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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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Space-operating Linux: An Operating System for computer Vision on Commercial-Grade Equipment in LEO

Space-operating Linux: An Operating System for Computer Visi...

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ieee conference on Aerospace

作者： Eric Miller Chris Heistand Deepak Mishra Small Satellite Research Laboratory University of Georgia GA STOKE Space Technologies WA

With recent exponential advances in AI-particularly with respect to the tremendous power and efficiency accessible for data processing-there is now a countless array of applications for aerospace missions and space exploration, even in experimental CubeSats. However, with the large volume of data acquisition required for satellite missions, downlinking presents an increasingly expensive bottleneck that drastically reduces mission efficiency. Hence, there is a skyrocketing demand to perform edge computation on board the payload system, often via a graphics processing unit (GPU). Multiple edge-computing CubeSat missions set to operate in Low Earth Orbit (LEO), including the University of Georgia's Multiview Onboard Computational imager (MOCI), house an Nvidia Jetson TX2i module to perform onboard computer vision. As is the case for many commercial off-the-shelf (COTS) devices used in CubeSats, the TX2i does not come radiation-hardened, and its most vulnerable component is its eMMC disk. Even with the option of radiation shielding, there is nevertheless a possibility of single event effects (SEEs) reaching the TX2i, calling for software-level mitigation as a final line of defense. The MOCI team and partners at Johns Hopkins University's Applied Physics Lab have developed Space Operating Linux (SOL), a minimized Yocto-based operating system with built-in redundancy designed to handle these environmental pressures. While SOL contains patches that enable real-time scheduling in Linux for time-sensitive reliability in flight, the methodologies of operating system minimization, software-based triple modular redundancy in persistent memory with associated bootloader modifications, and a RAM-based file system allow the device to rely less on its eMMC card and render it less prone to radiation-induced damage. Results from proton SEE tests on the device's chip exhibit lower expected error rates in LEO compared to stock devices. Additionally, the devices tested were less prone t

关键词： Space vehicles computer vision Operating systems Linux image edge detection Redundancy Low earth orbit satellites

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Detection of Jump Landing Performance of Basketball Player Using image processing and Support Vector Machine

Detection of Jump Landing Performance of Basketball Player U...

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international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Jessie R. Balbin Sebastian Paul O. Uy Royce D. Bumagat School of Electrical Electronics and Computer Engineering Mapua University Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

This study is dedicated to developing an analytical system aimed at evaluating the jump-landing technique employed by basketball players. This innovative system relies on applying the Support Vector Machine (SVM) image processing algorithm in conjunction with the Kinect sensor to recognize and assess the landing postures of the players. The primary objective of this research is to establish a detection system that can provide precise assessments of landing techniques by comparing them with evaluations made by experienced basketball trainers or coaches. The study mainly concentrates on jump landings in basketball, specifically on maneuvers like layups and rebound jumps. The research outcomes have demonstrated a robust correlation of 95.07 percent between the evaluations generated by the program and those provided by the coach. Rigorous statistical analysis has validated the accuracy of these assessments, which were gathered exclusively from the coaching staff of a single professional basketball team. The resulting system offers valuable insights for enhancing the jump-landing technique of basketball players, making notable contributions to injury prevention and overall performance improvement in the sport.

关键词： Support vector machines Correlation image recognition Statistical analysis Prevention and mitigation Humanoid robots Information technology

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BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification

BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray C...

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international conference on computer Vision (ICCV)

作者： Yuanhong Chen Fengbei Liu Hu Wang Chong Wang Yuyuan Liu Yu Tian Gustavo Carneiro Australian Institute for Machine Learning University of Adelaide Harvard Medical School Harvard University Centre for Vision Speech and Signal Processing University of Surrey

