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Research on Unmanned Aircraft Environment Perception Methods in Urban Low Altitude Complex Backgrounds 23

Research on Unmanned Aircraft Environment Perception Methods...

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Proceedings of the 4th International conference on Artificial Intelligence and Computer Engineering

作者： Jinyi Qu Wenping Gao Haoyu Wang Daquan Huang Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China China

ISBN: (纸本)9798400708831

With the continuous development of drone-related technologies, the application of UAV (Unmanned Aerial Vehicle) in urban low-altitude scenes is gradually increasing. Autonomous localization and perception of the surrounding environment are prerequisites for autonomous control. As a result, our team apply the VDO-SLAM algorithm to the scene of coexistence of dynamic and static obstacles in the complex background of urban low altitude. Effective segmentation of static background and moving objects is achieved by combining semantic segmentation and optical flow estimation algorithms. Since there is a lack of relevant public data for urban low-altitude scenarios, a binocular image dataset containing both static and dynamic objects is created by Airsim simulator, which is utilized for training a semantic segmentation model. In addition, a novel numbering maintenance algorithm is introduced for the semantic segmentation of continuous image sequences. The experimental results show that the algorithm can realize the autonomous perception of UAV posture and the motion state of dynamic objects in low altitude complex urban environment. This method provides the necessary self-pose information and reliable environmental data for autonomous obstacle avoidance of UAVs, and provides important support for the application of UAVs in urban low-altitude environment.

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ECG signal Analysis with QRS Complex Detection for Accurate Cardiac Abnormality Diagnosis

ECG Signal Analysis with QRS Complex Detection for Accurate ...

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International conference on ICT in Business Industry & Government (ICTBIG)

作者： Juhi Jain Chandan Kumar Roy Rishab Pastariya Avita Fuskele V. Kishore Kumar Harshit Dubey Dept of CSE-DS Gyan Ganga Institute of Technology & Sciences Jabalpur Dept. of ECE CMR College of Engineering and Technology Hyderabad Telangana India Department of Computer Science & Engineering IES College of Technology Bhopal Madhya Pradesh India Information Technology Department Jabalpur Engineering College Jabalpur Department of ECE Chennai Institute of Technology Chennai GGITS Jabalpur MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

To improve the ECG signal interpretation helping to diagnose the indicated cardiac pathologies, the research concentrates on the formation and analysis of the QRS complex detection methods only. The QRS complex is widely used for diagnosing most cardiac disorders such as arrhythmias and myocardial infarction among others. Proposing a series of QRS complex classifications that enhance the existing solutions in terms of sensitivity and accuracy, the suggested approaches are built using advanced signal processing methodologies combined with logical focalization governed by the fuzzy formalism. This proposed approach showed better results than the benchmark fuzzy rule-based classifiers: classifications based on classical ICA and PCA algorithms in favor of accuracy, sensitivity, specificity, and the F1 score. Essential strengths of this method were higher accuracy due to the improved detection algorithm highlighted, paving way for real-time ECG to monitor and diagnose cardiac disorders. This research has also pointed out the strategies for the advancement of other non-invasive replacements for cardiac monitoring by utilizing wearable or portable ECG devices and constructed a technique for the efficient and accurate QRS complex differentiation in ECG. The future work also includes the fine tuning of the proposed model, expanding the applicability of the model in the hospitals and improving the diagnostic accuracy by implementing more complex machine learning techniques.

关键词： Time-frequency analysis Accuracy Sensitivity Embedded systems Fast Fourier transforms Noise Electrocardiography Real-time systems Hardware Monitoring

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Performance evaluation of the UHF class 0 RFID tag for the proposed anti-collision algorithm

Performance evaluation of the UHF class 0 RFID tag for the p...

