Miracle-dz/UniFAR-FTUs huggingface dataset:
Gradient Microstructures and Mechanical Properties of Ti-6Al-4V/Zn Composite Prepared by Friction Stir Processing

In this work, a biomedical Ti-6Al-4V (TC4)/Zn composite with gradient microstructures was successfully prepared by friction stir processing (FSP). The microstructures and mechanical properties of the composite were systematically studied using scanning electron microscope (SEM), X-ray diffractometer (XRD), transmission electron microscope (TEM), atom probe tomography (APT), and microhardness test. The results show that TC4/Zn composite can be successfully prepared, and gradient microstructures varying from coarse grain to nanocrystalline is formed from the bottom to the upper surface. During FSP, adding Zn can accelerate the growth of β phase region, and the grain size significantly increases with the increasing rotation rate. The grain combination is the main mechanism for grain growth of β phase region. The deformation mechanisms gradually change from dislocation accumulations and rearrangement to dynamic recrystallization from the bottom to the upper surface (1.5 mm–150 μm from the upper surface). The composite exhibits slightly higher microhardness compared with the matrix. This paper provides a new method to obtain a TC4/Zn composite with gradient surface microstructures for potential applications in the biomedical field.
Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning

An accurate vision system to classify and analyze fruits in real time is critical for harvesting robots to be cost-effective and efficient. However, practical success in this area is still limited, and to the best of our knowledge, there is no research in the area of machine vision for date fruits in an orchard environment. In this work, we propose an efficient machine vision framework for date fruit harvesting robots. The framework consists of three classification models used to classify date fruit images in real time according to their type, maturity, and harvesting decision. In the classification models, deep convolutional neural networks are utilized with transfer learning and fine-tuning on pre-trained models. To build a robust vision system, we create a rich image dataset of date fruit bunches in an orchard that consists of more than 8000 images of five date types in different pre-maturity and maturity stages. The dataset has a large degree of variations that reflects the challenges in the date orchard environment including variations in angles, scales, illumination conditions, and date bunches covered by bags. The proposed date fruit classification models achieve accuracies of 99.01%, 97.25%, and 98.59% with classification times of 20.6, 20.7, and 35.9 msec for the type, maturity, and harvesting decision classification tasks, respectively.
The Effect of Icon Entropy, Icon Concreteness and Time on Human Selection Accuracy

This paper reports on an experimental study that included engineering faculty undergraduates (n = 400) to measure the effect of icon entropy and icon concreteness on selection accuracy within time constraint conditions (15 seconds, 10 seconds, and 5 seconds). The research was undertaken as an exploratory tool to study the potential effects and interactions of entropy, which has been poorly studied as a possible factor in icon selection outcomes despite existing knowledge that entropy is perceivable by humans and can influence their behaviors. This may be particularly important in small-screen computing where icon size and resolution may pose a challenge for perception, and where entropy may interfere further by introducing high salience. Results showed that conditions of high entropy resulted in lower categorization accuracy for both high and low concreteness icons. However, these differences were only significant under the strictest time constraint (5 seconds).
Design and Power Management of Solar Powered Electric Vehicle Charging Station with Energy Storage System

Global warming has led to the large adoption of Electric Vehicles(EVs) which appear to be the best replacement to IC engines. Due to increased number of EVs in the road, charging of the vehicles with conventional fossil fuel based grid is not economical and efficient. Thus, a renewable energy based charging station finds immense potential and control for electric vehicle charging. An electric vehicle charging station integrating solar power and a Battery Energy Storage System (BESS) is designed for the current scenario. For uninterrupted power in the charging station an additional grid support is also considered without becoming an extra burden to the grid. An efficient design of charging station with MPPT, PID and current control strategy is developed for the optimal power management between solar, BESS, grid with the EVs in the charging station. By taking dynamic charging needs of EVs, the design of charging station is formulated and validated in MATLAB/Simulink.
Design of Radiometric Thermography System for Object Recognition

The paper presents a custom-made radiometric thermography system which provides a full radiometric IR inspection. It contains heterogeneous stereo-vision system with RGB and thermal camera, as well as the processor unit with developed embedded software modules. The software units were realized using C++ programming language within ROS development environment. In order to obtain the better representation of points in the space as well as their projections on the cameras planes, the stereo calibration was performed. The correspondence between RGB and thermal images is represented with homography. The object detection is performed using OpenCV feature detectors, while the recognition is carried out by Hu moments computation and K-means clustering. For the showcase, incandescent light bulbs, LED light bulbs and quartz heaters are detected and recognized as such. The developed system is capable of providing a heterogeneous inspection of heating systems, power lines, etc.
Grouping and Labeling in Digital Learning Portfolios

