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3D object detection with low resolution depth map

3D object detection with low resolution depth map

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Huang, Wei-Ting Chen, Xiu-Zhi Chen, Yen-Lin Huang, Chieh-Sheng National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan Elan Microelectronics Corporation Artificial Intelligence Research and Development Department Hsinchu Taiwan

ISBN: (纸本)9798350324174

Obtaining high-resolution depth map from radar and lidar for training depth estimation network is a common approach in recent years, although the expensive price and high computational complexity cause it hard to be apply in real-world applications. In this research, we propose a novel concept that only requires low resolution depth map for training, but able to reach similar performance comparing to high-resolution depth map-based approaches. Refers to the experimental results, which evaluated on KITTI dataset, our proposed concept decreases 7% and 2% on the mAP of car and pedestrian comparing to the original approach in CaDDN, although it achieves similar performance on visualization result. © 2023 IEEE.

关键词： Object detection

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3D Vehicle Detection for Autonomous Buses

3D Vehicle Detection for Autonomous Buses

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chiu, Yi-Kai Chen, Xiu-Zhi Chen, Yen-Lin Huang, Chieh-Sheng National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan Elan Microelectronics Corporation Artificial Intelligence Research and Development Department Hsinchu Taiwan

ISBN: (纸本)9798350324174

The development of automatic driving technology and Advanced Driver Assistance Systems (ADAS) has become more and more mature, and has led to the development of 3D object detection technologies and discussions. Most of the researches are focus on the applications of small cars, but not common in large vehicles, such as buses and trucks, although such applications are more important for them. Therefore, this study mainly uses the bus data to verify the feasibility of the existed model, and by adjusting the loss function, the prediction accuracy of the class and bounding box can be improved. Based on our experimental results, we found that the model trained by KITTI dataset can predict acceptable result refers to the input images from the bus viewpoint. As a result, we can expect that the ADAS for large vehicles can be implemented based on the sample model structure, and its accuracy is able to be improved by fine-tuning based on more large vehicle viewpoint data. © 2023 IEEE.

关键词： Buses

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Compensation Method of Quantized Deep Learning Models for Edge Devices

Compensation Method of Quantized Deep Learning Models for Ed...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Xiu-Zhi Li, Jhen-Hao Chen, Yen-Lin Huang, Chieh-Sheng National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan Elan Microelectronics Corporation Artificial Intelligence Research and Development Department Hsinchu Taiwan

ISBN: (纸本)9798350324174

Quantization is one of the optimization methods for developing deep learning models for edge devices. Through converting the floating-point into 8bit integer or even lower bitwidth, the model's storage size can be reduced. As the rounding error exists during the quantization process, the model performance decreases. As a result, a method that can recover model performance is needed. In this research, a compensation method for improving the performance of quantized deep learning models is proposed, which make the quantized model can achieve equal or even better performance compared to the original floating-point model. © 2023 IEEE.

关键词： Deep learning

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Merlin, S. Sudha Malathy, K. Ravi, S. Arun, M. Raut, S.P. Fawareh, H. Dmi College of Engineering Department of Electrical and Electronics Engineering Palanchur Chennai India S.A. Engineering College Department of Mca Chennai India Department of Vlsi Microelectronics Chennai India Kings College of Engineering Dept of Computer Science and Engineering Punalkulam Thanjavur India Yeshwantrao Chavan College of Engineering Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The proliferation of consumer data can be attributed to 'the Internet of Things (IoT),' which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics. © 2024 IEEE.

关键词： CBFS Consumer Behavior Data Analysis ENCL Internet of Things IoT Marketing Strategy Model Evaluation

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Leveraging Temporal Patterns with LSTMs Networks for Financial Forecasting: A New Stastical Machine Learning Approach 2

Leveraging Temporal Patterns with LSTMs Networks for Financi...

