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检索条件"主题词=detection algorithms"

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Real-Time Baby Crying detection in the Noisy Everyday Environment

Real-Time Baby Crying Detection in the Noisy Everyday Enviro...

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IEEE Control and System Graduate Research Colloquium (ICSGRC)

作者： Lee Sze Foo Wun-She Yap Yan Chai Hum Zulaikha Kadim Hock Woon Hon Yee Kai Tee Universiti Tunku Abdul Rahman MIMOS Berhad

ISBN: (数字)9781728153131

ISBN: (纸本)9781728153148

Baby crying detection is an important component in child monitoring, diagnostics, as well as emotion detection systems. This study proposed a real-time baby crying detection algorithm that monitors the noisy environment for baby crying on a second-by-second basis. The algorithm detected baby crying through five acoustic features - average frequency, pitch frequency, short-time energy (STE) acceleration, zero-crossing rate (ZCR), and Mel-Frequency cepstral coefficients (MFCCs). The thresholds for each feature in classifying an audio segment as "crying" were set by extracting and examining the distribution of the features of noise-free crying and non-crying samples collected from an audio database freely available on the Internet. Later, the algorithm was tested using noisy crying and non-crying samples downloaded from YouTube, where an accuracy of 89.20% was obtained for the offline testing. In order to test the robustness and performance of the designed algorithm, online testings were also conducted using three customly composed noisy samples containing both crying and non-crying segments. The online accuracy obtained was 80.77%, lower compared to the offline testing which was mainly caused by the extra noise introduced by the experimental settings. With more advanced equipment, it should be possible to increase the online testing to be closer to the offline testing accuracy, paving the way to use the designed algorithm for reliable real-time second-by-second baby crying detection.

关键词： Pediatrics Feature extraction Noise measurement Real-time systems Classification algorithms Testing detection algorithms

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Multi-radar Signal Level Fusion detection Algorithm based on Fast Time Accumulation

Multi-radar Signal Level Fusion Detection Algorithm based on...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Fang Xuan Zhou Lin Nanjing Research Institute of Electronics Technology Nanjing China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

Aiming at the problem of multi-radar joint detection, the performance of non-coherent accumulation detection algorithm under fast time sampling is analyzed. In order to improve the detection performance, two fast time phase-coherent accumulation detection algorithms are designed based on Generalized Likelihood Ratio Test (GLRT): 1. Fusion detection algorithm based on rapid phase-coherent accumulation in a single station;2. detection algorithm based on overall signal fusion. From the perspective of theory and simulation, the performance of the two improved methods for detecting low-speed or stationary targets is better than that of the non-coherent accumulation detection method. Method 1 is suitable for distributed networking, while method 2 is suitable for centralized networking. The improved algorithm can extract target Doppler features while fusion detection, and provide information for target recognition and motion analysis.

关键词： Receivers Signal to noise ratio Radar Detectors Radar detection detection algorithms Time-frequency analysis

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Real Time Dynamic Threshold detection Algorithm Based on Local Weight and Application in Bridge Field

Real Time Dynamic Threshold Detection Algorithm Based on Loc...

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International Conference on Computer and Communications (ICCC)

作者： Hu Min Bai Xue Hu Xingchen SILC Business School Shanghai University SHU-SUCG Research Centre for Building Industrialization Shanghai University Shanghai China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Discovering bridge structural anomalies accurately and eliminating hidden risks timely are of great significance to ensure bridge safety. Therefore, this paper proposed the Real Time Dynamic Threshold detection Algorithm based on Improved Distance (RDTD). First, the Extended Frobenius Norm based on Local Weight (EFN_lw) is used to calculate the distance value of the multidimensional time subsequences; then, the Threshold Mechanism based on First Order Difference (TMFD) is designed to update the Normal Deviation Range (NDR) in time, and the anomaly judgment mechanism is to find outliers. The algorithm has the advantages of simple principle and small training sample size, at the same time, it can well take into account the requirements of real-time and accuracy. In order to verify the performance of the algorithm, two bridge engineering data sets in Shanghai are selected for test in this paper. The experimental results show that the algorithm can effectively find anomalies in a very short time while it has a certain promotion value and application value.

