Journal Information
Microprocessors and Microsystems: Embedded Hardware Design (MICPRO)
http://www.journals.elsevier.com/microprocessors-and-microsystems/
Impact Factor:
1.045
Publisher:
Elsevier
ISSN:
0141-9331
Viewed:
11599
Tracked:
14

Call For Papers
Microprocessors and Microsystems: Embedded Hardware Design (MICPRO) is a journal covering all design and architectural aspects related to embedded systems hardware. It includes different embedded system hardware platforms ranging from custom hardware via reconfigurable systems and application specific processors to general purpose embedded processors. Special emphasis is put on novel complex embedded architectures, such as systems on chip (SoC), systems on a programmable/reconfigurable chip (SoPC) and multi-processors on a chip (MPoC) as well as their communication methods, such as network-on-chip (NoC).

Design automation of such systems including methodologies, techniques and tools for their design as well as novel designs of hardware components fall within the scope of this journal. Novel applications that use embedded systems are also central in this journal. While software is not a part of this journal hardware/software co-design methods that consider interplay between software and hardware components with emphasis on hardware are also relevant here.
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Emerging Challenges and Opportunities in AI-enabled Embedded Systems for Signal Processing Applications
Submission Date: 2021-01-05

In the “big data” era, with the fast development of various artificial intelligence (AI) systems for the more disparate tasks, there is an emerging challenge in semiconductor technology that urges researchers to develop a new form of computer that can proactively analyze and learn from data, solve unknown issues using what it has learned, and work with the human brain's energy efficiency. Recent AI methods, such as deep learning, have enjoyed considerable success in various machine learning tasks because of their powerful learning ability. They have been broadly applied in many signal processing areas including computer vision, natural language processing, data mining, and time series prediction. To train such AI-based solutions, many days of computation on graphics processing unit (GPU) clusters are commonly required. Given the current boom of deep learning-based applications, these solutions will soon hit a power-performance wall with limits set in the cloud for energy usage and plateauing in the Complementary Metal Oxide Semiconductor (CMOS) scaling. Furthermore, there is growing interest in edge computing and smart cognitive assistants (ICAs), where AI must be available on energy-constrained mobile platforms, autonomous drones, and IoT sensor nodes, thus no longer requiring reliance on cloud-based services but also ensuring user data privacy. This opened to the need AI-enabled complex and efficient embedded architectures. This special issue aims to provide a forum for researchers and practitioners in academia and industry to present their latest research findings and engineering experiences in the configuration, implementation, optimization, and validation of AI/deep learning-based complex embedded architectures that can help to efficiently tackle real-world signal processing problems. Papers are invited in theory, modeling, algorithms, implementations and applications of applying AI/deep learning for various signal processing tasks to establish the latest efforts of the research in this area. Topics of interest include, but not limited to: • Smart AI/deep learning sensors and chips • Deep learning on complex embedded architectures • Deep learning/machine learning frameworks for complex embedded architectures • AI-systems on chip for the Internet of Things (IoT), Industry 4.0, smart transportation and autonomous robots • Programmable and reconfigurable chip for complexity-adapting AI solutions • Efficient memory and communication methods for high-throughput AI approaches • Embedded systems for large-scale smart healthcare. • Hardware/software co-design synthesis reconfigurable hardware. • Intelligent embedded system protocols for high-performance computing platforms. • Intelligent Embedded-aware protocols for data quality and integrity. • Intelligent methods timing analysis scheduling design. • Real-time networking and system on chip control. • Real-time embedded systems for Signal Processing - design implementation and performance evaluation. • Security dependability and fault tolerance of real-time and distributed embedded systems.
Last updated by Dou Sun in 2020-11-02
Special Issue on Artificial intelligence and Internet of People for hyper connected cyber physical systems
Submission Date: 2021-01-30