引用

International conference on Computer, Communications, and Control Technology (I4CT)

作者： Jahariah Sampe Dilla Duryha Berhanuddin MD Shabiul Islam Mohd Ambri Mohammed A.R. md Zain Universiti Kebangsaan Malaysia (UKM) Institute of Microengineering and Nanoelectronics (IMEN) Selangor Malaysia Universiti Kebangsaan Malaysia Bangi Selangor MY

The Radio Frequency Identification (RFID) system is an automatic identification technology of choice over other existing technologies. Nowadays, the ability to identify many objects simultaneously is crucial for more advanced applications such as to identify objects in the warehouse and at the supermarket. These applications require an efficient identification technique which can identify many objects at one time without long delay. Meanwhile, one of the main issues during the identification process is the tags collision which occurs when all these tags are simultaneously responding to the reader commands. The RFID applications also require the tag to be simple, small, cheap and memoryless. Therefore the research is to evaluate the the performance of the proposed tag architecture for the Fast Detection Anti-collision Algorithm (FDACA) using the Total Tag Replies parameter. The Total Tag Replies parameter for the Binary Tree and the proposed FDACA tags are explained. The tag architecture for both techniques are described in Verilog Hardware Description Language (HDL) and simulated using Xilinix software. Then both tag architectures are synthesized using Application Specific Integrated Circuit (ASIC) technology (0.18 μm Library, Synopsys Compiler and tools). The tags performances are evaluated based on the obtained synthesis parameters.

关键词： Radiofrequency identification Power demand Binary trees Uplink signal processing algorithms Bandwidth

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Heavy-Ball: A New Approach to Tame Delay and Convergence in Wireless Network Optimization

Heavy-Ball: A New Approach to Tame Delay and Convergence in ...

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Annual IEEE International conference on Computer Communications

作者： Jia Liu Atilla Eryilmaz Ness B. Shroff Elizabeth S. Bentley Department of Electrical and Computer Engineering The Ohio State University Air Force Research Laboratory Information Directorate

ISBN: (纸本)9781467399548

The last decade has seen significant advances in optimization-based resource allocation and control approaches for wireless networks. However, the existing work suffer from poor performance in one or more of the metrics of optimality, delay, and convergence speed. To overcome these limitations, in this paper, we introduce a largely overlooked but highly effective heavy-ball optimization method. Based on this heavy-ball technique, we develop a cross-layer optimization framework that offers utility-optimality, fast-convergence, and significant delay reduction. Our contributions are three-fold: i) we propose a heavy-ball joint congestion control and routing/scheduling framework for both single-hop and multi-hop wireless networks;ii) we show that the proposed heavy-ball method offers an elegant three-way trade-off in utility, delay, and convergence, which is achieved under a near index-type simple policy;and more importantly, iii) our work opens the door to an unexplored network control and optimization paradigm that leverages advanced optimization techniques based on "memory/momentum" information.

关键词： Delays Optimization Convergence Wireless networks Algorithm design and analysis Queueing analysis signal processing algorithms

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Multi-Group Multicast Beamforming in LEO Satellite Communications

Multi-Group Multicast Beamforming in LEO Satellite Communica...

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GLOBECOM 2022 - 2022 IEEE Global Communications conference

作者： Dapeng Wu Chen Qin Yaping Cui Peng He Ruyan Wang School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing China

ISBN: (纸本)9781665435413

This paper investigates user grouping and beam-forming design in multi-beam low earth orbit (LEO) satellite communication (SATCOM) systems. To serve a great many user terminals (UTs) with a limited number of beams and improve the system performance, we formulate the weighted sum rate (WSR) maximization problem subject to the constraints of the UTs grouping, the satellite total power, and the minimum rate requirements of UTs. For solving this problem, we propose a multi-group multi-beamforming (MGMBF) scheme. In this scheme, all UTs are firstly adaptively grouped based on the channel correlation coefficients. Further, the beam centers are determined to ensure that all UTs are covered. After UTs grouping, slack variables are introduced to convert the beamforming design into a difference-of-convex (DC) programming problem. Moreover, an iterative algorithm is presented to solve the problem based on the convex-concave procedure (CCP), in which the beamforming vectors and slack variables are updated jointly by solving the convex sub-problem. Simulation results demonstrate that the MGMBF scheme improves the WSR by 25.1% compared with the MBIM algorithm, verifying the significant advantages of the proposed scheme.

