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Massive machine-type communication (MTC) is expected to be one of the key concerns in future B5G and 6G. However, the explosive growth of MTC devices (MTDs) in Internet of Things (IoT) poses great challenges for the initial access procedure. To address this issue, we propose a cluster-aided collision resolution random access (CACRRA) protocol for grant-based random access (RA) in the distributed massive multiple input multiple output (mMIMO) systems, which takes full advantage of the spatial multiplexing provided by distributed mMIMO. By exploiting the difference of large-scale fading coefficients between different access points (APs) and MTDs, we further propose an exclusive clustering algorithm to cluster APs for each pilot. Moreover, the pilot collision resolution is performed distributedly in each cluster by leveraging the channel hardening and the favorable propagation of mMIMO channels, which achieves a large reuse factor of pilots. Simulation results show that the proposed CACRRA can efficiently resolve the pilot collision and significantly improve the access performance. Specifically, the proposed protocol can improve the RA throughput more than 9 times compared to the conventional protocol in incredibly crowded scenarios.

CH4 storage associated with adsorbed natural gas (ANG) technology is an issue attracting great concern. Following the Advanced Research Project Agency-Energy (ARPA-E) targeted deliverable capacity of 315 cm3·cm−3 (STP), hundreds of thousands of materials have been experimentally or theoretically evaluated, while the best results still show a 35% gap from the target. Moreover, recent theoretical research reveals that the target is beyond the possibility that real materials can be designed. To get rid of the awkward situation, we make attempts on investigating the CH4 delivery performance under other operation conditions. Methods of raising the discharge temperature (to infinite high) or elevating the storage pressure (to 25 MPa) have been proved to show limited effectiveness. In this work, it is found that the ARPA-E target can be achieved by using a decreasing storage temperature strategy. By taking 280 CoRE (computation-ready, experimental) COFs (covalent organic frameworks) as ANG materials, when reduce the storage temperature to 190.6 K, the highest deliverable capacity can reach 392 cm3·cm−3 (STP), and 16.1% CoRE COFs can surpass the target. The target is also achievable when storage at 220 K. Structure performance relationships study shows strong correlation between deliverable capacity and void fraction. Hence, 120 hypothetical COFs are generated to ascertain the optimum void fraction. In addition, the performance of 2D-COFs can be greatly enhanced by increasing the interlayer spacings, e.g. CH4 deliverable capacity (storage at 190.6 K) of ATFG-COF can be improved from 239 to 411 cm3·cm−3 (STP) when interlayer spacing is enlarged to 1.65 nm.

In this letter, a novel spatial group based access class barring (ACB) scheme is proposed for machine-to-machine (M2M) random access system with timing advance (TA) assisted machine-type devices (MTDs) identification and preamble reuse. Specifically, multiple ACB factors are assigned for groups to provide precise access control, and an optimization problem of determining the optimal ACB factors which maximize the throughput is formulated. To reduce the computational complexity, the optimal problem is further simplified to get a suboptimal solution. The performance advantages of the proposed scheme are verified by simulations.

Machine-type communication (MTC) is one of the most important enabling technologies for the emerging Internet of Things (IoT). However, the potential massive access will significantly degrade the access performance of MTC. To meet the massive access requirement of MTC, we propose a spatial filtering based random access (SFRA) scheme in massive MIMO cellular networks. Specifically, the angle of arrival (AOA) information provided by massive MIMO is utilized to distinguish collided MTC devices (MTDs). To improve the preamble utilization as possible, we further propose a novel preamble reuse scheme to eliminate the influence of multipath angular spread on preamble detection at the base station (BS). Theoretical analysis and simulation results both show that the proposed SFRA scheme can efficiently reuse preambles and considerably improve the access capacity. In typical urban environments, preambles can be perfectly reused for 9 times at most. Moreover, the average access delay of SFRA is only about one seventh of that of the conventional random access scheme when 10000 MTDs attempt to access the network simultaneously.

