6 to 0.9 seconds from the moment a target enters the frame.

关键词： Location awareness Deep learning Training Surveys Surveillance Transportation Rendering (computer graphics) Real-time systems Time factors Floods

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signal and data processing of small targets 2013

Signal and Data Processing of Small Targets 2013

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signal and data processing of small targets 2013

ISBN: (纸本)9780819497079

The proceedings contain 16 papers. The topics discussed include: small object detection via fast discrete curvelet transform;an examination of the application of space time adaptive processing for the detection of maritime surface targets from high altitude airborne platforms;improving variance estimation ratio score calculation for slow moving point targets detection in infrared imagery sequences;identification of human motion signature using airborne radar data;particle flow with non-zero diffusion for nonlinear filters, Bayesian decisions and transport;estimability of thrusting trajectories in 3-D from a single passive sensor with unknown launch point;a minimalist approach to bias estimation for passive sensor measurements with targets of opportunity;detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets;and efficient multiple emitter localization for fully decentralized large-scale/low-cost multimodal sensor networks.

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signal and data processing of small targets 2014

Signal and Data Processing of Small Targets 2014

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signal and data processing of small targets 2014

ISBN: (纸本)9781628410297

The proceedings contain 20 papers. The topics discussed include: application of rich feature descriptors to small target detection in wide-area persistent ISR systems;detection of small targets and their characterization based on their formation using an image feature network-based object recognition algorithm;advancement and results in hostile fire indication using potassium line missile warning sensors;plot enchaining algorithm: a novel approach for clustering flocks of birds;multiset singular value decomposition for joint analysis of multi-modal data: application to fingerprint analysis;tracking low SNR targets using particle filter with flow control;how to avoid normalization of particle flow for nonlinear filters, Bayesian decisions, and transport;and seven dubious methods to mitigate stiffness in particle flow with non-zero diffusion for nonlinear filters, Bayesian decisions, and transport.

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Proceedings of SPIE: signal and data processing of small targets 2008

Proceedings of SPIE: Signal and Data Processing of Small Tar...

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signal and data processing of small targets 2008

ISBN: (纸本)9780819471604

The proceedings contain 48 papers. The topics discussed include: an advanced missile warning processing suite;chemical detection and classification in Raman spectra;detection of small objects in multi-layered infrared images;spectral gating in hyperspectral-augmented target tracking;optical recognition of biological agents;pixel decomposition for tracking in low resolution videos;discriminating small extended targets at sea from clutter and other classes of boats in infrared and visual light imagery;a recurrent velocity filter for detecting large numbers of moving objects;feature-aided tracking in the urban environment;robust method for detecting an infrared small moving target based on the facet-based model;removal of bias due to propagation of estimates through nonlinear mappings;improving multiple target tracking in structured environments using velocity priors;and multisensor range-only tracking for a distributed architecture of imaging sensors.

关键词： Target tracking

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Proceedings of SPIE: signal and data processing of small targets 2007

Proceedings of SPIE: Signal and Data Processing of Small Tar...

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signal and data processing of small targets 2007

ISBN: (纸本)9780819468475

The proceedings contain 45 papers. The topics discussed include: monitoring of sensor covariance consistency;future prospects for algorithm development of tracking related processing;map integration in tracking;consistent covariance estimation for PMHT;computationally efficient assignment-based algorithms for data association for tracking with angle-only sensors;evaluation of a posteriori probabilities of multi-frame data association hypotheses;improved multitarget tracking using probability hypothesis density smoothing;spline filter for nonlinear/non-Gaussian Bayesian tracking;track-to-track association using informative prior associations;mitigation of biases using the Schmidt-Kalman filter;flow-rate control for managing communications in tracking and surveillance networks;and feature-aided tracking with hyperspectral imagery.

关键词： data processing

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