Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.     

Short packet communication in underlay cognitive network assisted by an intelligent reflecting surface

We propose short packet communication in an underlay cognitive radio network assisted by an intelligent reflecting surface (IRS) composed of multiple reconfigurable reflectors. This scheme, called the IRS protocol, operates in only one time slot (TS) using the IRS. The IRS adjusts its phases to give zero received cumulative phase at the secondary destination, thereby enhancing the end-to-end signal-to-noise ratio. The transmitting power of the secondary source is optimized to simultaneously satisfy the multi-interference constraints, hardware limitations, and performance improvement. Simulation and analysis results of the average block error rates (BLERs) show that the performance can be enhanced by installing more reconfigurable reflectors, increasing the blocklength, lowering the number of required primary receivers, or sending fewer information bits. Moreover, the proposed IRS protocol always outperforms underlay relaying protocols using two TSs for data transmission, and achieves the best average BLER at identical transmission distances between the secondary source and secondary destination. The theoretical analyses are confirmed by Monte Carlo simulations.     

Quantification of different effects occurring during fatigue tests on bituminous mixtures

Quantification of different effects (nonlinearity, heating, thixotropy, and fatigue) occurring during fatigue tests on bituminous mixtures is presented in this paper. A focus is given on the nonlinearity phenomenon. Continuous fatigue tests and a test with specific protocol (called fatigue tests to estimate biasing effects) were performed in tension/compression mode on cylindrical samples of the same material. The analysis of results reveals that reversible effects (nonlinearity, heating, and thixotropy) are important (more than 90% decrease at 100,000 cycles for a strain amplitude of 100 μm/m at 10 Hz) and cannot be ignored when interpreting classical fatigue tests. The nonlinearity effects respect the time‐temperature superposition principle, and they are more pronounced at “high” temperature (at the same frequency). Direction of nonlinearity curve in the Cole‐Cole axes is shown to be independent of temperature and frequency for the considered range.     

Development of a sequential robust–tolerance design model for a destructive quality characteristic

Robust design (RD) and tolerance design (TD) have received much attention from researchers and practitioners for more than two decades, and a number of methodologies for modeling and optimizing the RD and TD processes have been studied. However, there is ample room for improvement. Because most existing research considers RD and TD as separate research fields, the primary objective of this paper is to develop a sequential robust–tolerance design method to jointly determine the best factor settings and the closed-form solutions for the optimal specification limits. We then apply the proposed method to a destructive quality characteristic. Finally, a case study and sensitivity analyses are performed for verification purposes, and further studies are discussed.     

Optimal assembly plan generation: a simplifying approach

The main difficulty in the overall process of optimal assembly plan generation is the great number of different ways to assemble a product (typically thousands of solutions). This problem confines the application of most existing automated planning methods to products composed of only a limited number of components. The presented method of assembly plan generation belongs to the approach called “disassembly” and is founded on a new representation of the assembly process, with introduction of a new concept, the equivalence of binary trees. This representation allows to generate the minimal list of all non-redundant (really different) assembly plans. Plan generation is directed by assembly operation constraints and plan-level performance criteria. The method was tested for various assembly applications and compared to other generation approaches. Results show a great reduction in the combinatorial explosion of the number of plans. Therefore, this simplifying approach of assembly sequence modeling allows to handle more complex products with a large number of parts.     

A decision support system for fisheries management using operations research and systems science approach

In this paper, we present a general framework, using a systems science approach, for developing a decision support system (DSS) for fisheries management. Decision support systems are quantitative tools for managers to evaluate outcomes of their policies prior to implementation. Our fishery model considers multiple stocks and fisheries simultaneously in balancing catch among targeted and protected fish abundances. Since in the Northeastern US multispecies fishery the distribution of abundance, catch-per-unit-effort and bycatch vary geographically, we focus on a spatial management approach to address their spatial variability. The core component of this DSS applies operations research techniques of simulation and optimization to determine the optimal inter-annual and intra-annual fishing plans in terms of fishing efforts in each sub-area and the time period. The result is the recommended amount to catch from each fish species at each sub-area at any time period so that while management objectives for sustainability of fish stocks are satisfied, the value of landings is maximized. The graphical user interface of the proposed DSS helps users to define inputs, to set constraints and sub-area boundaries, and to visualize the outcomes.     

Cloud computing for small research groups in computational science and engineering: current status and outlook

Cloud computing could offer good business models for small computational science and engineering (CSE) research groups because these groups often do not have enough human resources and knowledge to manage the complexity of computational and data infrastructure for their research, while cloud computing aims to eliminate that complexity from the user. In this paper, we have analyzed current status of supporting tools for small CSE groups to utilize cloud computing. Although cloud computing is perceived as an interesting model, we have identified several issues that prevent a wide adoption of cloud computing from small CSE research groups. We also present recommendations for addressing these issues in order to attract small CSE groups to utilize cloud computing.     

Deposition on a flat substrate of gas molecules emerging from a cylindrical channel with an evaporating inner surface

The article investigates the possibility of controlling the rate of application of matter on a substrate at the expense of varying the temperature along a cylindrical channel, on the surface of which phase transitions take place.Academic Scientific Complex A. V. Luikov Institute of Heat and Mass Transfer, Academy of Sciences of Belarus, Minsk. Translated from Inzhenerno-Fizicheskii Zhurnal, Vol. 64, No. 2, pp. 160–167, February, 1993.     

Notes on ensembles of IoT,network functions and clouds for service-oriented computing and applications

Many advances have been introduced recently for service-oriented computing and applications (SOCA). The Internet of Things (IoT) has been pervasive in various application domains. Fog/Edge computing models have shown techniques that move computational and analytics capabilities from centralized data centers where most enterprise business services have been located to the edge where most customer’s Things and their data and actions reside. Network functions between the edge and the cloud can be dynamically provisioned and managed through service APIs. Microservice architectures are increasingly used to simplify engineering, deployment and management of distributed services in not only cloud-based powerful machines but also in light-weighted devices. Therefore, a key question for the research in SOCA is how do we leverage existing techniques and develop new ones for coping with and supporting the changes of data and computation resources as well as customer interactions arising in the era of IoT and Fog/Edge computing. In this editorial paper, we attempt to address this question by focusing on the concept of ensembles for IoT, network functions and clouds.