This paper describes an application of clustering and labeling methods in actual learning records of e-portfolios implemented through personal blogs. In the clustering process, we have used k-means algorithm with Euclidian distance and random initialization of the centroids, using the elbow method to determine the best value of k; we used centroid labels for labeling. Analysis of 4,241 instances of the model (posts) have shown that absence of formal descriptors was overcome through representation of the titles given to posts, suggesting its use for automatic structuring of contents of this nature.
Decision Support System for International Trade Analysis using FuzzyC4.5 based Predictive Analytics

It is really tough to manually examine the raw data. The Datamining strategies are used to detect the applicable information from uncooked data. The data mining algorithms are efficient for retrieving a specific pattern. In Datamining techniques decision trees are the most commonly used methods for predicting the outcome or behavior of a pattern because they can successfully and efficiently visualize the facts. Presently several decision tree algorithms are advanced for predictive analysis. Right here we gathered a dataset for rubberized mattress, from coir board CCRI, and applied the several decision tree algorithms on the data set and as compared every one. Every set of rules gives a completely unique choice tree from the input statistics. This paper focuses in particular on the Fuzzy c4.5 set of rules and compares one-of-a-kind choice tree algorithms for predictive analysis. Here by using predictive analytics, a decision can be made for each rubberized firms.
A Hybrid Task Scheduling Scheme for Heterogeneous Vehicular Edge Systems

Enhanced wireless communication improves the connectivity of vehicular networks in which vehicles are utilized as infrastructures for communication and computation. Thus, a new concept “Vehicular Edge Computing (VEC)” is formed. As VEC utilizes a collaborative multitude of near-user edge resources (i.e. vehicles) in the Internet of Vehicles, the capability of these joint resources becomes heterogeneous especially in their movements. Therefore, one critical problem is how to efficiently schedule each task under such mobile environments. For the reason, we propose a hybrid dynamic scheduling scheme (HDSS) that has the ability to optimize the task scheduling dynamically based on the changeable system environments. HDSS provides a decision function (DF) to select a better-performed scheduling algorithm from two provided candidates: the queue-based dynamic scheduling (QDS) algorithm and the time-based dynamic scheduling (TDS). QDS coincides with the Join-the-Shortest Queue scheme, which decides the scheduling by sorting out a server with the shortest queue-length; nevertheless, TDS is novel scheme that is designed to implement task allocation by estimating the waiting time of each server in order to select a server with the fastest response. Finally, this research generates formal models of each scheduling algorithm and the hybrid scheduling scheme to conduct performance evaluation with a fluid flow approximation technique. The analysis results in a superior performance of HDSS in the unstable VEC environments.
Representation Learning With Class Level Autoencoder for Intelligent Fault Diagnosis

Although representation learning has been proved to be a promising and effective solution for intelligent fault diagnosis, existing methods still encounter classification performance degradation due to large intra-class variations of real-world applications. In this work, we present a novel representation learning method, namely class level autoencoder (CLAE) for fault diagnosis which aims to learn representative and discriminative features from vibration signals. Specifically, we formulate a novel loss function for representation learning by jointly minimizing the basic and class level reconstruction errors to restrain intra-class variations in the feature space. In addition, we propose a new and simple feature pooling strategy to effectively fuse various meaningful local features to capture efficient and inherent fault pattern information about the input. Extensive experimental results on rolling bearing dataset demonstrate that our proposed method achieves very competitive results compared to state-of-the-art methods. The code of the proposed method is available at https://github.com/KWflyer/CLAE.
A novel hybrid architecture for classification of power quality disturbances

Power quality disturbances are introduced in the signals due to the increasing use of power electronic devices. To ensure reliability, security and adequate quality of power for consumption, the power quality disturbances need to be detected and classified accurately. This paper proposes an efficient algorithm for detecting and classifying the various power quality disturbances using a convolutional neural network (CNN) to extract various features from the input power signal which are then fed to the multi-class support vector classifier (MCSVC) to detect and classify the power quality disturbance events. It is observed from the simulation results and verified using an industrial dataset that the proposed model performs better than a normal convolutional neural network by approximately 10%. This work contributes to improving the quality of power delivered for industrial applications, making the operation of power systems economic, efficient and safe.
Comparative Performance Analysis of Karatsuba Vedic Multiplier with Butterfly Unit

