embedded systems research papers pdf

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Embedded Systems and Artificial Intelligence

Proceedings of ESAI 2019, Fez, Morocco

• Vikrant Bhateja 0 ,
• Suresh Chandra Satapathy 1 ,
• Hassan Satori 2

Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, India

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School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India

Department of computer sciences, faculty of sciences dhar mahraz, sidi mohammed ben abbdallah university, fez, morocco.

Gathers outstanding research papers presented at the ESAI 2019

Discusses new findings in embedded systems and artificial intelligence

Serves as a reference guide for researchers and practitioners in academia and industry

Part of the book series: Advances in Intelligent Systems and Computing (AISC, volume 1076)

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Table of contents (85 papers)

Front matter, embedded computing and applications, real-time implementation of artificial neural network in fpga platform.

• Mohamed Atibi, Mohamed Boussaa, Abdellatif Bennis, Issam Atouf

Design of a 0.18 μm CMOS Inductor-Based Bandpass Microwave Active Filter

• Mariem Jarjar, Nabih Pr. El Ouazzani

Improvement of the Authentication on In-Vehicle Controller Area Networks

• Asmae Zniti, Nabih El Ouazzani

Comparative Evaluation of Speech Recognition Systems Based on Different Toolkits

• Fatima Barkani, Hassan Satori, Mohamed Hamidi, Ouissam Zealouk, Naouar Laaidi

Stability Analysis and \(H_\infty \) Performance for Nonlinear Fuzzy Networked Control Systems with Time-Varying Delay

• Ahmed Ech-charqy, Said Idrissi, Mohamed Ouahi, El Houssaine Tissir, Ghali Naami

Driver Behavior Assistance in Road Intersections

• Safaa Dafrallah, Aouatif Amine, Stéphane Mousset, Abdelaziz Bensrhair

BER Performance of CE-OFDM System: Over AWGN Channel and Frequency-Selective Channel Using MMSE Equalization

• J. Mestoui, M. El Ghzaoui, K. El Yassini

Towards a Dynamical Adaptive Core for Urban Flows Simulations

• Hind Talbi, El Miloud Chaabelasri, Najim Salhi, Imad Elmahi

An Efficiency Study of Adaptive Median Filtering for Image Denoising, Based on a Hardware Implementation

• Hasnae El Khoukhi, Faiz Mohammed Idriss, Ali Yahyaouy, My Abdelouahed Sabri

Processing Time and Computing Resources Optimization in a Mobile Edge Computing Node

• Mohamed El Ghmary, Tarik Chanyour, Youssef Hmimz, Mohammed Ouçamah Cherkaoui Malki

Viscous Dissipation and Heat Source/Sink Effects on Convection Heat Transfer in a Saturated Porous Medium

• Elyazid Flilihi, Mohammed Sriti, Driss Achemlal

Fast and Efficient Decoding Algorithm Developed from Concatenation Between a Symbol-by-Symbol Decoder and a Decoder Based on Syndrome Computing and Hash Techniques

• M. Seddiq El Kasmi Alaoui, Saïd Nouh, Abdelaziz Marzak

SWIMAC: An Ultra-Low Power Consumption MAC Protocol for IR-UWB-Based WSN

• Anouar Darif

An Optimal Design of a Short-Channel RF Low Noise Amplifier Using a Swarm Intelligence Technique

• Soufiane Abi, Hamid Bouyghf, Bachir Benhala, Abdelhadi Raihani

Study and Design of a See Through Wall Imaging Radar System

• M. Abdellaoui, M. Fattah, S. Mazer, M. El bekkali, M. El ghazi, Y. Balboul et al.

