Journal Information
Microprocessors and Microsystems: Embedded Hardware Design (MICPRO)
https://www.journals.elsevier.com/microprocessors-and-microsystems
Impact Factor:
1.161
Publisher:
ELSEVIER
ISSN:
0141-9331
Viewed:
12653
Tracked:
16
Call For Papers
Microprocessors and Microsystems: Embedded Hardware Design (MICPRO) is a journal covering all design and architectural aspects related to embedded systems hardware. This 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-processor systems on a chip (MPSoC), as well as, their memory and communication methods and structures, such as network-on-chip (NoC).

Design automation of such systems including methodologies, techniques, flows and tools for their design, as well as, novel designs of hardware components fall within the scope of this journal. Novel cyber-physical applications that use embedded systems are also central in this journal. While software is not in the main focus of this journal, methods of hardware/software co-design, as well as, application restructuring and mapping to embedded hardware platforms, that consider interplay between software and hardware components with emphasis on hardware, are also in the journal scope.
Last updated by Dou Sun in 2021-02-02
Special Issues
Special Issue on Machine Learning and Blockchain for Cognitive Internet of Things
Submission Date: 2021-03-15

Cognitive Internet of Things (CIoT) is an emerging field where in IoT systems are made more intelligent and smarter using cognitive computing. The intuitive ability of IoT combined with cognitive power of Machine learning and security of blockchain is capable of building transformative techniques. CIoT with machine learning and blockchain presents enormous opportunities for building intelligent and smart applications. Machine learning enables cognitive computing to develop a “thinking” system. It allows the system to learn and analyze the new data as it comes. The blockchain technology provides more security to the data and disables any kind of breaches. The enormous data generated by connected devices essentially needs cognitive power to build intelligent systems. On the other hand, the security of this data is of utmost importance for various applications. In the coming decade it is estimated that more than 30 billion IoT devices will be generating data. Thus, there is an emerging requirement of development of technologies that can process, store and secure this data. The use of cognitive computing, Machine learning and blockchain will enable us to handle this data effectively. This special issue aims to publish high quality research papers that focus on the power of machine learning and blockchain for cognitive IoT. These technologies can be used together for automation, resource optimizations, sustainable systems and better security of data. Topics of interest include, but are not limited to: Innovative architecture, infrastructure, techniques for CIoT Intelligent models and applications for CIoT Intelligent systems for information fusion in CIoT Architectures and platforms for blockchain and IoT IoT and blockchain convergence IoT malicious transactions detection Blockchain schemes for decentralization in IoT Machine learning algorithms for IoT Machine Learning for decision support systems in IoT Cognitive aspects of Machine learning Knowledge-based techniques for IoT Optimization methods for IoT Automated reasoning in IoT Big data analytics to identify malicious behaviours on blockchain for IoT Intelligent blockchain driven IoT applications AI-enabled scalable Blockchain for IoT IoT data encryption and security
Last updated by Dou Sun in 2020-12-09
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 DATA SCIENCE ADVANCEMENTS IN SMART MOBILITY
Submission Date: 2021-04-15

Connected mobility has been identified as a key future market. In addition to bringing tremendous economic value and considerably reducing threats to human lives, it is contributing to appropriate constructions and optimized utilization of transportation systems and their communication networks. To this end, microsystem technologies enabling the interconnection of vehicles and road infrastructures as well as facilitating the acquisition and sharing of related contextual data are attracting increasing attentions. However, these technologies are still in their infancy. Considerable research and development efforts are, therefore, needed, particularly to prevent attacks against vehicles and transportation infrastructures, enable the integration of emergent technologies, process increasing volumes of mobility data, and allow an effective implementation of the rapidly growing paradigm of self-driving cars. We argue that new and adequate approaches to data acquisition, storage, processing, and sharing are necessary to enable seamless integration and use of emerging technologies within the ecosystem of connected mobility. In this special issue, we are addressing the following questions: (1) How could recent advances in miscro systems based data intelligence solve the current challenges of smart mobility? and (2) How could large volumes of structured and unstructured data science to stored, processed and manipulated in microsystems enabling smart mobilty? We, therefore, invite researchers, experts, and practitioners from the academy and the industry to share and promote their breakthroughs, applications, and solutions addressing the advances and challenges of connected mobility from a data science perspective. The topics of interest of this special issue are framed within the context of connected mobility from a data science perspective. They include, but are not limited to: Data science approaches and applications in microsystems Deep Learning and Convolutional Neural Networks for connected mobility Supervised and Unsupervised Learning for connected mobility Data mining processes for connected mobility Data representation for vehicular microsystems Data analytics for Vehicle Environment Perceptions Data science-based solution for road safety Cognitive and Context-aware Intelligence using microsystems Data intelligence for drone-based applications Data-driven intelligence using microsystems for counter-COVID systems
Last updated by Dou Sun in 2021-02-02
Special Issue on IOT ASSISTED DIGITAL TWINS IN SMART AGRICULTURE
Submission Date: 2021-04-30

