Miracle-dz/UniFAR-FTUs huggingface dataset:
System of Systems integration via a structured naming convention

During the last decade, technology changes and digitization have introduced numerous improvements and challenges in multiple areas of the industry, automation, and design of new architecture. However, the definitions of new naming conventions and identifiers that can be adapted to the new paradigm have not yet been considered.This paper analyzes aspects and characteristics that have to be included in the design of a naming convention consistent with the current Industry 4.0 requirements. As a result, this paper proposes a new naming convention for the Arrowhead Framework following the requisites and characteristics defined in the system of systems (SoS) integration.The proposed Arrowhead naming convention represents a renovated vision of the identification of services, systems, devices, and networks. A powerful tool in the quest of interoperability, security, service discovery, topology, and dynamic orchestration, as well as, a structured manner of defining meaningfully identifiers as help for developers
Hexa-Band Branch Line Coupler and Wilkinson Power Divider for LTE 0.7 GHz, LTE 1.7 GHz, LTE 2.6 GHz, 3.9 GHz, Public Safety Band 4.9 GHz, and WLAN 5.8 GHz Frequencies

A hexa-band branch line coupler (BLC) and Wilkinson power divider (WPD) for long term evolution (LTE) 0.7 GHz, LTE 1.7 GHz, LTE 2.6 GHz, 3.9 GHz, public safety band (PSB) 4.9 GHz, and wireless local area network (WLAN) 5.8 GHz frequencies have been proposed, designed, implemented, and analyzed in this brief. To realize the proposed BLC and WPD a hexa-band quarter wave transmission line (TL) with complete derivation for a hexa-band operation has been introduced. A good performance for the BLC/WPD has been achieved over all the six bands with maximum 0.42/0.21 dB amplitude imbalance and 4.81°/0.9° phase deviation. Compared to the most of the existing BLCs/WPDs, the proposed BLC/WPD has simpler design structure and, compact size than the existing hexa and quad-band BLCs/WPDs.
Reservoir Facies Classification using Convolutional Neural Networks

Distribution of Reservoir facies classes in a hydrocarbon reservoir is an important aspect for geologists to decide the location to drill a production well. Facies classes are based on rock characteristics that can indicate the locations of good quality sand, shale and hence the presence of hydrocarbon. However, due to the heterogeneous nature of earth subsurface, gathering facies information is challenging. Seismic data when properly correlated with the facies classes at the well locations can be suitable for effectively generating the facies information across the reservoir. Machine learning models had a great contribution in this field to successfully understand the reservoir characteristics from seismic data. However, advanced modelling techniques like deep learning still have not been explored in depth. In this paper, we put emphasis to understand the relation of seismic and facies classes using Convolution Neural Network (CNN) models, a popular deep learning model that takes into consideration the spatial relation present in the input data. CNN variations are adapted to improve the mapping relation of seismic to facies classes. When the models are compared with the traditional Machine Learning models, the CNN provided improved mapping relation as compared to the conventional models. More specifically, ResNet, an adaptation of CNN architecture with residual connections, outperformed all the other models with a Precision, Sensitivity and Specificity of 0.61, 0.69 and 0.84 respectively.
Effect of Sampling Strategies on Fine-grained Emotion Classification in Microblog Text

This study investigates the effect of diverse training samples on machine learning model performance for fine-grained emotion classification. Using four different sampling strategies (random sampling, sampling by topic and two variations of sampling by user), we found the class distribution of28 emotion categories to differ across the samples produced by each sampling strategy. However, combining different sampling strategies is complementary in generating sufficiently diverse training examples for the emotion classifiers. Based on support vector machine (SVM) and Bayesian network learning algorithms, our findings show that a classifier trained on combined data from the four sampling strategies performs better and is more generalizable than a classifier trained only on data from a single sampling strategy. Demonstrating how the diversity of the training samples affect the performance of emotion classifiers is the main contribution of this study.
Text Localization and Extraction from Background with Texture and Noise in Digital Images Using Adaptive Thresholding and Convolutional Neural Network