Analog Active Filter Component Selection Using Genetic Algorithm

• Asmae El Beqal, Bachir Benhala, Izeddine Zorkani

Preliminary Study of Roots by Georadar System

• Ahmed Faize, Gamil Alsharahi

Predictive Agents for the Forecast of CO2 Emissions Issued from Electrical Energy Production and Gas Consumption

• Seif Eddine Bouziane, Mohamed Tarek Khadir
• Conference Proceedings
• Artificial Intelligence
• Embedded Systems
• Fuzzy and Expert Systems
• Networked Embedded Systems
• Mobile Embedded Systems

Vikrant Bhateja

Suresh Chandra Satapathy

Hassan Satori

Book Title : Embedded Systems and Artificial Intelligence

Book Subtitle : Proceedings of ESAI 2019, Fez, Morocco

Editors : Vikrant Bhateja, Suresh Chandra Satapathy, Hassan Satori

Series Title : Advances in Intelligent Systems and Computing

DOI : https://doi.org/10.1007/978-981-15-0947-6

Publisher : Springer Singapore

eBook Packages : Intelligent Technologies and Robotics , Intelligent Technologies and Robotics (R0)

Copyright Information : Springer Nature Singapore Pte Ltd. 2020

Softcover ISBN : 978-981-15-0946-9 Published: 08 April 2020

eBook ISBN : 978-981-15-0947-6 Published: 07 April 2020

Series ISSN : 2194-5357

Series E-ISSN : 2194-5365

Edition Number : 1

Number of Pages : XXII, 910

Number of Illustrations : 174 b/w illustrations, 277 illustrations in colour

Topics : Computational Intelligence , Artificial Intelligence , Information Systems Applications (incl. Internet) , Systems and Data Security

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A Modular End-to-End Framework for Secure Firmware Updates on Embedded Systems

Firmware refers to device read-only resident code which includes microcode and macro-instruction-level routines. For Internet-of-Things (IoT) devices without an operating system, firmware includes all the necessary instructions on how such embedded systems operate and communicate. Thus, firmware updates are essential parts of device functionality. They provide the ability to patch vulnerabilities, address operational issues, and improve device reliability and performance during the lifetime of the system. This process, however, is often exploited by attackers in order to inject malicious firmware code into the embedded device. In this article, we present a framework for secure firmware updates on embedded systems. This approach is based on hardware primitives and cryptographic modules, and it can be deployed in environments where communication channels might be insecure. The implementation of the framework is flexible, as it can be adapted in regards to the IoT device’s available hardware resources and constraints. Our security analysis shows that our framework is resilient to a variety of attack vectors. The experimental setup demonstrates the feasibility of the approach. By implementing a variety of test cases on FPGA, we demonstrate the adaptability and performance of the framework. Experiments indicate that the update procedure for a 1183-kB firmware image could be achieved, in a secure manner, under 1.73 seconds.

Computer development based embedded systems in precision agriculture: tools and application

Low-power on-chip implementation of enhanced svm algorithm for sensors fusion-based activity classification in lightweighted edge devices.

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Smart homes assist users by providing convenient services from activity classification with the help of machine learning (ML) technology. However, most of the conventional high-performance ML algorithms require relatively high power consumption and memory usage due to their complex structure. Moreover, previous studies on lightweight ML/DL models for human activity classification still require relatively high resources for extremely resource-limited embedded systems; thus, they are inapplicable for smart homes’ embedded system environments. Therefore, in this study, we propose a low-power, memory-efficient, high-speed ML algorithm for smart home activity data classification suitable for an extremely resource-constrained environment. We propose a method for comprehending smart home activity data as image data, hence using the MNIST dataset as a substitute for real-world activity data. The proposed ML algorithm consists of three parts: data preprocessing, training, and classification. In data preprocessing, training data of the same label are grouped into further detailed clusters. The training process generates hyperplanes by accumulating and thresholding from each cluster of preprocessed data. Finally, the classification process classifies input data by calculating the similarity between the input data and each hyperplane using the bitwise-operation-based error function. We verified our algorithm on `Raspberry Pi 3’ and `STM32 Discovery board’ embedded systems by loading trained hyperplanes and performing classification on 1000 training data. Compared to a linear support vector machine implemented from Tensorflow Lite, the proposed algorithm improved memory usage to 15.41%, power consumption to 41.7%, performance up to 50.4%, and power per accuracy to 39.2%. Moreover, compared to a convolutional neural network model, the proposed model improved memory usage to 15.41%, power consumption to 61.17%, performance to 57.6%, and power per accuracy to 55.4%.

