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QR-Code Traffic Signs Recognition for ADAS Smart Driving System 6

QR-Code Traffic Signs Recognition for ADAS Smart Driving Sys...

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6th ieee international conference on Advanced systems and Emergent Technologies, IC_ASET 2023

作者： Jouini, Anis Cherif, Adnane Hasnaoui, Salem University Tunis El-Manar Laboratory of Signal Processing and Electric Systems Atssee Faculty of Science of Tunis Tunis Tunisia National Engineering School of Tunis University Tunis El-Manar Sys'Com Research Laboratory Tunis Tunisia

ISBN: (纸本)9798350321029

Today's self-driving cars recognize the meaning of road signs by taking a photo with a camera placed on the vehicle and analysing these images, but this process is difficult for machines since it consumes necessary time and memory for image processing (shapes and colors of signs). Using QR codes for road sign recognition is a promising method to improve road safety and driver navigation. QR codes store information about traffic signs, such as their meaning and location, and can be easily captured using an in-vehicle camera. This technology can be integrated with Advanced Driver Assistance systems (ADAS) to provide additional information to drivers in real time. For example, QR codes can be used to indicate speed limits, lane restrictions, weather conditions, or road obstacles. Drivers can also use QR codes to get information about nearby services and attractions. In this article, we present an intelligent QR-code traffic sign recognition system, which can detect the QR-code placed on road traffic signs. The result of the QR code detection is also used as parameters for ADAS (Advance driving assistant system) where it offers the possibility of an automated cruise control by classifying the speed signs or by activating the active suspension. When riding on a backstroke transmitting information to the microcontroller. © 2023 ieee.

关键词： Advanced driver assistance systems

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Reliable ML-Based Image processing and Compression for an Onboard Imaging Pipeline 15

Reliable ML-Based Image Processing and Compression for an On...

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15th Space Computing conference

作者： Garrett, Tyler Wildenstein, Diego Wang, Eileen Gretok, Evan George, Alan Univ Pittsburgh NSF Ctr Space High Performance & Resilient Comp S Pittsburgh PA 15260 USA

ISBN: (纸本)9798350384543;9798350384536

Sensing and onboard-processing capabilities of next-generation spacecraft continue to evolve. Enabled by advances in avionic systems, large amounts of data can be collected and stored on orbit. Nevertheless, loss of signal, communication delays, and limited downlink rates remain a bottleneck for delivering data to ground stations or between satellites. This research investigates a multistage image-processing pipeline and demonstrates rapid collection, detection, and transmission of data using the Space Test Program - Houston 7 - Configurable and Autonomous Sensor processing Research (STP-H7-CASPR) experiment aboard the international Space Station as a case study. Machine-learning (ML) models are leveraged to perform intelligent processing and compression of data prior to downlink to maximize available bandwidth. Moreover, to ensure accuracy and preserve data integrity, a fault-tolerant ML framework is employed to increase pipeline reliability. This pipeline fuses the fault-tolerant Resilient TensorFlow framework with ML-based tile classification and the CNNJPEG compression algorithm. This research shows that the imaging pipeline is able to alleviate the impact of limited communication bandwidth by using reliable, autonomous data processing and compression techniques to achieve reduced transfer sizes of essential data. The results highlight the benefits provided by resilient classification and compression including minimized storage use and reduced downlink time. Furthermore, the findings of this research are used to assess the feasibility of such a system for future space missions. The combination of these approaches enables the system to achieve up to a 98.67% reduction in data size and downlink time as well as the capacity to capture imagery up to 75.19x longer time period for a given storage size, respectively, while maintaining reconstruction quality and data integrity.

关键词： Data Compression Fault-Tolerant Computing Resilient Computing Image processing Image Classification Machine Learning Deep Learning Onboard processing

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Active IRS-Assisted Wireless Powered MEC systems with Non-linear Energy Harvesting Model 15

Active IRS-Assisted Wireless Powered MEC Systems with Non-li...

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15th ieee international conference on Wireless Communications and signal processing, WCSP 2023

作者： Chen, Pengcheng Yang, Yuxuan Jiang, Jie Lyu, Bin Yang, Zhen Jamalipour, Abbas Nanjing University of Posts and Telecommunications Nanjing210003 China University of Sydney SydneyNSW2006 Australia

ISBN: (纸本)9798350324662

intelligent reflecting surface (IRS) is a promising solution to assist the wireless power transfer (WPT) and task offloading in wireless powered mobile edge computing (WP-MEC) systems. However, to alleviate the double-fading attenuation of IRS-involved links, passive IRS is conventionally equipped with a large number of reflecting elements (REs), resulting in large surface size. To solve this issue, we investigate an active IRS-assisted WP-MEC system, in which active IRS is deployed to amplify the incident signals instead of just adjusting phase shifts as passive IRS. The beamforming at multi-antenna hybrid access point (HAP) is utilized for further performance improvement. To capture the sensitive properties of energy harvesting (EH) circuits, a practically non-linear EH model is adopted. The tasks are assumed to be bit-wise independent, and thus the partial offloading policy is adopted to the wireless devices (WDs). Accordingly, we propose an efficient compute-while-offload mode, i.e., the offloaded tasks are executed as soon as they are received by the HAP, thereby making full use of the finite edge computing capability. We aim to maximize the sum computational rate (SCR) of all the WDs by jointly optimizing time allocation, beamforming designs of active IRS and HAP, as well as offloading strategies of WDs. An algorithm based on alternating optimization (AO) is proposed to tackle the non-convexity of this problem. Numerical results demonstrate the superiority of deploying active IRS over passive IRS. © 2023 ieee.