关键词： Bridges Training Sensitivity Heuristic algorithms Real-time systems Safety detection algorithms

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Optimization of False-Overlap detection of Tile Assembly in Tile-based Rendering

Optimization of False-Overlap Detection of Tile Assembly in ...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Bowen Yang Meng Fan Mengqiao Han Yurong Geng Northwestern Polytechnical University Xi’an China Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

With the rapid development of mobile devices, terminal devices put forward more requirements on GPU's real-time performance and power consumption. In this paper, tile-based architecture is adopted to improve the utilization of Mobile GPU resources on Mobile Graphics Processor (MGP).This architecture improves the rasterization execution rate through tile-binning generated tile list. However, before the execution of tile-binning, the overlap test will accumulate a large amount of computation when calculating the overlap relationship between primitives and triangles, even if a reasonable detection algorithm is adopted. In this paper, we propose a new method for segmentation of primitives, design an overlap detection algorithm with this method, and analyze the feasibility. Finally, the algorithm is mapped to the tile assembly platform using sort display algorithm, which aims to provide a concise tile list structure and an efficient overlap detection algorithm for mobile graphics processors. By using this algorithm, we can reduce memory overhead by 15% to 35%, CPU latency by 24% to 55%, and computing resources by 32% to 45%.

关键词： Rendering (computer graphics) Mathematical model Graphics detection algorithms Approximation algorithms Transforms Task analysis

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A Crowd-Based Explosive detection System with Two-Level Feedback Sensor Calibration

A Crowd-Based Explosive Detection System with Two-Level Feed...

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IEEE International Conference on Computer-Aided Design

作者： Chengmo Yang Patrick Cronin Agamyrat Agambayev Sule Ozev A. Enis Cetin Alex Orailoglu University of Delaware Arizona State University University of Illinois at Chicago University of California San Diego

ISBN: (数字)9781665423243

Large, open, public events, such as marathons and festivals, have always presented a unique safety challenge. These sprawling events, which can take up entire city blocks or stretch for many miles, can draw tens to hundreds of thousands of spectators and in some cases have open admission. As it is impracticable to guarantee the subjection of every event-goer to a security screening, we propose a crowd-based explosive detection system that uses a multitude of low-cost ChemFET sensors which are distributed to attendees. As the sensors offer limited accuracy, we further propose a server-based decision-making framework that utilizes a two-level feedback loop between the sensors and the server and explores spatial and temporal locality of the collected data to overcome the inherent low-accuracy of individual sensors. We thoroughly explore two distinct detection schemes, stressing their performance under a myriad of conditions, thus showing that such a crowd-based detection system comprised of low-cost and low-accuracy sensors can deliver high detection accuracy with minimal false positives.

关键词： Explosives Sensitivity Servers Light emitting diodes Standards Peer-to-peer computing detection algorithms

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Ship detection on Single-Band Grayscale Imagery Using Deep Learning and AIS Signal Matching Using Non-Rigid Transformations

Ship Detection on Single-Band Grayscale Imagery Using Deep L...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Patrick Talon Juan Ignacio Bravo Pérez-Villar Anneley Hadland Nina Sofia Wyniawskyj David Petit Mark Wilson Deimos Space UK Ltd. ESRI UK BMT SCD

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

The potential of hazardous situations between mariners in conventional manned craft in proximity to other vessels and operating ASVs requires new solutions to improve utilisation of shared waterspace. This paper explores the possibility of combining AIS data and optical satellite imagery to provide exhaustive and near-real-time information to ASVs about surrounding ships.

关键词： Artificial intelligence Marine vehicles Satellites Optical imaging Training Image segmentation detection algorithms

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Pedestrian detection based on attention mechanism and feature enhancement with SSD

Pedestrian detection based on attention mechanism and featur...

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Communication, Image and Signal Processing (CCISP), International Conference on

作者： T T Feng H Y Ge School of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9781728185897

ISBN: (纸本)9781728185903

Some factors such as low resolution of small targets, limited target features, and noise interference affect the effect of pedestrian detection. Therefore, a pedestrian detection algorithm based on attention mechanism and feature enhancement with SSD is presented in this paper. It uses channel feature fusion to fuse non-adjacent convolutional layers to obtain significant edge gradient features and semantic information ***, through the optimization of the attention mechanism CBAM, the channel features and spatial features are coupled under different fusion detection layers to improve the feature weight of the pedestrian's salient region, so as to the detection accuracy of the algorithm has taken a big step forward. The improved algorithm is verified on the fusion dataset and the VOC2007TEST dataset, compared with the SSD algorithm, the detection accuracy of the improved algorithm model on the fusion dataset reaches 60.1%, with an increase of 7.2%, and that on the VOC2007TEST dataset reaches 78%, with an increase of 4.1%. The experimental results show that this method can effectively detect the small target pedestrian in the image, and reduce the error detection and omission detection, thus verifying its feasibility.

