Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality Monitoring System For Pork Meat Using Computer Vision

To realize the purpose of the study, the researchers implemented and evaluated a system that estimates the age of pork meat through the RGB features of its various segments whose images were captured using a custom-built platform. Furthermore, the study aimed to establish a relationship between the colors of a pork sample’s fat, meat, and miscellaneous area to estimate the age of the sample using computer vision techniques. In order to produce a reliable data model, the software’s dataset was established using sufficient number of pork meat images comprising specimens sampled on different days to include ages ranging from 0-7 days. Pork meat samples from each age group were individually placed in a specially-designed platform equipped with proper lighting and a high-resolution camera for taking photographs. Images of the samples were captured and subjected to image processing in order to produce data through feature extraction. By automating the image analysis of the entire data set, extracted data were processed and analyzed in order to reliably estimate the age of pork meat samples in a nondestructive manner. As part of the evaluation, this study determined the accuracy of three regression models: Linear, Multiple, and Polynomial regressions in estimating the age of pork meat. Upon evaluation, the result established that the age of pork meat can be estimated by the color of its fat. However, estimates were significantly improved by also using its meat and miscellaneous area. It could be surmised that using only the color of the fat is sufficient but would make estimations less accurate as compared to using all the aforementioned parts. Therefore, it is concluded that using the color of fat, combined with that of meat and other miscellaneous parts, the accuracy of estimating the age of pork meat can be improved. The system’s quality was also evaluated according to the standards for computer software set by ISO 25010 International Quality Standards. The over-all mean of 4.96 establishes that the system has “excellent” quality in terms of functional suitability, reliability, performance efficiency, usability, security, compatibility, maintainability, and portability.
Outdoor Experiment of mmWave Meshed Backhaul for Realtime Edge Content Delivery

In recent years, ultra-broadband communication using densification of millimeter-wave (mmWave) small cell base station (SC-BS) has attracted attention owing to its ability to accommodate increasing mobile data traffic. To make full use of mmWave access, deploying ultra-broadband backhauling lines such as optical fibers everywhere is an extremely expensive approach. mmWave meshed network is therefore a cost-efficient wireless backhaul architecture for mmWave overlay cellular network. Owing to its wide bandwidth and flexibility in link connection via beam steering and multi-hop relay, mmWave meshed network is furthermore suitable for dynamic construction of backhauling in adaptation to change of user traffic’s distribution via adaptive allocation of backhaul resources of densely co-located users (UE). On the other hand, as users nowadays want to experience services everywhere without disruption even when moving, it is desirable that UE-specific multimedia contents are located as close as possible to the UE via Mobile Edge Computing (MEC) technology. Covering all the above requirements, this paper aims to construct a real UE-centric edge content delivery system in which content server is re-located in adaptation to UE’s context information e.g. location, via dynamic routing over mmWave meshed backhaul network, enabled by Software Defined Network (SDN) technology. Using our developed WiGig device based testbed, outdoor experiment campaign is conducted in the university campus with various measurement scenarios. This paper describes the overall testbed architecture and the conducted outdoor experiment with preliminary results revealing the effectiveness of the proposed system.
Instruction-Based Timing Analysis in Pipelined Processors

Traditional timing analysis techniques for microprocessor design are based on the static analysis approach, in which clock frequency is set in accord with the worst-case delay in the processor circuit operation, regardless of the possible circuit inputs. Dynamic analysis approaches are calculating delays using varying circuit inputs, but are in general very slow. Recent approaches to static timing analysis are based on better-than-worst-case algorithms, which attempt to trade-off the pessimism of standard static analysis with the optimism of dynamic analysis.The work presented in this paper is concentrated on an enhanced implementation of static timing analysis for processor design, applying dynamic analysis through specific variations of circuit inputs. In particular, given that the circuit inputs are mostly instructions of the processor instruction set, we have chosen to vary the instruction opcode of that input. Thus, we have designed and implemented a timing analysis algorithm, which produces timing information for each instruction of the processor, allowing the designer to focus on improving the architecture of particular instructions rather than redesigning circuit parts.We have tested our algorithm on an OpenRISC processor design and collected timing information through an innovative use of the Synopsis PrimeTime tool. A comparison against timing information obtained through pure static timing analysis makes clear that our algorithm succeeds to give better-than-worst-case timing for the most common instructions, thus allowing the designer to deliver a better product.
Mining Traffic Accident Data for Hazard Causality Analysis

