Design and development of multiphase buck converters for voltage regulator modules

<p>Modern microprocessors in high-power applications require a low input voltage and a high input current, necessitating the use of multiphase buck converters. As per microprocessor computing complexity, the power requirements of the switching converter will also be more important and will be increasing as per load demand. Previous studies introduced some methods to achieve the advantages associated with multiphase regulators. This paper presents an effective closed closed-loop control scheme for multiphase buck converters that reduces ripple and improves transient response. It is suitable for applications that require regulated output voltage with effectively reduced ripple. The analysis began with a simulation of the entire design using the OrCAD tool, followed by the construction of a hardware setup. Experiments on a 200 Khz, 9 V, 12 A, 2-phase buck voltage regulator were conducted and the proposed experiment found to be useful.</p>

A quantum convolutional neural network on NISQ devices

Quantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks (CNN), which greatly reduces the computing complexity compared with its classical counterparts, with O((log2M)6) basic gates and O(m2+e) variational parameters, where M is the input data size, m is the filter mask size, and e is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the parameters are independent on the input sizes, making it friendly to near-term quantum devices. We demonstrate QCNN with two explicit examples. First, QCNN is applied to image processing, and numerical simulation of three types of spatial filtering, image smoothing, sharpening, and edge detection is performed. Secondly, we demonstrate QCNN in recognizing image, namely, the recognition of handwritten numbers. Compared with previous work, this machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel. It provides an efficient avenue to transform CNN to QCNN directly and opens up the prospect of exploiting quantum power to process information in the era of big data.

Design of a Power-Efficient Low Complexity Non Maximally Coefficient Symmetry Multi Rate Filter Bank for Wideband Channelization

A solution for existing and upcoming wireless communication standards is a software-defined radio (SDR) that extracts the desired radio channel. Channelizer is supposed to be the computationally complex part of SDR. In multi-standard wireless communication, the Software Radio Channelizer is often used to extract individual channels from a wideband input signal. Despite the effective channelizer design that reduces computing complexity, delay and power consumption remain a problem. Thus, to promote the effectiveness of the channelizer, we have provided the Non-Maximally Coefficient Symmetry Multirate Filter Bank. In this paper, to improve the hardware efficiency and functionality of the proposed schemes, we propose a polyphase decomposition and coefficient symmetry incorporated into the Non-Maximally Coefficient Symmetry Multirate Filter Bank. For sharp wideband channelizers, the proposed methods are suitable. Furthermore, polyphase decomposition filter and coefficient symmetry is incorporated into the Non-Maximally Coefficient Symmetry Multirate Filter Bank to improve the hardware efficiency, power efficient, flexibility, reduce hardware size and functionality of the proposed methods. To prove the complexity enhancement of the proposed system, the design to be the communication standard for complexity comparison.

A novel navigation system for an autonomous mobile robot in an uncertain environment

In this paper, we developed a new navigation system, called ATCM, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Morphin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The modular design of 6-steps navigation provides a holistic methodology to implement and verify the performance of a robot’s navigation system. The experiments on simulation and a physical robot for the eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment. Compared with the particle swarm optimisation, the dynamic window approach and the traditional Morphin algorithm for the autonomous navigation of a mobile robot in a static environment, ATCM achieved the shortest path with higher efficiency.

Swarm Differential Privacy for Purpose-Driven Data-Information-Knowledge-Wisdom Architecture

Privacy protection has recently been in the spotlight of attention to both academia and industry. Society protects individual data privacy through complex legal frameworks. The increasing number of applications of data science and artificial intelligence has resulted in a higher demand for the ubiquitous application of the data. The privacy protection of the broad Data-Information-Knowledge-Wisdom (DIKW) landscape, the next generation of information organization, has taken a secondary role. In this paper, we will explore DIKW architecture through the applications of the popular swarm intelligence and differential privacy. As differential privacy proved to be an effective data privacy approach, we will look at it from a DIKW domain perspective. Swarm intelligence can effectively optimize and reduce the number of items in DIKW used in differential privacy, thus accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW. The proposed approach is demonstrated through the application of personalized data that is based on the open-source IRIS dataset. This experiment demonstrates the efficiency of swarm intelligence in reducing computing complexity.

