Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation Between Speaker Gender and Perceptual Quality of Mobile Speech Signal

Perceptual evaluation of speech signals is a very crucial measure for quality of service in mobile speech communication. Several subjective and objective quality measures are being utilized to evaluate the perceptual quality of speech signals. Perceptual Evaluation of Speech Quality (PESQ) has been found to reliably predict the quality of processed speech signals with a higher correlation with the perceived quality. However, some studied have shown some issues with PESQ measure in specific environments or speech signals. This paper investigates the effect of speaker gender on PESQ measure of the perceptual quality of GSM Full Rate (GSM-FR) encoded speech signals. A Matlab experiment is carried out to encode 350 speech files from TIMIT corpus using GSM-FR vocoder and calculate the PESQ scores. The results clearly show that there is a significant difference in perceptual quality score between female and male speech signals which demonstrate another reliability issue of PESQ as a perceptual quality of speech signal in mobile communications.
An Improved Decomposition-based Multi-objective Evolutionary Algorithm with Enhanced Differential Evolution Strategy

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proved to have a significant advantage dispose of multi-objective optimization problems (MOPs) since its introduction. However, initial MOEA/D worsen diversity, Furthermore, it is easy to generate an inferior solution by using simulated binary crossover operation. Therefore, an improved MOEA/D with an enhanced differential evolution (MOEA/D-EDE) is proposed to solve above problems. The newness and advantages of this proposed MOEA/D-EDE include the following two aspects. First, several differential evolution operators are used to replace crossover operator in the original MOEA/D. Second, MOEA/D-EDE introduces an elite archive strategy, thereby significantly increases the convergence speed while ensuring the diversity. Finally, the proposed MOEA/D-EDE is studied on MOPs compared with several MOEA/D variants and other algorithms. Empirical results display that MOEA/D-EDE to enhance the performance.
Experimental Shielding Effectiveness Analysis of Metal Plate Influence inside an Enclosure with Aperture

This paper considers the experimental shielding effectiveness analysis of an enclosure with a metal plate placed at a certain angle inside. Dimensions of a metal plate are determined to affect the enclosure’s first resonant frequency in order to improve its protective function. A metal plate impact inside measured enclosure on the shielding effectiveness values especially at the first frequency as well as at the whole frequency range is presented.
A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras

The computational load associated with computer vision is often prohibitive, and limits the capacity for on-board image analysis in compact mobile robots. Replicating the kind of feature detection and neural processing that animals excel at remains a challenge in most biomimetic aquatic robots. Event-driven sensors use a biologically inspired sensing strategy to eliminate the need for complete frame capture. Systems employing event-driven cameras enjoy reduced latencies, power consumption, bandwidth, and benefit from a large dynamic range. However, to the best of our knowledge, no work has been done to evaluate the performance of these devices in underwater robotics. This work proposes a robotic lamprey design capable of supporting computer vision, and uses this system to validate a computational neuron model for driving anguilliform swimming. The robot is equipped with two different types of cameras: frame-based and event-based cameras. These were used to stimulate the neural network, yielding goal-oriented swimming. Finally, a study is conducted comparing the performance of the computational model when driven by the two different types of camera. It was observed that event-based cameras improved the accuracy of swimming trajectories and led to significant improvements in the rate at which visual inputs were processed by the network.
Design of Microstrip Bandpass Filter Based on Genetic Optimization

In this paper, Structural optimization based microstrip bandpass filter design is discussed. Conventional optimization mainly focuses on the dimensional optimization of the filter. Here, the focus is to design a bandpass filter using rectangular patch-based resonator. The obtained result is then optimized using Genetic Algorithm which is part of Evolutionary algorithm. In order to obtain an optimized result, we define a fitness function in terms of $S_{11}$ (dB) and $S_{21}$ (dB) parameters. The final design of the filter will possess maximum transmission co-efficient and minimum reflection co-efficient.
Fast Face Recognition Model without Pruning

This paper proposes a real-time high accuracy face recognition model which can be deployed on performanceconstrained embedded platforms or mobile terminals without pruning. In the proposed model, we optimize the network structure by balancing the memory access cost (MAC) and Floatingpoint Operations (FLOPs) and propose a new factor to measure the efficiency of learning parameters. Based on these two metrics, we designed two structures called Simple SqueezeFaceNet (SSN) and Channel-Split Network (CSN) to achieve a good balance between the high precision and high FPS (Frames per Second). After being trained with the refined MS1M-refine-v2 dataset, our architectures achieve 0.992 (or 0.990) accuracy on the Labeled Faces in the Wild (LFW) with 155 (or 180) FPS. This indicates that our models can also be applied to real-time multi-face recognition in video.
Detection and Boundary Extraction of Martian Impact Craters by a Pyramidal Approach