Over 1.25 million people are killed, and 20–50 million people are seriously injured by traffic accidents every year globally, according to the World Bank. This paper aims to identify patterns in traffic accident data, collected by Cyprus Police between 2007 and 2014. The dataset that was used includes information regarding 3 groups of accident properties: human, vehicle and general environmental or infrastructural information. Data mining techniques were used, and several patterns were identified. Five classifiers were evaluated using a preprocessed dataset, to extract accident patterns. Preliminary results indicate some of the main issues with regards to accident causalities in Cyprus that could be used for real time accident warnings.
Improve Indoor Positioning Accuracy Using Filtered RSSI and Beacon Weight Approach in iBeacon Network

Increasing the location accuracy of the Indoor Positioning System (IPS) is an important research area in localization. Utilizing mobile beacons in IPS environment has made localization more accurate and cost-effective. This paper proposes an Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator (RSSI) values using a Kalman filter. This algorithm takes both the distance and improved RSSI measurements between beacon nodes into consideration. Kalman filter applied on the RSSI measurements that eliminate noise of the signal and then applied on FRBW positioning algorithm. The proposed algorithm was applied using eight beacons. The results show that this FRBW approach has better positioning accuracy and minimum location error, and can be applied in IoT applications in smart city.
ArmChain - A Blockchain Based Sensor Data Communication For the Vehicle as a Mobile Sensor Network

The Internet of Things (IoT) is widely used in various aspects such as industry, manufacturing, health, agriculture and others. One of the uses of IoT is as an environmental monitoring system. In its application, IoT-based environmental monitoring systems are composed of one or more sensors placed in various locations and connected to each other using certain protocols. The problem arises when thousands of IoT devices connected together in one network and using the server-client connection model which raises several works in the synchronization process and access control management. This kind of network depends on a server as the center of control management that work as storing and distributing data from the sensor so that any data comes from the server is considered correct. To overcome this problem, it is proposed an IoT system that uses Blockchain technology which is a distributed data structure that forms blocks that do not require central authority and verification from third parties. From the experiments conducted can be evaluated that blockchain communication is much slower compared to MQTT communication protocol in terms of speed. The data transmission between node is measured with an average delay of 200ms. This system also visualizes data in graph with a high mobility data collection using vehicle so that it can be used to be an alternative of public participatorial measurement.
Uplink Performance of NOMA-2000 with Dynamic User Grouping

In some recent papers, the present authors revived an early non-orthogonal multiple access (NOMA) concept, which uses two sets of orthogonal signal waveforms and iterative interference cancellation. The beauty of this concept, which was introduced back in the year 2000, is that it fully avoids the power imbalance requirements of power-domain NOMA on which the current NOMA literature is heavily based. Using different type of receivers, these papers reported channel overload factors up to 25% on additive white Gaussian noise (AWGN) channels. In this paper, we investigate uplink performance on Rayleigh fading channels and we introduce a dynamic user grouping strategy, which leads to a substantial increase of the channel overload capability. Using this strategy, we show that the channel overload factor can be increased up to 100% at the expense of a virtually zero degradation of the signal-to-noise ratio (SNR).
Intelligent Sensor to detect Spectrum of Primary Users

Sensors play a vital role to sense primary user’s (PU) spectrum. In this article, we have devised an intelligent sensor that can detect PU’s spectrum band. In the proposed scheme, there are two sensors or detectors; working nature of detectors depends on signal-to-noise ratio (SNR) readings. Graphical results confirm that proposed scheme enhances system performance and outperforms the energy detector (ED) and ED-ADT-2013, energy detector and Cyclo-2010, adaptive spectrum sensing-2012, Energy Detection Technique for Adaptive Spectrum Sensing-2015, and estimated-signal-to-noise ratio-based adaptive threshold detector-2016 by 33.5 %, 57.0 %, 41.5 %, 28.2 % and 52.8 % at – 12 dB signal-to-noise ratio, respectively.
Ka-Band Microstrip Patch array Antenna for Satellite Communication

Microstrip patch antennas have been modeled and investigated at Ka-Band frequencies, to achieve high gain for satellite communications application. Structures of one, two, three and four elements arrays have been designed, modelled and simulated numerically using MATLAB and CST software at 29 GHz. Different configuration have been tested to find the optimum structure in terms of Gain, input impedance matching, phase matching and spacing between arrays. Various feeding techniques have been tested to find the optimum feed network, which is a combination of series and corporate feed. A gain of 12dB was achieved using the structure of four element arrays with beamwidth of 38 degrees.
Identifying Critical Load Locations for Power System Voltage, Angular and Frequency Stability