Deep learning methods have shown outstanding classification accuracy in medical imaging problems, which is largely attributed to the availability of large-scale datasets manually annotated with clean labels. However, given the high cost of such manual annotation, new medical imaging classification problems may need to rely on machine-generated noisy labels extracted from radiology reports. Indeed, many Chest X-Ray (CXR) classifiers have been modelled from datasets with noisy labels, but their training procedure is in general not robust to noisy-label samples, leading to sub-optimal models. Furthermore, CXR datasets are mostly multi-label, so current multi-class noisy-label learning methods cannot be easily adapted. In this paper, we propose a new method designed for noisy multi-label CXR learning, which detects and smoothly re-labels noisy samples from the dataset to be used in the training of common multi-label classifiers. The proposed method optimises a bag of multi-label descriptors (BoMD) to promote their similarity with the semantic descriptors produced by language models from multi-label image annotations. Our experiments on noisy multi-label training sets and clean testing sets show that our model has state-of-the-art accuracy and robustness in many CXR multi-label classification benchmarks, including a new benchmark that we propose to systematically assess noisy multi-label methods. Code is available at https://***/cyh-0/BoMD.

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Dense Convolution Neural Network for Complex land Cover Classification and change detection using Landsat OLI 8 hyperspectral image

Dense Convolution Neural Network for Complex land Cover Clas...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), international conference on

作者： K. Anitha Devi R. Priya Computer Applications School of Computing Sciences VISTAS Pallavaram Chennai Department of Computer Applications School of Computing Sciences VISTAS Pallavaram Chennai

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Hyperspectral image is employed to observe the earth surface with respect to spectral reflectance value on the electromagnetic spectral ranges of electromagnetic spectrum initiating on the visible regions to infrared region. It identifies the spectral feature space and classifies it with respect to minerals and land covers. Hyperspectral images have been proposed using existing unsupervised and supervised classifier towards classification. Multiple complications have been exploited on the hyperspectral images as it contains the Hughes phenomenon. In order to alleviate those problems, dense convolution neural network methodology is architected in this research. It capable of classifying the spectral images with reduced processing time. Initially pre-processing of the hyperspectral image is carried out to reduce the noise and improve the contrast of the hyperspectral images. preprocessed image is projected to linear discriminant analysis to extract the spectral end members and spatial endmembers of the image. Those extracted spectral and spatial endmembers is employed to Dense Connected Convolution Neural Network as it is highly capable in land cover classification and discrimination. Deep learning model containing the convolution layer and max pooling layer for feature map generation and fully connected layer is highly capable in generating the spectral indices to classify the land cover and detecting the land cover changes on the spatial features. Softmax layer employs the spectral index for classification and change detection. Cross entropy is employed to reduce the loss of the model. Further hyperparameter tuning is carried out using gradient decent to generate the high discriminant classes of the land cover. The experimental outcomes of the current model are verified on using hyperspectral image gathered from Landsat 8 OLI dataset. Performance of the current approach is evaluated with state of art approaches on measures like precision, recall and f measure on cro

关键词： Analytical models Convolution Computational modeling Current measurement Neural networks Land surface Feature extraction

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An IoT Integrated Sensor Technologies for the Enhancement of Hospital Waste Segregation and Management

An IoT Integrated Sensor Technologies for the Enhancement of...

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Innovative Data Communication Technologies and Application (ICIDCA), international conference on

作者： D. Selvakarthi D. Sivabalaselvamani M. Ahamed Wafiq G. Aruna M. Gokulnath Department of Electronics and Instrumentation Engineering Kongu Engineering College Erode Tamilnadu India Department of Computer Applications Kongu Engineering College Erode Tamilnadu India

Hospitals create an enormous volume of potentially hazardous trash. Currently, bandages, plastic papers, wasted pus, syringes, glucose drip bottles, other dangerous medical wastes, and containers are separated by hand, resulting in long-term health consequences such as tuberculosis, cancer, and infectious diseases. Rag pickers are currently responsible for the majority of waste separation. In this proposed system, waste will be detected using the Deep Learning technique; a model will be trained to distinguish various forms of medical waste. With the use of a flip attachment to a motor, waste will be easily segregated to the waste bin. There are two flips that open depending on the sort of trash detected, and the garbage is divided into compartments having four specific chambers. IoT-based web applications can be used to update the waste level, with respective times and dates. As a result, the overflowing of waste in the bin can be avoided. Here, medical waste is quickly and properly segregated to reduce manual labor and stop diseases from spreading throughout hospitals

关键词： Deep learning Training Waste materials Hospitals Tuberculosis image processing Manuals

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Review of Recent Systems for Detecting and Diagnosing Pronunciation Errors

Review of Recent Systems for Detecting and Diagnosing Pronun...