The rapid world population growth dramatically increases the requirements for an equally rapid increase of food production to feed the human community. This requirement has recently been covered through smart agriculture that can assist in increasing production efficiency. The farming sector faces new problems such as labor shortages, pressure for feeding populations increasing in size, and environmental degradation, as well as agricultural products and plants, which reduces the production of crops. The outbreak of the COVID-19 pandemic made the situation even worse putting global production at risk for an unknown time period. Digital twinning is a significant concept that to explore the relationship between real/physical and digital/virtual objects and can greatly assist towards improving several sectors of the digital economy including smart agriculture. Digital twins can be a primary method to control farming and can revolutionize agriculture with the assistance of Internet of Things (IoT) technologies to improve the production of crops. This removes otherwise necessary constraints on place, time, and human observation based on the Physical proximity to farming operations to enable remote monitoring, control, and coordination of farms. The digital twins may be enriched with precise detection of physical and virtual objects using sensors as well as satellite data. The growth of the Internet of Things in digital twins is pushing its way into all fields, including smart agriculture. It provides a better understanding of the situation in farms and areas based on process-related agriculture, such as irrigation, use of fertilizers and pestisides etc. for obtaining a more comprehensive view of the overall value chain in food production with strategic decisions. The significance of the IoT unplugs data collection and uses standard and low-cost worldwide Internet and Cloud Computing technologies as a core part of data transfer to resolve the challenges in smart farming. The use of digital twins with IoT in smart agriculture ensures several benefits such as productivity growth, food quality and pverall procuction efficiency improvement. Besides, this issue considers the utilization of unmanned aerial vehicles for crop monitoring and optimization and their contribution towards developing digital twins of agricultural processes. This topic illustrates the state-of-the-art Internet of Things based frameworks and architectures used in smart agriculture. This Special Issue invites reports on high quality research with innovative approaches and solutions for digital twins with IoT assisted smart agriculture management. The topics of interest for the special issue include: Digital twins integrated with IoT for smart farming Data-driven scenarios based on Digital twin leveraging AI; Cloud, edge, and fog computing for smart agriculture; Security for Digital twin in smart farming Cyberphysical systems (CPS) and modelling for smart agriculture; Data protection by user authentication and data encryption in smart agriculture. Metrics and modeling for sensing in smart agriculture; Farm services, applications and processes oriented towards agri-food systems IoT Platforms for applications in smart agriculture; Remote sensing from satellites or crewless aerial vehicles; Artificial intelligence and machine learning for smart agriculture Implementation and prototypes of connected farms and farming in the cloud Data-aware networking in smart agriculture; Big data techniques for IoT-based smart agriculture Management of IoT-based smart agriculture applications and services. Increasing environmental awareness in agriculture;
Last updated by Dou Sun in 2020-12-15
Special Issue on Advances in Multi-Core and Many-Core Systems
Submission Date: 2021-05-01

The ever-increasing global demand for computing power and improved performance drives the embedded systems towards multi-core systems. This is because of the reason that multi-core and many-core systems are formulating itself as a most significant choice of the solution across the electronics and communication sectors. However, its implementations are still at infancy and posses numerous research gaps. Some of the most critical challenges include risk associated with migrating legacy software to work with multi-core systems; what are the better technologies that can be used? Will the incorporation of virtual techniques can enhance the performance? Will it be cost-efficient and reliable?. Finding the answer to these questions can probably help the research community leverage the most powerful high-performance multi-core solutions that extensively offer advanced tools and techniques. Presently, computing networks are moving towards a new era of digital computing. However, the progress towards global network connectivity increases the need for higher computing power and makes the existing solutions to be inappropriate. Already, the high-end system applications such as networking and wireless infrastructure platforms have started to adopt multi-core and many-core solutions. As a matter of fact, it is often impossible for a single-threaded performance to offer consistent services without the integration of geometrically increasing power. This instance can potentially increase the system cost with reduced reliability measures. Thus, in order to retain the power at a controllable level, it has become a necessity for today’s applications to go for multi-core and many-core systems as an alternative to continually increasing frequency rate. Recent multi-core and many-core processors offer various levels of parallelism via numerous range of architectures for data-parallel execution. Hence, implementing multi-level parallelism has become a critical requirement for the current and future processors to cope with increasing demand. In this context, this special issue aims to bring-out the advances in multi-core and many-core systems for future generation computing systems. It offers a forum for researchers and industrial professionals to share their novel and innovative ideas against this background. The topics of interest include, but not limited to, the following: Advances in multi-core and many-core design, architectures, and programming models Energy-efficient design protocols for multi-core and may-core systems Low power design for multi-core and many-core system Advances in interconnection network infrastructures for multi-core and many-core systems Programming models, tools, and languages to support energy-aware multi-core and many-core computing Advances in system security for multi-core and many-core architectures Modern multi-core and many-core architectures Advanced network interface designs for Intra/inter-chip communication On-chip network architectures and deployment models for multi-core and many-core systems Advanced communication modeling and benchmarking technologies for multi-core and many-core approaches Emerging interconnect technologies for multi-core and many-core systems
Last updated by Dou Sun in 2020-12-15
Special Issue on Embedded system reconfigurable architecture for data mining and mobile applications
Submission Date: 2021-05-10