It has been proven that power system loads affect power system stability significantly and this influence is to an extent location dependent. This paper focuses on identification of the critical load locations in the power grid with respect to how heavily they affect power system voltage, frequency and angular stability indices. The random variation of loading is generated by Monte Carlo simulation at each of the locations and the locations are ranked considering different loading conditions so that a robust ranking can be obtained. The results show that the group of the most important load locations, is different for different system stability studies. The obtained results are illustrated on a modified version of the IEEE 68 bus NETS-NYPS test system and the simulations are performed in Matlab and DigSILENT/PowerFactory environment.
Deep Neural Network Based Poetic Meter Classification Using Musical Texture Feature Fusion

In this paper, a meter classification scheme is proposed using musical texture features (MTF) with a deep neural network (DNN) and a hybrid Gaussian mixture model-deep neural network(GMM-DNN) framework. The performance of the proposed system is evaluated using a newly created poetic corpus in Malayalam, one of the prominent languages in India and compared the performance with support vector machine (SVM) classifier. Initially, a baseline-mel-frequency cepstral coefficient (MFCC) based experiment is performed. Later, the MTF are fused with MFCC. Whilst the MFCC system reports an overall accuracy of 78.33%, the fused system reports an accuracy of 86.66% in the hybrid GMM-DNN framework. The overall accuracies obtained for DNN and GMM-DNN are 85.83%, and 86.66%, respectively. The architectural choice of DNN based classifier using GMM derived features on the feature fusion paradigm showed improvement in the performance. The proposed system shows the promise of deep learning methodologies and the effectiveness of MTF in recognizing meters from recited poems.
Automatic Classification of ECG Signals in WBAN Based on Convolutional Neural Network and Long-Short Term Memory Network

To achieve an accurate real-time analysis for the classification of Electrocardiogram (ECG) signals in Wireless Body Area Network (WBAN) has been a critical problem. Hence we propose an automatic classification method of ECG signals based on Convolutional Neural Network (CNN) and Long-Short Term Memory Network (LSTM). The CNN layers are mainly responsible to extract local temporal features and the LSTM layer is mainly responsible to encode the whole temporal history. It can automatically classify the four ECG signals in MIT-BIH Arrhythmia Database. The accuracy, sensitivity and specificity of the classification were 99.86%, 99.86% and 99.96% respectively. For 5289s long ECG beat signals, the above classification effect takes only 4.46s time. In addition, the model has the best classification effect for five types of ECG signals under the AAMI standard. The accuracy, sensitivity and specificity are 99.12%, 99.15% and 99.84%, the classification time is only 0.81s for 756s length ECG signals.
A Sophisticated Secured Smart Metering System

Security has become very important for the smart metering system to maintain its user privacy and prevent its vulnerability which may cause information misuse and prevent it from sabotaging the system. Cyber-attacks, if not prevented, may cause instability in the energy supply which in turn negatively affects the infrastructure of the grid. Previous metering systems have many disadvantages such as the presence of transfer keys that have no encryption, the use of large numbers of keys in initial state, and the collection of consumption readings which depends on the number of smart meters in the chain. In this paper, an innovative authentication and security system designed for smart metering in smart grids is proposed. This new system is distinguished by its ability to protect the two ways of communication and its use of a small set of keys while not adding much complexity to the message size. Combining routing security to protect the gateways and access control list that monitors and filters traffic to enhance the overall security, the proposed model in this article proved to be efficient and secure.
A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources

Nowadays, learning-based modeling system is adopted to establish an accurate prediction model for renewable energy resources. Computational Intelligence (CI) methods have become significant tools in production and optimization of renewable energies. The complexity of this type of energy lies in its coverage of large volumes of data and variables which have to be analyzed carefully. The present study discusses different types of Deep Learning (DL) algorithms applied in the field of solar and wind energy resources and evaluates their performance through a novel taxonomy. It also presents a comprehensive state-of-the-art of the literature leading to an assessment and performance evaluation of DL techniques as well as a discussion about major challenges and opportunities for comprehensive research. Based on results, differences on accuracy, robustness, precision values as well as the generalization ability are the most common challenges for the employment of DL techniques. In case of big dataset, the performance of DL techniques is significantly higher than that for other CI techniques. However, using and developing hybrid DL techniques with other optimization techniques in order to improve and optimize the structure of the techniques is preferably emphasized. In all cases, hybrid networks have better performance compared with single networks, because hybrid techniques take the advantages of two or more methods for preparing an accurate prediction. It is recommended to use hybrid methods in DL techniques.
An Infrastructure-Assisted Crowdsensing Approach for On-Demand Traffic Condition Estimation