In DSP processors or other applications which use multiply-accumulate units (MAC) etc., multiplication of large numbers is the main bottleneck. Multiplying two n-bit binary numbers requires n (n − 1 ) adders and n2 AND gates, which consumes more time, power and area for large n since the hardware scales as the square of n so, there is a need to design a binary multiplier which consumes lesser area, power and delay but in general there will be tradeoff between area, power and delay. With the shrinking of technology we can slightly compromise with area. This paper proposes an efficient method for signed binary multiplication using Urdhva-Tiryagbhyam technique, Karatsuba algorithm and efficient carry select adder. Urdhva-Tiryagbhyam technique is known for its low delay [8] as it produces partial products at same instant and sums them up. It is best suited when the number of bits in the multiplier and multiplicand are less than 16 [8], [14]. Whereas Karatsuba algorithm is applicable for multiplication of larger number of bits [5]. The proposed multiplier is implemented for a first stage butterfly unit [12] of a radix-2 FFT algorithm. This proposed design is implemented using Verilog HDL and synthesized in both 90 nm and 45 nm technology at multiplier level and in 45nm technology for butterfly unit using cadence RTL compiler and results are compared.
A Comprehensive Survey of OLAP: Recent Trends

A Multi-Dimensional Database (MDD) is a type of database for performing Data Warehousing and OLAP Operations. They are often made contribution for existing relational databases. The On-Line Analytical Processing (OLAP) is a Multidimensional database that is being used for discovery of information, which includes the abilities of complex analytical estimations, endless report views and prescient situation (conjecture) outline. OLAP discovers its applications in different Business Intelligence (BI) Objectives. Its main parameter is multidimensional facts and data. In this paper, we cover the architectural components and existing OLAP technologies. We have also figured out the usage of each existing technologies. Further, we have framed out the detailed analysis of research on OLAP.
Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance

The modern era internet is full of information pertaining to opinion of people through applications such as social media, micro blogging sites, review sites, personal blogs etc. Sentimental analysis is an area in text mining where opinion of people can be analyzed and classified into positive, negative or neutral. In this work, the sentiments of the tweets or reviews published in the twitter is identified by searching for the particular keyword in tweets and then evaluate the polarity of the tweets as positive and negative. The sentiments of the tweets that are tweeted on a twitter evaluated based on feature selection of each score words. In order to select the best features Naive Bayes Classifier (NBC) is used for training and testing the features of a words and also evaluating the sentiment polarity of each tweets. Performance evaluation parameters such as accuracy, precision and time is taken into consideration and compared with three machine learning classifiers, namely, Random Forest, Naive Bayes and Support Vector Machine(SVM).
How Complex is the Complexity of a Concept in Exploratory Search

Exploratory search is a specialized form of information retrieval and discovery. The main aim of the exploratory search is to investigate given information domain to acquire knowledge about it - that is, to learn something about that domain. The level of difficulty of information can affect the learning process and its success. Information with low complexity and relative ease may be uninteresting to learners, who might find it tedious and unrewarding. On the other hand, information with high complexity and difficulty can demoralize learners who perceive it as being impossible, triggering feelings of anxiety. Whether too complex or not complex enough, the seek for information can result in little or no learning. This paper aims to (i) identify features that indicate the complexity of the information represented by a concept; (ii) analyze the features in order to provide a coherent sequence of concepts that balances complexity and learners interest; and, (iii) discuss the challenges, difficulties, and validity of sequencing concepts based on features that indicate complexity. The results show that diversity and variety of concepts as well as detailed semantic descriptions and relations act as indicators for the ideal order of presentation of the information.
Architecting Racetrack Memory Preshift through Pattern-Based Prediction Mechanisms

