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EARLY SMOKE DETECTION IN FOREST AREAS FROM DCT BASED COMPRESSED VIDEO

EARLY SMOKE DETECTION IN FOREST AREAS FROM DCT BASED COMPRES...

引用

European signal processing conference

作者： A. Benazza-Benyahia N. Hamouda F. Tlili S. Ouerghi Communications Signal and Image Lab. (COSIM) SUP'COM Carthage University Smart Communication Systems Lab. (GRESCOM) SUP'COM Carthage University

ISBN: (纸本)9781467310680

Smart functions begin to be integrated into some camera acquisition systems for CCTV applications. In this regard, smoke detection based on the compressed video is a highly desirable functionality for the monitoring of forest that present a high risk of fire. In this paper, we propose a fast and early smoke detection method that measures the local fractal feature of smoke areas based on the Discrete Cosine Transform (DCT) coefficients. the latter are generally computed at the compression step which is concomitant in its acquisition. the two coding video standards MJPEG and MPEG2 are considered as they are widely available in the operating cameras. the novelty of our approach consists in resorting to a recursive DCT in order to improve the detection performance.

关键词： Smoke detection Compression video standard Hurst exponent Recursive DCT discrete cosine transforms DCT gene fire risks Smoke wooded area Video compression Videotapes COMPRESSED inspection methods video standards Cameras

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On-chip cell separation and manipulation using traveling wave dielectrophoretic force controlled by four-phase signal generator

On-chip cell separation and manipulation using traveling wav...

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Biomedical Engineering International conference (BMEiCON)

作者： Kata Jaruwongrangsee thitima Maturos Sakshin Bunthawin Anurat Wisitsoraat Manas Sangworasil Adisorn Tuantranont Biomedical signal and image processing laboratory King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Nanoelectronics and MEMS laboratory National electronics and computer technology center Pathumthani Thailand Biotechnology of Electromechanics Research Unit Prince of Songkla University Phuket Thailand

In this work, we present a device for cell manipulation and separation using travelling wave dielectophoretic (twDEP) force. the device consists of microchamber and 16 parallel electrode array controlled by four-phase signal. the dielectrophoretic PDMS chamber and Cr/Au parallel electrode array were fabricated by standard microfabrication techniques. the driving signals for the twDEP system are produced by a specially designed four-phase signal generator. the signal generator can be operated in a fixed or ±90° phase shift mode with varying frequency, applied voltage and shift interval. In addition, the signal conditions can be store to and retrieved from a built-in database. the twDEP system was tested with polystyrene microspheres suspension in de-ionized water and red blood cells in D-mannitol solution. Cells responded to the electric field in various mechanisms depending on the applied signals conditions. the results showed that the twDEP force occurred when the applied signals were 10 V (50 kHz-700 kHz) and 7 V (30 kHz-1 MHz) for 4.5 μm and 10 μm microspheres respectively. the mixed solution containing equal amount of 4.5 and 10 μm microspheres were used for separation test. Under the signal conditions for 10 μm microspheres, the microspheres were moved under twDEP force while the smaller microspheres were attached to the electrodes. therefore, the twDEP device can successfully manipulate and separate the microspheres of different sizes, and it can be further applied for cells selection.

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Multi-scale target detection for airfield visual navigation of taxiing aircraft

Multi-scale target detection for airfield visual navigation ...

引用

Neural Networks, Information and Communication Engineering (NNICE), International conference on

作者： Qijun Luo Jiagang Chen Xuerui Zhang Tao Zhang Tianjin Intelligent Signal and Image Processing Experiment Civil Aviation University of China Tianjin China

In the context of visual navigation-based civil aircraft assistance systems, the scene target scales captured by onboard cameras exhibit significant variations, and the computational power of airborne platforms is limited. To promptly and accurately identify threat targets in airborne scenic images, a lightweight multi-scale object detection algorithm based on improved YOLOv5s is proposed. Firstly, on the foundation of the Weighted Bidirectional Feature Pyramid Network (BIFPN), a Coordinate Attention (CA) mechanism is integrated to design the CA-BIFPN feature fusion network. this enhances feature representation for small targets and improves the model's learning capability for multi-scale targets. Secondly, a cross-level sub-network module is employed to lightweightly process the neck section, reducing the model's parameter count and thereby improving the overall network's detection speed. Experimental results on a self-constructed scene image dataset demonstrate that the proposed method achieves a detection frame rate of 89FPS, an increase of 5fps. the average precision mean (mAP) on the test set is 70.6%, a 3.4% improvement over the original YOLOv5s. the detection accuracy surpasses typical algorithms such as YOLOv6, YOLOv7, YOLOX, and Faster-RCNN.

