Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Uneven Shading by Neighboring Buildings and Clouds on the Conventional and Hybrid Configurations of Roof-top PV Arrays

Partial shading is the commonly encountered scenario of building roof-top based PV arrays that mainly occur due to the shadow of the neighbouring buildings and clouds resulting in unexpected losses and deteriorated system performance. The arrays are connected in various configurations to enhance the system performance during shading. In this paper, various conventional and hybrid interconnection configurations based on series-parallel (SP), bridge-linked (BL), and total cross tied (TCT) topologies of the roof-top PV arrays are examined under various partial shading scenarios caused by the neighbouring building and clouds. The investigation is done for a 9x9 roof-top array in MATLAB/Simulink environment considering various comparison parameters. It has been found that during 1.23%, 7.40%, 11.11%, 17.75%, 18.51%%, 22.22%, and 24.69% of total array shading, SP generated the maximum power whereas, during 30.86%, 61.72% shading, TCT has the generated only a slightly higher power as compared to SP. Hence, the study concludes that the configurations have a puny effect on the power generation of the arrays during uneven shading patterns caused by buildings and clouds.
Optimization and Robustness Analysis of a Spiral Resonators Array for Misalignment Recovering purposes in WPT Systems

The aim of the manuscript is to present a feasibility analysis for misalignment recovering purposes in a two coils Wireless Power Transfer system with the presence of a Spiral Resonators Array. The misalignment is faced by opportunely varying the values of capacitive loads with an ad-hoc optimization procedure. In a previous work, the authors have already presented a methodology able to compensate the system efficiency drop by applying a postprocessing approach based on an S-parameters matrix circuit representation of the entire system, and by changing the values of the capacitors which opportunely load each element of the SRA structure. Here, an optimization procedure followed by a Montecarlo method have been used in order to investigate the effect of the components tolerances and the sensitivity of the system. In this way, a more realistic modelization can be accomplished, guiding the designer to the realization of a more robust system.
Sensing Street-level Crowd Density by Observing Public Bluetooth Low Energy Advertisements from Contact Tracing Applications

Smart cities in current and future pandemics are expected to implement features that ensure social distancing in order to prevent the spread of infection. Technologies for sensing street-level crowd density are considered helpful in avoiding crowded situations; however, street-level crowd density is difficult to sense effectively using existing techniques. In this paper, we propose a method for sensing street-level crowd density with good accuracy by observing public Bluetooth low energy (BLE) advertisements from popular contact tracing applications. We conducted an experiment in major shopping districts in Tokyo by deploying our developed sensing devices and demonstrated that our method can estimate the street-level crowd density in 30-min intervals with high accuracy, compared to manually counting the number of pedestrians. Using this method, we have begun to publish the street-level crowd density on our website and a news program on Japanese television. Moreover, through long-term monitoring of the collected street-level crowd density data, we analyzed the factors that affect crowd density and constructed a model to predict crowd density from other factors with a coefficient of determination of 0.9 or higher using support vector regression.
Smart EM Surfaces for Future Wireless Communication Systems

Smart surfaces represent a promising technology that allow to make the wireless environment a smart reconfigurable space playing an active role in the information transfer process. In this work, a reflectarray-based smart surface is investigated for next-generations communications, Device-to-Device systems and Internet of Things. The preliminary design of a smart surface is presented which is able to focus and redirect the impinging electromagnetic wave along a desired direction. The designed smart surface is extensively analyzed, under different environment conditions.
A Low Phase Noise 11.8-15.3 GHz CMOS LC-VCO with High-Q Inductor

This work presents a 14GHz LC voltage controlled oscillator (VCO) by using high quality (Q) factor inductors. The LC-VCO consists of a NMOS cross-coupled pair and differential buffer for test purposes. To increase the VCO phase noise performance, double-turn parallel single-width inductor is used to achieve high tank quality factor. With 5-bit control bits, the VCO has a wide frequency tuning range (FTR) of 25.9 % (i.e. from 11.8 GHz to 15.3 GHz). At 14.64 GHz, the measured phase noise is -136.57 dBc/Hz @ 10 MHz offset frequency, which corresponds to a Figure of merit (FoM) of -188.25 dBc/Hz. Fabricated in a 65 nm CMOS process, the core VCO has an area of $210 \mu m \times 360 \mu m$.
D-Band Radiating Near field Channel Impulse Response Characterization using Time Delay Profile

In this paper, we present characterization and measurement of a D-band (110-170 GHz) radiating near field channel. Assuming 2-ray path model, Channel Impulse Response (C.I.R) has been theoretically improvised for a bandlimited frequency response obtained from V.N. A. The change in CIR with increase in radiating near field distance between the transmitter and receiver has been studied using time delay statistical parameter i.e. root mean square (RMS) delay spread variation with distance. Furthermore, the effect of change in height of transmitter and receiver on RMS delay has been analyzed theoretically and experimentally validated using near field measurement setup. The experiment has been conducted using D- band 16dBi horn antennas working as the transmitter and receiver.
Human Activity Recognition Based on Acceleration Data From Smartphones Using HMMs

