Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Frequency-Dependent Wall Insertion Loss Model at 3–6 GHz for Future Internet-of-Things Applications

A novel frequency-dependent wall insertion loss model at 3–6 GHz is proposed in this paper. The frequency-dependence of the wall insertion loss is modeled by the Fourier triangular basis neural network. A method to determine the optimal weighted vector and the number of the neurons is introduced. In addition, the impact of the wider continuous spectrum on the wall insertion loss is analyzed and extensive measurements are performed to validate the proposed model. The results obtained with the proposed model match better with the measured results than other models. The proposed model can be used in future indoor Internet-of-Things applications such as service computing.
Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making

Recently, large scale group decision making (LSGDM) problems have become a hotspot. This paper focuses on hesitant fuzzy LSGDM problems where decision makers (DMs) use hesitant fuzzy preference relations (HFPRs) to express their assessment information. HFPRs can represent the fuzziness and hesitancy of DM assessment information well. To improve the efficiency of hesitant fuzzy LSGDM problems, we first propose a reliability index-based consensus reaching process (RI-CRP). By assessing the ordinal consistency of DM's assessment information and measuring the deviation from collective opinion, the DM's opinion reliability index is given. To avoid unreliable information, we propose an unreliable DM management method to be used in the RI-CRP, based on the computation of DM's opinion reliability index. Moreover, an alternative ranking-based clustering (ARC) method with HFPRs is proposed to improve the efficiency of the RI-CRP. The similarity index between two DMs’ opinions is provided to ensure the ARC method can be effectively implemented. Compared with those clustering methods which need to preset several correlated parameters, the presented ARC method is more objective with a different approach based on the alternative ranking. A numerical example shows that the proposed ARC method and the RI-CRP are feasible and effective for hesitant fuzzy LSGDM problems.
Position Optimization Method for a Solar Tracking Device Using the Cast-Shadow Principle

Automated Photovoltaic (PV) Panels are accessible and reliable engines for harnessing energy delivered from the Sun rays. In this paper, we design an efficient solar tracking device composed of an Arduino UNO board, two stepper motors, a pair of specialized L298N circuits and an optocoupler. We also present a novel approach for orienting the solar panel towards the Sun based on the cast-shadow effect as well as a low-cost and sensorless solution for providing an automated variant of the PV panel. The implementation of a dedicated blocking mechanism for the stepper motors managed to reduce the overall power consumption of the system by 86.93% and thus eliminated the device's dependability from the AC network. The experimental results show a 45.21% voltage, 49.71% current and 58.82% power increase over the static PV panel by using monocrystalline solar cells.
Four Element Dual-band Sub-6 GHz 5G MIMO Antenna Using SRR-loaded Slot-Loops

In this paper, a four element MIMO configuration is proposed for possible application in sub-6 GHz 5G wireless access points working in 3 – 4 GHz frequency bands. The proposed antenna uses microstrip-fed square slot-loops loaded with split-ring resonators (SRRs) to achieve miniaturized multiband performance, where extended bandwidth is achieved in the higher frequency range by merging of two narrow bands. Ansys HFSS simulations are used to explain the design methodology of the proposed antenna, by studying its impedance bandwidth, mutual coupling, simulated 3D radiation patterns as well as surface current distribution features. The proposed MIMO antenna exhibits excellent diversity performance (ECC < 0.1) and MEG-ratio within ±3 dB in uniform propagation scenario. The robustness of the proposed MIMO antenna is further checked for indoor Gaussian / Uniform propagation environment for a wide- range of scenario parameters.
Experimental Evaluation of Group Communications Protocols for Tactical Data Dissemination

Network-centric warfare depends on the timely dissemination of battlefield information for Situation Awareness (SA) and Command and Control (C2). A significant percentage of SA and C2 data is sent to multiple recipients at the same time, which naturally calls for group communication protocols for efficient dissemination. Several variations of group communication protocols have been developed, including multicast-based and message bus-based approaches. However, a natural question arises about the suitability and performance of these protocols when deployed on tactical edge networks. These networks are typically wireless and characterized as Disrupted, Intermittent, and, Limited (DIL), with frequent disconnections and dynamic topologies. Constraints on bandwidth and variable latencies also complicate the environment. To answer the above question, we evaluate a number of group communications protocols in a realistic emulated tactical networking environment using the Anglova scenario. This paper presents results obtained from running different protocols in an experiment involving the dissemination of blue-force data for SA, with the objective of identifying advantages and disadvantages of different available solutions.
A Nonintersecting Multipath Routing Protocol for Wireless Sensor Networks