During the last ten years, Mars has been extensively explored and mapped by several NASA and ESA orbital missions, generating large datasets of high-resolution images. This type of information helps the understanding of impact processes occurring on the surface of celestial bodies. In this work, we introduced a novel automated approach for detection and boundary extraction of Martian impact craters. We implemented a pyramidal image representation and classical morphological operations, involving Hough transform (HT), which identified regions with a specific circular form. This was tested on 3D mesh data of Mars, provided by Mars Orbiter Laser Altimeter (MOLA). We will demonstrate the potential and usefulness of such automated approach in planetary science.
Multi-agent Reinforcement Learning in Spatial Domain Tasks using Inter Subtask Empowerment Rewards

In the complex multi-agent tasks, various agents must cooperate to distribute relevant subtasks among each other to achieve joint task objectives. An agent’s choice of the relevant subtask changes over time with the changes in the task environment state. Multi-agent Hierarchical Reinforcement Learning (MAHRL) provides an approach for learning to select the subtasks in response to the environment states, by using the joint task rewards to train various agents. When the joint task involves complex inter-agent dependencies, only a subset of agents might be capable of reaching the rewarding task states while other agents take precursory or intermediate roles. The delayed task reward might not be sufficient in such tasks to learn the coordinating policies for various agents. In this paper, we introduce a novel approach of MAHRL called Inter-Subtask Empowerment based Multi-agent Options (ISEMO) in which an Inter-Subtask Empowerment Reward (ISER) is given to an agent which enables the precondition(s) of other agents’ subtasks. ISER is given in addition to the domain task reward in order to improve the inter-agent coordination. ISEMO also incorporates options model that can learn parameterized subtask termination functions and relax the limitations posed by hand-crafted termination conditions. Experiments in a spatial Search and Rescue domain show that ISEMO can learn the subtask selection policies of various agents grounded in the inter-dependencies among the agents, as well as learn the subtask termination conditions, and perform better than the standard MAHRL technique.
Fault Analysis and Predictive Maintenance of Induction Motor Using Machine Learning

Induction motors are one of the most crucial electrical equipment and are extensively used in industries in a wide range of applications. This paper presents a machine learning model for the fault detection and classification of induction motor faults by using three phase voltages and currents as inputs. The aim of this work is to protect vital electrical components and to prevent abnormal event progression through early detection and diagnosis. This work presents a fast forward artificial neural network model to detect some of the commonly occurring electrical faults like overvoltage, under voltage, single phasing, unbalanced voltage, overload, ground fault. A separate model free monitoring system wherein the motor itself acts like a sensor is presented and the only monitored signals are the input given to the motor. Limits for current and voltage values are set for the faulty and healthy conditions, which is done by a classifier. Real time data from a 0.33 HP induction motor is used to train and test the neural network. The model so developed analyses the voltage and current values given at a particular instant and classifies the data into no fault or the specific fault. The model is then interfaced with a real motor to accurately detect and classify the faults so that further necessary action can be taken.
Design and Test for Packaging Machine Cutter Based on TRIZ Theory

Aiming at the problem of wasting plastic film when packaging with existing packaging machines on the market, the structure optimization design of the multi-milk bag packaging technology system is carried out by using the invention principle, and the experiment is verified. According to the problem of wasting plastic film when packaging the heat sealing machine to determine the technical system and establish the functional model of the system component. First, applying the contradiction analysis, object-field analysis and other methods to systematically analyze the problems existing in the technical system. Then, using the invention principle to generate a series of innovative design solutions. Finally, selecting the optimal solution and design the prototype based on considering the production cost, economic benefit, manufacturing difficulty and other factors. The experimental results show that the improved packaging machine packaging effect is better and saving the material.
Intelligent Deep Reinforcement Learning Based Resource Allocation in Fog Network

The increase in the smart devices have brought about the intelligence to the applications at the edge. The current day devices are rich in computations and communications which are in most cases are left under-utilized. These devices can be used to provide edge intelligence when deployed for computations on the streaming data from the IoT devices. To reduce the latency of sending the data to the cloud in case of real-time applications, a new paradigm of edge computing was introduced which meets the emerging challenge of handling latency aware applications on the fly. The other research issue to be handled with utmost importance is the load balancing which can be realized effectively by proper proactive resource allocation. Our proposed work involves the construction of SDN based Fog infrastructure wherein the research issue was tested and evaluated. The proposed Deep Reinforcement Learning algorithm helps in intelligent action selection based on the past experience data and the dynamic network parameters. Finally, the proposed work was compared with the state of the art OSPF algorithm in terms of service time and the load variance on increasing task allocation
LoPECS: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services