Racetrack Memories (RM) are a promising spintronic technology able to provide multi-bit storage in a single cell (tape-like) through a ferromagnetic nanowire with multiple domains. This technology offers superior density, non-volatility and low static power compared to CMOS memories. These features have attracted great interest in the adoption of RM as a replacement of RAM technology, from Main memory (DRAM) to maybe on-chip cache hierarchy (SRAM). One of the main drawbacks of this technology is the serialized access to the bits stored in each domain, resulting in unpredictable access time. An appropriate header management policy can potentially reduce the number of shift operations required to access the correct position. Simple policies such as leaving read/write head on the last domain accessed (or on the next) provide enough improvement in the presence of a certain level of locality on data access. However, in those cases with much lower locality, a more accurate behavior from the header management policy would be desirable. In this paper, we explore the utilization of hardware prefetching policies to implement the header management policy. "Predicting" the length and direction of the next displacement, it is possible to reduce shift operations, improving memory access time. The results of our experiments show that, with an appropriate header, our proposal reduces average shift latency by up to 50% in L2 and LLC, improving average memory access time by up to 10%.
Topologies and Control Schemes of Bidirectional DC–DC Power Converters: An Overview

Bidirectional DC-DC power converters are increasingly employed in diverse applications whereby power flow in both forward and reverse directions are required. These include but not limited to energy storage systems, uninterruptable power supplies, electric vehicles, and renewable energy systems, to name a few. This paper aims to review these converters from the point of view of topology as well as control schemes. From the point of view of topology, these converters are divided into two main categories, namely non-isolated and isolated configurations. Each category is divided into eight groups along with their respective schematics and a table of summary. Furthermore, the common control schemes and switching strategies for these converters are also reviewed. Some of the control schemes are typically applied to all DC-DC power converters such as PID, sliding mode, fuzzy, model predictive, digital control, etc. In this context, it should be noted that some switching strategies were designed specifically for isolated bidirectional DC-DC converters in order to improve their performance such as single phase shift, dual phase shift, triple phase shift, etc. The features of each topology and control scheme along with their typical applications are discussed, in order to provide a ground of comparison for realizing new configurations or finding the appropriate converter for the specific application.
Joint Scheduling and Deep Learning-Based Beamforming for FD-MIMO Systems Over Correlated Rician Fading

In this paper, we investigate the downlink transmission for full-dimension multiple-input multiple-output (FD-MIMO) systems over correlated Rician fading channels. With only statistical channel state information at the BS, the beamforming vectors of each user are decoupled from each other through the maximization of average signal-to-leakage-plus-noise ratio (SLNR) lower bound, and the optimal beamforming vector which involves the line-of-sight component and correlation matrix of both directions is derived. To reduce the time of acquiring the beamforming vector for each user, a deep learning (DL)-based algorithm is proposed. A sub-optimal analytical beamforming algorithm is also proposed for comparison. Based on the proposed beamforming method, the ergodic rate of each user is analyzed and simple closed-form approximations are derived, which are very useful in evaluating the system performance and user scheduling. Moreover, two user scheduling algorithms referred to as most dissimilar and minimum interference-to-signal factor algorithm are proposed, which greatly reduce the complexity compared to the sum rate based greedy scheduling algorithm. Simulation results validate the performance of the proposed beamforming and scheduling algorithms, and verify the accuracy of the derived ergodic rate approximations.
Exploring Spatial Symbiosis of Agriculture and Mining for Sustainable Development in Northwest Ghana

This paper explores the spatial relationship between mining and agricultural activities towards meeting the United Nations (UN) Agenda 2030 Sustainable Development Goals (SDGs) in Northwest Ghana. Agenda 2030 SDGs highlight the importance of poverty reduction, livelihood enhancement, and food security. A state's natural resources include both nonagricultural and agricultural resources. There is a renewed interest in large-scale mining in Ghana, entering into previously underexplored areas in the Northwest, an area dominated by agriculture. With the emergence of mining in this region, this study combines both satellite imagery, covering years 2000, 2010 and 2018, and ground truthing data to conduct baseline studies and assess changes in land use over time. We compared known data sets and field knowledge with satellite data to objectively measure changes in the distribution of surface water, farmlands and grasscover over time. The study finds increasing areas of surface water, abundant grasscover and farmlands within leases in the area. These growing abundance of land use and land cover types provide opportunities for commercial livestock keeping, extensive and intensive crop farming. The classified satellite images revealed the existence of more farmlands and potential cultivable areas than reported by agriculture extension offices. Most of these areas overlap with mining concessions and could be modelled for commercial food production and local job creation. The occurrence of mining and agricultural activities in rural subsistence farming communities often indicate conflict. However, a co-exitence of both sectors has a strong opportunity to drive inclusive growth for smallholder farmers; reduce poverty, generate income and uphold sustainable development.