Miracle-dz/UniFAR-FTUs huggingface dataset:
Automation Control of Thermal Conductivity Measurement Process Based on LabVIEW

An automation control program based on LabVIEW is designed to measure thermal conductivity of phase change material (PCM). The program is composed of file storage function of all test data and calculation results, temperature-controlled with only one heating plate or both heating plates, NI-DAQ-based data acquisition system, and steady state judgment system. During the testing procedure, the setting temperature is transmitted to heat plates while real-time parameters are read to the computer by using the Modbus protocol. When the temperature reach to stable state determined by the program automatically, the thermal conductivity of PCM is measured. The development of this automatic control program can realize the automatic test of thermal conductivity, and obtain the variation of temperatures and heat fluxes to research the phase change process in the future.
Dynamic Block Size Adjustment and Workload Balancing Strategy Based on CPU-GPU Heterogeneous Platform

In recent years, with the development of processor architecture, heterogeneous processors including CPUs and GPUs have become mainstream processors. In order to take full advantage of the computing power of heterogeneous cores, it is necessary to maintain workload balance between heterogeneous cores while the system is executing applications. And it is also important to optimize the efficiency of the application execution on the GPU by improving the thread organization. In order to improve performance, we propose a block size adjustment strategy that adapts to the current application and GPU environment. Based on this, we propose a strategy for balancing CPU-GPU workload that preferentially protects the core. These strategies optimize the execution time of applications on CPU-GPU heterogeneous platforms. Finally, we tested the actual effect of the strategy by running four benchmarks in a CPU-GPU heterogeneous environment. The experimental results show that the performance can be significantly improved by block size adjustment and workload balancing strategy. These two strategies reduce application execution time by up to 26.21% and 58.01%, respectively, compared to GPU-only execution time.
A Survey of Key Technologies for High Utility Patterns Mining

Recently, high utility pattern mining (HUPM) is one of the most important research issues in data mining. Because it can consider the non-binary frequency values of items in a transaction and the different profit values of each item. It has been widely used. First of all, this paper briefly describes the related concepts, formulas and examples of application for HUPM. Secondly, the key technologies for HUMP are introduced in detail, and they are divided into main methods including Apriori-based, tree-based, projection-based, list-based, data format-based, and index-based and so on. The paper further compares data sets, uses, advantages and disadvantages of algorithms, laid the foundation for the next research direction. Then, this article outlines the high utility derivative patterns, including high average utility pattern, high utility sequential pattern, and high utility compact pattern and so on. Because static data is difficult to meet the actual needs, this paper summarizes the efficient use of HUPMs’ methods over data streams, mainly based on incremental methods, based on the sliding window model methods, based on the time decay model methods and based on the landmark model methods and so on.
Evaluation of Image Informativeness in Multispectral Monitoring Systems with Image Fusion

In this paper an analysis of known methods of image informativeness is carried out. It was found that in many cases existing methods are not working correctly, in particular, give an inflated evaluation, which is not consistent with human visual perception. A new method that allows us to evaluate the image informativeness more adequately the image informativeness is proposed. The adequacy of the results, obtained with the help of the proposed method was confirmed by a sociological survey of a group of 30 respondents who gave an expert evaluation of the informativeness of test images. As a result, it was proved experimentally that the proposed method gives a more correct evaluate of the image informativeness.
Ferrite PM Optimization of SPM BLDC Motor for Oil-Pump Applications According to Magnetization Direction

This paper proposes optimizing the magnetization direction of isotropic ferrite permanent magnets (Fe-PM) to enable the maximization of back-electromotive force (B-EMF), torque density, and minimum torque ripple of surface permanent magnet brushless direct current motor. The concept design is divided into two steps. First, two general Fe-PM electric oil pump motor models with radial and parallel magnetization are selected and compared with the neodymium permanent magnet models. Secondly, a finite element method (FEM) analysis is carried out with two different magnetization directions for each of the models in open-circuit and load operation. For more accurate results, the effects of no-load B-EMF, magnetic field distribution, air-gap flux density, copper loss, and efficiency are investigated. Finally, the validity and superiority of the FEM design results are confirmed by manufacturing the prototype motor and performing an experiment.
Towards a DTN protocol for the Internet of Things

New technologies have been expanding rapidly in recent years. These new technologies require new communication systems, such as delay tolerant networks (DTN). However, establishing communications is not enough. Networks must allow data to be exchanged and these data must be understood by the entities that receive them. Although a lot of DTN routing protocol has been proposed for DTN networks, but with the emergence of new technological applications such as drones, the Internet of Things, etc., the DTN routing protocol is still in use. It is desirable to develop a feasible, reliable and robust protocol for this new range of applications. In this work, we will present a DTN protocol for the Internet of Things (IoT) applications.
A Reconfigurable Approximate Multiplier for Quantized CNN Applications