Getting Started with Secure Embedded Systems

Design principles for embedded systems, cyense: cyclic energy-aware scheduling for energy-harvested embedded systems, embedded systems software development, esqumo an embedded software quality model.

Embedded systems are increasingly used in our daily life due to their importance. They are computer platforms consisting of hardware and software. They run specific tasks to realize functional and non functional requirements. Several specific quality attributes were identified as relevant to the embedded system domain. However, the existent general quality models do not address clearly these specific quality attributes. Hence, the proposition of quality models which address the relevant quality attributes of embedded systems needs more attention and investigation. The major goal of this paper is to propose a new quality model (called ESQuMo for Embedded Software Quality Model) which provides a better understanding of quality in the context of embedded software. Besides, it focuses the light on the relevant attributes of the embedded software and addresses clearly the importance of these attributes. In fact, ESQuMo is based on the well-established ISO/IEC 25010 standard quality model.

Design Automation for Embedded Systems

An International Journal

• Addresses the systematic design of embedded systems
• Focuses on tools, methodologies and architectures, including HW/SW co-design, simulation and modeling approaches, synthesis techniques, architectures, and design exploration
• Meets the need for a truly multidisciplinary system design journal

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Confcall - 2018 (Volume 06 - Issue 14)

Embedded System Paper Document

• Article Download / Views: 2,616
• Total Downloads : 0
• Authors : Abitha. S
• Paper ID : IJERTCONV6IS14012
• Volume & Issue : Confcall – 2018 (Volume 06 – Issue 14)
• Published (First Online): 05-01-2019
• ISSN (Online) : 2278-0181
• Publisher Name : IJERT

Department of ECE,

PITS, Thanjavur, Tamil Nadu, India.

Abstract: An embedded system is a programmed controlling and operating system with a dedicated function within a larger mechanical or electrical system, often with real-time computing constraints. It is embedded as part of a complete device often including hardware and mechanical parts. Embedded systems control many devices in common use today.[3] Ninety-eight percent of all microprocessors are manufactured as components of embedded systems. Examples of properties of typical embedded computers when compared with general-purpose counterparts are low power consumption, small size, rugged operating ranges, and low per-unit cost. This comes at the price of limited processing resources, which make them significantly more difficult to program and to interact with. However, by building intelligence mechanisms on top of the hardware, taking advantage of possible existing sensors and the existence of a network of embedded units, one can both optimally manage available resources at the unit and network levels as well as provide augmented functions, well beyond those available. For example, intelligent techniques can be designed to manage power consumption of embedded systems. Modern embedded systems are often based on microcontrollers (i.e. CPUs with integrated memory or peripheral interfaces),[7] but ordinary microprocessors (using external chips for memory and peripheral interface circuits) are also common, especially in more-complex systems. In either case, the processor(s) used may be types ranging from general purpose to those specialized in certain class of computations, or even custom designed for the application at hand. A common standard class of dedicated processors is the digital signal processor (DSP).

Since the embedded system is dedicated to specific tasks, design engineers can optimize it to reduce the size and cost of the product and increase the reliability and performance. Some embedded systems are mass-produced, benefiting from economies of scale.

Embedded systems range from portable devices such as digital watches and MP3 players, to large stationary installations like traffic lights, factory controllers, and largely complex systems like hybrid vehicles, MRI, and avionics. Complexity varies from low, with a single microcontroller chip, to very high with multip

INTRODUCTION

What is Embedded System?

A precise definition of embedded systems is not easy. Simply stated, all computing systems other than general purpose computer (with monitor, keyboard, etc.) are embedded systems.

System is a way of working, organizing or performing one or many tasks according to a fixed set of rules, program or plan. In other words, an arrangement in which all units

assemble and work together according to a program or plan. An embedded system is a system that has software embedded into hardware, which makes a system dedicated for an application (s) or specific part of an application or product or part of a larger system. It processes a fixed set of pre-programmed instructions to control electromechanical equipment which may be part of an even larger system (not a computer with keyboard, display, etc).