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Detection and Removal of Clouds on Remote Sensing images

Detection and Removal of Clouds on Remote Sensing Images

引用

International conference for Convergence of Technology (I2CT)

作者： K Prinsa E Saritha Signal Processing and Embedded Systems GCE Kannur GCE Kannur

ISBN: (数字)9781538652329

ISBN: (纸本)9781538652336

To perform the cloud detection and removal, a novel automatic supervised approach is proposed. Here in the cloud detection task there are mainly two sections. one is training section where the detector is designed using the feature data and ground truth of the training image. the feature data is formed by extracting and stacking different features such as colour, statistical information, texture, structure, haar like features and reflectance. Other is testing section. After training the detector the feature data of the testing image is applied to get the saliency map. After that some cloud refinement techniques are applied to get the final cloud detected result. the complemented detected output is taken as a mask and applied to the testing image. then it is given to the cloud removal technique. Singular value decomposition is used in cloud removal. the quantitative analysis of efficiency of detection in proposed method is evaluated.

关键词： Clouds Feature extraction Testing Detectors Training image color analysis Mathematical model

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NOISE-AWARE SUPER-RESOLUTION OF DEPth MAPS VIA GRAPH-BASED PLUG-AND-PLAY FRAMEWORK

NOISE-AWARE SUPER-RESOLUTION OF DEPTH MAPS VIA GRAPH-BASED P...

引用

IEEE International conference on image processing

作者： Rong Chen Deming Zhai Xianming Liu Debin Zhao School of Computer Science and Technology Harbin Institute of Technology Harbin China

Depth information is being widely used in many real-world tasks, such as 3DTV, 3D scene reconstruction, multi-view rendering, etc. However, the captured depth maps in practice usually suffer from quality degradations, including low-resolution and noise corruption, which limit their further applications. Noise-aware super-resolution of depth maps is a challenging task and has received increasingly more attention in recent years. In this paper, we propose a novel method based on the plug-and-play scheme, which casts two powerful graph-based tools—the graph Laplacian regularizer and 3D graph Fourier transform—into a unified ADMM optimization framework. It can be performed in an iterative manner with easily treatable convex optimization sub-problems. Experiments results demonstrate that our method achieves superior performance compared with the state-of-the-art works with respect to both objective and subjective quality evaluations.

关键词： image resolution three-dimensional displays Laplace equations Task analysis image restoration image edge detection signal resolution

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Fan-chirp Transform based thermal Wave Imaging

Fan-chirp Transform based Thermal Wave Imaging

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Muzammil Parvez M J. Shanmugam V.S. Ghali Research Scholar Bharath Institute of Higher Education and Research (BIHER) Chennai India Professor Bharath Institute of Higher Education and Research (BIHER) Chennai India Professor Koneru Lakshmaiah Education Foundation AP Vaddeswaram India

ISBN: (数字)9781728163871

ISBN: (纸本)9781728163888

Non-stationary optical stimulus based infrared non-destructive testing modality has gained much attention from past decades. Various post processing techniques based on signal and image processing have been proposed for better visualization and quantification of subsurface defects for Non-Stationary thermal Wave Imaging (NSTWD. In present work, a new post processing approach named fan chirp transform is adapted to perfectly match the chirp rate from sample thermal response to facilitate better visualization of defects. the merit of proposed method is compared with the conventional post processing methods like Fast Fourier Transform (FFT) phase, pulse compression (PC) and principle component analysis (PCA). By considering the parameter of merits such as signal to noise ratio of defective pixels and full width at half maxima for defect sizing. It is observed that the adapted Fan-Chirp transform provides better defect detection and better signal to noise ratios.

关键词： Transforms Chirp Time-frequency analysis signal to noise ratio Heating systems Frequency modulation Fans

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Implementation of a Deep Learning Technique for Detection of Objects on Roads for Autonomous Vehicles

Implementation of a Deep Learning Technique for Detection of...