The performance analysis of different algorithms in a specific environment is an important topic of path planning research. This study focuses on the performance analysis and pros and cons of the A * algorithm and RRT algorithm in two-dimensional maze solving problems. Matlab software was used to simulate the experiment, and the maze complexity was defined as the number of branches and wall thickness. Then, the two groups of experimental complexity were divided from simple to complex. Finally, the time and presentation path of the two algorithms in each group of experiments were observed by transverse and longitudinal comparison. In two-dimensional maze path planning, A * is suitable for going through a maze efficiently, while RRT is suitable for exploring a maze evenly. These findings are significant for emergency search and rescue and exploration of unknown areas by unmanned vehicles.

A new evolving network model with community structures is proposed and analyzed. The formation of communities is driven by each node’s various preference of interest fields, denoted as interest vectors, whose mechanism is far different from other models with community structures. The theoretical analysis and numerical simulation show that the degree distribution of the model represents a transition between power-law and exponential, depending on parameters given, which is close to real-life networks. The model can generate a series of snapshots of the networks, varying with time, with well-defined communities, which could be taken as benchmark graphs for testing community detection algorithms. Because the community structure of each time slice proves to be reasonable, it can also reasonably represent the evolution of the community.

Auto-KWS 2021 challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to a customized keyword spotting task. Compared with other keyword spotting tasks, Auto-KWS challenge has the following three characteristics: 1) The challenge focuses on the problem of customized keyword spotting, where the target device can only be awakened by an enrolled speaker with his specified keyword. The speaker can use any language and accent to define his keyword. 2) All dataset of the challenge is recorded in realistic environment. It is to simulate different user scenarios. 3) Auto-KWS is a "code competition", where participants need to submit AutoML solutions, then the platform automatically runs the enrollment and prediction steps with the submitted code.This challenge aims at promoting the development of a more personalized and flexible keyword spotting system. Two baseline systems are provided to all participants as references., Comment: 5 pages, 2 figures

Nuclear power plant operating data are characterized by a large variety, strong coupling, and low data value density. When using machine learning techniques for fault diagnosis and other related research, feature selection enables dimensionality reduction while maintaining the physical meaning of the original features, thus improving the computational efficiency and generalization ability of the learning model. In this paper, a correlation-based feature selection algorithm is developed to implement feature selection of nuclear power plant operating data. The proposed algorithm is verified by experiments and compared with traditional correlation-based feature selection algorithms. The experiments and comparison results show that the proposed algorithm is effective in realizing the dimensionality reduction of nuclear power plant operating data.

© 2012 IEEE. Computer viruses can affect any consumer electronics that need an operating system, such as tablet, and smartphone. To address the issue of computer viruses, some detection systems that contain multiple antivirus engines are being used. The systems may save and share user-uploaded files to the community, which is not acceptable for users with high privacy requirement. This paper presents a private multiengine online virus detection system called MultiScan that can perform the isolated detection and update to guarantee that the uploaded confidential samples are not exposed to the Internet.

We consider the degree distribution design of the low-density parity-check (LDPC) code ensembles for symmetric Gaussian multiple-access channels (GMAC). To characterize the probability density function (PDF) of the message passing in the process of joint decoding, we propose a new scheme to construct the associated Gaussian mixture (GM) distribution, where each GM component is assigned according to the corresponding signal group transmitted by the users. By tracking the variation of the GM components in the iterative decoding process, more accurate mutual information can be obtained for the extrinsic information transfer (EXIT) chart analysis. Simulation results show that the performance of our proposed LDPC codes is better than that of the existing methods.

Non-orthogonal multiple access (NOMA) can increase the overall spectral efficiency of visible light communication (VLC) system. In this paper, we proposed a novel scheme to improve the bit error rate (BER) performance of a two-user NOMA VLC system by adjusting superposition constellation. The corresponding closed-form BER expressions are derived. Convex optimization is used to find the optimal parameters of the adjusted superposition constellation, where the overall BER is minimized. The BER performances are evaluated by theoretical analysis, Monte-Carlo (MC) simulation and experiment. The results illustrate that 8.9-dB and 8.1-dB signal-to-noise ratios (SNRs) are reduced to achieve BER of 10−3 for the two users, whose power allocation coefficients are 0.6 and 0.4, respectively.