Miracle-dz/UniFAR-FTUs huggingface dataset:
Oral manifestations of human papillomavirus infections

Papillomaviruses are one of the oldest viruses known, dating back 330 million years. During this long evolution, human papillomaviruses (HPV) have developed into hijackers of human cellular and immune systems in which they replicate and remain silent. Systematic studies on oral HPV infections and their outcomes are still scarce. Oral HPV infections have been linked to sexual behaviour, but recent evidence supports their horizontal, mouth‐to‐mouth, transmission. Most HPV infections in infants are acquired vertically from the mother during the intrauterine period, during delivery, or later via saliva. The best‐known benign clinical manifestations of HPV infection are oral papilloma/condyloma and focal epithelial hyperplasia. Evidence is emerging which suggests that some oral HPV infections might persist. Persistent HPV infection is mandatory for HPV‐associated malignant transformation. However, progression of HPV‐induced lesions to malignancy requires additional cofactors. In the early 1980s, we provided the first evidence that a subset of oral cancers and other head and neck cancers might be causally linked to HPV infection. This review summarizes current knowledge on the virus itself, its transmission modes, as well as the full spectrum of oral HPV infections – from asymptomatic infections to benign, potentially malignant oral lesions, and squamous cell carcinoma.
Sentiment Analysis of Real Time Twitter Data Using Big Data Approach

Online social media is an important platform over the globe to share once opinion. People go for online social media as it is easy to convey their opinions, to have an up-to-date knowledge about the ongoing trends on a daily basis. Twitter is one of the biggest and renowned online social media gets a large number of tweets each day on various topics. This huge volume of raw information can be used for Social, Industrial, Economic, or Government approaches by arranging as per our need and processing. Hadoop is the best tool for analyzing the twitter data as it processes the huge sets of data in parallel. Since twitter contains a variety of opinions on various topics it is necessary to analyze these opinions to know the customer behavior. Analysis is done using Hadoop and its ecosystem to know the polarity of every tweet. In this paper, we have discussed how effectively sentiment analysis is performed on the tweets gathered from twitter using Big data approach. We have used Apache Flume to stream ongoing twitter data into the Hadoop Distributed File System (HDFS). The pig scripts are written to extract tweet from the raw nested twitter data. The dictionary based method is used for sentiment analysis and tweets are classified into three groups such as positive, negative and neutral.
A Whole-Home Level Intrusion Detection System using WiFi-enabled IoT

The Internet of Thing (IoT) based applications can provide various services and be widely applied in intelligent home. With the tendency of house safety protection, the detecting accuracy and privacy of intrusion detection system, which detects the human motion in indoor environment, has become a continuing concern. Up to now, there are emerging many intrusion detection systems which employ different devices such as camera and infrared. However, the poor privacy and deployment of specialized devices are mainly disadvantages of the afore-mentioned systems for deploying in home environment. In this paper, we propose WLID, a whole-home level intrusion detection system based on RSSI (Received Signal Strength Indicator) measurements of WiFi in indoor complex environment. In order to expand the area of human presence detection, WLID cooperates with WiFi-enabled IoT devices such as smart TV, air conditioner and other smart devices. The detection system constructs a detection algorithm with the non-parametric statistical method by only using RSSI and realize whole-home level real-time detection by using software implementation. The experimental results show that WLID can achieve the consistent detection rate close to 100% in a practical home environment.
Super-Resolution Pulse-Doppler Radar Sensing via One-Bit Sampling

This paper investigates the delay-Doppler estimation problem of a pulse-Doppler radar which samples and quantizes the noisy echo signals to one-bit measurements. By applying a multichannel one-bit sampling scheme, we formulate the delay-Doppler estimation as a structured low-rank matrix recovery problem. Then the one-bit atomic norm soft-thresholding method is proposed to recover the low-rank matrix, in which a surrogate matrix is properly designed to evaluate the proximity of the recovered data to the sampled one. With the recovered low-rank matrix, the delays and Doppler frequencies can be determined and paired. Numerical experiments are performed to demonstrate the effectiveness of our method compared with the one-bit sparse signal recovery method based on discrete dictionary.
Sophisticated Online Learning Scheme for Green Resource Allocation in 5G Heterogeneous Cloud Radio Access Networks

