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Empowering Women’s Safety with smart IoT Technology: A Robust Protection System

Empowering Women’s Safety with smart IoT Technology: A Robu...

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International conference on signal processing and Communication (ICSPC)

作者： N.V.K. Ramesh Akhila Alaparthi G Sai Charan Rishitha Settipalli Pranathi Velga B. Veena Vani Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Electrical and Electronics Engineering National Institute of Technology Karnataka India

Nowadays, women’s safety has emerged as one of the world’s most pressing issues, and crime against women has significantly increased nationwide. the frequency of auto theft and accidents has increased significantly as compared to the previous decade. the value of a human life exceeds all other values, and it is more crucial to seek assistance when you do so than to provide a helping hand. Every day, women from all walks of life struggle to maintain their safety and defend themselves. In the presence of insensitive guys who regularly harass, assault, and violate the dignity of women, and in front of their wandering eyes. Public transport, especially public places, has become hunter territory. For these atrocities that women undergo current scenario A clever wearable safety tool for girls primarily based totally at the Internet of things is being implemented. It consists of a Arduino, Camera and a buzzer, and a button to activate the service. this unit is very remote and with the click of a button, it is activated by the victim when attacked, retrieves their current location and captures images of the victim Attacker via Esp32 Cam Module. A predetermined contact number will receive both the taken image and the location link. It monitors through the victim’s smartphone and avoids the usage of additional hardware or modules, keeping the device compact. Live video streaming is via webcam and live Position is obtained by GPS module. this paper makes a collision avoidance and detection systems. there are numerous initiatives being taken with the goal of saving lives. the proposed study intends to develop an IoT-based security system that prioritises protecting women.

关键词： Social networking (online) Webcams Pressing Streaming media signal processing Regulation Safety

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Study of wavelet de-noising in time-domain airborne electromagnetic data processing

Study of wavelet de-noising in time-domain airborne electrom...

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the 4th International conference on Environmental and Engineering Geophysics

作者： Dongwei L(u|¨)~1,Xuben Wang~1,Bin Chen~2,Lifeng Mao~1,Kai Zheng~1 1.College of information engineering,Chengdu University of Technology,Chengdu 610059,China 2.China Aero geophysical Survey and Remote Sensing Center for Land and Resources,Beijing 100083,China

this paper chooses different level white noises to add into time-domain airborne electromagnetic data,generated by using the forward *** characters of the noise, then making de-noising by soft-thresholding to the data with noise,wavelet db3 is *** the characters of wavelet db3 are showed in *** of mallat algorithm to decompose and reconstitute for de-noising and abstracting signal.H and K type three-layer earth model curves with noises are *** compared the results after *** it is found that the de-noising method has obtained very good results,and quantitative analysis was used to provide theoretical basis for time-domain airborne electromagnetic data processing in some *** it is proved that the de-noising method by soft-thresholding is reasonable and *** the range of noise levels in time-domain airborne electromagnetic data which can be processed was *** the same time,data quality has got obvious advance.

关键词： Time-domain airborne electromagnetic wavelet transform de-noising

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Adaptive learning algorithms for semantic object extraction

Adaptive learning algorithms for semantic object extraction

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European signal processing conference (EUSIPCO)

作者： Nikolaos Doulamis Anastasios Doulamis Stefanos Kollias National Technical University of Athens Athens GR

In this paper a novel approach is proposed for semantic video object segmentation. In particular, it is assumed that several neural network structures have been stored in a system database or memory. Each network have been learned to be appropriate to a specific application. then, a retrieval mechanism is introduced which selects that network from the memory which better approximates the current environment. Since, however, the retrieved network does not exactly correspond to the current conditions a small adaptation of its weights will be necessary in most cases. For that reason, an efficient training algorithm has been adopted based on both the former and the current network knowledge. the former one corresponds to knowledge existing in the memory while the latter is provided by the training set selection module based on the user assistance and a color segmentation algorithm. Experimental results are presented illustrate the performance of the proposed method to real life applications.

关键词： Training Neural networks Semantics Object segmentation image color analysis MPEG 4 Standard Computers

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3H-ObjectDet: A Target Detection Method Based on Haar wavelet Transform Downsampling and Hybrid Attention Mechanism for Medical Fracture image

3H-ObjectDet: A Target Detection Method Based on Haar Wavele...

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Electronic Information Engineering and Computer Communication (EIECC), International conference on

作者： Junmin Xue Yunbo Rao Qinwei Yao Jiangang Wu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Beijing Youan Hospital Affiliated to Capital Medical University Beijing China Operation Data Center of Postal Savings Bank of China Beijing China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

In recent years, in order to reduce the incidence of misdiagnosis and missed diagnosis in fracture diagnosis and maximize the protection of patients' lives and health, deep learning has achieved rapid development in the field of fracture diagnosis. Target detection models can obtain the location and category of fractures in images simultaneously, and the detection results output are more intuitive, making it convenient for clinical doctors to use directly. However, the following difficulties have troubled clinical use: (1) inefficient extraction of image features, (2) difficulty in accurate detection classification, and (3) large calculation volume causing delayed detection output. this paper proposed a medical fracture image target detection method based on wavelet transform downsampling and mixed attention mechanism, and made relevant innovations: (1) introduced a mixed attention mechanism instead of traditional convolution operation in the feature extraction of the backbone network, (2) innovated the detection classification and localization module to effectively identify features, and (3) simultaneously enhanced feature extraction ability through wavelet transform downsampling and reduced computation. the innovative detection model achieved a 21.2% increase in accuracy and a 10.1% increase in mAP@0.5 in the rib fracture dataset, while reducing computation by 14.8%. In the upper limb fracture and the hand fracture dataset, the high performance indicators have also been significantly increased, showing the high efficiency and generalization of the innovative detection model.