To simultaneously enable multiple autonomous driving services on affordable embedded systems, we designed and implemented LoPECS, a Low-Power Edge Computing System for real-time autonomous robots and vehicles services. The contributions of this paper are three-fold: first, we developed a Heterogeneity-Aware Runtime Layer to fully utilize vehicle’s heterogeneous computing resources to fulfill the real-time requirement of autonomous driving applications; second, we developed a vehicle-edge Coordinator to dynamically offload vehicle tasks to edge cloudlet to further optimize user experience in the way of prolonged battery life; third, we successfully integrated these components into LoPECS system and implemented it on Nvidia Jetson TX1. To the best of our knowledge, this is the first complete edge computing system in a production autonomous vehicle. Our implementation on Nvidia Jetson demonstrated that it could successfully support multiple autonomous driving services with only 11 W of power consumption, and hence proves the effectiveness of the proposed LoPECS system.
Multi-Scale Modeling and Simulation of DFIG-Based Wind Energy Conversion System

A multi-scale transients model of a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) is developed, implemented, and validated. All ac circuit and control quantities of the electrical part are modeled by analytic signals rather than just real signals. In addition to the real parts, the analytic signals also comprise orthogonal imaginary parts. While measured Fourier spectra of real ac quantities involve positive and negative frequency components, they only involve positive frequency components when extended and represented as analytic signals. With the introduced shift frequency operator, the analytic signal can now be shifted in the frequency domain to reduce its maximum frequency contents and thus allow for a larger time-step size in accordance with the Nyquist criterion. If the shift frequency is set equal to the ac fundamental frequency, then the affiliated ac voltages and currents become dynamic phasors. At a zero shift, however, instantaneous signals can be tracked as in an electromagnetic transients program (EMTP). This is illustrated here for the voltage sourced converters (VSC) of the WECS. By appropriate selection of the shift frequency, both electromagnetic and electromechanical transients are simulated efficiently. Studies involving wind power fluctuations, three-phase-to-ground fault and single-phase-to-ground fault confirm these claims.
Hierarchical Decomposition based Cooperative Coevolution for Large-Scale Black-Box Optimization

Most state-of-the-art decomposition approaches for cooperative coevolution adopt a static graph (variable interaction matrix) viewpoint to recognize the possible separability of real-valued objective functions. Although generally they work well for partially additively separable scenarios, all of them cannot solve non-separable problems and non-additively separable problems effectively. To tackle these issues, based on our recent theoretical advance, this paper proposes a hierarchical-decomposition-based cooperative coevolutionary framework for large-scale black-box optimization (LSBBO). Specifically, the well-known cyclically random decomposition strategy is embedded in a hierarchical fashion, in order to facilitate the continuous evolution of the best-so-far solution. Such a hierarchy provides a flexible way to make a proper trade-off between computational complexity and search performance as well as exploration and exploitation. Numerical experiments on the IEEE CEC’2010 LSBBO test suite showed the complementary performance of the proposed framework (when integrated with the automatic decomposition technique).
Design of the Controlling System of a Six-DoF Manipulator

Aiming at the requirement of experimental and exhibition, a six-degree-of-freedom control system with ATmega328P as the main controller, steering gear driving module as the motion unit of the manipulator is designed based on the analysis of the experimental application of the traditional manipulator control system. The system has two control modes: "handle control" and "automatic gesture action". According to the structural characteristics of the manipulator, the Denavit-Hartenberg(D-H) coordinate was obtained and its forward kinematic analysis was done. The experimental results show that the control system can quickly and accurately adjust the trajectory of the robot arm to complete the corresponding action, which can be used for experiments and exhibitions.
C-POS: A Context-Aware Adaptive Part-of-Speech Language Learning Framework