5G is the upcoming evolution for the current cellular networks that aims at satisfying the future demand for data services. Heterogeneous cloud radio access networks (H-CRANs) are envisioned as a new trend of 5G that exploits the advantages of heterogeneous and cloud radio access networks to enhance spectral and energy efficiency. Remote radio heads (RRHs) are small cells utilized to provide high data rates for users with high quality of service (QoS) requirements, while high power macro base station (BS) is deployed for coverage maintenance and low QoS users service. Inter-tier interference between macro BSs and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRANs. Therefore, we propose an efficient resource allocation scheme using online learning, which mitigates interference and maximizes energy efficiency while maintaining QoS requirements for all users. The resource allocation includes resource blocks (RBs) and power. The proposed scheme is implemented using two approaches: centralized, where the resource allocation is processed at a controller integrated with the baseband processing unit and decentralized, where macro BSs cooperate to achieve optimal resource allocation strategy. To foster the performance of such sophisticated scheme with a model free learning, we consider users’ priority in RB allocation and compact state representation learning methodology to improve the speed of convergence and account for the curse of dimensionality during the learning process. The proposed scheme including both approaches is implemented using software defined radios testbed. The obtained results and simulation results confirm that the proposed resource allocation solution in H-CRANs increases the energy efficiency significantly and maintains users’ QoS.
Motion Synthesis in Tensegrity Mechanisms

Lower weight and availability of multiple methods of changing the structure makes tensegrity mechanisms attractive for deployable mechanisms and robotics. Change of cable lengths is one way for these applications to be realized. It can allow a particular point in the mechanism to follow a certain path. This paper suggests three different methods of finding the sequence of cable lengths which achieves this path planning - parameter tuning, Probabilistic Global Search Lausanne (PGSL) and Simulated Annealing (SA). The NASA Tensegrity Robotics Simulator has been used for implementing these strategies in a three strut tensegrity simplex. The results suggest that parameter tuning is very slow. Amongst the other two, PGSL gives lower error and higher repeatability than SA.
Simplified Predictive Control of Unified Power Quality Conditioner

This paper presents the simplified model predictive control of unified power quality conditioner (UPQC). The discrete model of the converters is used along with the ripple filter requirements to design the control strategy for the switches. The predictive control ensures regulation of source current and load voltage to the desired levels by utilizing UPQC in spite of distortions in source voltage and load current. The proposed control avoids the use of linear controller or modulation technique. The performance of the adopted predictive control of UPQC is verified through laboratory experimental setup with voltage sag and nonlinear load.
Electricity Market Structure with Individual Pricing Mechanism

The most recent trends in modern electricity markets include active transformation of demand response programs into so-called intelligent demand response (IDR). This is mainly enabled by the presence and wider adoption of advanced demand side management (DSM) tools, including various smart metering, sensing and automation technologies. The larger penetration of IDR and DSM systems entails higher demand flexibility, which creates a favorable environment for electricity markets with high penetration of renewables. However, it is not easy to assess and measure the real effect of load flexibility in the context of the current market mechanisms. Specifically, the contribution of flexible consumers in the formation of electricity prices is not reflected fairly. The main reasons of this phenomena are related to the diversity of DR programs and unregistered population of price responsive consumers. In this paper, we propose a new electricity market mechanism with a centralized DR pool that guarantees a fair distribution of DR benefits, reflected in the proposed individual billing scheme. The proposed DR pool enables to pre-define each consumer's contribution to achieve system objectives. The efficacy of the developed day-ahead electricity market model is extensively compared with the conventional market schemes in the context of different levels of flexible loads' presence.
Spline-based energy-optimal trajectory planning for functionally redundant robots

The design of optimal trajectories for automatic machines is an effective tool to reduce their energy consumption. This work investigates the topic by proposing a method tailored for functionally redundant robots, i.e., the ones which have more degrees of freedom than the ones required by the task. The test case under consideration is a serial 3R robot used for a positioning task in a planar space, described by a sequence of via-points in the operative space. Speed limits and smoothness constraints, in terms of speed and jerk limitations are taken into account to ensure trajectory feasibility. The method finds the optimal time intervals between two consecutive via-points, as well as the optimal robot configurations at the via-points (i.e., the optimal solution of the inverse kinematic problem among the infinite ones). The results show the capability of the method in producing energy-efficient motion profile, and the improved results over the optimization of just the time intervals.
Ambient Intelligence in Automated Houses

Smart houses are invading nearly all modern companies under the flag of the booming field of home automation. This field is being implemented with the highest technologies to be integrated in daily life and be a non-neglect able part of everyone's life style. This field is growing at a fast pace as the field of smart devices was years ago. The main purpose of smart houses and home automation is to provide a more luxurious lifestyle to its users. However, throughout the span of the past years, there was not as much attention paid to the possibility of dangerous situations as the attention paid to providing luxurious welfare. The proposed home automation system in this work provides both luxurious and emergency features. There are six features discussed throughout this paper that shows how a house can deal with home tenant and the surrounding environment in case of emergency or dangerous situation.
The Impact of Social-Network Diffusion on Wireless Edge Resource Allocation