引用

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Thinakaran, K. Soman, Sandhya Anitha, L. Lakineni, Prasanna Kumar Dilli Ganesh, V. Taqui, Syed Noeman Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Simats Department of Computer Science and Engineering Chennai India Gitam University Dept of Computer Science Karnataka Bengaluru India Saveetha Engineering College Department of Management Studies Chennai India Gitam School of Technology Gitam University Department of Computer Science and Engineering Visakhapatnam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science-SIMATS Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

The intricate temporal patterns observed in financial markets might have an impact on market outlook and trading decisions. The challenge of effectively predicting these markets remains difficult due to the limitations inherent in conventional linear forecasting methods. The aim of this study is to assess the efficacy of Long Short-Terms Memory (LSTMS) networks, which are advanced recurrent neural network designs, in accurately forecasting financial time-series data. This evaluation will entail a comparison of the performance between LSTMS networks and classical models. The present study employs data derived from the S&P 500 index spanning a twenty-year timeframe for the purpose of analysis. The information comprises not just the daily closing prices but also incorporates trading volume and a range of technical indicators, including moving averages, relative strength index (RSI), and momentum. The information provided is extensive and serves as the foundation for the development and evaluation of the forecasting models. A sequence-to-sequence Long Short-Terms Memory (LSTMS) model was devised with the explicit objective of capturing the inherent temporal dynamics inherent in financial data. The research employed a rolling window approach, wherein a predetermined sequence of daily data was employed to predict the closing price of the subsequent day. The LSTMS architecture is composed of input layers, multiple LSTMS units, and output layers, with the incorporation of dropout layers in between to mitigate the problem of overfitting. In order to develop a benchmark, a comparison was conducted between the LSTMS approach and traditional models such as ARIMA and a basic RNN structure. © 2023 IEEE.

关键词： Financial forecasting Forecasting models Long Short-Term Memory Neural networks Relative strength index Temporal patterns

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Energy Efficient Deep-Edge Computing through Hardware Machine Learning

Energy Efficient Deep-Edge Computing through Hardware Machin...

引用

IEEE World Forum on Internet of Things (WF-IoT)

作者： Harry Koutsourelakis George Kornaros Marcello Coppola Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Iraklio Greece ST Microelectronics Grenoble France

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

It is currently challenging to accomplish high data processing with low latency, because large-scale, persistent, high-bandwidth connection is often required to gather huge volumes of data from platforms and sensors operating at the edge to allow processing in the cloud. Internet-of-Things (IoT) devices deployment in remote locations for environmental monitoring with machine learning (ML) techniques, Internetof-Medical Things, IoT-based precision agriculture, all can operate with limited battery, while robustness and reliability is required. Machine learning algorithms employed in IoT devices improves efficiency as well as decision-making with real-time situational awareness and the capacity to react swiftly and accurately to impending actions or threats. This work presents a methodology to develop AI-based applications for Internet-ofThings environments by using the machine learning core inside the STM Mems Sensor LSM6DSOX and the toolchain Unico, Unicleo and WEKA. In particular, we demonstrate an efficient implementation that combines the target IoT platform's compilation toolchain and platform-specific resource limitations into the model selection process, to leverage embedded hardware machine learning for automated monitoring of the detection of a falling object.

关键词： Machine learning algorithms Microprocessors Machine learning Hardware Energy efficiency Robustness Real-time systems

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Multi-Task Deep Learning Model for Autonomous Driving: Object Detection, Semantic Segmentation, and depth Estimation

Multi-Task Deep Learning Model for Autonomous Driving: Objec...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Wang, Yih-Chen Lin, Jhe-Li Chen, Yen-Lin Huang, Chieh-Sheng Lai, Ming-Liang National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan Elan Microelectronics Corporation Artificial Intelligence Research and Development Department Hsinchu Taiwan National Taipei University of Technology Graduate Institute of Intellectual Property Taipei Taiwan

ISBN: (纸本)9798350324174

In the field of autonomous driving, many models based on deep learning methods have been constructed to solve computer vision tasks related to this domain, such as object detection, semantic segmentation, and depth estimation. Each model can provide different information about the surrounding environment of a vehicle to assist in driving. However, for applying in the real world, more detailed information about the surrounding environment is essentially required. In this study, we propose a model based on the concept of multi-task learning. This model is an encoder-decoder architecture mainly consisting of an encoder using the hard-parameter sharing technique and three decoders for individual tasks. Therefore, this model can handle object detection, semantic segmentation, and depth estimation at the same time. Our proposed multi-task model has been verified to perform well on the public dataset Cityscapes and has higher generalizability than other models. © 2023 IEEE.