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international conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Karim Dabbabi Abdelkarim Mars Faculty of Sciences of Tunis Research Unite of Analysis and Processing of Electrical and Energetic Systems Tunis Tunisia Research Laboratory in Algebra Number Theory and Intelligent Systems Faculty of Sciences of Monastir Monastir Tunisia

Nowadays, computer Aided Language Learning (CALL) frameworks have attracted a lot of attention because of their capability, adaptability, and flexibility in improving people's language skills. The field of Mispronunciation Detection and Diagnosis (MDD) is considered one of the applications of these frameworks and has recently benefited from the rapid innovation spawned in deep learning models and different acoustic, phonetic, and linguistic features. The various manner of combinations of different features and deep learning architectures for different MDD systems require huge amount of annotated textual data to be pretrained and hence achieved good performances. However, providing a large amount of annotated textual data for pre-training is a tedious and time-consuming operation. For this, a pre-trained acoustic model from unannotated data can be a good alternative. In this article, we present different MDD systems categorized according to the pre-training mode: pre-trained approaches without annotated text data and pre-trained approaches with annotated text data. Experimental results, advantages, disadvantages and additional improvements for each method explored for each category are presented and discussed.

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Disease Detection in Areca Nut Using Deep Learning

Disease Detection in Areca Nut Using Deep Learning

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ieee international conference on Cloud Computing in Emerging Markets (CCEM)

作者： Kiran Kumar B S Varshini U Manohar N Tian Jipeng Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Computer Science College Zhongyuan University of Technology Zhengzhou Henan Province China

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360066

The areca nut finds use in multiple industries. It is used in the production of betel quid, in traditional medicine, and in cosmetics and oral care products. However, it is important to consider health risks and adhere to safety and quality standards in its industrial applications, although some of the research has not depicted multiple diseases in shells. The effects of various diseases affecting areca nuts can be attributed. In our research we developed a novel dataset consisting of 1332 samples that was developed for the detection of diseases in areca nuts such as button shedding, fruit rot, fruit crack, and exfoliation. In this research, five convolutional models, namely VGG-16, DenseNet121, MobileNetV3-large, ResNet-50, and ResNet-18, were developed for the detection of areca nut disease. Among them, the proposed model using ResNet-18 achieved an accuracy of 98.12% with an inference time of 0.334 milliseconds on the validation dataset. This research contributes to the sustainability and profitability of this vital agricultural crop.

关键词： Accuracy Profitability Computational modeling Production Safety Sustainable development Standards Diseases image classification Residual neural networks

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INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation processing

INR-Arch: A Dataflow Architecture and Compiler for Arbitrary...

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ieee international conference on computer-Aided Design

作者： Stefan Abi-Karam Rishov Sarkar Dejia Xu Zhiwen Fan Zhangyang Wang Cong Hao Georgia Institute of Technology Georgia Tech Research Institute University of Texas Austin

An increasing number of researchers are finding use for nth-order gradient computations for a wide variety of applications, including graphics, meta-learning (MAML), scientific computing, and most recently, implicit neural representations (INRs). Recent work shows that the gradient of an INR can be used to edit the data it represents directly without needing to convert it back to a discrete representation. However, given a function represented as a computation graph, traditional architectures face challenges in efficiently computing its nth-order gradient due to the higher demand for computing power and higher complexity in data movement. This makes it a promising target for FPGA acceleration. In this work, we introduce INR-Arch, a framework that transforms the computation graph of an nth-order gradient into a hardware-optimized dataflow architecture. We address this problem in two phases. First, we design a dataflow architecture that uses FIFO streams and an optimized computation kernel library, ensuring high memory efficiency and parallel computation. Second, we propose a compiler that extracts and optimizes computation graphs, automatically configures hardware parameters such as latency and stream depths to optimize throughput, while ensuring deadlock-free operation, and outputs High-Level Synthesis (HLS) code for FPGA implementation. We utilize INR editing as our benchmark, presenting results that demonstrate 1.8-4.8x and 1.5-3.6x speedup compared to CPU and GPU baselines respectively. Furthermore, we obtain 3.1-8.9x and 1.7-4.3x lower memory usage, and 1.7-11.3x and 5.5-32.8x lower energy-delay product. Our framework will be made open-source and available on GitHub.****https://***/sharc-lab/inr-arch