Recently, the task of text localization attracts many attentions. In this paper, we propose a method to localize individual characters of text from background with texture and noise in digital images using adaptive thresholding, width-to-height ratio, and convolutional neural network. The proposed method consists of three main steps: preprocessing, text candidate localization and classification, and character candidate localization and classification. In preprocessing step, images are enhanced by average filter in order to reduce noise and make the texture background harmonious. Then text candidates in an image are localized and classified using adaptive thresholding and width-to-height ratio of bounding boxes. Finally, character candidates of each text are localized and a convolutional neural network is used to classify character candidates as either character or non-character. The experiments were conducted on a dataset obtained from ICDAR2013 containing the training set of 229 images and the testing set of 233 images. From the experimental results, the proposed method can correctly localize text with the accuracy of 71.87%
Public Cognitive Risk Transmission of Technological Innovation on Network with Community Structure

Considering that network community may have a great impact on public cognitive risk transmission of technological innovation, we introduce a network model with community structure, and its topological properties are analyzed. Next, a model of public cognitive risk transmission in technological innovation is constructed based on the network, and the model describes the transmission mechanism of public cognitive risk in the interactions. Final, we analyze how the strength of community structure, innovativeness and cognitive similarity, influence the transmission of public cognitive risk by simulations. Analysis results show that whether the public can form a consensus on the risk of technological innovation is highly correlated with the strength of community structure and cognitive similarity. Furthermore, the interaction between the public will amplify (or reduce) the public cognition risk of technological innovation under the impact of public innovativeness, when public innovativeness is high, the interaction will reduce the public's cognitive risk of technological innovation.
Review on Reliability of Power Electronic Components in Photovoltaic Inverters

This paper focuses on the topic of reliability analysis and lifetime evaluations for various power electronic components in a photovoltaic (PV) inverter. The basic indices used in reliability from the mathematical and customers’ points of view are discussed. The most critical components like insulated gate bipolar transistors (IGBT), metal oxide semiconductor field effect transistor (MOSFET), heatsinks and capacitors, which contributes in failure are identified and the mathematical indices are used to evaluate the reliability of hese components. In addition, this review identifies the failures and their causes for various components, providing an insight for keys aspects of performance enhancement. Further, different methods used for analyzing the performance and reliability are discussed for better understanding.
A Portable Functional Near-Infrared Spectroscopy System Using 4-wavelength LEDs

Recently many countries are facing the problem of a rapid aging society. Due to the geriatric brain diseases, the research on brain imaging in a normal environment is becoming more important. Functional near-infrared spectroscopy (fNIRS) has proven its effectiveness in measuring the concentration changes of blood chromophores in the human body. In this study, we develop a light emitting diode (LED) based fNIRS instrument capable of measuring the concentration changes of multiple chromophores. We used 4-wavelength LEDs of 770 nm, 810 nm, 850 nm, and 890 nm for our system for calculating not only the concentrations of hemoglobin, but also the concentrations of oxidizing enzyme which increase or decrease during the oxygen exchange process. Along with calculating more chromophores, this device will provide more accurate oxyhemoglobin and deoxyhemoglobin values. The LED driver circuit is designed using a serial peripheral interface (SPI) based output expansion chip. Photodiode (PD) is used to measure the intensity of light after absorbing and scattering during passing through the medium. The control system is designed using the microcontroller unit (MCU) and to enable bidirectional communication with the host computer at 4 Hz by using serial communication. We used the phantom model to test the functionality of the system. We attached LED and PD to the phantom and tested it under the same conditions with actual channel configuration. The developed fNIRS system showed a total of 32 acquired constant light intensity values of 8 channels.
Small Cells Enabled by Crowdsourced Radio Units Mounted on Parked Vehicles for Smart City

A large number of small cells are densely deployed in the era of 5G and beyond 5G mobile networks to achieve high rate and low latency data transmission. Small cells are composed with fixed ground radio units (RUs) in the conventional networks, and thus the utilization rate of them is drastically deteriorated due to the spatio-temporal patterns of mobile traffic demand. To address this problem, adaptive network architectures have been investigated to reconfigure networks on demand using small cells mounted on moving objects such as vehicles and drones. However, it has been difficult to establish stable communication link to ground nodes in high-speed environments with these schemes. Therefore, this paper proposes a concept of small cells composed with crowdsourced RUs mounted on parked vehicles. They are activated based on the traffic demand in surrounding area by road side units (RSUs) assuming a smart city. The proposed idea is based on the correlation between the distribution of people and the occupancy rate of parking lots. It can efficiently improve user throughput with relatively small number of RUs. The feasibility of the proposed scheme is validated through numerical analysis, computer simulations, and experimental results.
Feature Extraction Using an RNN Autoencoder for Skeleton-Based Abnormal Gait Recognition