关键词： Feature extraction detection algorithms Shape Semantics Object detection Deep learning Visualization

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The loop closure detection Algorithm Based on Bag of Semantic Word For Robot Navigation

The loop closure Detection Algorithm Based on Bag of Semanti...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Pengcheng Yu Xiaogang Ruan Xiaoqing Zhu Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

Simultaneous localization and mapping (SLAM) is mainly used to solve the problem of mobile robot navigation in an unknown environment. The loop closure detection is a key step in visual SLAM and plays an important role in building consistent maps and reducing pose cumulative errors. A new loop closure detection method based on the bag of semantic word (BOSW) is proposed in this paper, extracting semantic descriptors by using a neural network pre-trained on the scene recognition data set. Traditionally, the visual bag of word (BoW) model adopts the features such as ORB and SURF which is easily affected by the environment. With advantages of better ability to deal with changes in lighting, the convolutional neural network can extract the abstract features of the image. For these reasons, we proposed a new algorithm that combines the bag of word model and the convolutional neural network. The feature maps, extracted by the VGG16 neural network which is trained on the dataset named Places365 (VGG16-Places365), are used as the semantic descriptors instead of ORB descriptors. The workflow of building the vocabulary of the semantic words, extracting the feature vector of the image and computing the similarity score is presented. The performance of BOSW is evaluated by a comparison study with the traditional visual bag of word model and Inception-based deep learning method which use Inception's fully connected layer output as the feature vector of the picture. Overall, the experiments on New College datasets verified that our mode show stronger generalization and can achieve higher accuracy in the loop closure detection.

关键词： Feature extraction Semantics Visualization detection algorithms Simultaneous localization and mapping Clustering algorithms Vocabulary

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detection of Errors in Three-Phase Line Models Using Synchronized Phasor Measurements

Detection of Errors in Three-Phase Line Models Using Synchro...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Ramtin Khalili Ali Abur Northeastern University Boston MA USA

ISBN: (数字)9781728171005

ISBN: (纸本)9781728171012

Recent proliferation of distributed energy sources in distribution or sub-transmission systems necessitates close monitoring of these three-phase power grids which typically operate under unbalanced loading conditions. Unlike the transmission systems where the network equations are commonly based on the positive sequence component models, a detailed three phase model will have to be used in implementing network applications for these systems. In the specific case of the state estimator, where measurement and parameter errors may bias the solution, bad data and parameter error detection algorithms should also be incorporated. Implementing the state estimator and error detection algorithms for three-phase systems impose additional computational burden and modifications to the state estimation code. This paper proposes a practical solution to avoid these issues by using synchronized phasor measurements and modal decoupling. The previously developed parameter error detection algorithm based on the normalized Lagrange multipliers (NLM) test is applied to the measurements independently in each mode in parallel, not only saving CPU time but also avoiding new code development for a three-phase estimator. Different parameter error scenarios are created and tested to verify the effectiveness of the proposed error detection approach.

关键词： Power measurement Measurement uncertainty Transmission line measurements Mathematical model Synchronization detection algorithms State estimation

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Comparison Between Block-Wise detection and A Modular Selective Approach

Comparison Between Block-Wise Detection and A Modular Select...

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International Conference on Awareness Science and Technology (iCAST)

作者： Huitao Wang Kai Su Intisar MD Chowdhunry Qiangfu Zhao Yoichi Tomioka The University of Aizu System Intelligence Laboratory Aizu-Wakawatsu Japan

ISBN: (数字)9781728191195

ISBN: (纸本)9781728191201

On-road risk detection and alert system is a crucial and important task in our day to day life. Deep Learning approaches have got much attention in solving this noble task. In this paper, we have performed a comparative study on two recent architectures that handle the on-road risk detection task, which are Block-Wise detection and Modular Selective Network (MS-Net). In the Block-Wise detection, we have used the VGG19, VGG19-BN, and ResNet family as the backbone network. On the other hand, for MS-Net we have used the ResNet-44 as the router and ResNet-101 as the expert network. In this experiment, we evaluate our model on an “on-road risk detection dataset”, which was created by our research group using an RGB-D sensor mounted on a senior car. On this dataset, we can achieve an accuracy of 89.40% for MS-Net. For the Block-Wise detection model, we can achieve an accuracy of 90.51% if we use ResNet-50 as the backbone network. However, if we choose the network models used in MS-Net, we can double the inference speed. Thus, compared with Block-Wise detection, we think the overall performance of MS-NET is better, and is potentially more useful for driving assistance of elderly drivers.

关键词： Object detection Senior citizens Task analysis Proposals Feature extraction detection algorithms Deep learning

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