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Non-Homogeneous Haze image Formation Model Based Single image Dehazing

Non-Homogeneous Haze Image Formation Model Based Single Imag...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Banala Revanth Manoj Kumar Sanjay K. Dwivedi DCS BBA University Lucknow UP India

image dehazing is an image processing technique that involves removing the haze effect from a given image. Restoration of hazy images is currently an established computer vision issue due to its applications in the real world, like automated vehicles, surveillance, etc. This paper proposes a single image dehazing model by estimating atmospheric light and transmission map simultaneously using two networks. Atmospheric light and transmission map are estimated by a non-homogeneous light estimation network and a novel refined transmission map estimation network. The non-homogeneous light estimation network has two subnetworks: a white-balanced network and a deep convolutional neural network called Global Atmospheric Light Estimation Network. The white-balanced error network for correction of white-balanced error in a hazy image and a Global Atmospheric Light Estimation Network for estimation of global atmospheric light in a corrected white-balanced error hazy image. A refined transmission map estimation network is used to create a refined transmission map of the hazy image. The design of a refined transmission map estimation network is based on U-Net with skip connections, which estimates and refines the transmission map of the input haze image. The non-homogeneous haze image formation model takes both estimated global atmospheric light and a refined transmission map as input to restore the hazy-free image. The global atmospheric light estimation network and refined transmission map estimation network are trained using mean square error (MSE) and hybrid loss functions, respectively. In terms of structural similarity index Matric (SSIM) and peak signal-to-noise ratio (PSNR), the proposed model is equal to the cutting-edge approaches on the NR-Haze, I-Haze, and O-Haze datasets.

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Expression of Concern for: Enhancing Tone Recognition in Large-Scale Social Media Data with Deep Learning and Big Data processing

Expression of Concern for: Enhancing Tone Recognition in Lar...

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ieee international conference on Artificial Intelligence and computer applications (ICAICA)

作者： Xingyu Huo School of Theater Film and Television Communication University of China Beijing China

ISBN: (数字)9798350323313

ISBN: (纸本)9798350323320

Huge processing power, big data, and deep learning algorithms have made major technical improvements possible. Technology is developing to analyse, predict, and meet our unmet requirements. Speech is the primary human communication method, and when a microphone sensor is employed, it may be utilised to support human-computer interaction (HCI). A new field of study in human-computer interaction (HCI) is quantifying emotion recognition from speech signals using these sensors. This research has applications in virtual reality, healthcare, emergency call centres, behaviour assessment, and human-reboot interaction. Our main contributions in this study are to: (i) further develop discourse feeling acknowledgment (SER) exactness over the cutting edge; what's more (ii) bring down the figuring cost of the proposed SER model. The method involved with recognizing a speaker's feelings from their discourse signal is known as discourse feeling acknowledgment. The three essential strides in the feeling acknowledgment process are highlight extraction, include determination, and classifier. Improving a framework's capacity to perceive discourse feelings using different element extraction techniques is the essential objective of this work. The work centers around the pre-handling of the sound examples that are gotten, where channels are utilized to wipe out commotion from discourse tests. Worldwide discriminative elements are learned in completely associated layers, though neighborhood stowed away examples are learned in convolutional layers that bring additional consideration to down-example the component maps rather than the pooling layer. Discourse feeling classification is finished utilizing a SoftMax classifier. It shows the worth and viability of the proposed SER approach and exhibits that it is so relevant t

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