关键词： active intelligent reflecting surface (active IRS) beamforming designs non-linear energy harvesting model Wireless powered mobile edge computing (WPMEC)

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Modelling and tolerant control of the time-delay networked control system based on pole configuration 4

Modelling and tolerant control of the time-delay networked c...

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4th international conference on intelligent Control, Measurement and signal processing, ICMSP 2022

作者： Xu, Housheng Li, Jimeng Shenyang JianZhu University Department of Scienc Shenyang China

ISBN: (数字)9781665486583

ISBN: (纸本)9781665486583

For networkeded control systems with time delays, while considering the influence of external disturbances, a criterion is derived to ensure that the actuator is robust when a fault occurs, and a robust H_{infty} fault-tolerant controller design method is given. A fault diagnosis system is designed and fault-tolerant control is implemented using the closed-loop poles as fault information. The closed-loop system poles are used as input to an Extreme Learning Machine (ELM) fault diagnosis model to achieve fault-tolerant control of the different channels of the system actuator. Finally, a simulation example is used to verify the effectiveness of the described method. © 2022 ieee.

关键词： Closed loop systems

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Natural Language processing for Marketing 9

Natural Language Processing for Marketing

引用

9th international conference on signal processing and intelligent systems, ICSPIS 2023

作者： Dewi, Kania Evita Heryandi, Andri Widiastuti, Nelly Indriani Universitas Komputer Indonesia Informatic Engineering Department Bandung Indonesia

ISBN: (纸本)9798350369922

Promotion is one effort to increase consumers. The use of the Internet as a promotional medium has been widely used by companies because it can save costs in reaching consumers. Social media can be a platform for marketing, one of the social media that is widely used today is Twitter. However, promotions often fail if they are given to the wrong consumers. So it is necessary to identify consumers correctly when carrying out promotions. This research aims to identify users who are looking for a place to study, by analyzing messages sent via social media. In this research, the dataset from Twitter is processed first so that it is free from noise. The initial stages carried out in the research were deleting messages in the form of advertisements, filtering so that the data was clean of hashtags, links and symbols other than letter characters;tokenizing;stemming;remove stop words so that the data is clean of words that are felt to be unrelated to the topic. As a result of the initial stage, the dataset was then subjected to feature extraction using TF-IDF. The results of data set feature extraction are fed into machine learning to determine which users are looking for campuses and which users are just talking about campuses. The final result of this research is to get usernames from social media which will be promotional targets. The testing carried out in this research is more about testing the accuracy of machine learning to determine the right users who will be potential customers from campus. In this research, cross validation is used. The accuracy of Support Vector Machine (SVM) and Naive Bayes (NBC) reaches 96%. Even though in terms of accuracy it is very good, the recall and precision values are 0%. So the research objectives were not achieved. This happens because the dataset is unbalanced. Therefore, in future research it is necessary to consider the imbalance dataset problem. © 2023 ieee.

关键词： Feature extraction

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LEARNING TRAJECTORY-CONDITIONED RELATIONS TO PREDICT PEDESTRIAN CROSSING BEHAVIOR 29

LEARNING TRAJECTORY-CONDITIONED RELATIONS TO PREDICT PEDESTR...

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ieee international conference on Image processing (ICIP)

作者： Zhou, Chen AlRegib, Ghassan Parchami, Armin Singh, Kunjan Georgia Inst Technol Sch Elect & Comp Engn Ctr Signal & Informat Proc OLIVES Atlanta GA 30332 USA Ford Motor Co Dearborn MI USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In smart transportation, intelligent systems avoid potential collisions by predicting the intent of traffic agents, especially pedestrians. Pedestrian intent, defined as future action, e.g., start crossing, can be dependent on traffic surroundings. In this paper, we develop a framework to incorporate such dependency given observed pedestrian trajectory and scene frames. Our framework first encodes regional joint information between a pedestrian and surroundings over time into feature-map vectors. The global relation representations are then extracted from pairwise feature-map vectors to estimate intent with past trajectory condition. We evaluate our approach on two public datasets and compare against two state-of-the-art approaches. The experimental results demonstrate that our method helps to inform potential risks during crossing events with 0.04 improvement in F1-score on JAAD dataset and 0.01 improvement in recall on PIE dataset. Furthermore, we conduct ablation experiments to confirm the contribution of the relation extraction in our framework.

关键词： Intent Prediction Relation Learning Pedestrian Crossing

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A Pattern Recognition Framework for signal processing in Metaverse 8

A Pattern Recognition Framework for Signal Processing in Met...

