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real-time Dog Detection and Alert System using Tensorflow Lite embedded on Edge Device 1

Real-Time Dog Detection and Alert System using Tensorflow Li...

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1st ieee International Conference on Industrial Electronics: Developments and applications, ICIDeA 2022

作者： Swain, Satabdi Deepak, A. Pradhan, Ankit Kumar Urma, Sharad Kumar Jena, Swarna Prabha Chakravarty, Sujata Centurion University of Technology and Management Department of CSE Odisha India

ISBN: (数字)9781665421492

ISBN: (纸本)9781665421492

Wild and domestic animals constitute a massive part of the animal kingdom. There are approximately 8.7 million different species of animal available. Still, many species are being discovered. Cats and dogs are the most common animal in the society where people live and work. Human beings are also social animals who know society's living norms. However, animals like dogs and cats are not aware of the rules and norms to be followed in society. Hence it is necessary to keep track of the animals in the restricted areas to avoid sudden death, which happens accidentally. So, this paper proposed a real-time dog detection model with email notifications using embedded ML running on Raspberry Pi for the University campus. Machine learning models like SSD, YOLO v3, and YOLO v4 are used to detect the dog and send the notification to the concerned authority. It has been found that the YOLO v3 model outperforms all other models with the highest accuracy of 94%, whereas SSD and YOLO v4 Achieved an accuracy of 89% and 63%, respectively. © 2022 ieee.

关键词： Animals

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Mini-YOLOX: A Lightweight Network for real-time embedded applications

Mini-YOLOX: A Lightweight Network for Real-Time Embedded App...

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Midwest symposium on Circuits and Systems (MWSCAS)

作者： Ahmed N. El-Zeiny Adham Hassan Hassan Mostafa Ahmed H. Khalil Electronics and Communications Engineering Department Cairo University Zewail City of Science and Technology University of Science and technology Nanotechnology and Nanoelectronics Program Giza Egypt

In this paper, we present an efficient real-time object detector designed for embedded system applications. Although increased memory and power consumption pose significant challenges, our network adopts techniques to overcome them. Depthwise and pointwise separable convolutions are used across the backbone, neck, and head of the network to reduce the number of parameters. To compensate for accuracy loss, we employ several techniques including starting with a larger baseline, using depthwise separable convolution mixed with blueprint separable convolution in the backbone, replacing the SiLU activation function with the Mish activation function, and switching from Adam to SGD optimization algorithms. These techniques help to achieve a state-of-the-art object detection network called Mini-YOLOX, which optimizes YOLOX-S from the YOLOX series and reaches only ∽57% of its parameter size and ∽52% of its floating-point operations with better accuracy. Mini-YOLOX achieves impressive results with only 5.08M parameters and 13.95 GFLOPs, resulting in a 40.9 AP score in just 5.71 ms.

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Low-Cost Privilege Separation with Compile time Compartmentalization for embedded Systems

Low-Cost Privilege Separation with Compile Time Compartmenta...

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ieee symposium on Security and Privacy

作者： Arslan Khan Dongyan Xu Dave Jing Tian Purdue University

embedded systems are pervasive and find various applications all around us. These systems run on low-power microcontrollers with real-time constraints. Developers often sacrifice security to meet these constraints by running the entire software stack with the same privilege. Existing work has utilized compartmentalization to mitigate the situation but suffers from a high overhead due to extensive runtime checking to achieve isolation between different compartments in the system, resulting in a rare adoption. In this paper, we present Compartmentalized real-time C (CRT-C), a low-cost compile-time compartmentalization mechanism for embedded systems to achieve privilege separation in a linear address space using specialized programming language dialects. Each programming dialect restricts the programming capabilities of a part of a program, formalizing different compartments within the program. CRT-C uses static analysis to identify various compartments in firmware and realizes the least privilege in the system by enforcing compartment-specific policies. We design and implement a new compiler to compile CRT-C to generate compartmentalized firmware that is ready to run on commodity embedded systems. We evaluate CRT-C with two real-time Operating Systems (RTOSs): FreeRTOS and Zephyr. Our evaluation shows that CRT-C can provide compartmentalization to embedded systems to thwart various attacks while incurring an average runtime overhead of 2.63% and memory overhead of 1.75%. CRT-C provides a practical solution to both retrofit legacy and secure new applications for embedded systems.

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Optimal Driving Conditions and Angles for an Ultrasound-Actuated Needle Device

Optimal Driving Conditions and Angles for an Ultrasound-Actu...