Content providers (CPs) increasingly deploy network infrastructures that oftentimes reach up to the wireless network edge, i.e. base stations or small cells. Hence, they are interested in optimizing resource allocation and relevant performance metrics for that infrastructure. On the other hand, mobile apps featuring streaming content (e.g. video, music) come with social-networking and content-sharing capabilities among users. These need to be taken into account in resource allocation since they decisively shape content demand. In this work, we introduce mathematical optimization problems about resource allocation at the wireless network edge, which obtain interesting twists when social-network diffusion is considered. Specifically, we consider, (i) the problem of content caching and user targeting through the recommender system of the app, with the goal to maximize the social diffusion effect of cached content, and (ii) the problem of user targeting through the mobile app recommender system, so that the available wireless bandwidth is utilized as efficiently as possible.
Construction and evaluation of the Mandarin Chinese matrix (CMNmatrix) sentence test for the assessment of speech recognition in noise

Abstract Objective: Development of the Mandarin Chinese matrix (CMNmatrix) sentence test for speech intelligibility measurements in noise according to the international standard procedure. Design: A 50-word base matrix representing the distribution of phonemes and lexical tones of spoken Mandarin was established. Hundred sentences capturing all the co-articulations of two consecutive words were recorded. Word-specific speech recognition functions, speech reception thresholds (SRT: signal-to-noise ratio (SNR), that provides 50% speech intelligibility) and slopes were obtained from measurements at fixed SNRs. The speech material was homogenised in intelligibility by applying level corrections up to ± 2 dB. Subsequently, the CMNmatrix test was evaluated, the comparability of test lists was measured at two fixed SNRs. To investigate the training effect and establish the reference data, speech recognition was measured adaptively. Study sample: Overall, the study sample contained 80 normal-hearing native Mandarin-speaking listeners. Results: Multi-centre evaluation measurements confirmed that test lists are equivalent in intelligibility, with a mean SRT of −10.1 ± 0.1 dB SNR and a slope of 13.1 ± 0.9 %/dB. The reference SRT is −9.3 ± 0.8 and −11.2 ± 1.2 dB SNR for the open- and closed-set response format, respectively. Conclusion: The CMNmatrix test is suitable for accurate and internationally comparable speech recognition measurements in noise.
Online Localization of Multiple Moving Speakers in Reverberant Environments

This paper addresses the problem of online multiple moving speakers localization in reverberant environments. The direct-path relative transfer function (DP-RTF), as defined by the ratio between the first taps of the convolutive transfer function (CTF) of two microphones, encodes the inter-channel direct-path information and is thus used as a localization feature being robust against reverberation. The CTF estimation is based on the cross-relation method. In this work, the recursive least-square method is proposed to solve the cross-relation problem, due to its relatively low computational cost and its good convergence rate. The DP-RTF feature estimated at each time-frequency bin is assumed to correspond to a single speaker. A complex Gaussian mixture model is used to assign each observed feature to one among several speakers. The recursive expectation-maximization algorithm is adopted to update online the model parameters. The method is evaluated with a new dataset containing multiple moving speakers, where the ground-truth speaker trajectories are recorded with a motion capture system.
CNFET Based Reconfigurable First Order Filter

The CNFET based digitally programmable differential voltage current conveyor has been used to realize a new voltage mode reconfigurable first order multifunctional filter. The proposed filter offers first order low pass, high pass and all pass filter responses from a single topology. The proposed filter's pole-$\omega 0$ can be controlled through an n-bit digital control word. The realized filter uses one digitally controlled differential voltage current conveyor, one capacitor and one resistor. The proposed filter operates under low supply voltage of ±0.9V. The realized voltage mode filter circuit is simulated by using HSPICE. The results thus obtained justify the theory.
Sparse Bayesian Learning for DOA Estimation with Recursive Grid-Refining

The modeling error for off-grid direction-of-arrival (DOA) estimation can be alleviated by the technique of linear approximation, but cannot be fully eliminated, especially for coarse grids. In this paper, we not only adopt a linear approximation to cover the off-grid gap, but also try to combine it with the idea that all the sampled locations can be viewed as the adjustable parameters. Then, we utilize an expectation-maximization (EM) to recursively refine the grid points, rather than updating the coefficients in the linear approximation directly. In this way, the refined grid points will tend to the true DOAs after several iterations. Simulation results illustrate that the proposed method can give a fast DOA estimation while remain a reasonable accuracy.
Cache Access Fairness in 3D Mesh-Based NUCA