关键词： Correlation coefficient Satellites Multicast algorithms Array signal processing Simulation System performance Low earth orbit satellites

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Anti-blockage Beam Training for Massive MIMO Millimeter Wave Systems

Anti-blockage Beam Training for Massive MIMO Millimeter Wave...

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IEEE Vehicular Technology conference

作者： Zhaoqiang Li Danpu Liu Xiaoyong Wu Feng Yu Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing P.R. China Huawei Technologies Co. Ltd Beijing P.R. China

Due to the short wavelength of millimeter wave (mmWave) and high directional beamforming, the 60GHz massive MIMO systems are highly vulnerable to link blockage. Beam switching to unblocked direction is an effective solution to overcome blockage. To this end, a set of backup beam pairs for beam switching must be identified at initial beam training. In this work, a low complexity beam training scheme with support for backup beam identification is proposed. Considering sparsity and clustered characteristics of mmWave channels and high probability for adjacent beams to be simultaneously blocked, we propose a new criterion for the selection of backup beams based on peak beam grouping, and design two beam grouping algorithms. The detailed procedure for beam switching when a blockage occurs is also given. Simulation results show that the proposed beam training scheme achieves near-optimal performance at initial beam training stage. Furthermore, the new method for identifying backup beam pairs is more effective to improve the spectral efficiencies of systems under blockage environments.

关键词： Training Structural beams Array signal processing Switches Radio frequency MIMO communication Antenna arrays

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Design space exploration of HSDPA subsystem algorithms and architectures

Design space exploration of HSDPA subsystem algorithms and a...

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IEEE conference on Vehicular Technology (VTC)

作者： Yiqun Ge A. Wellig J. Zory Geneva Laboratory Advanced System Technology Switzerland

A new transport channel type was introduced in 3GPP to support high-speed data packet access (HSDPA). It makes efficient use of valuable radio frequency resources. More flexibility and resource sharing leads to more control. A separate shared control channel (HS-SCCH) is defined to carry convolutionally encoded control data over the air interface to allow for fast adaptation to system dynamics. Thus, low-latency HS-SCCH subsystems are key building blocks impacting major design choices of the underlying HSDPA architecture. We systematically analyze different algorithm and architecture candidates at various levels of abstractions. At the functional level, formalizing the complexity, i.e. operations, memory size and access rate, we show how algorithm and architecture transformations of the classical max-log-MAP and Viterbi algorithm are traded-off with communication performance. At the architecture level, DSP implementation strategies are discussed. Cycle estimates are presented for two STMicroelectronics DSP platforms - the ST122 and a small, application-specific DSP. Finally, we propose a low-latency, energy-efficient software implementation of HS-SCCH decoding referred to as short-sequence Viterbi-like approach.

关键词： Algorithm design and analysis Space exploration Multiaccess communication Digital signal processing Control systems Computer architecture Radio frequency Resource management Viterbi algorithm Energy efficiency

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Building a research data science platform from industrial machines

Building a research data science platform from industrial ma...

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IEEE International conference on Big Data

作者： Fang Cherry Liu Fu Shen Duen Horng Chau Neil Bright Mehmet Belgin Partnership for An Advanced Computing Environment (PACE) Georgia Institute of Technology Atlanta Georgia School of Computational Science and Engineering Georgia Institute of Technology Atlanta Georgia