Dealing with large amount of information and finding interesting knowledge from them become a huge problem nowadays. Data-mining applications are on a very huge demand in all aspects of human life. Increasingly, the exponential growth of information demands computing platforms with higher processing power. Providing more processing powers to embedded mobile (portable) devices is a challenging problem because mobile devices have stringent constraints such as area, power consumption, memory bandwidths, cost, etc. to overcome this challenge effectively and efficiently, optimized hardware architectures are needed. A significant amount of time approximately 93% to 98% is spent on data transfer between the external memories, which is a major performance bottleneck. Hardware designs need to be developed towards reduction of the memory access latency. Reconfigurable systems, exploiting a mixture of the traditional CPU-centric instruction-stream-based processing with the decentralized parallel application-specific data-dominated processing, provide a drastically higher performance and lower power consumption than the traditional CPU-centric systems. Embedded systems are real time systems, including sensing, interfacing, processing and / or actuating sub systems and involve in their implementation various mixtures of digital and analog hardware and embedded software. Extra hardware in optimization techniques results in larger area. It is important to consider the speed-space trade-offs, especially in mobile and embedded devices. It is necessary to create partial and dynamic reconfigurable hardware architectures for the selected data-mining application to reduce the on-chip occupied area. It is also necessary to introduce architectures and techniques to address the on-chip memory bandwidths limitations on FPGAs. Other challenges include Improvements in code (HDL) optimization, power consumption in mobile and embedded devices. Potential topics included, but not limited Run-Time Partial Reconfiguration of hardware architectures for data mining and mobile applications Efficient Embedded Architectures for power Management in mobile devices Dynamic partial reconfigurable hardware architecture for mobile and embedded devices Embedded and reconfigurable architectures, techniques, and methodologies for data mining applications on portable devices Reconfigurable hardware architecture for mobile and embedded devices Secure, and Predictable Software/hardware Architecture for data mining and mobile applications FPGA implementations of data mining algorithms in portable devices
Last updated by Dou Sun in 2020-12-15
Special Issue on Design methodologies and tools for Integrated 5G/6G technologies and their applications
Submission Date: 2021-06-15