The Internet made the connectivity between people across the globe possible at an unprecedented scale and pace. The next wave of connectivity is coming much faster to interconnect objects and create a smart cyber-physical environment. Now, we see a shift of technology driven focus to human accessibility using digital twinning. Internet-of-People (IoP) represents the mapping of social individuals that refer to people as cyber entities. Apart from traditional focus such as on data collection, modeling, and ubiquitous intelligence for a wide range of applications of crowd sourced, Internet-based personal information, it is essential to include citizen services in the smart community. Along with wearable, AI based gestural interface, automatic speech recognition pay way for hyper-connected cypher physical systems (CPS) when we have data from whole community. With these, the emerging social computing and brain science will allow further incorporation of the social and mental worlds into the so-called Hyper World. Due to the pervasiveness of IoP and its impacts on human activity, it has quickly emerged as a hot and important interdisciplinary field. It represents the mapping of social individuals and their interactions with smart devices to the internet. In essence, Internet-of-People promotes the idea that a smart society is only possible if there is a better, smarter, and secure collaboration bridging machine intelligence and human intelligence, between devices and people, where technology artificial intelligence is applied from a human-centric standpoint. This Special Issue will act as a premier forum for sharing theoretical, experimental and operational results in the relevant fields and it focuses on cutting-edge research from academia, industry and practitioners, with emphasis on original, novel, innovative and impact-oriented research providing insights into smarter hyper-connected societies' needs, processes, services and frameworks using the Internet-of-People paradigm. This Special Issue aims at promoting original and unpublished research works Artificial intelligence and Internet-of-People for hyper connected cypher physical systems. Authors are invited to submit high quality papers to the key areas of AI in IoT and Smart CPS, including the following (but not limited to). · AI based smart wearable computing · Internet of sensing, thinking and creation · Interoperability and user-engagement approaches · Crowd sensing and human intelligence · AI and IoT for human happiness · AI-based universal accessibility in the IoT · IoT and AI for child safety and monitoring · Sentiment analysis and affective computing · AI-powered smart devices · Social networks and behavior analysis · AI-based hyper connected smart society · Internet-of-People (Child/Adult/Elderly) – Technology Implication · Energy Efficiency Problems in above Systems · Security Issues in AI-based IoP/IoT
Last updated by Dou Sun in 2020-08-30
Special Issue on Embedded Artificial Intelligence and Machine Learning for Real-Time Cyber Physical Systems and Networks
Submission Date: 2021-02-15

Sensors and embedded technologies shall contribute a lot for success of the smart world. From intelligent devices in domestic automation to healthcare systems, the industrial fulfillment of these smart items is the result of broad developments in autonomous system that has brought boom in overall performance, while lowering the power/energy consumption. The wireless connectivity pattern is now heading towards billions of intelligent sensors in the IoT. Specifically, these billions of intelligent devices with sensors will enable a huge amount of information exchange that needs artificial intelligence (AI) and machine learning (ML) for various applications. Artificial Intelligent techniques will enable positive solution to the smart world for building the Internet of Things (IoT) platform for variety of applications. The open IoT cyber platform offers a configuration for building large scale IoT applications depending on data collected from a complex infrastructure of sensors and smart devices. Various challenges shall emerge in realizing such infrastructure, one of them being IoT data and quality of services (QOS) demand on real time Cyber Physical Systems (CPS) based applications. Machine learning has been well recognized as an effective AI tool for researchers to handle the problems in real time CPSs, such as environment, e-health, assisted living, automatic surveillance and agriculture. Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, and then make sophisticated decision, based on the learned practices. The imagine pattern will need to address significant complexity. Submissions on the following potential topics with a focus on the application of AI and ML are welcome, but not limited to: Cybersecurity for ML ML-based Cybersecurity smart for CPS & IoT Real time multiprocessor system Architectures for image and video processing Smart devices and platforms Real time remote access system Decision support systems for real time Real time transportation system Real time industrial systems Healthcare systems
Last updated by Dou Sun in 2020-11-02
Special Issue on Cyber physical Microsystems for signal processing and navigation systems
Submission Date: 2021-02-28

