Finding Dense Components in Large-Scale Network Using Randomized Binary Search Tree

Given a simple undirected graph G=(V, E), the density of a subgraph on vertex set S is defined as a ratio between the number of edges |E(S)| and the number of vertices |S|, where E(S) is the set of edges induced by vertices in S. Finding the maximum density subgraph has become an intense study in recent years, especially in the social network era. Being based on a greedy algorithm that connects with a suitable graph data structure, we have reduced its time complexity by using a randomized binary search tree, also called treap. We make the complexity analysis in both time and memory requirements, including computational experiments in large scale real networks.

Developing a Smart Device for Measuring and Monitoring Ammonia Concentration

This paper presents an approach for design and realization of a smart device for measuring and monitoring ammonia concentration. The developed system included two parts: hardware and software, in which the hardware of the system has been developed based on the ATMEGA328P microcontroller in order to collect data from the ammonia sensor MQ135. Meanwhile, the software is developed for signal processing and warning according to different thresholds. Collected data by the system is then transmitted to a computer via the wireless communication protocol. In addition, an interface is also designed on computers to collect and monitor data from sensors. The developed system allows to measure ammonia concentration up to 300ppm and has been tested not only at the laboratory but also at NPK factory of Lamthao Fertilizers and Chemicals Joint Stock Company. Experimental results show the desired performances of the developed system.

Adaptive Noise Filter for Real-Time Stress ECG Signal Analysis

The stress Electrocardiogram (ECG) gives more efficient results for the diagnosis of cardiovascular diseases, which may not be apparent when the patients are at rest. However, the noise produced by the movement of the patient and the environment often contaminates the ECG signal. Motion artifact is the most prevalent and difficult type of interference to filter in stress test ECG. It corrupts the quality of the desired signal thus reducing the reliability of the stress test. In this work, we first describe a quantitative study of adaptive filtering for processing the stress ECG signals. The proposed method uses the motion information obtained from a 3-axis accelerometer as a noise reference signal for the adaptive filter and the optimal weight of the adaptive filter is adjusted by the Modified Error Data Normalized Step-Size (MEDNSS) algorithm. Finally, the performance of the proposed algorithm is tested on the stress ECG signal from the subject.

Design of a High-Efficiency GaN High-Electron Mobility Transistor Microwave Power Amplifier

This study presents a design procedure to obtain high-efficiency for microwave power amplifier. The designed amplifier uses a GaN high electron mobility transistor as an active device. Matching networks including input and output networks are realized using Megtron6 substrate microstrip lines. The designed amplifier operates at 2.1 GHz band. The simulated results show that the amplifier delivers a maximum power-added efficiency of 73.2% at output power and power gain of 47.8 dBm and 13.8 dB, respectively. This promising designed performance makes this amplifier to be an excellent candidate for use in modern wireless communication systems like radar, mobile network, and satellite communications.

Application of Convolution Neural Network in Design and Fabrication of Robots for Transporting Goods in Factories

Nowadays, the use of freight robots in factories will help people reduce their labor force and move into difficult and dangerous places more easily. Only smart robots will help people move equipment, goods to destinations that have been designed to own lanes in factories or remotely control these cargo robots to move following the demands of the controller. Goods will be delivered to the right place, helping to reduce labor costs for factories, increase productivity, thereby increasing the profits of businesses. Understanding the necessity of the design of transport robot and the development of artificial intelligence field along with the development of some types of embedded computers, the research team proposed a method to use convolutional neural networks deployed on the embedded computer platform to design a smart robot model to transport goods in the factory.

