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Software/Hardware Co-design for Multi-modal Multi-task Learning in Autonomous systems 3

Software/Hardware Co-design for Multi-modal Multi-task Learn...

引用

ieee 3rd international conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Hao, Cong Chen, Deming Georgia Inst Technol Atlanta GA 30332 USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781665419130

Optimizing the quality of result (QoR) and the quality of service (QoS) of AI-empowered autonomous systems simultaneously is very challenging. First, there are multiple input sources, e.g., multi-modal data from different sensors, requiring diverse data preprocessing, sensor fusion, and feature aggregation. Second, there are multiple tasks that require various AI models to run simultaneously, e.g., perception, localization, and control. Third, the computing and control system is heterogeneous, composed of hardware components with varied features, such as embedded CPUs, GPUs, FPGAs, and dedicated accelerators. Therefore, autonomous systems essentially require multi-modal multi-task (MMMT) learning which must be aware of hardware performance and implementation strategies. While MMMT learning has been attracting intensive research interests, its applications in autonomous systems are still underexplored. In this paper, we first discuss the opportunities of applying MMMT techniques in autonomous systems, and then discuss the unique challenges that must be solved. In addition, we discuss the necessity and opportunities of MMMT model and hardware co-design, which is critical for autonomous systems especially with power/resource-limited or heterogeneous platforms. We formulate the MMMT model and heterogeneous hardware implementation co-design as a differentiable optimization problem, with the objective of improving the solution quality and reducing the overall power consumption and critical path latency. We advocate for further explorations of MMMT in autonomous systems and software/hardware co-design solutions.

关键词： Location awareness Power demand Autonomous systems Quality of service Learning (artificial intelligence) sensor fusion Hardware

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Tile-Based Architecture Exploration for Convolutional Accelerators in Deep Neural Networks 3

Tile-Based Architecture Exploration for Convolutional Accele...

引用

ieee 3rd international conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Chen, Yang-Tsai Yen, Yu-Xiang Chen, Chun-Tse Chen, Tzu-Yu Huang, Chih-Tsun Liou, Jing-Jia Lu, Juin-Ming Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan Natl Tsing Hua Univ Dept Elect Engn Hsinchu Taiwan Ind Technol Res Inst Informat & Commun Res Labs Hsinchu Taiwan

ISBN: (纸本)9781665419130

This paper presents the tile-based DCNN accelerator architecture. The hierarchical interconnection networks enable distributed data delivery to maximize the data bandwidth both for the conventional and depthwise convolution layers. An exploration tool is also developed to optimize architectural parameters under the trade-off among specific performance, power, and area. The case study shows that our accelerator can outperform the state-of-the-art by up to 36% faster and up to 25% lower energy on different modern DCNNs. The experiment also justifies the scalability of our approach.

关键词： Multiprocessor interconnection Convolution Circuits and systems Scalability conferences Neural networks distributed databases

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IOT Based Temperature and Oxygen Pulse Scan Entry System 3

IOT Based Temperature and Oxygen Pulse Scan Entry System

引用

3rd international conference on Communication, computing and Industry 4.0, C2I4 2022

作者： Kulkarni, Pavankumar Umachagi, Hamjad Ali Bakshi, Mariya Sultana BLDEA's V.P.Dr.P.G.H CET Department of EEE Vijaypur India

ISBN: (纸本)9781665474979

In this project the system has been designed in the manner to detect the body temperature and oxygen saturation level (SpO2) of the person. To detect the body temperature and oxygen pulses we are using the respective sensors. The sensors we are using in this project is MLX90614 ESF thermometer and MAX30100 is integrated with the pulse oximeter. Here we have used the two Node MCU (ESP 32) for the different sensors, one Node MCU is connected to the temperature sensor and another Node MCU is connected to the pulse oximeter. The components we are using in this project is Eco friendly. The software we are using in this project is ArdUINO UNO R3 IDE (Integrated development board) and To show the results we are making use of Blynk app. The maximum errors we get in this project is about 2%. When it comes to health monitoring, it has shown the good results. This is the best way to minimize the spread of the COVID-19. It is a low cost and high functionality which makes it use in the different places like Hospitals, Malls, Sports etc. © 2022 ieee.

关键词： COVID-19 IOT Node MCU Pulse Oximeter Temperature sensor

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Design of Real-time Centroid Extraction System for Shack–Hartmann sensor Based on FPGA

Design of Real-time Centroid Extraction System for Shack–Ha...

引用

Electronic Technology, Communication and Information (ICETCI), ieee international conference on

作者： Shen Xiaocui Huang Zhangqin Gao Han Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

Hartmann wavefront sensor is applied widely in adaptive optics systems. Considering the real-time performance requirements and processing amount of Hartmann sensors in computing the centroids of points, this article proposes an FPGA-based real-time image processing and centroid extraction system. The system adopts FPGA as the core processor and uses the connected domain labeling algorithm and the gray centroid method to complete the centroid calculation. In addition, the calculation results are output simultaneously through multiple DAs to drive the device under test to achieve image wavefront restoration. At the same time, the image data is uploaded to the host computer in real-time through the Ethernet interface. The system has excellent performance in real-time image processing, image output quality, and system control stability. Experiments show that it is an efficient technical means of providing real-time wavefront processing measurement for the Hartmann sensor in various application scenarios, such as adaptive optical correction.