Quantized CNNs, featured with different bit-widths at different layers, have been widely deployed in mobile and embedded applications. The implementation of a quantized CNN may have multiple multipliers at different precisions with limited resource reuse or one multiplier at higher precision than needed causing area overhead. It is then highly desired to design a multiplier by accounting for the characteristics of quantized CNNs to ensure both flexibility and energy efficiency. In this work, we present a reconfigurable approximate multiplier to support multiplications at various precisions, i.e., bit-widths. Moreover, unlike prior works assuming uniform distribution with bit-wise independence, a quantized CNN may have centralized weight distribution and hence follow a Gaussian-like distribution with correlated adjacent bits. Thus, a new block-based approximate adder is also proposed as part of the multiplier to ensure energy efficient operation with awareness of bit-wise correlation. Our experimental results show that the proposed adder significantly reduces the error rate by 76-98% over a state-of-the-art approximate adder for such scenarios. Moreover, with the deployment of the proposed multiplier, which is 17% faster and 22% more power saving than a Xilinx multiplier IP at the same precision, a quantized CNN implemented in FPGA achieves 17% latency reduction and 15% power saving compared with a full precision case.
Conservation Voltage Reduction Case Study

A Conservation Voltage Reduction (CVR) is an efficient method to manage the load demand in distribution power systems. This paper proposes the utilization of the CVR method on Washington EMC’s power system and Georgia Transmission Corporation facilities. The utility’s peak power consumption is reduced to determine the figures of wattage and money saved. The CVR technique is implemented within the ANSI standards and with an efficient CVR, factor to maintain the efficient performance of the system and satisfy the consumers’ needs. The principle of operation, concepts of modeling, and substation CVR management are illustrated. The CVR methodology including utility circuit, smart meters’ data, management, savings, and analysis is presented. Then, experimental CVR scenarios, smart meters’ experimental CVR data, CVR steady-state analysis and experimental CVR management using SCADA are investigated and analyzed. After that, a CVR dynamic prediction model is established using the Neural Network (NN) and validated by means of comparisons with actual data and the Gaussian model. Power saving, power demand, yearly money-saving real data from EMC utility, and real temperature data from NOAA is used to create the CVR dynamic model with a minimum average error percentage of 0.22 % w.r. t the real-data. Finally, a SCADA system operation is discussed along with the potential future improvements and research directions.
Characterizing Depressive Related Speech with MFCC

The experimental results from comparative study of acoustical properties in speech as emotional indicator based on spectral characteristics of speech signal have formerly been studied and reported for its quantitative information in association with the emotional states in persons suffering depression. This symptom affects speech production system of speaker, which modulates in spoken sound. MFCC has been reported for its characteristic change corresponding to severity of depression. The sixteenth MFCCs from remitted, depressed and suicidal patient groups were extracted, statistically tested and classified in pairwise fashion by using ML, LS and LMS classifiers. The best score of classification can be obtained at 0.2487 in error based on ML classifier with 80% of MFCC samples in testing phase. Results suggest the dominant property of MFCC in separation between suicidal and recovering speakers from depression.
Thompson Sampling-Based Heterogeneous Network Selection Considering Stochastic Geometry Analysis

We propose a sophisticated network selection scheme based on multi-armed bandits and stochastic geometry for heterogeneous cellular networks. In the system model, a user seeking the best network tries to estimate the density of active interferers for every network through the repeated observation of signal-to-interference power ratio (SIR), which shows the randomness induced by randomized interference sources and fading effects. The purpose of this study is to enable the user to identify the network with the lowest density of active interferers while considering the communication quality during exploration. In order to resolve the trade-off between getting more observations on uncertain networks and using a network that seems better so far, we employ a bandit algorithm called Thompson sampling (TS), which is known for its empirical effectiveness. We take two ideas into consideration to enhance TS. First, noticing that the statistical SIR model given by stochastic geometry is useful for capturing the relationship between observed SIR and density of active interferers, we propose to incorporate the statistical model into TS. Second, TS requires us to sample from the posterior distribution of the density parameter for each network, while the distribution obtained through stochastic geometry is much more complicated to generate samples than well-known distribution; we reveal that such a sampling process is achieved with the help of the Markov chain Monte Carlo method. The simulation results show that the proposed method enables a user to find the best network more efficiently than well-known bandit algorithms such as an ∊-greedy strategy.
Boosting the Profitability of NVRAM-based Storage Devices via the Concept of Dual-Chunking Data Deduplication

