A New Combined Boost Converter with Improved Voltage Gain as a Battery-Powered Front-End Interface for Automotive Audio Amplifiers

High boost DC/DC voltage conversion is always indispensable in a power electronic interface of certain battery-powered electrical equipment. However, a conventional boost converter works for a wide duty cycle for such high voltage gain, which increases power consumption and has low reliability problems. In order to solve this issue, a new battery-powered combined boost converter with an interleaved structure consisting of two phases used in automotive audio amplifier is presented. The first phase uses a conventional boost converter; the second phase employs the inverted type. With this architecture, a higher boost voltage gain is able to be achieved. A derivation of the operating principles of the converter, analyses of its topology, as well as a closed-loop control designs are performed in this study. Furthermore, simulations and experiments are also performed using input voltage of 12 V for a 120Wcircuit. A reasonable duty cycle is selected to reach output voltage of 60 V, which corresponds to static voltage gain of five. The converter achieves a maximum measured conversion efficiency of 98.7% and the full load efficiency of 89.1%

Analytical approximations to charged black hole solutions in Einstein-Maxwell-Weyl gravity

The Homotopy Analysis Method (HAM) is a useful method to derive analytical approximate solutions of black holes in modified gravity theories. In this paper, we study the Einstein-Weyl gravity coupled with Maxwell field, and obtain analytical approximation solutions for charged black holes by using the HAM. It is found that the analytical approximate solutions are sufficiently accurate in the entire spacetime outside the black hole's event horizon, and also consistent with numerical ones for charged black holes in the Einstein-Maxwell-Weyl gravity.Comment: 17 pages, 6 figures. arXiv admin note: text overlap with arXiv:2308.0350

Retraction and Generalized Extension of Computing with Words

Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.Comment: 13 double column pages; 3 figures; to be published in the IEEE Transactions on Fuzzy System

Three-Phase Detection and Classification for Android Malware Based on Common Behaviors

Android is one of the most popular operating systems used in mobile devices. Its popularity also renders it a common target for attackers. We propose an efficient and accurate three-phase behavior-based approach for detecting and classifying malicious Android applications. In the proposed approach, the first two phases detect a malicious application and the final phase classifies the detected malware. The first phase quickly filters out benign applications based on requested permissions and the remaining samples are passed to the slower second phase, which detects malicious applications based on system call sequences. The final phase classifies malware into known or unknown types based on behavioral or permission similarities. Our contributions are three-fold: First, we propose a self-contained approach for Android malware identification and classification. Second, we show that permission requests from an Application are beneficial to benign application filtering. Third, we show that system call sequences generated from an application running inside a virtual machine can be used for malware detection. The experiment results indicate that the multi-phase approach is more accurate than the single-phase approach. The proposed approach registered true positive and false positive rates of 97% and 3%, respectively. In addition, more than 98% of the samples were correctly classified into known or unknown types of malware based on permission similarities.We believe that our findings shed some lights on future development of malware detection and classification

Offloading in P4 Switch Integrated with Multiple Virtual Network Function Servers

Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two transformative technologies that offer distinct benefits. SDN virtualizes the control plane by separating it from the data plane, while NFV virtualizes the data plane by moving network functions from hardware and implementing them in software. Therefore, combining SDN and NFV can fully exploit the benefits of both technologies. As Programming Protocol-independent Packet Processors (P4) become popular due to its flexibility, traditional SDN switches are being replaced by P4 switches. In the P4+NFV architecture, network functions can be provided in both P4 switches (PNF) and NFV servers (VNF). However, to minimize packet delay, the offloading problem between P4 switches and NFV needs to be addressed. The novelty of our paper lies in investigating the offloading problem and evaluating the impact of employing multiple VNFs with varying computing capacities within the P4+NFV architecture. We also use M/M/1 queuing theory to derive the average packet delay and propose an optimization solution based on gradient descent to find out the optimal offloading probabilities of various VNF servers. Results show that optimal offloading from P4 switch to multiple VNFs can reduce the average packet delay from 4.76% to 40.02%

Three-Tier Capacity and Traffic Allocation for Core, Edges, and Devices for Mobile Edge Computing

In order to satisfy the 5G requirements of ultra-low latency, mobile edge computing (MEC)-based architecture, composed of three-tier nodes, core, edges, and devices, is proposed. In MEC-based architecture, previous studies focused on the controlplane issue, i.e., how to allocate traffic to be processed at different nodes to meet this ultra-low latency requirement. Also important is how to allocate the capacity to different nodes in the management plane so as to establish a minimal-capacity network. The objectives of this paper is to solve two problems: 1) to allocate the capacity of all nodes in MEC-based architecture so as to provide a minimal-capacity network and 2) to allocate the traffic to satisfy the latency percentage constraint, i.e., at least a percentage of traffic satisfying the latency constraint. In order to achieve these objectives, a two-phase iterative optimization (TPIO) method is proposed to try to optimize capacity and traffic allocation in MEC-based architecture. TPIO iteratively uses two phases to adjust capacity and traffic allocation respectively because they are tightly coupled. In the first phase, using queuing theory calculates the optimal traffic allocation under fixed allocated capacity, while in the second phase, allocated capacity is further reduced under fixed traffic allocation to satisfy the latency percentage constraint. Simulation results show that MEC-based architecture can save about 20.7% of capacity of two-tier architecture. Further, an extra 12.2% capacity must be forfeited when the percentage of satisfying latency is 90%, compared to 50%.This work was supported in part by H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant number 761586), and Ministry of Science and Technology, Taiwan for financially supporting this research under Contract No. MOST 106-2218-E-009-018

Relativistic quantum scarring, spin-induced phase, and quantization in a symmetric Dirac billiard system

Acknowledgments The work at Lanzhou University was supported by NSFC under Grant Nos. 12175090, 11775101, and 12047501, and by the 111 Project under Grant No. B20063. The work at Arizona State University was supported by the Air Force of Scientific Research through Grant No. FA9550-21-1–0186.Peer reviewedPostprin