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Early retention or initial threshold voltage shift (IVS) is one of the key reliability challenges in charge trapping memory (CTM) based 3D NAND flash. Re-program scheme was introduced in quad-level-cell (QLC) NAND (Shibata et al., 2007, Lee et al., 2018, Shibata et al., 2019, and Khakifirooz et al., 2021), and the IVS improvement by re-program scheme was reported. In this work, it is found that re-program can suppress ~81% of IVS in 3D NAND, which is much more significant than that of 2D NAND ~50% (Chen et al., 2010). The mechanisms of IVS improvement by re-program scheme in 3D NAND are investigated. Both vertical de-trapping in the BE-tunneling oxide and charge lateral migration (LM) in the charge-trap layer are suppressed in re-program. Re-program is effective in vertical de-trapping suppression both in checker-board pattern (C/P) and solid-board pattern (S/P) cases, and is effective in LM suppression only in C/P case. Furthermore, the LM improvement by re-program scheme is more pronounced with gate length (Lg) and inter-gate space (Ls) scaling down, showing potential in the reliability improvement of advanced 3D NAND technologies.

Knowing the infection status of surrounding people can be a huge advantage for both self-protection and spreading control in an epidemic outbreak or a flu season. Recently, Bluetooth-based mobile apps have been implemented to perform the contacting tracking. However, self-status estimation and tracking are of the same importance yet less investigated. Unfortunately, existing apps cannot enable users to establish the necessary relationship between others’ infection status and their own status. Meanwhile, traditional infection models are either used for statistics in the general population or have strong constraints that cannot be applied for such usage. In this paper, we propose BASE, a Bluetooth App-based Self-Estimated infection model, which can be integrated with this kind of app to calculate individual user’s own infection probability. BASE only requires basic inputs that are acquirable from any smartphone with Bluetooth 2.0 and above. More importantly, BASE can work under practical scenarios in terms of scalability, mobility, and sensing inaccuracy. Furthermore, a learning model is integrated to calibrate the model by studying the propagation characteristics of heterogeneous diseases. The merits of BASE are proved theoretically and validated against extensive simulations. To the best of our knowledge, this is the first work that investigates the individual’s infection model based upon Bluetooth neighbor discovery techniques.

Smart manufacturing systems have a highly heterogeneous structure that leads to complexity in satisfying the requirements of various underlying application domains. To achieve smart control and intelligent automation, one core issue is the realization of horizontal and vertical integration of heterogeneous industrial automation components. In this paper, a system architecture for a smart manufacturing process is proposed, in which the open platform communications unified architecture (OPC UA) technology is utilized to integrate various industrial field networks into the top-level factory energy management system (FEMS). The proposed architecture is verified to be reasonable and feasible through the implementation of the integrated system in a lab-level experiment.

Real-timeliness and reliability are two basic requirements for industrial wireless sensor networks (IWSNs). In this paper, periodic heterogeneous traffics with delay constraints are concerned in an IWSN with multiple access points (thus forming a redundant star topology). In order to improve the reliability while meeting the real-timeliness and fairness, we propose a fairness-aware scheduling (FAS) algorithm, in which three heuristic rules are first designed based on a novel penalty mechanism. Then, in each slot field devices are classified into six priorities according to the above three rules. Finally, the current slot is allocated to field devices in accordance with their priorities and communication ranges in a space division multiplexing way. A performance study is carried out by simulation, and the results show that the proposed FAS algorithm performs much better than existing works in terms of reliability and fairness.

Massive number of devices must operate for a long time with very limited chance to contact energy sources in current Machine-to-Machine (M2M) network, so power saving mechanism is very essential. In this paper, we analyze energy efficiency of Contention Tree Algorithm(CTA), which is one popular contention based data transmission method currently, and propose one enhanced CTA: Power Saving CTA(PS_CTA). In PS_CTA, each device can know its data transmission slots by one new feedback information from base station and be in the sleep until it needs to transmit data. PS_CTA also uses short payload contention strategy to decrease power consumption further. Simulation results show that PS_CTA can improve energy efficiency greatly.

For the unlicensed spectrum, the occupied bandwidth requirement is demanded by some regulations. The legacy scheme of Physical Random Access Channel (PRACH) for Long Term Evolution (LTE) cannot satisfy it. In this paper, we propose a novel PRACH transmission scheme to satisfy the requirement of unlicensed spectrum based on preamble pairing, and show some other candidates. We analyze and evaluate the key performances, including TA estimation accuracy, preamble multiplexing capability and cubic metric, of the proposed scheme and other candidates. Both theoretical analysis and simulation result show that our scheme outperforms the other candidates for the performances.

2D black phosphorus is good for use in photodetection devices due to its direct bandgap [1]. However, light absorptions in these 2D materials are often very low due to its ultra-thin nature. Numerical simulations were used to design and optimize the plasmonic structures. The optimized bowtie structure devices were fabricated on black phosphorus on transparent substrate. Photocurrent measurements showed strong light polarization dependence in our device (2 times difference), as well as stronger responsivity (6 times higher) compared with our previous work thanks to the enhanced light absorption by the plasmonic structure [2].