A general-purpose definition of embedded systems is that they are devices used to control, monitor or assist the operation of equipment, machinery or plant. Embedded reflects the fact that they are an integral part of the system. In many cases, their embeddedness may be such that their presence is far from obvious to the casual observer.

CHARACTERISTICS:

Embedded systems are application specific & single functioned; application is known apriori, the programs are executed repeatedly.

Efficiency is of paramount importance for embedded systems. They are optimized for energy, code size, execution time, weight & dimensions, and cost.

Embedded systems are typically designed to meet real time constraints; a real time system reacts to stimuli from the controlled object/ operator within the time interval dictated by the environment. For real time systems, right answers arriving too late (or even too early) are wrong.

Embedded systems often interact (sense, manipulate & communicate) with external world through sensors and actuators and hence are typically reactive systems; a reactive system is in continual interaction with the environment and executes at a pace determined by that environment.

They generally have minimal or no user interface.

At Embedded World 2013 (Nuremberg, February 26-28), Fraunhofer researchers will demonstrate 'Smart Farming' – how the interaction of machines in cyber-physical systems operates safely and securely.Climate change, population growth and increasingly scarce resources are putting agriculture under pressure. Farmers must harvest as much as possible from the smallest possible land surface. Until now, the industry confronted this challenge with innovations in individual sectors; intelligent systems regulate engines in order to save fuel, for instance.

With the aid of satellites and sensor technology, farming equipment can automatically perform the field work; in doing so, they are more efficiently able to distribute seed, fertilizer and pesticides on the land. Nonetheless,

optimisation is gradually hitting its limits. The next step is to network these individual systems into cyber-physical production systems. These map the entire process electronically, from the farm computer to the harvesting operation, substantially increasingefficience And quality.

available for identifying pests.

Agriculture is under huge pressure due to population growth, scarce resources and climate change. Today farmers are required to harvest maximum from the smallest piece of land. Thus, this field requires assistance of something remarkable like embedded system. Several complexities are involved in farming, as farmers need to have sound understanding of climatic conditions and they must be able to change the farming process depending upon the climatic conditions. Farming practices even change according to the soil conditions and therefore computational assistance help a lot to farmers. At Embedded World (booth 228 in Hall B5) researchers from the Fraunhofer Institute for will demonstrate how agriculture will be able to benefit from networked systems in the future. Experimental Software Engineering IESE in Kaiserslautern For their exhibit, an piece of farm equipment moves across a plot of land within an agricultural diorama. Located at the edge of the farmland are two tablet PCs. Visitors to the trade show can use them to start up the automated control of the farm equipment. Six screens are suspended above the model farm. They display the processes behind the automation, showing how software manages the functionality. The visualization is intended to help visitors understand the challenges of, and solutions to, interconnecting embedded and IT systems. With intelligent networking, farmers can improve farming productivity.The networking of agricultural operations is not limited to simple task management for agricultural machinery. Besides seed and fertilizer producers, sensor technology and data service providers are joining in the mix, offering geodata and weather data; smartphone apps are also To assist and help the farmers, scientists have come up with precision farming process that optimises the complete agriculture work. This process aims to maximize the output while keeping inpt to the minimum. This farming practice

is currently implemented in Kerala by KAU and ICFOSS, where they are looking to setup smart agriculture that would provide actual data of soil with this platform would take information from satellites and suggest the best farming practice accordingly.

Precision farming process also aims to assist farmers with market information, value-added options and post-harvest advices. In future, this system also eyes to solve labour issues by coming up with robotic farm equipment like sensor-based sprinklers, which would perform the farming practices that are usually performed by the labourers. In several countries, precision farming has gained lot of significance and the latest one to join is the Holland. This country is currently developing driverless tractors using Real Time Kinematic and GPS that will prove to be effective and cost-efficient for use in large farmlands.