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ieee International symposium on applications of Ferroelectrics (ISAF)

作者： Youheng Zeng Ashraf Agweder Zhihong Huang Graeme McLeod School of Physics Engineering and Technology University of York York United Kingdom School of Science and Engineering University of Dundee Dundee United Kingdom Institute of Academic Anaesthesia University of Dundee Dundee United Kingdom

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

As a commonly used technique in regional anesthesia and biopsy, ultrasound-guided needle procedure still faces difficulties in precisely locating the needle tip. Previous study has shown the potential of using real-time electrical impedance measurements from an ultrasonic needle device as an indicator of the needle tip location. The device applies a Langevin piezoelectric ultrasound transducer to actuate a general anesthesia needle. An adaptive control system, controlled in LabVIEW, allows for real-time impedance measurements. This study seeks to explore the impact of different insertion angles and driving conditions on needle localization using electrical impedance measurements from the device. Gelatin phantoms of 10% and 20% concentration, each with an embedded grape, were used as the testing objects. The results identified a constant current of 10 mA and an insertion angle of 60° as the optimal conditions, supporting the enhanced accuracy of needle tip localization through this method.

关键词： Location awareness Ultrasonic imaging Impedance measurement Transducers Pipelines Needles Anesthesia Acoustics real-time systems Testing

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Personalised and Adjustable Interval Type-2 Fuzzy-Based PPG Quality Assessment for the Edge

Personalised and Adjustable Interval Type-2 Fuzzy-Based PPG ...

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ieee International Conference on Fuzzy Systems (FUZZ-ieee)

作者： Miranda, Jose A. Lopez-Ongil, Celia Andreu-Perez, Javier Ecole Polytech Fed Lausanne Embedded Syst Lab Lausanne Switzerland Univ Carlos III Madrid Elect Teclmol Dept Leganes Spain Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

ISBN: (纸本)9798350332285

Most of today's wearable technology provides seamless cardiac activity monitoring. Specifically, the vast majority employ Photoplethysmography (PPG) sensors to acquire blood volume pulse information, which is further analysed to extract useful and physiologically related features. Nevertheless, PPG-based signal reliability presents different challenges that strongly affect such data processing. This is mainly related to the fact of PPG morphological wave distortion due to motion artefacts, which can lead to erroneous interpretation of the extracted cardiac-related features. On this basis, in this paper, we propose a novel personalised and adjustable Interval Type-2 Fuzzy Logic System (IT2FLS) for assessing the quality of PPG signals. The proposed system employs a personalised approach to adapt the IT2FLS parameters to the unique characteristics of each individual's PPG signals. Additionally, the system provides adjustable levels of personalisation, allowing healthcare providers to adjust the system to meet specific requirements for different applications. The proposed system obtained up to 93.72% for average accuracy during validation. The presented system has the potential to enable ultra-low complexity and real-time PPG quality assessment, improving the accuracy and reliability of PPG-based health monitoring systems at the edge.

关键词： Photoplethysmography Fuzzy Personallsation Signal Quality Assessment Physiological Monitoring

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Optimizing Deep Learning on Sustainable embedded Systems: A Study of Handwritten Digit Recognition with CNN and OpenCV

Optimizing Deep Learning on Sustainable Embedded Systems: A ...

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ieee Region 10 International Conference TENCON

作者： Chiang Liang Kok Chee Kit Ho R Vicknesh Charles Lee Yit Yan Koh College of Engineering The University of Newcastle Australia Engineering Cluster Singapore Institute of Technology Singapore

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

This senior thesis develops a real-time handwritten digit identification system using a Raspberry Pi 3B+ with a camera module, leveraging a lightweight CNN optimized with MNIST. The project highlights the effective implementation of deep learning on edge computing devices through seamless integration of CNN, TensorFlow Lite, and OpenCV's real-time image processing. The system is both cost-effective and precise, enabling real-time digit recognition tasks. This proposed work illustrates the potential of AI applications in education, industry, and commerce, setting the stage for future advancements in embedded AI systems.

关键词： Deep learning Industries Handwriting recognition embedded systems Image processing real-time systems Convolutional neural networks Artificial intelligence ieee Regions Edge computing

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Smart Infant Monitoring System Using Computer Vision and AI

Smart Infant Monitoring System Using Computer Vision and AI

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2023 ieee International Conference on Electro Information technology, eIT 2023

作者： Singh, Gurpreet Shekhar, Abhishek Raj Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

The new era of technology is being greatly influenced by the field of artificial intelligence. Computer vision and deep learning have become increasingly important due to their ability to process vast amounts of data and provide insights and solutions in a variety of fields. Computer vision, deep learning and signal analysis have been used in a growing number of applications and services including smart devices, image, and speech recognition, healthcare, etc., one such device is an infant monitoring system. It monitors the daily activities of the infant such as their sleeping patterns, sounds, and movements. In this paper, deep learning and computer vision libraries were used to develop algorithms to detect whether the infant was in any uncomfortable situation such as sleeping on its back, face being covered and whether the infant was awake. The smart infant monitoring system detects the infant's unsafe resting situation in real time and sent immediate alerts to the caretaker's device. This paper presents the design flow of a smart infant monitoring system consisting of a night vision camera, a Jetson Nano, and a Wi-Fi internet connection. The pose estimation and awake detection algorithms were developed and tested successfully for different infant resting/sleeping situations. The smart infant monitoring system provides significant benefits for safety and an improved understanding of infants' sleep patterns and behavior. © 2023 ieee.