Due to constrained resources, stable and reliable routing protocols have always been a research focus in sensor networks. In order to improve network stability, based on the breadth-first search, this paper proposes a non-intersecting multi-path routing algorithm based on the breadth-first search. The proposed algorithm constructs two disjoint paths transmitting the data collected by sensing nodes to sink node to improve the reliability of the routing protocol, which can well solve common-link problem and/or the common-node problem in multipath routing protocols. Moreover, this algorithm can also be easily applied to multi-sink node network and be extended to multi-path routing (more than two non-intersecting paths). The experiments in this study show that the packet loss rate of the algorithm does not increase when the number of network nodes increases. In addition, this algorithm can significantly reduce the average routing length and improve routing reliability in multi-sink node networks.
Exploring Social Media Network Landscape of Post-Soviet Space

The “post-Soviet space” consists of countries with a substantial fraction of the world’s population; however, unlike many other regions, its social media network landscape is still somewhat under-explored. This paper aims at filling this gap. To this purpose, we use anonymized data on user friendships at VK.com (also known as VKontakte and, informally, as “Russian Facebook”), which is the largest and most popular social media portal in the post-Soviet space with hundreds of millions of user accounts. Using the VK network snapshots from October 2015 to December 2016, we conduct a “multiscale” empirical study of this network by considering connections among individual users, cities, and countries. Our findings indicate that the VK users form a small-world network with basic characteristics consistent with Facebook and other social media networks. In addition, the analysis of modularity-based communities within the user scale network reveals a pattern of geographical separation of the identified communities mostly along the borders between countries. However, the comparison of the two network snapshots suggests that some of these communities may be “blending” within the network, whereas other communities remain “self-contained. ” Furthermore, the analysis of city scale and country scale networks identifies cities and countries that are most “central” (in the context of certain metrics) in the VK network.
Shuffled Structure for 4.225 GHz Antireflective Plates: A Proposal Proven by Numerical Simulation

A newly proposed antireflective selector with shuffled structure is reported in this paper. The proposed idea is made of two different quarter wavelength (QW) slabs and numerically supported by the one-dimensional simulation results provided by the method of characteristics (MOC) to function as an antireflective selector. These two QW slabs are characterized by dielectric constants $\varepsilon_{rA}$ and $\varepsilon_{rB}$, uniformly divided into $N$ and $N+1$ pieces respectively which are then shuffled to form an antireflective plate with $B(AB)_{N}$ structure such that there is always one $\varepsilon_{rA}$ piece between two $\varepsilon_{rB}$ pieces. Another is $A(BA)_{N}$ structure where every $\varepsilon_{rB}$ piece is sandwiched by two $\varepsilon_{rA}$ pieces. Both proposed structures are numerically proved to function as QW plates. In order to allow maximum transmission through the proposed structures, the two dielectric constants are chosen to have the relation of $(\varepsilon_{rA})^{2}=\varepsilon_{rB} > 1$. The advantages of the proposed structures over the traditional anti-reflection coating techniques are two components with two thicknesses and to shuffle to form new QW structures. The design wavelength used to validate the proposed idea is 71 mm corresponding to a frequency about 4.225 GHz. The computational results are shown in both time and frequency domains revealing that the proposed structures produce minimum reflections around the frequency of interest.
How Does the Data set Affect CNN-based Image Classification Performance?

Convolutional neural networks (ConvNets or CNNs) have been proven very effective in areas such as image recognition and classification. Especially in the field of image classification, the CNN-based method has achieved excellent performance. Performance is an important indicator for evaluating whether a CNN-based classification method is excellent, so it is important to study which factors affect performance. As we all know, image classification performance is affected by the network structure itself and the size of the data set. In particular, data set size have a significant impact on performance. While for most people, a large number of data set are difficult to obtain. Thus, we consider a question of this approach: How does the size of the data set affect performance? In order to clarify this issue, there are 35 groups experiment performed with 5 times experiment in each group (175 experiments in total). For each k-classification experiment, we do 5 groups by increasing the size of the training set. Observe changes in accuracy to analyze the effect of data set size on difference. For the same CNN-based network, experimental results of average accuracy illustrate that the larger the training set, the higher the test accuracy. However, when the training data set are insufficient, better results can be obtained. Furthermore, in each group experiment, the more categories that are classified, the more obvious the performance change. Results of this paper not only can guide us to do experiments on image classification, but also have important guiding significance for other experiments based on deep learning.
Comparison between deploying an IoT system designed to be used online and one designed to be used offline