Data Encoding Improvement and Size Reduction of Chipless RFID Tag Using Short-circuit Line Effect and Angle-based Divided Loops

This paper focuses on a short circuit and concentric loop effects to improve the structure dimension as well as enhance the ability to encode electromagnetic wave data for chipless RFID tags. The tag is composed of star-shaped rings that are concentrically nested together. The following factors help reduce the tag size: using the phenomenon of electromagnetic backscattering, assessment by the Radar Cross Section (RCS), the antenna-free tag only includes the multi-frequency resonators. By inserting short circuit lines combined with concentric loops, the number of resonant peaks increases exponentially without increasing the tag overall dimension. In addition, by inserting the short circuit lines at different angles between the two consecutive loops, we can adjust the resonant frequency in the frequency bands, thereby changing the value of the bit by shifting the frequency position. The overall dimension of the resulted tag is 13mmx13 mm and the tag is aimed to be a printable tag on the flexible substrate to minimize the fabrication cost. With the 18-vertex star-shaped tag with five concentric loops, this chipless RFID tag can encode up to 16 bits with high resolution and large data density.

Fuzzy Adaptive Controller Design for IPMSM with System Uncertainties and Disturbances Considering

This paper proposed a T-S fuzzy model based adaptive fuzzy controller for the interior permanent magnet synchronous motors. Firstly, the T-S fuzzy model of the system is built based on the nonlinear dynamic model. Next, the adaptive fuzzy controller is designed to deal with the problems of system uncertainties and external disturbances. This controller includes two phases, one is for system stability and one for compensating the effect of the unknown components. The stability of the system, as well as the convergence of the adaptive law, is mathematically proven through Lyapunov theory. Finally, some simulations are done to verify the effectiveness of the presented scheme. The simulation results show that the proposed algorithm has a good response to the change of reference input, the system parameters variation, and the sudden change of the load torque.

Integrated Transient Stability Analysis with Multi-Large-Scale Solar Photovoltaic in Distribution Network

Using renewable energy sources (RES) to face energy shortages in the context of rising load demand is a central issue in national power system planning. With low investment rates, rapid return on capital as well as many incentives from the government, solar energy is being more invested than other types of RES, as it can be seen as a rooftop and large-scale Photovoltaic (LSPV) system. However, if the LSPV penetrates too much into the grid, it will weaken the stability of the system, especially when a fault occurs. This paper aims to evaluate the transient stability of an actual distribution grid (DG) under different penetration levels of LSPV by ETAP software. The frequency and voltage responses will be presented in the results along with the rotor angle variation of a conventional generator located near the LSPV.

Multi-Objective Optimization Design of Standard Industrial Motor

This paper presents a method to optimally design electrical machines. Unlike the traditional design method “tries-and-errors iterative process”, the optimal design approach consists of combining optimization algorithms and multi-physics models to reach the optimum design. A case study of designing a standard industrial motor of 6 HP with multi-objectives and constraints is chosen in order to test this optimization methodology. The Pareto solution results of two conflicting objectives between the efficiency and the active mass of this machine are reached to help designers and customers selecting the best compromised design of motor of 6 HP in terms of cost and consuming energy.

Cluster-Based Routing Approach in Hierarchical Wireless Sensor Networks toward Energy Efficiency using Genetic Algorithm

Energy efficiency is one of the important factors when exploiting Wireless Sensor Networks, especially for increasing lifespan and performance. In the network nowadays, the number of sensor nodes can reach hundreds or thousands and can be arranged in complex hierarchical architecture. Besides, the current sensor nodes have a small size, limited battery source but are operated in vast areas. The clustered-based method has been an effective and potentially extensible means of boosting the management and operation of such large-scale networks and minimizing the overall energy consumption. In this paper, the issue of arranging and routing the nodes in the sensor network in a hierarchical manner is investigated, in which each lowest level sensor nodes are grouped in a cluster with a common cluster head, then the cluster-head plays an intermediate role transmit the information back and forth between the sensor nodes and the base station. In this way, the route to exchange information can not only be optimized with respect to the distance but also for energy spent on the communication. In order to do so, this paper proposed a novel method based on a Genetic Algorithm to establish a routing protocol to achieve energy optimization. The results demonstrate that this approach can decrease the energy consumption according to the optimized routing through clustering and increase the performance superior to the other clustering schemes.