Embedded electronic systems contain a combination of software and hardware, both analog and digital. Although simple systems can be implemented with a single, off the-shelf microcontroller, a digital signal processor or a conventional microprocessor and associated software, more complex systems that have critical requirements regarding aspects such as area, speed, and power consumption call for innovative design. Various target architectures can be considered for matching different requirements. Solutions may include dedicated processors and/or ASICs, or even multi-processor platforms, combined with dedicated analog and RF front ends. The design of such complex embedded systems and analog front ends encompasses a suite of different technologies, tools, and design styles. A complete design environment must consider system specification, partitioning among software, digital hardware and analog parts, and synthesis of software and hardware parts and interfaces. As the penetration of internet-based services such as video streaming, broadband connections have become an increasingly indispensable element of modern life, so the demand for advanced network technologies is growing stronger than ever. The dynamic 5G technology is one such concept anticipated to make a significant mark over the coming years. Indeed, it has resulted in the evolution and advancement of several verticals such as 5G chipsets, 5G enterprises, 5G broadcast, and more. Defined with faster speed, increased bandwidth, higher capacity and low latency issues, 5G is likely to contribute to the several technological breakthroughs in fields ranging from smartphones and IoT connected devices to smart cities and autonomous cars. An important consideration in the growth of this technology is the establishment of a robust 5G infrastructure.5G chipsetsare an integral part of this infrastructure, which is why several vendors across the technological domain are showing a strong commitment towards developing sophisticated chipset solutions. 5G chipsets refer to a set of electronic elements within integrated 5G circuits, which facilitate the transmission of 5G packets on IoT devices, smartphones, portable hotspots, and even mobile network-equipped notebook PCs. 6G will eventually replace5G, but currently 6G is not a mature technology, and is instead in the early research phase. Mobile telecoms companies are much too focused on 5G to deal with 6G in any significant way at the moment, and for the near future. In this context, this special issue offers a platform for researchers and practitioners worldwide to present their novel and innovative ideas on design methodologies for integrated 5G/6G technologies. We invite unpublished articles presenting interactive system models, conceptual designs, and technological advancements related to the theme of this special issue. Potential topics included, but not limited Embedded systems in software defined radio for 5G/6G applications On chip reconfigurable antennas and devices for 5G/6G communication systems Reconfigurable FPGA programming model architecture for 5G layers Design and implementation of embedded hardware switch for 5G/6G network architecture Novel packing and system integration platform for low cost and high performance 5G/6G systems Design and implementation of microservice architecture for 5G/6G satellite edge computing framework and its applications Hardware prototyping for filter bank multicarrier for 5G/6G mobile communication systems Design and integration of receiver circuits for 5G/6G communication systems Architecture design of area efficient high-speed crypto processor for 6G Reconfigurable intelligent processor for 6G technologies and its applications Design and implementation of UAV wireless communication system for 6G networks Reconfigurable system in cyber twin for 6G network security Hardware/software co-design in agile chip development for 6G and its applications
Last updated by Dou Sun in 2021-02-02
Special Issue on Enabling Technologies for Intelligent Embedded Systems: Changing the Landscape of Research and Development
Submission Date: 2021-06-30

In today’s era of connected digital world, Intelligent Embedded Systems (IES) represent a novel and promising generation of embedded systems and have the capacity of reasoning about their external environments and adapt their behavior accordingly. While embedded systems are a very mature technology overall, with the steady advancement of new and more powerful processors, the technology now enables the next-generation of intelligent devices, machines, equipment, and factories. Technologies such as smart sensing, RFID tagging, embedded internet, edge computing, and predictive data mining all work to permeate intelligence and decision making into the physical world with the ultimate aim of continually enhancing human experience in real-time. “Big data” is another important technology, in which analytics provides real-time insights, which need to be actioned upon quickly to support decisions, gain better value, and mitigate risk. Moreover, artificial intelligence (AI), and particularly the machine learning, has been intensively applied to deal with large-scale heterogeneous data to help innovate and transform businesses. The convergence of these two technology paths is highly promising, and opens up new avenues of IES. The aim of this special issue is to discuss how large-scale data systems and AI can be leveraged to enhance the learning, reasoning, and decision-making in embedded systems, in real-time. Data governance, data integration, data storage, data quality and data security are some criticalities associated with this problem, while conventional embedded system architectures and protocols are used to prepare data are inadequate. Unlike traditional data sets that are commonly associated with embedded systems, big data tends to be unstructured, multi-modal, and in the case of human-centric text, perhaps multi-lingual. The incompleteness, fuzziness and uncertainty make it even more intricate to tap and analyse information using contemporary tools. We invite researchers to discuss intelligent embedded system design methods, optimization techniques, protocols and architectures. Novel approaches to information discovery and decision making which use multiple intelligent technologies such as machine learning, deep learning, artificial intelligence, natural language processing and image recognition among others are required to understand data & then generate insights. We also welcome implementation papers on analyzing and processing of big data and practical data-driven decision making by discovering and understanding knowledge from the data. The topics of interest include, but are not limited to: Intelligent embedded system design methods, optimization techniques, architectures, and protocols for high performance/parallel computing platforms. AI-empowered modelling of embedded systems for big data. Intelligent embedded system-aware protocols for data quality and integrity. Intelligent embedded computer vision and natural language processing systems AI in mobile embedded systems, wearables, and robotics Efficient memory and communication methods for high-throughput AI approaches Embedded systems for large-scale smart healthcare, avionics, transportation, and automotive Machine learning and embedded computing in advanced driver assistance systems ML algorithms for energy-efficient operation and low-power processing Green / low-power embedded software and embedded hardware Emerging hardware technologies in big data systems AI-systems on chip for the Internet of Things (IoT), Industry 4.0, smart transportation and autonomous robots Deep learning/machine learning frameworks for complex embedded architectures Fault-tolerance, reliability, and security for embedded IoT applications and deep learning systems
Last updated by Dou Sun in 2021-02-02
Special Issue on Deep Reinforcement Learning for Medical Applications on Embedded Devices
Submission Date: 2021-06-30