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8th international Iranian conference on signal processing and intelligent systems, ICSPIS 2022

作者： Daneshfar, Fatemeh Jamshidi, Mohammad Behdad University of Kurdistan Department of Computer Engineering and Information Technology Sanandaj Iran Pilsen Czech Republic

ISBN: (纸本)9781665476232

Recognizing emotions in Metaverse and the real world at the same time is a significant issue that is less addressed today and is very attractive to many psychologists. In this paper, using a simple machine learning (ML) network called echo state network (ESN), speech emotion recognition (SER) has been done in both the Metaverse and the real world. Due to the recursive structure of the reservoir used in ESNs, they will have limitations for modeling high-dimensional data (because of high memory consumption). In this paper, a new structure is presented to empower ESNs for high-dimensional signal processing. To reduce the complexity caused by hyper-complex data, an octonion-based nonlinear ESN (ONESN) has been proposed. In addition, two different scenarios are designed and analyzed for how to demonstrate the functionality of the proposed networks. © 2022 ieee.

关键词： Machine learning

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ECG Heartbeat signal Classification and Detection of Cardiac Abnormalities using Deep Learning 1

ECG Heartbeat Signal Classification and Detection of Cardiac...

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1st international conference on Circuits, Power, and intelligent systems, CCPIS 2023

作者： Tiwary, Akash Kumar Rout, Prakash Kumar Tripathy, Dhananjaya Nayak, Debasish Silicon Institute of Technology Department of Electronics and Communication Engineering Bhubaneswar India Silicon Institute of Technology Department of Electronics and Instrumentation Engineering Bhubaneswar India

ISBN: (纸本)9798350315769

cardiovascular diseases (CVDs) are evolved as the general chronic diseases that create major threats to the health of human beings. The ECG machine can be used to track the phenomena of repetitive heart relaxations and contractions by placing the electrodes on the skin of the patient. Generally, ECG signals comprise of various types of waves, such as T wave, P wave, and QRS complex wave. The symptoms related to any cardiac disease can be traced from the morphological and statistical characteristics of these ECG waves. In recent years, deep learning (DL) has been used for medical diagnosis successfully and it helps to detect the abnormality in functioning of heart automatically with the help of ECG signals. In this paper we try to implement a combination of various deep learning techniques and pre-processing techniques for categorical classification of ECG signals and cardiac abnormalities. As many of the previous works focus on deep learning methods, we will try to incorporate a few pre- processing methodologies to the datasets extracted to get different perspective. © 2023 ieee.

关键词： Electrocardiograms

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Design of Cloud-Edge-End Collaborative Framework for Power Supply intelligent Inspection Operation Scenario Based on Unmanned Aerial Vehicle 8

Design of Cloud-Edge-End Collaborative Framework for Power S...

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8th ieee Information Technology and Mechatronics Engineering conference, ITOEC 2025

作者： Wang, Wei Qiu, Ping State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Zhejiang International Studies University Hangzhou China

ISBN: (纸本)9798331517915

As a new type of tool with powerful perception, monitoring, and emergency response capabilities, unmanned aerial vehicles (UAVs) have the characteristics of flexibility, maneuverability, and high-altitude inspection. UAVs can greatly improve the efficiency of power facility maintenance and repairing, reduce personnel risks, and have gradually become an important auxiliary means for power supply operation and management. Currently, research on cloud-edge collaborative computing using UAVs has been applied to power systems, especially in their daily maintenance. However, there is not much discussion on building a systematic UAV-based cloud-edge collaboration system, especially to meet the requirements of power supply companies. Aiming at the problems such as the limited distance of image transmission, the lack of timeliness and visualization of image data processing in the application process of UAVs, in this paper, a "cloud-edge-end"collaborative framework has been proposed to achieve fast data return over the distance under signal coverage, and use the characteristics of local edge computing to quickly solve image preprocessing. © 2025 ieee.

关键词： Drones

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Vocal Intelligence Training System for Music Performance Majors based on Multi-point signal Recognition and Fusion 6

Vocal Intelligence Training System for Music Performance Maj...

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6th international conference on intelligent Computing and Control systems, ICICCS 2022

作者： Zhao, Mei Mianyang Teachers' College Sichuan621000 China

ISBN: (纸本)9781665410359

Vocal intelligence training system for the music performance majors based on multi-point signal recognition and fusion is studied in the paper. In the process of signal acquisition, whether it is MIDI signal acquisition or audio signal acquisition, there is a certain delay. The delay of the recording equipment, the slow signal processing speed and the lag of the software feedback all bring a lot of core inconvenience to the software implementation process. Hence, to face this challenge, a novel multi-point signal recognition and fusion model has been designed. The recognition algorithm combines the core advantages of both supervised learning and unsupervised learning, and can use a small amount of labeled sample data and a large amount of unlabeled sample data for identification. Furthermore, the vocal information is selected to be tested. Through the comparison testing, the accuracy of the designed model is reflected to be 95.98% which is much higher than the traditional ones. © 2022 ieee.

关键词： Information fusion

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