Smartphones are among the most popular wearable devices to monitor human activities. Several existing methods for Human Activity Recognition (HAR) using data from smartphones are based on conventional pattern recognition techniques, but they generate handcrafted feature vectors. This drawback is overcome by deep learning techniques which unfortunately require lots of computing resources, while generating less interpretable feature vectors. The current paper addresses these limitations through the proposal of a Hidden Markov Model (HMM)-based technique for HAR. More formally, the sequential variations of spatial locations within the raw data vectors are initially captured in Markov chains, which are later used for the initialization and the training of HMMs. Meta-data extracted from these models are then saved as the components of the feature vectors. The meta-data are related to the overall time spent by the model observing every symbol for a long time span, irrespective of the state from which this symbol is observed. Classification experiments involving four classification tasks have been carried out on the recently constructed UniMiB SHAR database which contains 17 classes, including 9 types of activities of daily living and 8 types of falls. As a result, the proposed approach has shown best accuracies between 92% and 98.85% for all the classification tasks. This performance is more than 10% better than prior work for 2 out of 4 classification tasks.
Can recurrent models know more than we do?

Model interpretation is an active research area, aiming to unravel the black box of deep learning models. One common approach, saliency, leverages the gradients of the model to produce a per-input map highlighting the features most important for a correct prediction. However, saliency faces challenges in recurrent models due to the “vanishing saliency” problem: gradients decay significantly towards earlier time steps. We alleviate this problem and improve the quality of saliency maps by augmenting recurrent models with an attention mechanism. We validate our methodology on synthetic data and compare these results to previous work. This synthetic experiment quantitatively validate that our methodology effectively captures the underlying signal of the input data. To show that our work is valid in a real-world setting, we apply it to functional magnetic resonance imaging (fMRI) data consisting of individuals with and without a diagnosis of schizophrenia. fMRI is notoriously complicated and a perfect candidate to show that our method works even for complex, high-dimensional data. Specifically, we use our methodology to find the relevant temporal information of the subjects and connect our findings to current and past research.
Time-Varying Characteristics for V2V Channels in Complicated Scenarios

Wireless channel characterization and modeling is the foundation of vehicle-to-vehicle (V2V) communication systems. In this paper, the time-varying channel characteristics under three complicated scenes, namely, viaduct, tunnel and cutting, are measured and studied. Specifically, based on the targeted 5.9 GHz channel measurements, a detailed analysis of the time-varying power and delay of multipath components (MPCs) is presented. The research in this paper is helpful to the investigation of V2V channel propagation mechanism and the design of communication system.
Repair Delay Analysis of Mobile Storage Systems Using Erasure Codes and Relay Cooperation

Mobile storage systems (MSSs) which store popular content in mobile devices can reduce the heavy traffic burden on the base station (BS). To deal with data loss caused by the mobility of devices, erasure codes are widely used in practice to improve system reliability. The duration to repair all lost data is defined the repair delay. However, the repair delay via device-to-device communications is usually large due to intermittent contacts among devices within a given communication range, which causes files to be irreparable and permanently lost. This paper focuses on analyzing the repair delay of the MSSs through D2D communications. We adopt a coded repair process running periodically and derive analytical expressions of the average repair delay by taking into account factors, i.e., limited communication range, device mobility, and coded repair scheme. Moreover, considering that devices need multipath contacts to repair lost data for large files, we propose a relay cooperation repair scheme, in which some mobile devices can act as relay nodes to transmit data cooperatively. Furthermore, we design a heuristic algorithm to optimize the number of relay devices and the amount of data allocated to each path, with the objective of minimizing the average repair delay. We find that maximum distance separable codes can yield smaller average repair delays for small files, and the average repair delay can be reduced by selecting faster moving devices to participate. With the file size increasing, the relay cooperation repair scheme can significantly improve repair efficiency in MSSs using the proposed heuristic algorithm.
Robotic Assistance Operation for Effective On-site Network Maintenance Works

While the introduction of softwarelization technologies such as SDN and NFV transfers the main focus of network management from hardware to software, the network operators still have to deal with various and numerous network and computing equipment located in network centers. Toward fully automated network management, we believe that a robotic approach will be significant, meaning that robots will handle network-facility management works on behalf of humans. This paper focuses on robotic assistance for on-site network maintenance works. Currently, attendance at the on-site vendor's works (e.g., hardware check, hardware installation/replacement and high impact update of software etc.) is one of the major tasks of network operators. The network operators monitor the work progress for human error prevention and safety improvement. In order to reduce the burden of this, we propose three functionalities, namely attendance at on-site meetings, progress check by periodical patrol, and remote control of robots, which support the various forms of attendance. The paper presents our implementation of enabling these supports, and reports the results of experiments conducted in a commercial network center.
QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback

Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user’s information need so as to improve the search results. Previous PRF methods mainly select expansion terms with high occurrence frequency in top-retrieved documents or with high semantic similarity with the original query. However, existing PRF methods hardly try to understand the content of documents, which is very important in performing effective query expansion to reveal the user’s information need. In this paper, we propose a QA-based framework for PRF called QA4PRF to utilize contextual information in documents. In such a framework, we formulate PRF as a QA task, where the query and each top-retrieved document play the roles of question and context in the corresponding QA system, while the objective is to find some proper terms to expand the original query by utilizing contextual information, which is similar answers in QA task. Besides, an attention-based pointer network is built to understand the content of top-retrieved documents and select the terms to better represent the original query. We also show that incorporating the traditional supervised learning methods, such as LambdaRank, to integrate PRF information will further improve the performance of QA4PRF. Extensive experiments on three real-world datasets demonstrate that QA4PRF significantly outperforms the state-of-the-art methods.
A Partial Computation Offloading Strategy for Microcell-femtolet based Future Generation Edge-Cloud Network

Fast and low power offloading is an emerging area of interest for future generation mobile network. This paper proposes a partial computation offloading strategy for microcell-femtolet based edge-cloud network. In the microcell-femtolet network microcells contain femtolets inside their coverage area to provide good communication and computation facilities to the user devices at indoor region. An indoor user is connected with the femtolet and an outdoor user is connected with the microcell base station in the microcell-femtolet network scenario. When a computation has to be partially offloaded, the task is partitioned into two segments, one is locally executed and the other one is offloaded to the femtolet or edge server according to the user's presence at indoor or outdoor region. The theoretical and experimental results illustrate that the proposed partial offloading scheme reduces the latency and power consumption of the mobile device than the cloud based approach.
MCF tree-based Clustering Method for Very Large Mixed-type Data Set

Several clustering methods have been proposed for analyzing numerous mixed-type data sets composed of numeric and categorical attributes. However, existing clustering methods are not suitable for clustering very large mixed-type data sets because they require a high computational cost or a large memory size. We propose a novel clustering method for very large data sets using a mixed-type clustering feature (MCF) vector with summary information about a cluster. The MCF vector consists of the CF vector and a histogram to summarize the mixed-type values. Based on the MCF vector, we propose an MCF tree, along with a distance measure between the MCF vectors representing two clusters. Unlike previous studies that summarize a data set based on a fixed memory size, we estimate a small initial memory size of the data set for building the tree. Then, the memory size is adaptively increased to estimate a more accurate threshold by reflecting the size reduction in the re-built tree. Our theoretical analysis demonstrates the efficiency of the proposed approach. Experimental results on very large synthetic and real data sets demonstrate that the proposed approach clusters the data sets significantly faster than existing clustering methods while maintaining similar or better clustering accuracy.
Self-Supporting Circularly Polarized Backfire Helix Feed Antenna with Reflector and Director for Deep Dish Reflector Antennas in the L-Band

Backfire helix antennas are particularly interesting candidates for use as feeds of reflector antennas due to their natural circular polarization, lightweight construction, and minimal blockage. An additional reflector element can provide further degrees of freedom to optimize the illumination efficiency, reduce the backlobe and increase cross-polarization isolation. The result is an efficient, lightweight and versatile feed antenna, particularly well-suited for deep dish reflector antenna applications in the L-band.
Substrate Integrated Slab Waveguide and Broadband Transition from CPW

In this paper, a new broadband transition between conductor backed coplanar waveguide (CB-CPW) and substrate integrated slab waveguide (SISW) filled with a perforated dielectric medium with air vias is proposed. In order to achieve low effective permittivity and increased single-mode bandwidth without affecting the cutoff frequency of fundamental mode, the two rows of air vias are implemented in this structure. Moreover, the curved metallic row in classical substrate integrated waveguide (SIW) is used to reduce overall loss and improved impedance bandwidth of the proposed design. The proposed structure is simulated and optimized by ANSYS HFSS ver.19 and further validated experimentally. The experimental results are in agreement with the simulated results over the entire frequency band (12–18 GHz). Within the desired frequency range, the following results are obtained: return loss (RL) 15 dB, insertion loss (IL) below 0.92 dB, and overall loss variation 2–30%. This transition is suitable for Ku-band applications such as antenna, filter, amplifier, and other microwave devices.
Design of a 50 Hz Electromagnetic Energy Harvester