Given the increase in cache capacity over the past few decades, cache access efficiency has come to play a critical role in determining system performance. To ensure efficient utilization of the cache resources, <italic>non-uniform cache architecture</italic> (NUCA) has been proposed to allow for a large capacity and a short access latency. With the support of networks-on-chip (NoC), NUCA is often employed to organize the last level cache. However, this method also hurts cache access fairness, which denotes the degree of non-uniformity for cache access latencies. This drop in fairness can result in an increased number of cache accesses with overhigh latency, which leads to a bottleneck in system performance. This paper investigates the cache access fairness in the context of NoC-based 3-D chip architecture, and provides new insights into 3-D architecture design. We propose <italic>fair-NUCA</italic> (F-NUCA), a co-design scheme intended to optimize cache access fairness. In F-NUCA, we strive to improve fairness by equalizing cache access latencies. To achieve this goal, the memory mapping and the channel width are both redistributed non-uniformly, thereby equalizing the non-contention and contention latencies, respectively. The experimental results reveal that F-NUCA can effectively improve cache access fairness. When F-NUCA is compared with the traditional static NUCA in a simulation with PARSEC benchmarks, the average reductions in average latency and latency standard deviation are 4.64%/9.38% for a <inline-formula> <tex-math notation="LaTeX">$4 \times 4 \times 2$ </tex-math></inline-formula> mesh network, as well as 6.31%/13.51% for a <inline-formula> <tex-math notation="LaTeX">$4 \times 4 \times 4$ </tex-math></inline-formula> mesh network. In addition, a 4.0%/6.4% improvement in system throughput can be achieved for the two scales of mesh networks, respectively.
CFO Estimation for OFDM-Based Massive MIMO Systems in Asymptotic Regime

Massive multiple input multiple output (MIMO) plays a pivotal role in the fifth generation (5G) wireless networks. However, the carrier frequency offset (CFO) estimation is a challenging issue in the uplink of multi-user massive MIMO systems. In fact, frequency synchronization can impose a considerable amount of computational complexity to the base station (BS) due to a large number of BS antennas. In this paper, thanks to the properties of massive MIMO in the asymptotic regime, we develop a simple synchronization technique and derive a closed form equation for CFO estimation. We show that the phase information of the covariance matrix of the received signals is solely dependent on the users' CFOs. Hence, if a real-valued pilot is chosen, the CFO values can be straightforwardly calculated from this matrix. Hence, there is no need to deal with a complex optimization problem like the other existing CFO estimation techniques in the literature. Our simulation results testify the efficacy of our proposed CFO estimation technique. As we have shown, the performance of our method does not deteriorate as the number of users increases.
Sparsity-Enabled Step Width Adaption For Linearized Bregman Based Algorithms

Iterative algorithms based on linearized Bregman iterations allow efficiently solving sparse estimation problems. Especially the Kaczmarz and sparse least mean squares filter (LMS) variants are very suitable for implementation in digital hardand software. However, when analyzing the error of such algorithms over the iterations one realizes that especially at early iterations only small error reductions occur. To improve this behavior, we propose to use sparsity-enabled step width adaption. We show simulations results demonstrating that this approach significantly improves the performance of sparse Kaczmarz and sparse LMS algorithms.
Optimal Design of Solar-Wind Hybrid System Using Teaching-Learning Based Optimization Applied In Charging Station For Electric Vehicles

This paper presents an optimal design of a solar-wind hybrid system with energy storage for Electric Vehicle Charging Stations (EVCS). The number of solar panels, wind turbines and batteries are optimized to minimize the energy production costs. The cost of the system includes initial investment and maintenance’s costs. Cost function is optimized using Teaching Learning Based optimization (TLBO) algorithm. The proposed optimization procedure is simulated in the cases of only solar panel; only wind turbine as well as solar-wind hybrid system. The results demonstrate the ability of the proposed algorithm in designing and optimizing of hybrid system.
Transient Control of Dynamic Inductive EV Charging and Impact on Energy Efficiency when Passing a Roadside Coil Section

This paper discusses the transient control of a system for dynamic inductive power transfer to an electric vehicle when passing a road-side coil section. A series-series-compensated topology with minimum number of passive components is assumed and a slight detuning of the resonance frequencies is introduced to limit the maximum induced current on the vehicle-side. A simple strategy for independent control of the road-side and vehicle-side coils is presented for ensuring strictly controlled charging power. The dynamic response of the resonant currents and of the power transfer is assessed by a small-scale experimental setup designed to represent a freight truck in scale 1:14. Experimental evaluation of the energy transfer efficiency is performed for high and low vehicle speeds and with different strategies for activating the control loops. The results demonstrate that the proposed strategy can ensure very short initialization transients and close-to-optimal operation at rated power when the vehicle-side coil is passing above the roadside coil, even at relatively high traveling speed. The concept of road utilization ratio is introduced to quantify the effect of length and layout of the road coil sections, as well as the dynamic transients, on the energy transfer capability of the system.