Global Structure of Determining Matrices for a Class of Differential Control Systems

This paper developed and established unprecedented global results on the structure of determining matrices of generic double time-delay linear autonomous functional differential control systems, with a view to obtaining the controllability matrix associated with the rank condition for the Euclidean controllability of the system. The computational process and implementation of the controllability matrix were demonstrated on the MATLAB platform to determine the controllability disposition of a small-problem instance. Finally, the work examined the computing complexity of the determining matrices.

Novel Criteria of Stability for Delayed Memristive Quaternionic Neural Networks: Directly Quaternionic Method

In this paper, we fixate on the stability of varying-time delayed memristive quaternionic neural networks (MQNNs). With the help of the closure of the convex hull of a set the theory of differential inclusion, MQNN are transformed into variable coefficient continuous quaternionic neural networks (QNNs). The existence and uniqueness of the equilibrium solution (ES) for MQNN are concluded by exploiting the fixed-point theorem. Then a derivative formula of the quaternionic function’s norm is received. By utilizing the formula, the M-matrix theory, and the inequality techniques, some algebraic standards are gained to affirm the global exponential stability (GES) of the ES for the MQNN. Notably, compared to the existing work on QNN, our direct quaternionic method operates QNN as a whole and markedly reduces computing complexity and the gained results are more apt to be verified. The two numerical simulation instances are provided to evidence the merits of the theoretical results.

Anonymous Blockchain-based System for Consortium

Blockchain records transactions with various protection techniques against tampering. To meet the requirements on cooperation and anonymity of companies and organizations, researchers have developed a few solutions. Ring signature-based schemes allow multiple participants cooperatively to manage while preserving their individuals’ privacy. However, the solutions cannot work properly due to the increased computing complexity along with the expanded group size. In this article, we propose a Multi-center Anonymous Blockchain-based (MAB) system, with joint management for the consortium and privacy protection for the participants. To achieve that, we formalize the syntax used by the MAB system and present a general construction based on a modular design. By applying cryptographic primitives to each module, we instantiate our scheme with anonymity and decentralization. Furthermore, we carry out a comprehensive formal analysis of our exemplified scheme. A proof of concept simulation is provided to show the feasibility. The results demonstrate security and efficiency from both theoretical perspectives and practical perspectives.

ANALYSIS OF FAST ALGORITHM OF MATRIX-VECTOR MULTIPLICATION FOR THE BANK OF DIGITAL FILTERS

Algorithms of implementation of vector-matrix multiplication are presented, which are intended for application in banks (sets) of digital filters. These algorithms provide significant savings in computational costs over traditional algorithms. At the same time, reduction of computational complexity of algorithms is achieved without any performance loss of banks (sets) of digital filters. As the basis for the construction of algorithms proposed in the article, the previously known Winograd method of multiplication of real matrices and vectors and two versions of the method of type 3M for multiplication of complex matrices and vectors are used. Methods of combining these known methods of multiplying matrices and vectors for building digital filter banks (sets) are considered. The analysis of computing complexity of such ways which showed a possibility of reduction of computing complexity in comparison with a traditional algorithm of realization of bank (set) of digital filters approximately in 2.66 times – at realization on the processor without hardware multiplier is carried out; and by 1.33 times – at realization on the processor with the hardware multiplier. These indicators are markedly higher than those of known algorithms. Analysis of sensitivity of algorithms proposed in this article to rounding errors arising by digital signal processing was carried out. Based on this analysis, an algorithm is selected that has a computational complexity smaller than that of a traditional algorithm, but its sensitivity to rounding errors is the same as that of a traditional algorithm. Recommendations are given on its practical application in the development of a bank (set) of digital filters.

The extremal pentagon-chain polymers with respect to permanental sum

The permanental sum of a graph G can be defined as the sum of absolute value of coefficients of permanental polynomial of G. It is closely related to stability of structure of a graph, and its computing complexity is #P-complete. Pentagon-chain polymers is an important type of organic polymers. In this paper, we determine the upper and lower bounds of permanental sum of pentagon-chain polymers, and the corresponding pentagon-chain polymers are also determined.