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International conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Kakarla. Deepti S. Aruna Deepti Ch. Neetu K.R. Deepthi Dept of ECE Vasavi college of Engineering Hyderabad India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Autonomous driving and vehicle safety things profoundly depend on object detection to guarantee precise an robust right-time performance under different environmental conditions. the effectiveness of this systems depends on precise and efficient processing algorithms. the manuscript aim is to contrast the correctness of three notable image processing algorithms in the context of auto driving: Convolutional Neural Networks (CNN), Retina Net, and You Only Look Once v8 (YOLOv8). the assessment is performed using the Roboflow and kaggle dataset. the first algorithm under assessment is the Convolutional Neural Networks(CNN), Retina Net, and You Only Look Once v8 (YOLOv8). Each algorithm's unique approach to object detection is reviewed, observing their strengths and weaknesses in accurately identifying things in complex road situations. A comparative analysis is performed using the Robo flow and Kaggle dataset, covering various real-world contexts. the algorithms are evaluated based on correctness, considering occurrences with and without occlusion and truncation. Computation time,a vital aspect for real-time applications, is also considered. the precision-recall curve is applied as a metric to determine the algorithms' overall performance. Retina Net, with its focal loss mechanism, is reviewed for its capability to manage occluded and truncated things and its influence on overall correctness. YOLOv8 emerges as a standout performer, displaying superior correctness in discovering challenging road target things under complex situations and adverse weather conditions. Its right-time deployment capabilities, combined with high correctness, position it as a compelling choice for autonomous driving systems. In conclusion, this comparative analysis shines a light on the strengths and deficiencies of notable image processing algorithms for object discovery in autonomous driving systems. YOLOv8 stands outas the most accurate and efficient algorithm, excelling in challenging real-

关键词： YOLO Accuracy Roads image processing Simulation Vehicle safety Retina Real-time systems Convolutional neural networks Autonomous vehicles

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Unlocking Insights: Generating Synthetic Data with GANs Across Diverse Datasets

Unlocking Insights: Generating Synthetic Data with GANs Acro...

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

作者： A Padmavathi Bongu Lahari Udatha thanuja Harshitha Dwaram Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Generative adversarial networks (GANs) are a cutting-edge approach to generative modelling in deep learning. GAN's was proposed in 2014 by Ian Goodfellow. Since then, there has been significant growth in adversarial networks. New break-throughs and innovative approaches in generative adversarial networks can radically elevate and increase the quality of synthetically generated images by extracting patterns from the original datasets. Among the major advancements of GAN, image synthesis is the most prominent and extensively studied appli-cation. the concept of adversarial training, where two neural networks compete against each other, has introduced a novel paradigm for learning complex patterns in the images. the paper emphasizes the vital role of GANs in strengthening and fine-tuning datasets, honing further research to create GANs capable of producing high quality synthetic samples with constrained practice of data.

关键词： Deep learning Training Ethics Neural networks Reinforcement learning signal processing Generative adversarial networks Mathematical models Standards Synthetic data

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Implementation and Evaluation of image Transmission Protocol in Wireless Multimedia Sensor Networks 2021

Implementation and Evaluation of Image Transmission Protocol...

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2021 4th International conference on Sensors, signal and image processing

作者： VanKhoe Ta Hoon Lawrence Oh Department of Electrical Electronic and Computer Engineering University of Ulsan South Korea

ISBN: (纸本)9781450385725

In wireless multimedia sensor networks (WMSNs), a multimedia sensor node captures an image and sends it to a server via multiple wireless hops. In this process, an image transmission protocol has some stringent requirements such as reliability in data transmission, energy consumption of nodes, and end-to-end delay. this paper implements an image transmission protocol called the pipelined cooperative transmission (PCT) protocol on WMSNs and evaluates its suitability in the viewpoint of the requirements. According to experiments, the protocol could deliver an image of 34 Kbytes within less than 0.4 seconds without any loss of packet and also about 94% of totally generated images, even with a long-distance multi-hop topology and the high interference of WiFi signals.

关键词： energy end-to-end delay pipelined cooperative transmission implementation and evaluation Wireless multimedia sensor networks reliability

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Dangerous Behavior Detection Based on Convolutional Neural Network Algorithm

Dangerous Behavior Detection Based on Convolutional Neural N...

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signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Hua Zhang College of Information Science and Technology Bohai University Jinzhou China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Behavior detection is an important research direction in the field of deep learning. CNN has achieved great success in the field of image classification, which promotes the research and development in the field of behavior detection. this paper studies dangerous behavior detection based on CNN algorithm. First of all, the problems of traditional risky behavior detection algorithms are studied, as well as the research progress and development trend of risky behavior detection based on CNN. then, we study the basis of CNN algorithm, construct CNN network model, and study the working process of convolutional layer, pooling layer and fully connected layer. then, we study CNN algorithm optimization, including Softmax regression optimization and back propagation algorithm optimization. Finally, Kth data set was selected for simulation experiment, and parameters such as learning rate, convolution kernel and Batch Size were scheduled. On this basis, comprehensive test of risky behavior was carried out. the results show that the CNN algorithm optimized in this paper can improve the accuracy of behavior recognition, which is of great significance in laboratory safety and other fields.

关键词： Training Solid modeling Accuracy Video sequences signal processing algorithms Safety management Feature extraction Convolutional neural networks Detection algorithms Optimization

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