With the latest advance in the non-volatile random-access memory (NVRAM), NVRAM is widely considered as the mainstream for the next-generation storage mediums. NVRAM has numerous attractive features, which include byte addressability, limited idle energy consumption, and great read/write access speed. However, owing to the high manufacturing cost of NVRAM, the incentive of deploying NVRAM in consumer electronics is lowered due to the consideration of profitability. To resolve the profitability issue and bring the benefits of NVRAM into the design of consumer electronics, avoiding storing duplicate data on NVRAM becomes a crucial task for lowering the demand and deployment cost of NVRAM. Such observation motivates us to propose a data deduplication extended file system design (DeEXT) to boost the profitability of NVRAM via the concept of dual-chunking data deduplication while considering the characteristics of NVRAM and duplicate data content. The proposed DeEXT was then evaluated by real-world data deduplication traces with encouraging results.
Descriptive Feedback on Interns’ Performance using a text mining approach

Descriptive feedback is a powerful tool to identify the strength and the areas that need improvement of a certain program. Text analysis using the text mining approach is the most common application in the text processing field. This study aimed to determine the potentials and the imperfections of the IT interns while taking the internship program using a Naive Bayes text classification. Text dataset from the internship program of the IT curriculum under the College of Computer Studies and Information Technology (CCSIT) of Southern Leyte State University (SLSU) was the input of the study. This study applied the text mining application using the Naïve Bayes text classification to evaluate the performance of the IT interns. Results show that the IT interns of the program were proficient and technically competent but had a problem with their communication skills. The model has obtained an acceptable accuracy rating of 87.55% in predicting the performance of the students. Based on the results, the IT interns were efficient in the IT-related task. However, there is a need to improve interns’ communication skills. The management should think about a possible bridging program that will help the student to develop their communication skills.
Low-Cost, Practical Data Confidentiality Support for IoT Data Sources

IoT is one of the most discussed topics in distributed systems currently, having new applications and interesting solutions developed every day. Nevertheless, to make applications possible and widespread, the costs of data acquisition and transmission must be overcome. Raspberry Pi 3, in this sense, has become a good cost-effective solution for many IoT applications. In this paper, we present a low-cost, secure Smart Meter built on Raspberry Pi 3. This solution is divided into three stages: secure data acquisition, data ciphering and secure data transfer. The main contribution consists on the level of achieved data protection, which goes beyond the regular authentication and secure communication protocols. Through this approach, sensitive data can be kept secure even if the cloud provider is untrusted or an adversary gets remote access to the Raspberry. This is a flexible, simple, efficient, and low-cost solution that can be applied in many other IoT application contexts.
Implementation of a Polyphase Filter Bank Channelizer on a Zynq FPGA

This paper describes the design and implementation of a 16-channel polyphase filter bank (PFB) channelizer. The PFB channelizer structure implements a resource-efficient multichannel digital receiver for a set of frequency division multiplexed (FDM) signals that exist in a single sampled data stream. The implementation is based on the Zynq® field programmable gate array (FPGA) and aims to exploit the potential for data reuse and flexibility offered by the PFB channelizer structure. General design criteria are summarized for the 16-channel polyphase filter bank channelizer. Python and Vivado simulation results of the performance and operation for the design are also presented.
Buffer Management for Identifying Crypto-Ransomware Attack in Environment with No Semantic Information

Crypto-ransomware is one of the most dangerous threats to cybersecurity. Many countermeasures against ransomware have been proposed in the vertical layers: cloud systems, host systems and storage devices. Recently, storage devices have attracted considerable interest for data recovery against ransomware. However, the usage of the previous storage-level approaches is limited to flash-based storage devices. In this paper, we propose a ransomware-aware buffer management policy of internal buffers in storage devices, which allows our proposal to be generalized to various types of storage devices (e.g., hard disk drives and flash-based storage devices). Our policy aims at capturing the representative access patterns of three ransomware families at the internal buffers. It leverages the low-level features at storage devices where there is no semantic information (e.g., file name) on files. The experimental results show that the proposed policy makes the internal buffers keep the information relevant to the synthetic and realistic patterns of ransomware. In addition, it is shown that a simple ransomware detector using the internal buffers managed by our policy detects a realistic ransomware sample in the various sized buffers.
An Empirical Evaluation of Machine Learning Techniques for Chronic Kidney Disease Prophecy