关键词： Semantic Segmentation

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Optimum Design of 1-D Leaky-Wave Antennas based on Partially Reflecting Sheets

Optimum Design of 1-D Leaky-Wave Antennas based on Partially...

引用

2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Fuscaldo, Walter Galli, Alessandro Jackson, David R. Institute for Microelectronics and Microsystems National Research Council Rome00133 Italy Sapienza University of Rome Dept. Information Engineering Electronics and Telecommunications Rome00184 Italy University of Houston Dept. of Electrical and Computer Engineering HoustonTX77204-4005 United States

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

The properties of one-dimensional (1-D) unidirectional leaky-wave antennas (LWAs) based on partially reflecting sheets (PRS) are analyzed here. In particular, we derive conditions for maximizing the gain and provide analytical formulas to determine the pattern bandwidth. Design equations are finally obtained to optimize the gain of a practical PRS-based 1-D unidirectional LWA propagating a TE mode. © 2022 IEEE.

关键词： Traveling wave antennas

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Wideband Circularly Polarized FOWLP Antenna in Package for Satellite Communications

Wideband Circularly Polarized FOWLP Antenna in Package for S...

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Electronic Components and Technology Conference (ECTC)

作者： Kai Bo Zheng Mei Sun Yong Xin Guo IME attached student from Dept. of Electrical and Computer Engineering National University of Singapore (NUS) Singapore A*STAR (Agency for Science Technology and Research) Institute of Microelectronics (IME) Singapore Electrical and Computer Engineering Dept. National University of Singapore Singapore

This paper presents a wideband circularly-polarized Antenna-in-Package (AiP) element implemented in Fan-Out Wafer-Level-Packaging (FOWLP) technology for satellite communication. The AiP element features a meta-surface radiator with a single-ended aperture coupled feeding structure to achieve a wideband and circular polarization performance. By stacking a top mold of 500μm, a middle mold of 90μm and a 42μm RDL layer at the bottom, with a size of 10 mm×10 mm×0.652 mm, the AiP element achieves a Return Loss > 17.95 dB, an Axial Ratio < 2.67 dB, and a Gain of 7.19-7.88 dBi over 27–31 GHz. The antenna design concept is further confirmed using printed circuit board (PCB) technology for fast prototyping and evaluation.

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INCL: An Effective Deep Learning Model to Identify Road Potholes by Using Image Based Neural Classification Logic

INCL: An Effective Deep Learning Model to Identify Road Poth...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Shasikala, Y. Kantha, M.Rajitha Dinesh, M. Nagamani, K. Bonde, A. Afanehsuha AdityaUniversity Departmentof Computer Applications Surampalem India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh Prakasam District India Dept of Vlsi Microelectronics Chennai India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Prakasam India AndhraPradesh Prakasam District India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan Universiry of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

This paper presents a deep learning-based approach for detecting road potholes using image-based neural classification logic. The methodology involves collecting road images from publicly available datasets, particularly from Kaggle, and augmenting the dataset to improve model performance. Preprocessing steps such as resizing, standardizing, and annotating the images were employed to ensure consistency. A pre-trained Faster R-CNN model was fine-tuned for pothole detection, using convolutional neural networks (CNNs) to extract features and classify the road images into pothole and non-pothole categories. The model was trained using transfer learning and tested under varying road and environmental conditions, including day, night, dry, and wet scenarios. The model achieved an accuracy of 96.5% in dry daylight conditions, with a slightly lower performance of 85.4% during wet nighttime conditions. Additionally, precision and recall were evaluated, with the model achieving a precision of 95.3% and a recall of 97.6% for daytime conditions. The system was implemented in a real-time environment, allowing for real-time pothole detection and reporting through a mobile or web application. Future work will aim to improve performance under challenging conditions and enhance real-time deployment efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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