Deep reinforcement learning (DRL) uses feedback from the agent to make decisions in complex problems under uncertainty. Medical applications often require processing large volumes of complex data in a challenging environment. Deep reinforcement learning can process this data by analyzing the agent's feedback that is sequential and sampled using non-linear functions. The deep reinforcement learning algorithms commonly used for medical applications include value-based methods, policy gradient, and actor-critic methods. The recent advances in the increased computational capabilities of architectures like field-programmable gate array (FPGA), graphics processing units (GPU), and digital signal processors (DSP) have made it possible to infer deep reinforcement learning algorithms on them. However, efficient implementation of these architectures should consider the issues related to their portability, wearability, and power consumption. The main objective is to provide a platform for scientists, researchers, industry experts, and scholars to share their innovative contributions in deep reinforcement learning for medical applications on various embedded devices (ED). Research articles describing only a proof of concept are not encouraged. Authors are solicited to develop novel deep reinforcement learning algorithms on medical data and implement them either on FPGA, GPUs, or DSP. The special issue invites authors to submit papers that analyze the portability, wearability, power consumption of the deep reinforcement learning algorithms implemented either on FPGA, GPU, or DSP. The deep reinforcement learning topic includes but not restricted to: Monte Carlo Tree Search and Deep Q-network Dual Gradient Descent and Conjugate Gradient Trust Region Policy Optimization and Proximal Policy Optimization. Actor-Critic using Kronecker-Factored Trust Region Linear Quadratic Regulator and Iterative Linear Quadratic Regulator Twin-Delayed Deep deterministic policy gradient Guided Policy Search and Model-Based Learning with Raw Medical Videos Inverse Reinforcement Learning and Meta-learning Very efficient ED for DRL in medical applications in terms of power consumption, processing efficiency and flexibility Neuromorphic and/or brain-inspird architectures implementing DRL techniques Efficient mapping of DRL applications to ED New learning approaches for DRL targeting ED High-level programming language support, tools, frameworks, and system software for DRL in medical applications implemented on ED Security and Reliability issues for DRL on ED DRL ED implementation in cyber-physical systems for healthcare, well-being and personal assistance (elderly, disability), sports and medicine, rehabilitation, instrumentation, lab-on-chips
Last updated by Dou Sun in 2021-02-02
Special Issue on Hybrid artificial intelligent in embedded system for smart industry
Submission Date: 2021-07-15

Hybrid artificial intelligence (H-AI), has emerged. H-AI is dedicated to investigating models, methods, technologies, and systems that enable and support the synergy, symbiosis, and augmentation of human and artificial intelligence. This provides a promising approach to the technical and ethical challenges–humans and machines can each focus on what they are good at, meanwhile humans are still largely in control in decision making. An embedded system (unlike a stand-alone computer) is a cyber system being an inseparable part of a certain larger system (e.g. product or infrastructure). It serves a specific aim (e.g. monitoring, control etc.) in this larger system through (repeatedly) executing specific computation and communication processes required by its application. It is application-specific. It has to be especially designed or adopted to adequately serve the execution of these specific computation processes, and satisfy the application’s requirements related to such attributes as functional behaviour, reaction speed or throughput, energy consumption, geometrical dimensions, price, reliability, safety, security, etc. Typically, embedded systems are (reactive) real-time systems, include sensing, interfacing, processing and/or actuating sub-systems, and involve in their implementation various mixtures of digital and analogue hardware, and embedded software. Smart industry system is a combination of humans, embedded systems, advanced computation, communication and control with the aim to achieve specific goals at an optimized level. This special section will provide a platform for scientists, engineers and industrial practitioners to present their latest theoretical and technological advancements in smart industry. Potential topics included, but not limited Computational intelligence for embedded system design Smart sensors, online monitoring and diagnosis for smart manufacturing Intelligent embedded software for smart industry Novel secure designs for embedded software and systems for smart industry Intelligence embedded system for quality modeling, analysis and optimization AR-assisted assembly, disassembly, diagnosis, and equipment maintenance Augmented reality and wearable computing for greater equipment or process awareness Embedded system for digital simulations and digital twins
Last updated by Dou Sun in 2021-02-02
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 System and Device Architectures, Challenges and Future Directions of 6G-Enabled Wireless Communications for IoT Applications
Submission Date: 2021-07-30