The paper presents a comparative analysis of a growing technology, which is IoT, between two systems that are using this technology, one which can be accessed globally and another system with local access which is gathering data over a period of time and then connects to internet for a limited period of time to transmit and receive informations. We survey the ability to work with Internet of Things (IoT) and ways how we can work offline. The analysis focuses on the distinctive advantages and disadvantages, between online and offline control, by highlighting the specificity of each, that we believe should be addressed in an IoT system solution, and discuss how they are approached by the proposed solutions.
Demand Control of Baseboard Heaters: Lessons Learned from 50-home Pilot Study

This paper presents results from a pilot study in 50 occupied homes in Fredericton, New Brunswick, Canada to evaluate a demand response (DR) strategy applied to residential electric baseboard heating loads. The underlying principle is based on storing electricity as thermal energy in the building envelope and household contents before the grid peak demand period, and then discharging that stored energy to maintain conditions for thermal comfort during the peak period. During January and February 2016, a total of 13 DR interventions were executed. This field test showed that a load shift up to a maximum of 3.4 kW, with an average of 1.4 kW, per household was possible for the first hour, depending on the outdoor temperature. We observed a large variation in load shift between homes, partly depending on the thermostat set-point profiles, building and occupant characteristics. The approach holds significant potential for shifting peak loads in locations where electricity is a major source of energy for space heating.
A New Image Denoising Method Based on Wavelet Multi-scale Registration Fusion

Image denoising is an eternal research topic. In this paper, a new image denoising method based on wavelet multiscale registration fusion is proposed to solve the problem that it is easy to lose the edge and texture details of the image in the denoising process. First of all, we can get multiple sets of wavelet coefficients by using different wavelet bases to decompose the same noisy image. Then, the obtained wavelet coefficients are processed by the improved wavelet threshold shrink to get multiple denoising images of the same noisy image. At last, we use the fusion registration algorithm proposed in this paper to fuse the edge feature of multiple denoising images to get the final denoising image. The experiments prove that this method not only can effectively overcome the pseudo gibbs phenomenon caused by the hard threshold method, but also can overcome the image distortion phenomenon caused by the soft threshold method. More importantly, compared with existing methods, this method can effectively preserve the edge detail and texture features of the image and the image has a better visual effect after fusion registration. Therefore, it has a better application value.
All-Optical Fiber Power-Frequency Electric Field Sensor Using a Twin-Fbg Based Fiber-Optic Accelerometer with Charged Polyimide

In this report, we demonstrate a novel fiber-optic power frequency electric-field sensor. In the sensor structure, a charged polyimide-jacketed fiber cantilever is adopted to form an electric-field sensitive element. The lightweight fiber cantilever can vibrate in power frequency under the actions of the induced alternative electric field force. The vibrating amplitude of fiber cantilever is proportional to the strength of the electric field to be measured, which is optically detected by using a twin-FBG based fiber-optic Fabry-Perot interferometer which is embedded in the fiber beam. The concept and operation principles of this sensor technology including the detailed theoretical analyses will be presented. A sensor prototype is made and some preliminary experimental results for proving sensor concepts as well as for evaluating sensor's basic performances including the sensitivity, dynamic response and directionality will be presented. Experimental results for investigating the actual field distribution surrounding energized structures using this sensor prototype also are demonstrated. These experimental results further verify the feasibility of the proposed sensor technology applying in the power industry for power-frequency electric field sensing.
Generation of Perfectly DC Balanced Codes for Visible Light Communications

In visible light communications (VLC), direct current (DC) level balancing is important to maintain constant illumination while the LEDs are being used for communication. In this paper, given codeword length of $n$ bits, we provide a relationship between maximum possible input bits, $k$, and $n$ for a perfectly DC balanced $kb/nb$ code. We propose an algorithm to generate the code book of these perfectly DC balanced codes that avoid flickering and maintain consistency in the brightness. The performance of the proposed codes is compared with several existing codes in terms of code rate, Hamming distance, frame error rate (FER), and bit error rate (BER). The numerical results show that the proposed codes provide perfect DC balance and perform better in terms of minimum Hamming distance, FER, and BER without significant loss in code rate. Further, we derive a lower bound on average Hamming distance for the proposed perfectly DC balanced codes for VLC.
AF Relaying in a Massive Mu-Mimo Ofdm System

Due to the market demands, current communication systems evolve rapidly from existing 4G standard to a future 5G, that implies higher capacity and flexibility. In 4G systems the orthogonal frequency-division multiplexing (OFDM) modulation and multiple-input multiple-output (MIMO) technique are used to enable higher rates for mobile users, while in 5G it is expected to further increase the performances by using a significantly higher number of antennas (massive MIMO) and cooperative relays for solving the problem of coverage and capacity at the cell border. In this work, massive multiuser MIMO OFDM (MV-MIMO) systems, with 10 to 100 antennas are used, without relay nodes, to evaluate the bit error rate (BER) as a function of the Signal to Noise Ratio (SNR). Then, the system is further developed, by introducing amplify-and-forward (AF) relays as intermediaries between the transmitter and the receiver to see if the performances are improved.
Damping Power Oscillations in the Inverter-Dominated Microgrid