关键词： Face recognition

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Research on efficient operation technology of ship general performance prediction app in complex environment 21

Research on efficient operation technology of ship general p...

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21st International symposium on Distributed Computing and applications for Business Engineering and Science, DCABES 2022

作者： Qian, Weidong Xu, Yuan Tian, ZhiFeng Lin, Da Sun, GanJiang China Ship Scientific Research Center China Cssc Orient Wuxi Software Technology Co. Ltd Wuxi China

ISBN: (纸本)9781665454629

Aiming at the problems of real-time data transmission and system scheduling in complex environments such as ship general performances prediction app, operation system, software and hardware environment, operation time and task priority, this paper analyzes the operation status of heterogeneous app at home and abroad, and discusses in detail the key technologies of multi-threaded asynchronous execution and intelligent decision scheduling of heterogeneous app, so as to ensure the reliable, real-time and efficient operation of heterogeneous app, so as to significantly reduce the design cycle, Improving the operation efficiency of the ship general performances prediction app can effectively reduce the risk of subsequent physical tests. © 2022 ieee.

关键词： Application programs

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Design and Optimization of a Wearable Sonomyography Sensor for Dynamic Muscle Activity Monitoring

Design and Optimization of a Wearable Sonomyography Sensor f...

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2023 ieee Applied Sensing Conference, APSCON 2023

作者： Kamatham, Anne Tryphosa Mukherjee, Biswarup Indian Institute of Technology Centre for Biomedical Engineering Delhi India

ISBN: (纸本)9781665461634

Sonomyography is a non-invasive modality that uses ultrasound for sensing muscle activity. The ability of this modality to robustly sense muscle activity with good resolution makes it suitable as a biomechatronic control interface. However, the most sonomyography-based studies utilize B-mode ultrasound imaging which require large form-factor clinical ultrasound imaging devices. In order to extend the utility of sonomyography techniques to wearable systems, the size of the ultrasound system and associated sensors needs to be greatly reduced. In this paper, we design and optimize the performance of a wearable ultrasound sensor for muscle activity monitoring applications. We fabricated a custom ultrasound transducer with a backing layer by mixing varying amounts of Tungsten powder to epoxy resin to optimize the bandwidth of the transducer. Our results showed that increasing the proportion of Tungsten improves the bandwidth of the transducers. Following optimization of the transducer design, we developed a wearable sonomyography sensor and demonstrated real-time measurement of dynamic muscle activity during hand open-close movement. © 2023 ieee.

关键词： Bandwidth

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Accurate and Efficient Depth Completion Based on Attention

Accurate and Efficient Depth Completion Based on Attention

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2024 Photonics and Electromagnetics Research symposium, PIERS 2024

作者： Ding, Ruoyun Zhao, Yuxing Lin, Lan Tongji University Department of Electronic Science and Technology Shanghai China

ISBN: (纸本)9798350375909

Depth completion is an essential task for applications such as simultaneous localization and mapping (SLAM), autonomous driving, and 3D reconstruction. It aims at restoring pixel-wise depth from sparse depth (usually captured by LiDAR or RGB-D cameras). This process can be further improved by leveraging RGB information. With multi-modality data fusion, the model is allowed to learn complementary features, thus achieving robust and accurate depth estimation. In recent years, intelligent robots and autonomous vehicles have shown great advances, but it is still challenging for them to perceive environment depth in real-time with limited hardware resources. Moreover, most existing depth completion methods choose to trade accuracy at the cost of high computational complexity, which inhibits their application in real-time robotic *** this paper, we propose a deep learning-based method with attention mechanism to estimate accurate depth maps from sparse data efficiently. Specifically, we present a convolution neural network consisting of two lightweight symmetric encoder-decoder branches to extract color and depth information respectively;each branch outputs a coarse depth map and a corresponding confidence map, which are then fused adaptively. Furthermore, we perform attention-based fusion between two branches to learn multi-modality features. According to the characteristics of the depth completion problem, a loss function is designed to criteria the region with significant depth variation. This design helps to reduce blurriness and distortion in object boundaries, which is a general defect in the estimated depth map. We train and evaluate our network on the KITTI dataset. Experimental results demonstrate that our method fuses depth and color information effectively, and also estimates high-resolution depth maps with clear edges at relatively low computational cost. © 2024 ieee.

关键词： Intelligent robots

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