In the current age of technology, comprehension of the English language is very important. This is because that often innovation and discoveries are done by English speaking countries. And thus, it is considered as being the most important and widely used international language. Further, the need for learning an additional language has increased due to globalization in an unprecedented manner. Therefore, English is being taught as an additional language at various levels of education. In traditional classrooms of non-English speaking countries, students only learn English in their language classes whereas they have few opportunities to practice English outside the classroom. However, the specialized nature of mobile technologies has opened a new horizon for learners that make information available to people at any time, at any place, and in any form.The number of smartphone users is increasing every year and more than 4.78 billion smartphone users are in the world today.It can be understood that learning increases when the learning process is integrated with daily life routine activities. The context-awareness is one of the commonly used techniques that provide specialized content to different learners based on their context parameters. The word ’context’ refers to any information that can be used to characterize the situation of a particular entity. The context parameter includes location, time, concentration level, Interest and learner’s knowledge. Based on these context parameters, a Context-aware Part-Of-Speech (C-POS) learning system is designed, implemented and evaluated. Different experiments are conducted to evaluate the performance of learners’ which is increased by 9%. The evaluation of results provides evidence that the C-POS learning system can be successfully adapted by the non-natives speakers to learn the English language effectively.
EEG signal Extraction Utilizing Null Space Approach

The aim of this paper is to apply the Null space algorithm to extract Electroencephalography (EEG) signals and remove the Electrocardiogram (ECG) artifact. First, the EEG signals are modelled using the linear mixture model. Then, the Null space algorithm is applied to extract all EEG signals. Simulation results, using synthesized EEG data, show that the model is successfully extracting all the unknown EEG signals and the readiness potential, as well. Results, using real EEG data, show that the model is successfully extracting the unknown EEG signal and removing the ECG artifacts. The algorithm is tested using the correlation and the signal-to-noise ratio extraction metrics, and the results show considerable improvements as compared to fast independent component analysis (FastICA) and parallel linear predictor (PLP) algorithms.
Personalized Local Event Search Based on SNS Data Analysis

Since the Olympic Games were decided to be held in Tokyo in 2020, the number of foreign tourists visiting the city has been increasing rapidly. Accordingly, tourists have been seeking more sightseeing information. While guidebooks are good for pointing out popular tourist attractions, it is more difficult for tourists to get various types of detailed information that is suitable for each of them. We developed a tourist information distribution system that sends information corresponding to places and times. In addition to information on events that can be taken from existing travel books and the Web, the system enables users to get "event information" that is suitable for each of them in terms of hobbies by acquiring information on a wide variety of large and small events from a social networking service (SNS). In addition, we examined the preferences of foreigners visiting Japan to make the system more user-friendly. By estimating their preferences on the basis of social media streams and considering location information and time information, we aim to distribute event information that tourists can use on the spot at a particular time.
Patient Care Management for Physicians: Reducing Handoffs in the ED

The Emergency Department (ED) is an environment prone to high error rates with severe consequences. Prior studies report that miscommunication contributes to 80% of the serious medical errors. Handoffs, transfer of patient care from one physician to another, are a common occurrence and predisposed to errors as a result of interruptions and high workload. Moreover, the Institute of Medicine reported that a majority of treatment delays are a result of communication errors associated with shift change. A simulation model was developed to test various physician to patient assignment policies to minimize the number of handoffs and reduce the workload at the end of a shift. Using a policy that restricts a physician from receiving high acuity patients in the last two hours of the shift, as well as limits the maximum number of patients per physician to five, the number of handoffs can be reduced by as much as 22%.
An Object Oriented Approach to Fuzzy Actor-Critic Learning for Multi-Agent Differential Games

This paper presents a new form of the multi-agent fuzzy actor-critic learning algorithm for differential games. An object oriented approach to defining the relationships between agents is proposed. We define the fuzzy inference system as a network structure and define attributes of the agents as rule sets that fired and rewards associated with the fired rule set. The resulting fuzzy actor-critic reinforcement learning algorithm is investigated for playing the differential pursuer super evader game. The game is played in a continuous state and action space to simulate a real world environment. All the robots in the game are simultaneously learning.
Design of Efficient 2–4 Modified Mixed Logic Design Decoder

This paper proposes a Modified-Mixed Logic Design (MMLD) for the decoders, which comprises full swing Gate diffusion input (GDI) technique, conventional complementary metal oxide semiconductor (CMOS) and Dual value logic (DVL). Two modernized topologies: 14T and 15T decoders are designed for minimizing power dissipation and propagation delay respectively. Each traditional and inverting decoder is enforced in every case. Compared to the conventional CMOS logic, the proposed decoder gives full swing with reduced count of the transistors. The spread of Cadence (Virtuoso) simulation at 45nm is used for implementing this proposed mixed logic decoder at different frequencies with different various supply voltages, which shows reduction in power dissipation & propagation delay as compared to typical CMOS and existing mixed logic design.