An improved Proportional-Derivative (PD) droop control strategy is proposed in this paper for stable operation and improved disturbance response of the islanded inverter-dominated AC microgrid. The proposed strategy (i) is simple and easy to implement. , (ii) does not require communication between distributed energy resources, and (iii) significantly improves microgrid dynamic response and stability. Performance of the proposed improved PD droop control strategy is investigated using a detailed and realistic study system, and its effectiveness is verified using extensive simulation studies in the PSCAD/EMTDC software. The study results indicate that the proposed control strategy enables effective voltage and frequency regulation, i.e., limited deviations, in the inverter-dominated microgrid without causing power oscillations between inverters.
Patch-Based Approach for the Fusion of Low-Light Image Pairs

When taking pictures in a low-light scene with artificial lighting, we often encounter the following problem: we can use short exposure setting which yields a dim, noise image but with a sharp outline, or we can use a longer exposure setting which yields a bright, saturated image but with blurred areas. In many cases, none of those images is good enough. Good brightness and color information are retained in longer-exposure images, whereas sharp outlines are retained in shorter ones. In this paper, we present a patch-based method to combine such image pair into a better one. In our method, we firstly perform a coarse-to-fine strategy to detect inconsistent pixels caused by moving objects, then we draw information from the two exposures based on a novel patch-based technique. Experimental results show that the proposed method effectively preserves sharp edges of the short-exposure image, and maintains the color, brightness, and details of the long-exposure image.
Multi-mode Study of Deep Learning Applications in Acoustic Signal Processing

This proposed an intelligent acoustic signal processing system based on deep neural networks (DNNs). Employing deep learning algorithms, our system can perform acoustic scene classification, audio tagging and (rare) sound event detection, achieving a high performance in terms of accuracy, error rate and so on. An attracting feature of the system is that it integrate four working modes together, which can do parallel processing of an audio record and give results from different aspects.
Pavement Distress Detection Based on Transfer Learning

With the rapid development of highway construction in China, more and more attention has been paid to highway maintenance. The traditional manual detection and recognition methods cannot meet the needs of highway development, so the research of detection and recognition technology based on road image has become particularly important. In recent years, deep learning has shown very high performance in target detection. Based on transfer learning, this paper reuses part of the network of pavement crack detection based on Faster R-CNN to improve the performance of pavement distress detection.
Pet Hair Color Transfer Based On CycleGAN

Generative adversarial networks (GANs) have shown great performance on image-to-image translation tasks. Many approaches have been proposed for translation of human face images, scene pictures and artful paintings, but few works considered about translating a pet image. In this paper, we propose a method based on cycle-consistent adversarial network (CycleGAN) to solve pet hair color transfer problem. Given a pet image, our model can translate its hair color into a desired one while keeping its other features unchanged, which makes our generated images seem quite realistic. We do several improvements on CycleGAN including doing segmentation to avoid the influence of background, and using spectral normalization to improve the quality of generated images. We build a large pet image dataset consisting of a total number of 7. 5K images, categorized by different hair colors. Our proposed method is trained and tested on this data set and the results show the promising performance on translating between white and orange hair color of dog images.
Estimation of Transmission Line Sequence Impedances using Real PMU Data

Accurate knowledge of transmission line parameters in general, and sequence impedances, in particular, plays an important role in state estimation, fault detection, and adjustment of relay settings. Line parameter estimation using online methods has attracted considerable interest with the widespread installation of phasor measurement units (PMUs). Although various methods have been proposed in the literature for line parameter estimation, most of them have been tested on purely synthetic datasets. A synthetic dataset does not capture the nuances of real data, such as measurement invariance and realistic field noise. Therefore, the algorithms developed using synthetic datasets may not be as effective when used in practice. In this paper, a three-stage test procedure is developed to compare the performance of two algorithms, namely, moving-window total least squares (MWTLS) recursive Kalman filter (RKF), on real PMU data. The results prove that RKF is better than MWTLS. This paper also proposes using ASPEN data as an initial estimate to RKF for further improving its performance. Finally, to circumvent the problems faced due to data dropouts, an auto regressive integrated moving average (ARIMA) model is implemented to predict the variations in sequence impedances.