The advanced industrial system entirely deals with raw information from data statistics , data analysis, it’s interpretation, data visualization techniques which communicates the message clearly and efficiently through smart inertial sensor-based signal processing navigation system with to ensure safe and secure cooperation, and also contribute to new standards and regulations. The development of the Internet of Things will allow the advancement of much time and safety-critical embedded cyber physical systems (CPS). The new challenge of microsystems be to design, processing and interfacing integrated circuits, middleware, software programme and implement highly distributed and connected digital technologies. These technologies will be embedded in a multimode of increasingly autonomous physical components with various dynamics and satisfying multiple critical constraints including security, power efficiency and high performance. Micro and nano-electromechanical devices are hover to change integrated microsystems and provide paradigm shifts in sensing, communication, and navigation. Among them, micro and nano systems enable integrated platforms for environmental and biological sensing, inertial sensing, as well as energy harvesting, and are the building blocks for spectrum processing and timing applications. This special issue focus on the design and deployment of the next generation of smart sensors which utilize front-end microelectronic, microelectromechanical system, and processing technologies. Potential topics included, but not limited MEMS and NEMS system devices and structures for signal processing MEMS in optical signal processing and beamforming for navigation systems · Signal processing technique for integrating MEMS-based low-cost initial unit and GPS in vehicular navigation MEMS integration utilizing dynamic neural networks for vehicular navigation Wireless signal processing microelectromechanical pressure sensor for harsh environments Signal processing technique for improving the accuracy of MEMS sensors in navigation system MEMS inertial-GPS integrated system using wavelet denoising techniques for navigation system Design of an interface circuit for MEMS capacitive sensors for navigation system · Advanced and intelligent interface circuits for capacitive MEMS sensors for navigation system Intelligent sensor technology in autonomous vehicles · Vision based sensors and communication in intelligent transportation system and connected vehicles Intelligent multi sensor modules for intelligent transportation system Intelligent sensors for ubiquitous and low power vehicles speed monitoring Applications of signal sensors in vehicle detection Intelligent sensors in machine vision for robot navigation Intelligent sensors in machine vision for application on science and industry Vision based sensors for human assistive systems Intelligent sensors for satellite navigation and information fusion Intelligent sensors for improving smart home security Intelligent bio sensor for agricultural applications
Last updated by Dou Sun in 2020-07-30
Special Issue on Adaptive and Reconfigurable Embedded Systems
Submission Date: 2021-04-15

Adaptive systems find application in a plethora of scenarios and, recently, with the rise of Cyber-Physical Systems, have gained more attention in the research community. Generally, these systems have a reconfigurable nature since they are designed to cope with external (environmental) and internal changes at run-time, for example, by adjusting their behavior and architecture. Embedding such systems in devices constrained in terms of energy consumption and computational capabilities requires additional design efforts, especially when these devices have to be deployed in real-time environments or scalability is a requirement. This Special Issue aims at spreading new and on-going research exploring emerging approaches and techniques that address the challenges of the design of energy-efficient, adaptive, and reconfigurable embedded systems and SoCs. Potential topics include, but are not limited to: Self-aware adaptation in FPGA-based systems FPGAs and reconfigurable hardware accelerators FPGA architectures for distributed adaptive computing systems FPGA-based adaptive architectures for correlated multi-stream processing Low power design of reconfigurable SoCs / MPSoCs Adaptive computing for inter-process communication in FPGA architectures On-chip and multi-chip interconnection systems Adaptive real-time embedded processors Novel reconfigurable architectures for real-time applications Performance modelling of emerging high performance computing architectures Machine learning techniques in adaptive computing architectures Adaptive computing in middleware and virtual machines in embedded architectures Approximate computing in FPGAs Verification and evaluation techniques of the aforementioned topics Industrial case studies regarding the aforementioned topics
Last updated by Dou Sun in 2020-11-03
Special Issue on Artificial Intelligence for Self-Organizing Smart Transportation System
Submission Date: 2021-07-29