In skeleton-based abnormal gait recognition, using original skeleton data decreases the recognition performance because they contain noise and irrelevant information. Instead of feeding original skeletal gait data to a recognition model, features extracted from the skeleton data are normally used. However, existing feature extraction methods might include laborious processes and it is hard for them to minimize the irrelevant information while preserving the important information. To solve this problem, an automatic feature extraction method using a recurrent neural network (RNN)-based Autoencoder (AE) is proposed in this paper. We extracted features from skeletal gait data by using two RNN AEs: a long short-term memory (LSTM)-based AE (LSTM AE) and a gated recurrent unit (GRU)-based AE (GRU AE). The features of the RNN AEs are compared to the original skeleton data and other existing features. We evaluated the features by feeding them to various discriminative models (DMs) and comparing the recognition performances. The features extracted by using the RNN AEs are more easily recognized and robust than the original skeleton data and other existing features. In particular, the LSTM AE shows a better performance than the GRU AE. Compared to single DMs fed with the original skeleton directly, hybrid models where the features of the RNN AEs are fed to DMs show a higher recognition accuracy with fewer training epochs and learning parameters. Therefore, the proposed automatic feature extraction method improves the performance of skeleton-based abnormal gait recognition by reducing laborious processes and increasing the recognition accuracy effectively.
Design of High-power Fully Automatic Charging Device

With the rapid development of electric vehicles and charging facilities, the demand for high-power charging technology is becoming more and more clear. But the weight of charging cables increases dramatically with the increase of current, which is inconvenient to users. The existing automatic charging robot has a small supporting capacity and cannot be compatible with the high current demand of high-power liquid cold vehicle connector. At the same time, the user's charging time is random, and outdoor lighting conditions have a great influence on the visual recognition effect. Therefore, a high-power fully automatic charging device for complex light source environments is designed. The device uses the hybrid structure of joint bearing and linear bearing, as well as six degrees of freedom mechanical structure based on the complementary approach of parallel electric cylinder and flexible soft shaft to solve supporting capacity problem for the rough charging cable. At the same time, the concept of multi-sensor collaboration is introduced, based on the traditional image recognition technology, the application of ultrasonic technology, infrared technology and ring purple light development technology is used. X-Y plane positioning is realized by the minimum distance iteration algorithm, and the Z-axis positioning is achieved by the split wide-angle rangefinder based on ultrasonic-infrared ranging technology. Therefore, realizing the vehicle inlet in the day-time outdoor complex environment and night environment of 3D accurate positioning is achieved, which can help promote the intelligent and convenient development of the charging service system.
Design of Liquid Metal Battery Equalization System Based on Bidirectional Flyback Circuit

Liquid metal battery (LMB), with characteristics such as long service life, high working current and large capacity, is a potential energy storage battery that can be used for power grid level energy storage. According to the test for charging and discharging characteristics of LMB made in this paper, lower working voltage than conventional batteries makes the design of equalization system difficult. An active equalization circuit based on bidirectional flyback circuit is put forward in this paper. Equalization System including a control module and an acquisition module is designed based on this circuit. This equalization system is tested on LMB with 200Ah capacity and test results show significant improvement of equalization of cells.
Performance Analysis of Cache Size and Set-Associativity using simpleScalar Benchmark

The ever growing space between the high-speed processor and slower main memory has always remained a performance bottleneck. Attempts have been made to address this challenge through the advent of smarter memories, an improved memory hierarchy, and deployment of high speed bus controllers. Another important dimension is to add cache units to the processor in order to benefit from the temporal and spatial, and even instruction locality and therefore, reduce the processormemory gap that ultimately results in a performance boost. In this paper, we demonstrate that increasing the cache size in order to reduce the capacity based misses results in further optimizing the performance. This results in increasing the associativity and block size and thus, reduce the conflict and compulsory based misses. We discuss the cache parameters set to achieve the minimum miss rate for the simpleScaler suite including Rijndael, Sha, Compress, Go, and Dijkstra benchmarks. Explicitly, we reduce the miss rate by using different cache configurations such as increasing the cache size, altering the block size, and varying the associativity. Notably, we achieve a minimal possible miss rate for the Rijndael benchmark, which is 104. The results show the validity of an n size cache and x associativity, which is equal to an n/2 cache size and 2x associativity – 2: 1 cache rule of thumb.
Analysis and 3D Representation of Visual Information from fMRI of Ventral Temporal Cortex Using Neural Network and Support Vector Machine