We consider an Uplink License Assisted Access (LAA) system in which LTE systems compete transmission resources with WiFi systems. Before sending any signals, a listen-before-talk (LBT) process is mandated for LAA system: users are required to sense the environment and can only transmit if the channel is considered idle. To guarantee that all users are aligned for transmission, a designation period of time, referred as the LBT gap is designed at the beginning of each subframe. We proposed a mechanism to calculate the optimal length of this LBT gap based on the condition of the environment, the payload size and the LBT process counter size for each individual user. The proposed mechanism can achieve a balance between the probability of successful transmission and the efficiency use of the resource. With an optimized LBT gap, we further incorporate the probability of successful transmission into the multi-user scheduling algorithm design. Our simulation results show that the mechanism of deciding the optimal LBT gap and the corresponding scheduling algorithm can significantly improve the throughput of LTE system and reduce the delay of end-to-end transmission delay.

AdaBoost is a well-known simple and effective boosting algorithm for classification. It, however, suffers from the overfitting problem in the case of overlapping class distributions and is very sensitive to label noise. To tackle both problems simultaneously, we consider the conditional risk as the modified loss function. This modification leads to two advantages: it is able to directly take into account label uncertainty with an associated label confidence; it introduces a “trustworthiness” measure on training samples via the Bayesian risk rule, hence the resulting classifier tends to have superior finite sample performance than the original AdaBoost when there is a large overlap between class conditional distributions. We scrutinize the re-weighting procedure and classifier combination rule to show its adaptive ability on the mitigation of class noise and overfitting. Extensive experimental results using synthetic data and real-world data sets from UCI machine learning repository are provided. The empirical study shows high competitiveness of the proposed method in predication accuracy and robustness when compared with the original AdaBoost and several existing robust AdaBoost algorithms.

WirelessHART, an international standard of wireless industrial networks, adopted the concept of graph routing to meet the requirements of short latency and high reliability. However, WirelessHART networks also require longer network lifetimes when implemented for industrial applications. Designing a reasonable form of graph routing is one method to achieve a balanced consumption of network energy and, hence, a longer network lifetime. We propose a new energy-balancing graph-routing algorithm for WirelessHART networks based on the breadth-first search (BFS) algorithm, which divides nodes into layers. To increase the network lifetime, we define routing responsibility to estimate the data traffic at any given node and use graph shaping to transfer the traffic of high routing responsibility nodes to lower ones. Simulation results show that the proposed graph-routing algorithm extends the lifetime of WirelessHART networks, and helps guarantee end-to-end communication with minimal hopping.

Demand response (DR) used in smart grid (SG) can enhance the reliability of the power system as well as reduce the energy costs for customers. One of the major consumers of electrical energy is industry. In this study, we develop a hardware-in-the-loop (HIL) simulator to demonstrate how to practically implement DR in industrial facilities. The HIL simulator includes an energy management system (EMS), a monitoring and control system (MCS), an industrial Ethernet backbone network based on RAPIEnet protocol, and a wireless field network based on ISA100.11a protocol. The results show that the electricity demand of industrial facilities can be shifted from peak to off-peak demand periods to improve the reliability of the electrical grid.

Soft computing is tolerant of imprecision, uncertainty, partial truth, and is used to cope with complicated problems. In this paper, we take advantages of various soft computing techniques, fuzzy systems, neural networks, and evolutionary computations, to construct an ensemble classification model, the Enhanced Adaptive Hierarchical Classifier (EAHC). The EAHC utilizes fuzzy systems to gain extra data attributes and hence is able to construct classification models with higher classification accuracy. Furthermore, we integrate several data mining technologies, such as, decision tree, self-organizing map, and fuzzy IF-THEN rules which are used to build basic classifiers of EAHC. The integration also leads to EAHC achieving a better classification results. The EAHC has been applied to solve a critical, real-world problem, namely rainfall intensity classification. EAHC can achieve various goals through setting different fitness functions. Experimental results show that the proposed model is able to achieve high accuracy for rainfall intensity retrieval and outperforms previously published methods. Finally, we apply EAHC to a real typhoon case, which demonstrates relatively high agreement between GPROF and our algorithm.

A semi-track air-cushion vehicle (STACV) which combines an air-cushion lifting system with a semi-track propulsion system is an efficient solution for heavy-duty vehicles working on soft terrain, such as the vehicles for agricultural, oil industrial and military purposes. Focusing on optimizing fuel economy of the vehicle, five main issues are studied in this paper. Firstly, based on the analyses of resistances for the STACV in a sandy loam working condition, a theoretical model for fuel consumption of 100 km, which is an evaluation index for fuel economy, is established. Secondly, through simplified by a group of constraint equations based on the physical structure of the vehicle, the running parameters and control target (fuel consumption of 100 km) could be expressed by the following two measurable and adjustable parameters, fan rotational speed and vehicle forward speed. Thirdly, a genetic algorithms (GA) model is designed to optimize the fuel consumption and to get the relevant parameters off line as control objectives of status variables of a control system. Fourthly, the control system with a fuzzy PID controller and a GA-based PID controller is built to achieve the minimal fuel consumption by regulating fan rotational speed and vehicle forward speed respectively. Fifthly, simulations are performed in a MATLAB/Simulink software environment. The result illustrates that this control system works efficiently and steadily.

This paper introduces the unique structure of a semi-tracked air-cushion vehicle. A theoretical model for total required power of the vehicle is deduced. The optimum state of minimum power consumption is obtained under a certain soil condition. A control scheme using the self-tuning PID fuzzy logic controller is proposed. Simulation results show that the vehicle can drive steadily on the optimum state by the control system.