关键词： wavelet transforms Technological innovation Attention mechanisms Accuracy Quantization (signal) Computational modeling Object detection Feature extraction Medical diagnostic imaging Ribs

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User interaction in content-based video coding and indexing

User interaction in content-based video coding and indexing

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European signal processing conference (EUSIPCO)

作者： Paulo Correia Fernando Pereira Institute Superior Técnico - Institute de Telecomunicações Av. Rovisco Pais 1096 Lisboa Codex Portugal

the current level of request for applications involving content-based video coding and video information retrieval is increasing the relevance of systems able to somehow `understand' the content of video sequences. this type of systems will enable the provision of services where enhanced user interaction with the visual content is supported. Such an object-based video coder is being specified by ISO under the MPEG-4 project [1], while the standardization of video description capabilities to support content-based video indexing and retrieval is being considered by MPEG-7 [2]. In this paper, we discuss the issue of video content identification and characterization, and the importance of providing the means for the user to interact with the analysis process so that the achieved results can have a meaningful and powerful semantic value.

关键词： Feature extraction Indexing image segmentation Transform coding Object recognition Encoding Real-time systems

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Cartoon-recognition using video & audio descriptors

Cartoon-recognition using video & audio descriptors

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European signal processing conference (EUSIPCO)

作者： Ronald Glasberg Amjad Samour Khalid Elazouzi thomas Sikora Communication Systems Group Technical University of Berlin Berlin Germany

We present a new approach for classifying mpeg-2 video sequences as `cartoon' or `non-cartoon' by analyzing specific video and audio features of consecutive frames in real-time. this is part of the well-known video-genre-classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7 [12]. In our method the extracted features from the visual descriptors are non-linearly combined using a multilayered perceptron and then considered together with the output of the audio-descriptor to produce a reliable recognition. the results demonstrate a high identification rate based on a large collection of 100 representative video sequences (20 cartoons and 4*20 non-cartoons) gathered from free digital TV-broadcasting.

关键词： Streaming media Feature extraction Visualization Video sequences Mel frequency cepstral coefficient Multimedia communication image color analysis

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Research on image Dimensionality Reduction Algorithms 2021

Research on Image Dimensionality Reduction Algorithms

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

作者： Zulun He Jingjun Zhang Shanghai Dehong International school China Ningbo Zhenhai Jiaochuan Academy China

ISBN: (纸本)9781450385725

this paper summarizes the principle of the Scale Invariant Feature (SIFT), Principal Component Analysis (PCA), and PCANet. Also, the paper uses IPython to realize the similarity comparison using SIFT and handwriting recognization using PCANet, then calculate the precision and recall of the result. the main thing of the SIFT is to find the key point descriptor through the Scale-space, Gaussian Pyramid, and Difference of Gaussian Pyramid (DoG), while the primary purpose of PCA is to find the eigenvalue and eigenvector through calculating the covariance. the combination of the PCA and neuro network, PCANet is divided into three stages.

关键词： PCA SIFT PCANet

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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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Estimation of the Surface Quality Of Galvanazed Steel: the Method Of Decomposing the image Into Layers 2021

Estimation of The Surface Quality Of Galvanazed Steel: The M...

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

作者： Anton Nikolaev Oxana Logunova Evgeny Garbar Mikhail Arkulis Palvan Kalandarov Nosov Magnitogorsk State Technical University Russia Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Uzbekistan

ISBN: (纸本)9781450385725

the aim of the study is to increase the reliability of information on the surface quality of galvanized coiled steel in the automated assessment system. the paper presents examples of images of a surface with defects that are visually close but have a different classification. the authors decompose the image into layers and begin to classify the surface defects of materials on the brightness histogram for each layer and image with defects. the research was carried out for the conditions of a large metallurgical enterprise of the Russian Federation. As a result of the study, it was proved that for the bar charts of brightness by image layers, the data used to identify the type of defect are presented.

关键词： image of defects hard sliver surface image rolling skin quality of rolled products quality assessment methods image layers

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SIW-based Network Antenna Design and Analysis

SIW-based Network Antenna Design and Analysis

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International conference on image and signal processing and their applications (ISPA)

作者： Ahcene Abed Aissa Amrouche Hind Kraimia Ahmed Bouchekhlal Redha Bendoumia Abdelghafour Herizi Laboratory of Electrical Engineering (LGE) University of M'sila M'sila Algeria Scientific and Technical Research Center Fir the Development of Arabic Language CRSTDLA Algiers Algeria Department of Electronics DIC Laboratory University of Blida 1 Algeria Telecommunications Department Higher School of Signals (HSS) Tipaza Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

this work is concerned with the design of an antenna array using SIW technology, where the basic element is a rectangular patch antenna. the main goal is to improve the radiation pattern. this technology allows this type of antenna to be produced at a lower cost and easily integrated into communications systems. the antenna was designed in the HFSS simulator on a FR-4 Epoxy substrate with a dielectric constant of ε r =4.4 and a thickness of 1.6mm. three structures have been proposed: the array antenna (AR2), the array antenna (AR4), and the SIW array antenna (SAR4). this paper proposes to insert metal vias between the four elements of the AR4 antenna to direct the antenna radiation towards the main lobe. the antenna resonates at two different frequencies: 6.1 GHz with a matching coefficient S 11 =17.77 dB and 6.85 GHz with a matching coefficient S 11 =16.63dB.

关键词： Wireless communication Dielectric constant Array signal processing Simulation Resonant frequency Dielectric resonator antennas Substrates

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