The sophistication and the upcoming commercialization of communication networks of the fifth generation (5G) will accommodate an exponential growth in mobile data demand and the diverse needs of vertical industries as part of a much wider global digital economy, such as retail, transport, and health. Wireless networking systems and innovations beyond the fifth-generation of (5G) networks need to be more implemented. Wireless data traffic by 2030 (Source: ITU) is forecast at 4394 EB, and most advanced technologies will not be enabled by 5G. The sixth-generation network (6G) is expected to improve 5G capabilities in order to allow millions of connected devices and apps to run seamlessly with high data rates and low latency. In support of massive wireless interconnectivity, with extremely complex service requirements, 6G will therefore play a key role. Efficient and smart preparation is required to meet the exceptional demands of ubiquitous connectivity in future 6G enabled wireless networks. Due to the Internet of Things (IoT) heterogeneity, 6G wireless network random access and resource allocation mechanisms should take into account IoT requirements through intelligent algorithm implementations and protocol design as well as advanced signal processing and communication technologies. Modern random access techniques such as MIMO, OFDMA, NOMA, sparse signal processing or new techniques in the field of orthogonal architecture are good candidates for IoT-enabled 6G wireless network. Additionally, 6G-enabled IoT networks could be a domain of application of emerging Artificial Intelligence (AI) technologies exploiting the knowledge that can be generated for applications like spectrum management, energy efficiency, IoT discovery, and cognitive technologies etc. accelerating innovation in these fields. The combined use of the 6G and AI technologies represent a multidisciplinary domain towards developing efficient modern IoT systems. This special issue is intended to provide a detailed review of emerging technologies, advanced architectures and potential challenges focusing on the role of system and device architectures for 6G-enabled IoT. The topics of interest include, but are not limited to the following: System and device architectures for 6G-enabled wireless communication Implementation of resource allocation and energy efficient algorithms in 6G-enabled wireless networks Performance vs. implementation efficiency aspects of random access for 6G-enabled IoT Impact on design and implementation complexity of novel Information theory foundations of 6G-enabled IoT Wireless signal processing techniques for 6G-enabled IoT Real-time sensing, learning, and decision making for 6G-enabled IoT Novel AI and Machine learning frameworks for 6G-enabled IoT Intelligent hardware and systems against vulnerabilities under different threat models and risk management in 6G enabled wireless communication Interoperability between 6G and various IoT systems AI-assisted intelligent spectrum management in 6G networks Blockchain-enabled resource management and sharing for 6G communications Breakthrough technologies and concepts Reconfigurable antennas and devices for the next generation systems and services
Last updated by Dou Sun in 2021-02-02
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 Recent Advances in Artificial Neural Networks and Embedded Systems for Multi-Source Image Fusion
Submission Date: 2021-08-16

Multi-source visual information fusion can help the robotic system to perceive the real world, and image fusion is a computational technique fusing the multi-source images from multiple sensors into a synthesized image that provides either comprehensive or reliable description. At present, a lot of brain-inspired algorithms methods (or models) are aggressively proposed to accomplish this task, and the artificial neural network has become one of the most popular techniques in processing multi-source image fusion in this decade, especially deep convolutional neural networks. This is an exciting research field for the research community of image fusion and there are many interesting issues that remain to be explored, such as deep few-shot learning, unsupervised learning, application of embodied neural systems, and industrial applications. How to develop a sound biological neural network and embedded system to fuse the multiple features of source images are basically two key questions that need to be addressed in the field of multi-source image fusion. Hence, studies of image fusion can be divided into two aspects: first, new end-to-end neural network models for merge constituent parts during the image fusion process; Second, the embodiment of artificial neural networks for image fusion systems. In addition, current booming techniques, including deep neural systems and embodied artificial intelligence systems, are considered as potential future trends for reinforcing the performance of image fusion. This Research Topic focuses on the new ideas, models, and methods in artificial neural networks and embedded systems for multi-source image fusion. We welcome all Specialty Grand Challenge, Perspective, Brief Research Report, Original Research Articles, and Reviews. Themes to be investigated may include, but are not limited to: Neural Network Models and Techniques: -Deep Convolutional Neural Networks for Image Fusion -Generative Adversarial Networks for Image Fusion -Neurodynamic Analysis for Image Fusion -Learning Systems for Image Fusion -Fuzzy Neural Networks for Image Fusion -Bionic Image Fusion for Robotic System Feature Extraction and Fusion Strategies: -Image Feature Extraction based on Deep Neural Networks -Intelligent Sensing-based Decision Support Systems for Image Fusion -Feature Presentation Methods for Image Fusion -Fused Image Quality Assessment -Image fusion Strategies on Neural Networks -Adaptive Image Fusion Strategies for Robotic System Techniques on Real-World Applications: -Industrial Applications of Image Fusion -Embedded Learning System for Image Fusion -Real-Time Image Fusion System -System on Chip for Image Fusion -Model Acceleration for Image Fusion -Lightweight Image Fusion Techniques for Robotic System Keywords: Artificial Neural Networks, Embedded Learning System, Feature Extraction, Fusion Strategy, Image Fusion Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Last updated by Xin Jink in 2021-01-20
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
Special Issue on Intelligent Decision Making Methods for Embedded Devices in IoT Environments
Submission Date: 2021-08-20