Self-organizing smart transportation system is an emerging area of research, and its application is getting increased attention from industrial as well as the academicians of our recent times. Globally speaking, mobility becomes an integral part of urban areas; this is especially true when dealing with smart cities. Smart transportation systems take advantage of technologies such as the internet of things (IoT), cloud computing, and big data analytics to enhance various means of transportation services. Self-organizing the smart transportation system is a significant shift of paradigm in smart transportation systems in which the transportation facilities are arranged between agents with transportation demand and agents with transportation supply more effectively across the peer-to-peer network. In contrast to traditional intelligent transportation systems, self-organizing smart transportation systems function in an automated way in a decentralized manner. It offers sustainable transportation services through a network of interconnected sensors and smart devices, which offers more efficient, sophisticated, and robust transportation services to the end-users. In such systems, enhancing the functionalities of smart devices, networking technologies, and regulatory measures is of greater importance, and it is often difficult as it requires the most advanced level of advanced intelligent technologies. In this context, this special issue aims to bring out advances in artificial intelligence (AI) for self-organizing smart transportation systems. It is well-known that AI forms an integral part of IoT applications, and smart transportations systems are not an exception in this regard. Appropriate use of AI technologies offer robust services across self-organized smart transportation systems with enhanced passenger safety, reduced CO2 emission, improved traffic management, and congestion facilities. To the point, AI as technology can widely empower machines with human intelligence to provide more customized transportation services to the end-users. For now, AI plays a significant role in smart transportation applications. However, it has some complexities when dealing with fully automated transportation systems with no human interventions. Hence, bringing in advancement in AI for self-organizing smart transportation will contribute to numerous benefits such as autonomous driving cars, traffic management systems, delay predictions, route navigations, and various other features in an efficient way. This especially can enhance future generation requirements of smart transportation systems. Topics of interest for the special issue include, but not limited to, the following: AI for autonomous vehicle and traffic management systems Self-adaptive AI algorithms for self-organizing smart transportation systems Future of AI-empowered self-organizing smart transportation systems challenges and opportunities Blockchain assisted distributed machine learning solutions for self-organizing smart transportation systems AI-assisted cloud/fog computing-based advanced network architectures for self-organizing smart transportation systems AI-empowered traffic management and congestion control solutions for self-organizing smart transportation systems Role of IoT and AI in future generation self-organizing smart transportation systems AI-assisted sensor technologies for navigation management in self-organizing smart transportation systems Deep learning and artificial intelligence for autonomous driving of vehicles in self-organizing smart transportation systems Role of ethical computational intelligence in self-organization smart transportation systems Implications of human-computer interaction and cognitive computing in self-organization smart transportation systems
Last updated by Dou Sun in 2020-11-17
Special Issue on AUTOMATION & INTELLIGENCE WITH EMBEDDED SYSTEMS FOR CYBER PHYSICAL SYSTEMS
Submission Date: 2021-08-15

In today’s scenario of the technical world, every industry requires some automation and intelligence that is combined in the form of embedded systems. Embedded systems are generally hardware components, which are fused with additional capabilities using customized software. In general, embedded systems are programmed with microprocessors or microcontrollers that are used predominantly to accomplish any particular task. Thus, the size of the embedded systems varies with different applications. Embedded systems comprise three components namely the physical hardware, application-specific software and Real-Time Operating System (RTOS). Most of the embedded systems are task-oriented focus on particular system functionalities. It possesses significant advantages such as low cost, low power, small-sized and high-performance systems that work predominantly with the dynamic real-time environment. However, as an individual technical entity, embedded systems fail to cope with emerging disruptive technical requirements. This is due to the reason that there exists a crucial gap between the physical and the information world in embedded applications. In contrast, convergence with present-day advanced techniques can bridge the gap and result in significant technological advancements. For instance, the pacemaker is a well-known embedded application; integrating the Internet of Things (IoT) technologies with it could assist in effective real-time health data analysis and emergency assistance. In the contemporary age of sophisticated technology, internet and Cyber-Physical Systems (CPS) forms an obvious part of the day to day life. Cyber-Physical Systems (CPS) is an integration of physical and logical systems to comprise interaction between digital, analog, and human components. These systems act as an establishment factor for various applications such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), smart cities, industrial internet, smart grid, and several other smart systems (e.g., cars, building, parking, home, etc.). In general, CPS enables composite interaction between various heterogeneous cyber and physical components. The complex nature of Cyber-Physical Systems (CPS) leads to various purposeful and accidental disturbances across the network making the behavior prediction (normal or faulty system behavior) a difficult process. As an active measure, the convergence of the Cyber-Physical System (CPS) with embedded systems can significantly enhance both the sectors and offer numerous benefits. Further, it automates various systems processes with advanced intelligence measures. This special issue offers an excellent platform for the researchers to present their novel views and solutions on embedded systems and Cyber-Physical System (CPS) for automation and intelligence measures. The following topics are welcome but not restricted to: Innovation and automation in smart cities with embedded systems and cyber-physical systems (CPS) Frontiers in next generation high performance computing with embedded systems and cyber-physical systems (CPS) Automation intelligence in robotics with embedded systems and cyber-physical systems (CPS) Role of cyber-physical systems (CPS) and embedded systems in Internet of Things (IoT) smart cities (smart healthcare, smart transportation, smart buildings, etc.) Ubiquitous and persuasive computing with Internet of Things (IoT), embedded systems and cyber-physical systems (CPS) Artificial intelligence for Embedded and cyber-physical system (CPS) applications Concerted effort of Internet of Things (IoT), blockchain, cyber-physical system (CPS), Artificial Intelligence (AI) and embedded systems in neural and mental healthcare Design methodologies and architectural framework for sustainable cyber-physical system (CPS) with embedded systems Cloud-fog-edge computing for sustainable Internet of Things (IoT) and Cyber-Physical Systems (CPS) A new era of embedded computing with advanced technologies in embedded systems and cyber-physical systems (CPS) Combined effect of cyber-physical systems (CPS) and embedded systems for advancement insmart autonomous unmanned vehicle systems (UVS) Embedded system for intelligent mobile cyber-physical systems (CPS) Energy-efficient low power architectures for cyber-physical systems (CPS) using embedded systems Intelligent embedded systems architectures for cyber-physical systems (CPS) with federated learning (FL) and artificial intelligence (AI) techniques Design for resilience in cyber-physical systems (CPS) with embedded computing.
Last updated by Dou Sun in 2020-07-30
Special Issue on Role of Embedded Systems in Internet of Medical Things
Submission Date: 2021-08-20