Energy harvesting has shown to be an effective technology to power wireless sensors and devices for Internet of the Things and Smart City related applications. In this paper, we propose a new design of a free-standing magnetic energy harvester around overhead power lines. The I-shaped core with a pair of magnetic collector plates at both ends can significantly increase the effective permeability and output power of the energy harvester.
ToA-Based Localization of Far-Away Targets: Equi-DOP Surfaces, Asymptotic Bounds, and Dimension Adaptation

This paper studies the Dilution of Precision (DOP) in the Time-of-arrival (ToA)-based localization of targets outside the anchors’ convex hull. In the far-away target regime, we derive a closed-form expression of the DOP that reveals a linear asymptotic scaling law. We characterize the asymptotic DOP bounds, and equi-DOP surfaces/contours in 3D/2D localization scenarios, which quantifies the reliability of location estimates on the target's trajectory. Motivated by vehicular applications, we propose a range-aided dimension adaptation scheme. Here the localization dimension is adapted in real-time using a single range measurement such that the maximum/root-mean-square (rms) DOP does not exceed a threshold. Since high-accuracy localization of far-away targets is infeasible due to linear DOP scaling with distance, this scheme prioritizes high-dimensional tracking of nearby targets while monitoring far-away targets with range-only measurements.
Wide-Band Linear Polarizer Based on a Frequency Selective Surface

A Frequency Selective Surface built on an FR4 substrate, that exhibits linear polarized transmission field for a generally polarized incident electromagnetic waves in the frequency range below 10 GHz is introduced. Outgoing waves are linearly polarized on two orthogonal directions, in two different frequency bands. The operation of the proposed structure is wide-band, the two bands intersecting at −10 dB. A parametric study demonstrates flexibility in design, also the structure being tested for oblique incidence
Efficient Rejection of Artifacts for Short-Term Few-Channel EEG Based on Fast Adaptive Multidimensional Sub-Bands Blind Source Separation

Artifacts rejection is crucial to electroencephalogram (EEG) application. Short-term few-channel EEG (e.g., in real-time detection of stress level and motor imagery) brings new challenges for removing artifacts due to less data. The existing artifact removal methods cannot guarantee both effectiveness and efficiency for removing artifacts from short-term few-channel EEG recordings. Consequently, we propose a fast adaptive sub-bands’ blind source separation method to remove artifacts from short-term few-channel EEG recordings effectively and efficiently. First, noise-assisted fast multivariate empirical mode decomposition (NA-FMEMD), as a fast adaptive multidimensional sub-bands’ decomposition method, is used to decompose short-term few-channel EEG recordings into multidimensional sub-bands. Then canonical correlation analysis (CCA), as a blind source separation method, is used to estimate artifact-related and EEG-related sources. Finally, EEG-related sources are intelligently selected and reconstructed as clean EEG recordings. The results demonstrate that our method takes at least five times less computing time for 2-s few-channel EEG recordings than state-of-the-art methods with similar effectiveness, using the same computer and software. Therefore, our method enhances the efficiency of removing artifacts from short-term few-channel EEG recordings while ensuring effectiveness, and it is more suitable for real-time processing.
Principal Multipath Component Analysis for Outdoor Microcell Scenario at 39 GHz

Tremendous research funding and efforts have been invested in millimeter-wave (mmWave) communication systems for fifth-generation (5G) and beyond wireless communications. Understanding the influence of the surrounding environment on the propagation channel is vital to mmWave system design and deployment. In this paper, principal multipath components (MPCs) are studied in in an outdoor microcell scenario at 39 GHz. The direction-scan channel sounding measurement is conducted in a typical urban living district. By adapting the 3D environment model and material parameters, the ray-tracing simulator is tuned to match the simulated MPCs with the measurement. Based on this, the main propagation mechanisms and influential objects are identified. The method, observations, and analysis in this work are useful for understanding mmWave channel and developing communication systems.
Complex Shannon Entropy Based Learning Algorithm and Its Applications

Nowadays, adaptive filters in complex domain have received increasing attention since they can be applied to a wider scenario than real valued cases. As two typical information theoretic criteria, the minimum complex kernel risk-sensitive loss (MCKRSL) and maximum complex correntropy criterion (MCCC) have shown robustness to non-Gaussian noises in complex domain adaptive filtering. However, since both criteria cannot consider the probability distribution of error, they are not optimal in the presence of some non-Gaussian noises. This paper firstly defines a complex Shannon entropy in terms of the probability distribution of error. Then, a novel adaptive filter is proposed using the minimum complex Shannon entropy (MCSE) criterion. More significantly, the convergence behavior of the MCSE algorithm is discussed, and thus the steady-state excess mean square error (EMSE) is calculated for theoretical analysis. Finally, simulated results on system identification and channel estimation validate the obtained theoretical results and the merits of the proposed MCSE algorithm.