Evaluating the Repair of System-on-Chip (SoC) using Connectivity

This paper presents a new model for analyzing the repairability of reconfigurable system-on-chip (RSoC) instrumentation with the repair process. It exploits the connectivity of the interconnected cores in which unreliability factors due to both neighboring cores and the interconnect structure are taken into account. Based on the connectivity, two RSoC repair scheduling strategies, Minimum Number of Interconnections First (I-MIN) and Minimum Number of Neighboring Cores First (C-MIN), are proposed. Two other scheduling strategies, Maximum Number of Interconnections First (I-MAX) and Maximum Number of Neighboring cores First (C-MAX), are also introduced and analyzed to further explore the impact of connectivity-based repair scheduling on the overall repairability of RSoCs. Extensive parametric simulations demonstrate the efficiency of the proposed RSoC repair scheduling strategies; thereby manufacturing ultimately reliable RSoC instrumentation can be achieved

Spare Line Borrowing Technique for Distributed Memory Cores in SoC

In this paper, a new architecture of distributed embedded memory cores for SoC is proposed and an effective memory repair method by using the proposed Spare Line Borrowing (software-driven reconfiguration) technique is investigated. It is known that faulty cells in memory core show spatial locality, also known as fault clustering. This physical phenomenon tends to occur more often as deep submicron technology advances due to defects that span multiple circuit elements and sophisticated circuit design. The combination of new architecture & repair method proposed in this paper ensures fault tolerance enhancement in SoC, especially in case of fault clustering. This fault tolerance enhancement is obtained through optimal redundancy utilization: Spare redundancy in a fault-resistant memory core is used to fix the fault in a fault-prone memory core. The effect of Spare Line Borrowing technique on the reliability of distributed memory cores is analyzed through modeling and extensive parametic simulation

ieee access special section radio frequency identification and security techniques

Radio Frequency Identification (RFID) systems have been receiving much attention in the last few decades due to their effective role in our everyday life. They propose different solutions to many vital applications. Moreover, RFID systems are the backbone of modern Internet-of-Things (IoT) and Near-Field Communication (NFC) systems. Extending the capacity of such systems and making them more secure is the desired objective of the research community

A High-level Optimum Design Methodology for Multimodal Biometric Systems

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A New Statistical Reconstruction Method for the Computed Tomography Using an X-Ray Tube with Flying Focal Spot

Abstract This paper presents a new image reconstruction method for spiral cone- beam tomography scanners in which an X-ray tube with a flying focal spot is used. The method is based on principles related to the statistical model-based iterative reconstruction (MBIR) methodology. The proposed approach is a continuous-to-continuous data model approach, and the forward model is formulated as a shift-invariant system. This allows for avoiding a nutating reconstruction-based approach, e.g. the advanced single slice rebinning methodology (ASSR) that is usually applied in computed tomography (CT) scanners with X-ray tubes with a flying focal spot. In turn, the proposed approach allows for significantly accelerating the reconstruction processing and, generally, for greatly simplifying the entire reconstruction procedure. Additionally, it improves the quality of the reconstructed images in comparison to the traditional algorithms, as confirmed by extensive simulations. It is worth noting that the main purpose of introducing statistical reconstruction methods to medical CT scanners is the reduction of the impact of measurement noise on the quality of tomography images and, consequently, the dose reduction of X-ray radiation absorbed by a patient. A series of computer simulations followed by doctor's assessments have been performed, which indicate how great a reduction of the absorbed dose can be achieved using the reconstruction approach presented here

intelligent neural network design for nonlinear control using simultaneous perturbation stochastic approximation spsa optimization

Simultaneous Perturbation Stochastic Approximation (SPSA) Optimization Adrienn Dineva*, Annamaria R. Varkonyi-Koczy**, Jozsef K. Tar*** and Vincenzo Piuri**** *Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, Budapest, Hungary *Doctoral School of Computer Science, Universita' degli Studi di Milano, Crema, Italy **Institute of Mechatronics & Vehicle Engineering, Obuda University, Budapest Hungary ** Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia ***Institute of Applied Mathematics, Obuda University, Budapest, Hungary **** Department of Computer Science, Universita' degli Studi di Milano, Crema, Italy E-mail: * [email protected], ** [email protected], ***[email protected], ****[email protected]

IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications

Smart health is bringing vast and promising possibilities on the road to comprehensive health management. Smart health applications are strongly data-centric and, thus, empowered by two key factors: information sensing and information learning. In a smart health system, it is crucial to effectively sense individuals’ health information and intelligently learn from its high-level health insights. These two factors are also closely coupled. For example, to enhance the signal quality, a sensing array requires advanced information learning techniques to fuse the information, and to enrich medical insights in mobile health monitoring, we need to combine “multimodal signal processing and machine learning techniques” and “nonintrusive multimodality sensing methods.” In new smart health application exploration, challenges arise in both information sensing and learning, especially their areas of interaction

IEEE Access special section editorial: collaboration for Internet of Things

The network of objects/things embedded with electronics, software, sensors, and network connectivity, Internet of Things (IoT), creates many exciting applications (e.g., smart grids, smart homes, and smart cities) by enabling objects/things to collect and exchange data so that they can be sensed and controlled. To fulfill IoT, one essential step is to connect various objects/things (e.g., mobile phones, cars, and buildings) so that they can "talk" to each other (i.e., collect and exchange data). However, substantial case studies show that simply connecting them without further collaboration among the objects/things when "talking" to each other leads to unnecessary energy consumption, uncertain security, unstable performance, etc., for IoT. Therefore, collaboration for IoT is very important. Specifically, there are a lot of critical issues to consider in terms of how to achieve robust collaboration among the objects/things for IoT. For instance, how to conduct collaboration among the objects/things so that more energy-efficient communication can be achieved for IoT? How to conduct collaboration among the objects/things so that computing with higher performance can be achieved for IoT? How to improve the security of IoT with collaboration among the objects/things? How to enhance the Quality of Service of IoT with collaboration among the objects/things? How to minimize the overhead costs when objects/things are collaborating in IoT

IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts

Smart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, and evidence-based medical decision support. To build and utilize the smart health big data, advanced data sensing and data mining technologies are closely coupled key enabling factors. In smart health big data innovations, challenges arise in how to informatively and robustly build the big data with advanced sensing technologies, and how to automatically and effectively decode patterns from the big data with intelligent computational methods. More specifically, advanced sensing techniques should be able to capture more modalities that can reflect rich physiological and behavioral states of humans, and enhance the signal robustness in daily wearable applications. In addition, intelligent computational techniques are required to unveil patterns deeply hidden in the data and nonlinearly convert the patterns to high-level medical insights