Chronic Kidney Disease (CKD) implies that the human kidneys are harmed and unable to blood filter in the manner which they should. The disease is designated “chronic” in light of the fact that harm to human kidneys happen gradually over a significant time. This harm can make wastes to build up in your body. Many techniques and models have been developed to diagnos the CKD in early-stage. Among all techniques, Machine Learning (ML) techniques play a significant role in the early forecasting of different kinds ailments. ML techniques have been used for achieving analytical results which is one of the instruments utilize in medical analysis and prediction. In this paper, we employ experiential analysis of ML techniques for classifying the kidney patient dataset as CKD or NOTCKD. Seven ML techniques together with NBTree, J48, Support Vector Machine, Logistic Regression, Multi-layer Perceptron, Naïve Bayes, and Composite Hypercube on Iterated Random Projection (CHIRP) are utilized and assessed using distinctive evaluation measures such as mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), recall, precision, F-measure and accuracy.The experimental outcomes accomplished of MAE are 0.0419 for NB, 0.035 for LR, 0.265 for MLP, 0.0229 for J48, 0.015 for SVM, 0.0158 for NBTree and 0.0025 for CHIRP. Moreover, experimental results using accuracy revealed 95.75% for NB, 96.50% for LR, 97.25% for MLP, 97.75% for J48, 98.25% for SVM, 98.75% for NBTree, and 99.75% for CHIRP. The overall outcomes show that CHIRP performs well in terms of diminishing error rates and improving accuracy.
An Energy-Efficient Collaborative Scheme for UAVs and VANETs for Dissemination of Real-Time Surveillance Data on Highways

In addition to military applications, Unmanned Aerial Vehicles (UAVs) are now finding their ways in civilian and public domains. Surveillance of highways with the use of a helicopter is a costly option that could be replaced with a more economical option of UAVs. Unlike MANETs in general and VANETs in particular, UAV networks have unique characteristics due to the association of factors like dynamic topology, high mobility, and limited energy. Keeping in view the real-time aspect of road surveillance, the use of UAVs with the existing routing protocols is not a straight forward option. A fleet of drones en route to surveillance on the highways may face frequent link failures due to its dynamical sparse linear architecture. To compensate for the sparse topology, a collaboration of UAVs with VANETs might be a good choice but as such integration of the two networks could create too much overhead. In this article, a proactive energy-efficient and reliable Collaborative scheme between UAVs and VANETs, termed as CUV, is presented. To avoid flooding in the entire network, Base station Association Messages (BAM), in a unicast manner, are forwarded till the end-nodes. Although BAM is received by each node which ensures connectivity with the BS but only certain nodes are allowed to retransmit. Furthermore, to take care of the loss of BAM messages due to the presence of hidden nodes, a novel mechanism for link repair inside a BAM interval is introduced. The proposed scheme is simulated in Network-Simulator-2 and compared with the well known proactive OLSR and reactive AODV protocols.
Physical Layer Security of Digital Front End Based Internet of Things Communication in Power Systems

Internet of Things Communication in Power Systems (IOTCPS) have been widely used both in smart grid and in-home applications such as real-time detection of equipment power and remote control of electrical network. However, security is still a major problem. A way to enhance security is to adopt physical layer security. By padding data to scrambler and interleave before transmitting, security can be quite improved. On the basis of this traditional communication link, in this paper, we propose a novel method to enhance security of IOTCPS. Digital front end is added to mask center frequency and bandwidth of transmitted message with artificial data. To access system security, we compare bit-error-rate (BER) performance of a legitimate receiver and an eavesdropper. Results shows that this method enhances security significantly.
Analysis of AC 500kV XLPE Submarine Cable Insulation Laboratory Aging Condition Based on Frequency Domain Dielectric Spectroscopy

With the rapid rise of new energy industry and its rapid development all over the world, there is a great market for submarine cable products. The world's first class AC 500kV cross-linked polyethylene (XLPE) insulated submarine cable will soon be put into operation in the “Zhoushan-Ningbo” interconnection project. Aging of insulation materials after operation will be an important factor restricting the safe operation of the 500kV XLPE cable. Taking into consideration that the frequency domain spectroscopy (FDS) is a nondestructive testing (NDT) method, in this paper, the changes of dielectric spectroscopy of the AC 500kV XLPE submarine cable material during 130°C thermal aging process were studied. The aged samples show increasing dielectric constant and dielectric loss. Besides, Iε‘ is defined to estimate the aging condition of XLPE samples. It can be clearly seen that the samples severely aged when the Iε’ exceeds 1. The Iε’ can be used to diagnostic the aging condition of the AC 500kV submarine cable insulation material. Besides, the ε‘rand tanδ in the lower frequency of the seriously aged sample increase significantly, and the inflection points of tanδ curves for the seriously aged sample move to higher frequency significantly.
Research of Inertial Instrument Error Identification Algorithm for Spacecraft Test

The inertial instrument is the important part of spacecraft control system, its measurement precision is related to the control precision and service life of the spacecraft. During the testing process of spacecraft, the key point is to obtain accurate model of the actual system. In order to identify the actual inertial navigation system error model from test data, a new identification method is proposed. Through the reverse process of strapdown inertial navigation, the ideal inertial instrument output data can be solved from navigation result. The error compensation system can be established, with ideal data input and the actual data of the inertial instrument output. Finally, the error model and error coefficient of inertial instrument can be obtained with the system identification method. The test results show that the method is effective and has reference value and directing significance for engineering application.