The Internet of Things (IoT) becomes as one of the key technological developments that provide smart infrastructure for the cloud-edge services by interconnecting physical devices and virtual things between mobile applications and embedded devices. Several embedded software and hardware systems had been developed by developers to assist IoT systems in gathering information about safety-critical fields. By increasing development of IoT ecosystems, Intelligent Decision Making (IDM) paradigm is emerging as a high potential solution for processing and navigating the information of embedded IoT devices. IDM for embedded devices in IoT systems investigates the massive quantity of complex data to help industry, transportations, medical systems, microcontrollers and other smart applications. IoT, embedded devices, sensors, microprocessors, manual data entry and online sources are a few complex data sources for IDM. IDM make use of machine learning techniques to enhance the process of complex making decisions and prediction. AI tools such as Metaheuristic, Fuzzy Logic, artificial neural networks, deep learning and intelligent agents can be integrated to IDM for embedded systems in IoT. Finally, IDM-based embedded devices can be very beneficial to a range of IoT systems where complex and critical decisions are made under time pressure, decision-makers are on the move, and the environment is dynamic and uncertain. Despite the importance of decision making methods on embedded software and hardware systems in IoT environments, this special issue invites researchers to publish selected original papers presenting intelligent trends to solve new challenges of IDM methods. We also are interested in review articles as the state-of-the-art of this topic, showing recent major advances and discoveries, significant gaps in the research and new future issues. Topics are as below but are not limited to: • Memetic-based IDM for embedded applications in IoT • Machine learning methods for IDM in embedded IoT systems • IDM for the embedded computer-aided diagnostic system. • IDM for industrial embedded IoT applications. • Embedded medical instrumentation and healthcare technologies in IoT • Decision making for Wireless Body Area Network (WBAN) in wearable IoT systems • Formal analysis of IDM-based embedded devices in IoT • Energy prediction on embedded sensor-based IDSS systems in IoT • Security and privacy aspects of embedded systems based on IDM in IoT • Blockchain technology on IDM-based embedded IoT systems • Big data management based on IDM in IoT systems • IDM on vehicular communications in IoT systems • IDM for robotics and micro-robotic embedded systems in IoT • Smart city and smart home based on IDM in embedded IoT • Decision-making enabled embedded smart farming and agriculture in IoT • Decision-making on multi-processor systems on a chip in IoT applications
Last updated by Dou Sun in 2021-02-02
Special Issue on Intelligent Sensors and Microsystems for Smart Cities
Submission Date: 2021-10-20

With the rise of new tendencies and technologies such as Artificial Intelligence (AI), Big Data, Internet of Things (IoT), and next generation mobile communications, the development of ICT solutions will continue its trend of miniaturization, integration, and intelligence. Sensors and microsystems are an organic combination of microelectronics, Micro-Electro-Mechanical Systems (MEMS), optoelectronics and other technologies, characterized by miniaturization and systematization. Sensors and microsystems are miniaturized by advanced integration methods, which produce new functions at the system level and greatly increase function density. In intelligent sensors and microsystems, the sensor module is used to acquire and collect data; the processing module is used for information conversion, calculation, storage, and system control; the communication module is used for information exchange and transmission; the execution module is used for the implementation of instructions and system actions; and the energy supply module is used for energy supply and optimized management. Compared with traditional SoC chips, MEMS components, optoelectronic modules, or integrated microsystems, intelligent sensors and microsystems constitute a qualitative leap in connotation and extension, that is not limited to the characteristics of high performance, ultra-precision, and dexterity. Smart cities use various information technologies or innovative ideas to integrate the systems and services of the city. Intelligent sensors and microsystems are a dynamic, open, developing, and evolving concept. Advanced scientific and technological innovations such as AI, new generation communication, and quantum information are advancing by leaps and bounds. Breakthroughs in various new materials, structures, devices, and processes are also emerging. These cutting-edge technologies will continue to inject vitality into the technological innovation of intelligent microsystems. The application demand brought by social and economic development will drive the continuous breakthrough of intelligent sensors and microsystems technologies. Smart city support and applications also relate to the planning, management, operation, and long-term development of the city. The development of the modern city is digital-driven. In the process of urban planning, the application of leading digital technology and intelligent technology must be considered. Thus, this special issue aims to provide a relevant opportunity for scholars in related fields to communicate and share applications and achievements of intelligent sensors and microsystem technology in the field of smart city development, and disseminate the latest research information and progress among the public. The research topics of interest for this special issue include but are not limited to: Intelligent Sensors and Microsystems in Smart City Operation and Construction Intelligent Sensors and Microsystems for Visualization of Urban Spaces Intelligent Sensors for the Urban Internet of Things Wireless Intelligent Sensors for Intelligent Transportation Systems Innovative Intelligent Sensors and Microsystems for Smart Homes Intelligent Sensors for the Urban Thermal Pipeline Waste Detection Filling Sensors for Smart Cities Intelligent Sensors and Microsystems for the Smart Grid MEMS Microsystems for Smart Cities Automotive Sensors and Microsystems Public Safety Sensors and Microsystems Middleware for Intelligent Sensors and Microsystems Cloud and Edge Cloud Architectures to Integrate Intelligent Sensors in Smart Cities Security and Privacy Issues for Intelligent Sensors in Smart Cities Interoperability Issues for Intelligent Sensors in Smart Cities Scalability Issues for Intelligent Sensors in Smart Cities Large-scale Deployment Experiences of Intelligent Sensors and Microsystems Environments
Last updated by Dou Sun in 2021-02-02
Special Issue on Intelligent Sensors and Microsystems for Smart Cities
Submission Date: 2021-10-20