Internet of medical things (IoMT) is an integration of medical devices and applications through which it connects to the healthcare information technologies over a network of interconnected devices. These devices possess the ability to generate, collect, analyze, and transfer data to perform a variety of healthcare-related services. In an IoMT environment, medical devices such as wearables and sensors continuously track end-users health information through medication-tracking systems, sensor-enabled medical wearable devices, and medical supply tracking applications. However, with the growing trend of innovation and technology, IoMT has acquired a massive number of users in recent times, and it may continue to multiply in the coming years. Already around 60% of the global healthcare organizations have successfully deployed IoMT solutions, and it is expected to grow more with global advancements. This digital transformation could potentially create a revolution in IoMT systems with a requirement for more advanced intelligent devices incorporated with accurate data collection and information processing capabilities. Embedded systems are considerably an important area of the research and play a crucial role in the development of IoMT applications. They effectively sense the information and transfer it across the IoT networks for numerous processes. In general, embedded devices are more user friendly, and they can be easily developed, customized, and programmed based on user requirements. Some of the significant properties of embedded systems, such as reduced power consumption, lesser maintenance requirements, real-time computing facilities, and high availability act as key enablers of IoMT applications with a distinct set of innovative functionalities. Thus, bringing in advancements against this background can significantly offer IoMT-optimized solutions for the growing healthcare market with the competitive edge of opportunities. This special issue focuses on theories, methodologies, architectures, and applications of embedded systems for IoMT applications. It further encourages the authors to make submissions on a broad range of IoMT devices, new methods of efficient information processing systems for wearables, embedded systems based on IoMT architectures, algorithms, and their novel applications in real-time practices. Papers focusing on innovative embedded devices for IoMT devices to meet the increasing global medical requirements are specially invited. Topics for the special issue include, but are not constrained to the following: Advances in intelligent embedded system architectures for IoMT Programming models for IoT enabled embedded systems for IoMT Innovative design methodologies and platforms for embedded systems in IoMT New trends in information and communication technologies for efficient data processing in IoMT with embedded systems In-memory computing for IoMT applications with embedded systems Application related case studies for intelligent embedded systems and their real-world practices Edge computing assisted IoMT embedded systems from hardware as well as the software perspective Efficient design methodologies and architectures for wearable computing Emerging applications, services, and management models for IoMT using embedded systems Advances in sensor, actuator, and M2M communication networks for IoMT using embedded applications Secure design of embedded devices for IoMT environment
Last updated by Dou Sun in 2020-11-17
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