ISBN: (纸本)9781467390064

Data Science research has a long history in academia which spans from large-scale data management, to data mining and data analysis using technologies from database management systems (DBMS's). While traditional HPC offers tools on leveraging existing technologies with data processing needs, the large volume of data and the speed of data generation pose significant challenges. Using the Hadoop platform and tools built on top of it drew immense interest from academia after it gained success in industry. Georgia Institute of Technology received a donation of 200 compute nodes from Yahoo. Turning these industrial machines into a research Data Science Platform (DSP) poses unique challenges, such as: nontrivial hardware design decisions, configuration tool choices, node integration into existing HPC infrastructure, partitioning resource to meet different application needs, software stack choices, etc. We have 40 nodes up and running, 24 running as a Hadoop and Spark cluster, 12 running as a HBase and OpenTSDB cluster, the others running as service nodes. We successfully tested it against Spark Machine Learning algorithms using a 88GB image dataset, Spark DataFrame and GraphFrame with a Wikipedia dataset, and Hadoop MapReduce wordcount on a 300GB dataset. The OpenTSDB cluster is for real-time time series data ingestion and storage for sensor data. We are working on bringing up more nodes. We share our first-hand experience gained in our journey, which we believe will benefit and inspire other academic institutions.

关键词： Hardware Sparks Digital signal processing Ecosystems Distributed databases Cloud computing

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A new variable step size of NLMS algorithm for acoustic feedback cancellation in hearing aids

A new variable step size of NLMS algorithm for acoustic feed...

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2014 International conference on advanced Control, Automation and Robotics (ACAR2014)

作者： X.Wang J.K.Zuo Q.Y.Wang R.Y.Liang G.C.Tang C.R.Zou Key Laboratory of Underwater Acoustic signal Processing of Ministry of Education Southeast University School of communication Engineering Nanjing Institute of Technology Guangzhou University

ISBN: (纸本)9781784660468

This paper presents a new Variable Step Size of the Normalized Least Mean Square(VSS-NLMS) algorithm for acoustic feedback cancellation in hearing aids. The new variable step size is designed to automatically vary according to the filter update state. The filter update state can be detected adaptively via normalizing the difference between the long term and the short term average of the filter coefficients' energy. The performance of the proposed algorithm is compared with the fixed step size NLMS, VSS-NLMS based on recursive error energy and VSS-NLMS based on estimated variances. Simulation results illustrate that the proposed algorithm has better performance than other algorithms in terms spectral distortion, PESQ and average misalignment.

关键词： hearing aids acoustic feedback cancellation variable step size long term average short term average

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Improved Cassava Plant Disease Classification with Leaf Detection

Improved Cassava Plant Disease Classification with Leaf Dete...

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Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Ming Xuan Chai Yao Deng Fam Quinito Norman Octaviano Chih-Yang Pee Lai-Kuan Wong Mas Ira Syafila Mohd Hilmi Tan John See Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia School of Mathematical and Computer Science Heriot-Watt University (Malaysia) Putrajaya Malaysia

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Cassava (Manihot esculenta Crantz), a crucial food source for millions, has experienced significant crop yield losses due to various plant diseases. To address this issue, extensive research has been conducted on the Cassava 2020 dataset released by the Makerere AI Lab. Images in this dataset are captured in complex real-world environments, adding difficulty to the classification process. While several deep learning models have shown promising results, we believe that reducing interference from overlapping leaves and complex backgrounds could further enhance classification performance. To accomplish this, up to five leaves of interest have been identified and annotated in each image. These annotated leaves are then used to train a detection model: Faster R-CNN with ResNet-101, designed to automatically detect leaves of interest in the images. The identified leaves are separated from the background through a masking process, producing masked images (Mi), where i indicates the maximum number of leaves in the masked images, with i ranging from 1 to 5. The masked images M1 to M5 are then trained on various CNN models; i.e. EfficientNetB1, DenseNet121, ResNet50, and Xception, for Cassava disease classification. The results show that classifiers trained with M3 improve the accuracy by 2.13% to 3.06% compared to models trained with original images. The main contribution of this work is a novel technique capable of identifying leaves of interest from complex backgrounds to produce masked images, which improve the accuracy of various CNN-based classification models, and a novel leaf annotation where up to 5 leaves of interest have been annotated for each image in Cassava 2020 dataset. Annotated images can serve as a valuable resource for advanced cassava disease analysis, detection, and classification.

关键词： Training Plant diseases Accuracy Annotations Interference Information processing Feature extraction Detection algorithms Faces Residual neural networks

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