With the widespread use of smartphones and the continuous increase of their capabilities, a new sensing paradigm has emerged: mobile crowdsensing. The concept of crowdsensing implies the reliance on the crowd to perform sensing tasks and collect data about a phenomena of interest. Due to the benefits it offers in terms of time and cost savings in terms of sensors’ deployment and maintenance, the concept of mobile crowdsensing is now being adopted in the area of intelligent transportation. In this context, drivers or pedestrians equipped with sensor enabled smartphones collaborate to collect information about roads and traffic. However, the current solutions proposed for the use of crowdsensing for the collection of traffic related data adopt an opportunistic continuous sensing approach, which entails high resource consumption on the server and mobile device side, a high communication overhead, while offering little control of the users over the sensing activity. In this paper, we address these limitations by proposing an infrastructure-assisted on-demand crowdsensing approach for the real time detection and prediction of traffic conditions in an area of interest. Our approach combines the strengths of mobile crowdsensing, with the support of the mobile infrastructure, a multi-criteria algorithm for the participants’ selection, and a deductive rule-based model for traffic condition estimation. The proposed solution was validated through a combination of prototyping and simulated traffic traces, and the results show a significant reduction in terms of resources’ consumption and network overhead, while reaching high accuracy for the traffic condition estimation.
An Ablative Pulsed Plasma Thruster Based on Multianode Electrode Geometry

This article presents a special surface discharge achieved by a multi-anode electrode geometry (MAEG). This surface discharge can be applied to the ablative pulsed plasma thruster (APPT) and solve the problem of beam divergence. The MAEG changes the discharge characteristics of the surface discharge and extends the $Z$ -pinch existing within the thruster to the plasma plume outside of the nozzle compared with the conventional electrode geometry (CEG). The enhanced-MAEG further promotes the confinement effect of plasma plume by the $\theta $ -pinch. The discharge phenomenon shows that the visible plasma plume lengths of the MAEG and the enhanced-MAEG have been prolonged effectively compared with that of the CEG. The electron densities of the MAEG and the enhanced-MAEG in axial direction have also been measured to reach 3.5 and 4 times of that of the CEG. Through microthrust measurement, the MAEG APPT and the enhanced-MAEG APPT have been proven to have superior performance in terms of the impulse bit and the thrust-to-power ratio than that of the CEG APPT. The MAEG and the enhanced-MAEG could achieve improved propulsion performance with its system advantages by special electrode geometries. It has been found that the MAEG and the enhanced-MAEG have the potential to be used in the field of microsatellites propulsion.
Investigation of Scattering Intensity Dependencies on the Optical System Parameters in Cross-Correlation Spectrometer

The cross-correlation method is an optical method used for studying particle sizes in liquid dispersed samples. In this work, we describe our estimations of the intensity of laser radiation scattering in a cross-correlation spectrometer depending on a number of parameters of the optical system, such as the focal length of the lens system, beam divergence, and the angle between incident beams. With help of presented calculations, one can increase the power of detected scattered signal and signal-to-noise ratio in cross-correlation spectrometers scheme.
BPFHV: Adaptive Network Paravirtualization for Continuous Cloud Provider Evolution

Cloud providers are continuously evolving their virtual networking infrastructure to improve performance and functionality. This evolution, however, typically stops at the virtual NIC interface, since any change in that domain would require impractical upgrades in the running VMs with the collaboration of the customers who own them. This could hinder many important evolutions, like the transition to newer revisions of the VirtIO standard. To overcome this problem we propose BPFHV, a new paravirtualized network meta-device that is able to dynamically change its internal operation under the hypervisor control. BPFHV comes with a set of hypervisor-provided callbacks that the guest must call to complete datapath operations, such as posting a new packet for transmission. By injecting new callbacks, the hypervisor can dynamically change the behaviour of the device and of its guest driver even after the initial deployment of a VM. We describe our prototype implementation on the QEMU hypervisor with Linux guests, reusing the eBPF infrastructure for code injection. We show some preliminary experimental results and discuss some possible further applications.
Exact Analytical Method for Active Magnetic Bearings With Rotor Eccentricity

This article presents an exact analytical method for active magnetic bearings to obtain the magnetic field distribution with rotor eccentricity. Based on the subdomain method and the perturbation method, the active magnetic bearing is divided into two regions: the air gap and stator slots. Meanwhile, the zeroth-order and first-order equations in the polar coordinate are obtained and solved by the method of separation of variables, respectively. The magnetic field distribution is obtained by superimposing the first-order solution into the zeroth-order solution according to the perturbation method. The proposed analytical method has provided an effective way to study the magnetic bearing and other electromagnetic actuators. By using the Maxwell stress tensor theory, the unbalanced magnetic force is calculated to obtain the displacement stiffness and current stiffness. Finally, the consequences of magnetic field distribution and the unbalanced magnetic force are compared with those by the finite-element method. The result validates the correctness of the analytical method.