Ventral Temporal Cortex (VTC) is a part of human brain that is responsible for the identification of different types of visual stimuli. The proposed model revolves around the analysis of VTC from fMRI of 6 subjects who were watching certain objects during the scan. Firstly, dataset was processed and an Artificial Neural Network model was trained to predict which object was being viewed by the subject based on the fMRI images. With proper training, approximately 90% accuracy was achieved on an average. Secondly, Support Vector Machine (SVM) algorithm was used to compare the activity of VTC for processing images of 2 different objects and the comparison was mapped into 2D anatomical MRI image of the corresponding brain. Thirdly, a 3D anatomical model was constructed from a series of 2D anatomical MRI slices and the active portion of the VTC was presented in the 3D structure. Finally, all the findings and the potential future works were analyzed and discussed.
Scale-Iterative Upscaling Network for Image Deblurring

Machine learning based methods for blind deblurring are efficient to handle real-world blurred images, whose blur may be caused by various combined distortions. However, existing multi-level architectures fail to fit images of various scenarios. In this paper, we propose a scale-iterative upscaling network (SIUN) that restores sharp images in an iterative manner. It is not only able to preserve the advantages of weights sharing across scales but also more flexible when training and predicting with different iterations to fit different images. Specifically, we bring in the super-resolution structure instead of the upsampling layer between two consecutive scales to restore a detailed image. Besides, we explore different curriculum learning strategies for both training and prediction of the network and introduce a widely applicable strategy to make SIUN compatible with different scenarios, including text and face. Experimental results on both benchmark datasets and real blurred images show that our method can produce better results than state-of-the-art methods. Code is available at https://github.com/minyuanye/SIUN.
An Incremental Learning Based Gesture Recognition System for Consumer Devices Using Edge-Fog Computing

Gesture based systems are attracting more and more researchers to develop a single point control for consumer devices. Most of the existing works use wearable devices or camera based solutions, thus requiring additional resources. This paper presents an incremental learning based gesture recognition system that uses gyroscope sensor of Edge device (mobile phone) to recognize gesture of user and select the function of consumer device. Accelerometer sensor of the mobile phone is then used to control the magnitude of the selected function. User also gives speech input along with the gesture, which is recognized by the Fog device (laptop) and its result is used by the system for incremental learning. The system is implemented by developing an application on the mobile phone and various experiments are performed for validating the accuracy of the system.
Influence of Congestion Control Algorithms on Head-of-Line Blocking in MPTCP-based Communication

The computers manufactured today are equipped with multiple communication interfaces. However, they cannot be used simultaneously to transfer the data due to design restrictions of the primary networking protocols, especially TCP. Its legacy, single-flow versions allow for engaging only one interface at a time for a given application stream. Recently, this inopportune limitation has been relaxed by the standardization of a multipath version–MPTCP. Still though, only one type of congestion control algorithm is used to control the flow of data, despite multiple choices available in the modern operating systems, which leads to non-optimal transfer dynamics. In addition, the multipath connection, although deemed to achieve higher throughput, may arrive at inferior performance when the transmission at the individual paths is throttled, e.g., as a result of drops. This phenomenon-Head of Line (HoL) blocking – poses a serious challenge to wide adoption of MPTCP. In this work, the influence of popular TCP congestion control algorithms, operating at the path and MPTCP level, on HoL blocking phenomenon is examined experimentally. The investigated scenarios, involving a low-end client communicating via heterogeneous, public networks with a high performance data server, set the basis for recommendations about efficient congestion control algorithm selection in common network applications. The default algorithm configuration is found ill-suited in the majority of the considered cases, yielding priority to nonhomogeneous settings.
Upsampling Algorithms for Autoencoder Segmentation Neural Networks: A Comparison Study

This paper compares nine different upsampling methods used in convolutional neural networks in terms of accuracy and processing speed. The process of image segmentation using autoencoder neural networks consists of the image downsampling in the encoder and correspondingly of image upsampling in the decoder part of the network to achieve original image resolution. This paper focuses on the upsampling process in the decoder part of the standard U-Net neural network. Three different interpolations are compared with and without subsequent lxl convolution layers and three transpose convolution layers for image upsampling using different size convolutional cores. The experiment has shown that the best practical results were achieved using simple nearest neighbor interpolation upsampling taking into consideration the computational time needed. The network using nearest neighbor interpolation upsampling achieved pixel accuracy of 99.47% and has shown fast training time and convergence in comparison with other networks using different upsampling methods. The data used in this work consist of a lumbar CT spine segmentation dataset.