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A USRP based UHF Wireless sensor Node and Fusion Centre for Aquaponics System Monitoring 3

A USRP based UHF Wireless Sensor Node and Fusion Centre for ...

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3rd international conference for Emerging Technology, INCET 2022

作者： Nandesh, O.N. Shetty, Rikitha Alva, Saniha Paul, Aditi Sure, Pallaviram M S Ramaiah University of Applied Sciences Department of Electronics and Communication Engineering India

ISBN: (纸本)9781665494991

Tremendous rise in smart and sustainable agricultural systems such as the Aquaponics, demand continuous system monitoring for predictive analytics and timely control. Many such monitoring systems have been developed in the literature using microcontrollers, which transmit the sensed data to cloud using off-the-shelf components. However, these do not exploit underutilized Ultra High Frequency (UHF) transmission bands and have fixed carrier frequency, transmit and receive signal processing chains. Contrarily, this paper discusses the development of a wireless sensor node and fusion centre at the underutilized 470 MHz UHF band, using microcontroller and programmable Universal Software Radio Peripheral (USRP) modules, for temperature and pH monitoring of a smart aquaponics system. Analog data frame comprising temperature and pH values is acquired, digitized using 32-bit Pulse Code Modulation (PCM) and a frame header is added. The frame is then transmitted to a remote fusion center using 470 MHz carrier, where the received symbols are decoded to retrieve the analog frame. Average Bit Error Rate (BER) of the wireless link is obtained as 0.003 at a receiver gain of 20 dB. Hence, reduced frame loss/improved link reliability is ensured by repeated transmissions. The sensor node transmits each analog frame 50 times, while the fusion center is programmed to collect 20 successive frames. At the fusion center, these 20 frames are pre-processed to estimate the actual analog frame. The developed monitoring system acquired temperature and pH time series data for a total duration of 12 hours with 1 minute sampling period. © 2022 ieee.

关键词： Predictive analytics

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A comprehensive Review: Cardiac Disease Detection using IoT based Wearable sensor and Deep Learning Techniques 3

A comprehensive Review: Cardiac Disease Detection using IoT ...

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3rd international conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Chitra, B. Ambeth Kumar, V.D. Anna University Panimalar Engineering College Department of Cse Chennai600123 India Panimalar Engineering College Department of Cse Chennai600123 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The clinical field is making huge measure of information that doctors can't unravel and utilize productively. Additionally, rule-based master frameworks are wasteful in settling convoluted clinical assignments or for making experiences utilizing Big Data. The Deep Learning has arisen as a more precise and powerful innovation in a wide scope of clinical issues like analysis, expectation, and intercession. Deep Learning is a portrayal learning technique that comprises of layers that change information nonlinearly, subsequently, uncovering various leveled connections and designs. Wearable technologies can provide creative solutions to medical care problems. Certain Wearable Technology uses, like weight management or ongoing work tracking, are intended to assist people in avoiding sickness and maintaining their health. These wearable gadgets were being used for other purposes by heart patients and executives who are unwell. Wearable apps can have a direct effect on treatment direction. In this audit, Wearable Technology applications in medical services and Deep Learning application papers that utilization organized information, and sign and imaging modalities from cardiology are concentrated exhaustively. The benefits and constraints of condition of-craftsmanship strategies Deep Learning in Cardiology and wearable advances that likewise apply in medication by and large, while proposing specific bearings as the most practical for clinical use. © 2022 ieee.

关键词： Decision making

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Proceedings - 2022 3rd international conference on Computation, Automation and Knowledge Management, ICCAKM 2022

Proceedings - 2022 3rd International Conference on Computati...

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3rd international conference on Computation, Automation and Knowledge Management, ICCAKM 2022

ISBN: (纸本)9781665453196

The proceedings contain 44 papers. The topics discussed include: design of nonlinear systems with the integral state controller;an insight into applications of deep learning in cancer classification;convolutional neural networks for MRI-based brain tumor classification;a statistical review on machine learning based medical diagnostic systems for chronic kidney disease;computation model to forecast the tsunami wave propagation by 2-dimensional multi-resolution cellular automata for the 2004 Indian Ocean tsunami;blockchain technology in biometric database system;impact of upsurge in Covid-19 cases on Indian stock market: a machine learning approach;disruptive technologies and digitalization in insurance: improving the value chain of insurance;classifying customers’ journey from online reviews of amazon fresh via sentiment analysis and topic modelling;self-powered mems sensor system for monitoring of ocean parameters;heart failure prediction using machine learning algorithm;and security oriented deadline aware workflow allocation strategy for infrastructure as a service clouds.