Another example of precision farming can be witnesses at Distributed Root Garden, which have been setup by MIT researchers. This garden consists of tomato plants that are nurtured by Robots and right from watering the plants to providing regular nutrients to studying plant condition to optimally harvest the tomatoes, every practice is taken care of by the robots. Every plant has a sensor that provides plants status to the robots. The entire garden is equipped with sensors to provide map and respective positions of the plants so that robots can act according to the plants condition. Presently robots predict the fruits condition like when it would ripe and be ready for harvest and the time when the plant would require the next nutrients. The students are free to conduct research on this garden to make it better and usable by farmers. the assistance of sensors to a cloud-based platform. After proper data interpretation.

https://en.wikipedia.org/wiki/Embedded_system

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embedded system engineering research papers IEEE PAPER

Embedded system 2020, embedded system 2019, embedded system 2018, embedded system 2016, embedded system 2015, embedded system research papers, fuzzy logic research papers, new embedded papers added june 2012, robotics engineering research papers.

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Embedded Computing Systems

An embedded system is any computer that relies on its own microprocessor and is a part of a larger system. It is usually embedded as a part of a complete device that serves a more general purpose. Our society is increasingly depending on embedded computing systems such as robots, unmanned aerial vehicles self-driving cars, unmanned underwater vehicles, military and aerospace electronics. This paper provides a brief introduction on embedded computing systems.

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Wearable computers are computer devices or systems integrated into the clothing or attached to the body of a person. The evolution of wearable computing devices, driven by the confluence of information and communication technology, has changed the way people use online services by keeping them always connected. This paper provides a brief introduction to wearable computing with its technical issues and challenges that must be addressed to reap the huge benefits.

Innovative Research Publications , Adebowale E Shadare

Mobile health is the creative use of emerging mobile devices to deliver and improve healthcare practices. It integrates mobile technology with the health delivery with the premise of promoting a better health and improving efficiency. Mobile health has become an increasingly important issue in a number of disciplines such as health communication, public health, and health promotion. This paper provides a brief introduction to mobile health.

European Scientific Journal ESJ

Cyber-physical systems (CPSs) are smart systems that depend on the synergy of cyber and physical components. They link the physical world (e.g. through sensors, actuators, robotics, and embedded systems) with the virtual world of information processing. Applications of CPS have the tremendous potential of improving convenience, comfort, and safety in our daily life. This paper provides a brief introduction to CPSs and their applications. Introduction The term " cyber-physical system " (CPS) was coined in 2006 by Helen Gill of the US National Science Foundation (Henshaw, 2016). As the name suggests, CPS has both cyber (software control) and physical (mechanism) elements. Cyber-physical systems (CPSs) are engineered systems that are designed to interact seamlessly with networks of physical and computational components. These systems will provide the foundation of our critical infrastructure and improve our quality of life in many areas. CPSs and related systems (such as IoT and industrial Internet) have the potential to impact various sectors of the economy worldwide. CPS is basically an engineering discipline, focused on technology and modeling physical processes (differential equations, stochastic processes, etc.) with mathematical abstractions. In a CPS, computing elements coordinate and communicate with sensors, which monitor cyber and physical indicators and actuators. CPS is also similar to the Internet of Things (IoT), sharing the same basic architecture (Cyber-physical system, 2017). It is also related to embedded systems. While an embedded system is

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Supervisory Control And Data Acquisition (SCADA) is a control system for smooth managing large-scale, automated industrial operations. When applied to electric power industry, it can help the industry to save time and money, reduce operational costs, and improve efficiency. It provides real-time monitoring and automation for smart power grid, a promising power delivery system of the future. This paper provides a brief introduction on the application of SCADA in electric power systems.

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Nanocomputing refers to computing devices built from nanoscale components. It is an emerging technology that is at the early stage of its development. An exciting anticipation of nanocomputing is the smaller system size it will provide and the ability to construct systems that use many orders of magnitude more components than in the past. This paper provides a brief introduction to nanocomputing.

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Computer Science > Artificial Intelligence

Title: consciousness in artificial intelligence: insights from the science of consciousness.

Abstract: Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.

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• Download a PDF of the paper titled Consciousness in Artificial Intelligence: Insights from the Science of Consciousness, by Patrick Butlin and 18 other authors PDF
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