With the rise of new tendencies and technologies such as Artificial Intelligence (AI), Big Data, Internet of Things (IoT), and next generation mobile communications, the development of ICT solutions will continue its trend of miniaturization, integration, and intelligence. Sensors and microsystems are an organic combination of microelectronics, Micro-Electro-Mechanical Systems (MEMS), optoelectronics and other technologies, characterized by miniaturization and systematization. Sensors and microsystems are miniaturized by advanced integration methods, which produce new functions at the system level and greatly increase function density. In intelligent sensors and microsystems, the sensor module is used to acquire and collect data; the processing module is used for information conversion, calculation, storage, and system control; the communication module is used for information exchange and transmission; the execution module is used for the implementation of instructions and system actions; and the energy supply module is used for energy supply and optimized management. Compared with traditional SoC chips, MEMS components, optoelectronic modules, or integrated microsystems, intelligent sensors and microsystems constitute a qualitative leap in connotation and extension, that is not limited to the characteristics of high performance, ultra-precision, and dexterity. Smart cities use various information technologies or innovative ideas to integrate the systems and services of the city. Intelligent sensors and microsystems are a dynamic, open, developing, and evolving concept. Advanced scientific and technological innovations such as AI, new generation communication, and quantum information are advancing by leaps and bounds. Breakthroughs in various new materials, structures, devices, and processes are also emerging. These cutting-edge technologies will continue to inject vitality into the technological innovation of intelligent microsystems. The application demand brought by social and economic development will drive the continuous breakthrough of intelligent sensors and microsystems technologies. Smart city support and applications also relate to the planning, management, operation, and long-term development of the city. The development of the modern city is digital-driven. In the process of urban planning, the application of leading digital technology and intelligent technology must be considered. Thus, this special issue aims to provide a relevant opportunity for scholars in related fields to communicate and share applications and achievements of intelligent sensors and microsystem technology in the field of smart city development, and disseminate the latest research information and progress among the public. The research topics of interest for this special issue include but are not limited to: Intelligent Sensors and Microsystems in Smart City Operation and Construction Intelligent Sensors and Microsystems for Visualization of Urban Spaces Intelligent Sensors for the Urban Internet of Things Wireless Intelligent Sensors for Intelligent Transportation Systems Innovative Intelligent Sensors and Microsystems for Smart Homes Intelligent Sensors for the Urban Thermal Pipeline Waste Detection Filling Sensors for Smart Cities Intelligent Sensors and Microsystems for the Smart Grid MEMS Microsystems for Smart Cities Automotive Sensors and Microsystems Public Safety Sensors and Microsystems Middleware for Intelligent Sensors and Microsystems Cloud and Edge Cloud Architectures to Integrate Intelligent Sensors in Smart Cities Security and Privacy Issues for Intelligent Sensors in Smart Cities Interoperability Issues for Intelligent Sensors in Smart Cities Scalability Issues for Intelligent Sensors in Smart Cities Large-scale Deployment Experiences of Intelligent Sensors and Microsystems Environments
Last updated by Dou Sun in 2021-02-02
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