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Evaluating the Performance of Machine Learning Models for Dynamic Resource Allocation in NFV 3

Evaluating the Performance of Machine Learning Models for Dy...

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3rd international conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Gamal, Gameel Al-Shaikh, Marya Saeed, Mogeeb A. Hazza'a, Ahmed G. Alomary, Ahmed Alshehabi, Reem Taiz University Al-Saeed Faculty of Engineering & It Computer Network and Distributed Systems Dep Yemen

ISBN: (纸本)9798350305333

Network Function Virtualization (NFV) is an emerging architecture that leverages virtualization technology to decouple software from hardware. The management of virtual network functions (VNFs) poses a significant challenge in NFV, given the dynamic nature of the system. Resource allocation to VNFs must adjust to accommodate varying traffic loads, but this process can lead to substantial delays. To address this issue, resource estimation models can be applied to predict the resources required for each VNF and optimize dynamic resource allocation. Our study utilized machine learning (ML) techniques to train models using real data from VNFs, encompassing performance metrics and resource needs. The models, once trained, were successful in accurately forecasting the necessary resources for handling particular traffic loads concerning each VNF. The evaluation utilizing real-world data demonstrates the importance of selecting a good model for dynamic resource allocation by contrasting seventeen different ML algorithms with a common fixed allocation model. Using FRA in particular could lead to significant resource over-allocation, a high number of VNF instances, and needless high delay, all of which could lead to high prices and poor service quality. Although linear approximation performs admirably in the assessment, more potent techniques like KNN, SVR, or Boosting outperform it because they can more precisely forecast the actual VNF resource requirements. Overall, using ML to allocate resources dynamically can help cut down on resource waste while enhancing service quality. © 2023 ieee.

关键词： Network function virtualization

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A DNN-Based GLMB Algorithm for Multi-sensor Multi-target Tracking with Dense Group Clutter 1

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3rd international conference on Autonomous Unmanned systems, ICAUS 2023

作者： Zhang, Yongquan Shang, Aomen Li, Siwei Su, Zhenzhen Ji, Hongbing School of Electronic Engineering Xidian University Xi’an710071 China Science and Technology on Electronic Information Control Laboratory Chengdu610036 China

ISBN: (数字)9789819710911

ISBN: (纸本)9789819710904

Multi-sensor multi-target tracking (MTT) with dense group clutter is facing great challenges in data fusion. One is the high computational complexity caused by the dense group clutter;another is that existing fusion algorithms do not have uniform fusion guidelines. To handle these problems, we present a novel multi-sensor MTT algorithm in this paper, which is based on the generalized labeled multi-Bernoulli (GLMB) filter and distributed deep neural networks (DNN) track fusion under the multi-sensor (MS) system, named the MS-GLMB-DNN algorithm. Firstly, a clutter pre-processing is elaborated, it is based on a CFDP (cluster by finding density peaks) algorithm to reduce the effect of the dense group clutter. Then, the GLMB filter is applied to the DNN track fusion framework for effectively fusing various local tracks. Experimental results indicate that, compared with the commonly used covariance intersection (CI) fusion algorithm, the proposed algorithm can effectively eliminate the effect of clutter on target tracking results, reduce computational complexity, and improve tracking accuracy. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Deep neural networks

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Multi-Layer Message Signature Scheme with Threshold-based distributed PKG and Dynamic Leader Selection 3

Multi-Layer Message Signature Scheme with Threshold-based Di...

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3rd ieee international conference on Blockchain computing and Applications (BCCA)

作者： Bezzateev, Sergey Afanasyeva, Aleksandra Zhidanov, Konstantin Ometov, Aleksandr Univ Aerosp Instrumentat St Petersburg Russia Enecuum Network Ltd Victoria Seychelles Tampere Univ Tampere Finland

ISBN: (纸本)9781665421829

The development generally drives the evolution of modern systems towards distributed ones, generally, in blockchain and distributed Ledger Technology domains. Ensuring the reliable digital signature of messages and the data security of messages signed with a digital signature from unauthorized interference is an essential part of enabling the operation of the paradigms as mentioned earlier. This work proposes a method for signing messages in the distributed system by introducing a two level nodes' hierarchy, distribution of the Public Key Generator (PKG) master key the nodes of the second level so that a distributed PKG threshold scheme is formed from those. Further, one or more second-layer leaders are selected by the nodes of the first message level in such a way that the aggregate content of the messages determines the value of the current identifier and, thus, the public key are formed by the nodes of the second level forming a distributed threshold PKG, shares of the session secret key corresponding to the identifier and the public key. The session secret key is recovered by one or more second level nodes selected as the leader, and the current message is signed using the corresponding session secret key.

关键词： Digital signature Blockchain distributed systems Secret sharing

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