Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Sideband Selection for Periodic Interference Cancellation

This paper proposes adaptive sideband selection for periodic interference cancellation using digital signal processing. The proposed selection enables the periodic interference cancellation to attain better performance by providing sideband signals having higher ICNR(interference to carrier and noise power ratio). An algorithm that selects such sideband signals is shown in this paper. The proposed selection achieves 18dB better SINR(output signal to interference and noise power ratio) performance than random selection in the channel with CIR(carrier to interference power ratio) of 10dB and CNR(carrier to noise power ratio) of 30dB.
Revisit of Region-Feature Combinations in Facial Analysis

Facial analysis has significant applications in human-computer interaction and multimedia communication research. In this paper, we aim to investigate the impact of facial regions and features in the facial analysis tasks. In particular, we compare the performances of various facial features extracted from different regions in two specific tasks, namely, age group prediction and twin recognition. Furthermore, we also evaluate the fusion of handcrafted features and deep learning features in both tasks. Through the experiments, we highlight different impacts of facial regions and facial features combination in facial analysis tasks.
Classification of Musical Instruments with Convolutional Neural Networks

This paper presents one solution to a problem of classifying musical instruments with convolutional neural networks. Mel frequency cepstral coefficients are used for audio features extraction, and neural network architecture is modelled after the LeNet architecture. During the learning process, Adam optimization is used, along with negative log likelihood loss function. In the end, results are given and it is concluted that this solution has, at least 4% better accuracy on the validation set, than any other published solution.
Development of a Real-time Evaluation Support System Using Physiological Index: Case Study of a Simulator-based Ship Handling Exercise

In the maritime society, the training of a ship handling is carried out by a ship bridge simulator. The education using the simulator is popular worldwide because we are able to educate the same scenario, and repeat it for a lot of students until they can understand well. Moreover, we can make a scenario for the dangerous conditions and situations that we have never tried on the real vessel. The evaluation of simulator training is subjectively; it is professional subjective evaluation that comes from their experiences. In this paper, we propose a real time evaluation support system using a physiological index as the one of system of simulator systems. The system evaluates mental workload on the real time, and the system supports instructors to monitor student's mental workload during their training. The instructors understand not only their performance but also their mental workload to evaluate the training.
On-Chip Antennas in SiGe BiCMOS Technology: Challenges, State of the Art and Future Directions

This paper reviews recent works on on-chip antennas utilizing different integration concepts in Silicon-Germanium (SiGe) BiCMOS technologies. Challenges as well as key design parameters of the on-chip antennas are addressed and a novel antenna integration concept based on a micromachining technique is described. The utilization of the on-chip antennas in different applications is also discussed. The integration of the on-chip antennas in the radar transceivers allows for a high miniaturization potential of the sensors. Compact high-resolution radar sensors at frequencies above 100 GHz become feasible and attractive for short range applications.
Traffic Modelling Challenges on 10 Gbit/s Links Using Netmap Platform

Network devices are using growing number of 10 Gbit/s links. Such throughput upgrade imposes new challenges on creating high speed packet generators that can provide real network conditions. Traffic generators implemented as software solutions have challenges in achieving required performance, as they are commonly using kernel network stack for packet I/O. We describe different techniques for implementing Poisson traffic model in software-based traffic generator that is using netmap API as fast packet I/O.
A Compact Single-Layer Q-Band Tapered Slot Antenna Array With Phase-Shifting Inductive Windows for Endfire Patterns

A wideband millimeter-wave (mmW) tapered slot antenna (TSA) array operating in the Q-band is reported, aiming at the upcoming fifth-generation wireless communications. The antenna element is comprised by a TSA with two arrays of comb-shaped slits symmetrically cut on the edges of the two fins, respectively, which enables a degree of miniaturization for the radiator. Importantly, the geometrical structures of adjacent elements are flipped such that the fins of two neighboring TSAs are electrically connected. A miniaturized T-shaped eight-way power divider based on substrate integrated waveguide (SIW) technology is used to feed the linear array, where adjacent output channels are out-of-phase. In order to compensate the phase errors caused by the conventional compact T-shaped eight-way power divider, a well-tuned matching circuit composed of two consecutive inductive windows embedded inside the SIW is employed behind each TSA element, resulting in an enhanced gain and a low sidelobe level over a wide bandwidth. A prototype of the proposed antenna is fabricated using standard printed circuit board process and characterized, showing a good agreement between measured results and simulation predictions. The fabricated antenna experimentally achieves an $S_{11} < -10$ dB bandwidth of 13.0% (38.97–44.45 GHz), within which the gain is higher than 13.4 dBi and the sidelobe level is below −14.6 dB. Its directive endfire radiation patterns and wideband operation make it a suitable candidate for various mmW wireless communication systems.
Nouvelles fonctionnalités pour la visualisation des données textuelles et des résultats : Pour une approche ergonomique des dispositifs lexicométriques.

Un certain nombre d’initiatives vise aujourd’hui a comparer les logiciels et a mettre en commun les pratiques et les fonctionnalites. Beaucoup d’etudes et d’analyses mobilisent deux voire trois outils simultanement, dans le but de porter des eclairages differents sur des corpus identiques. Si certaines mesures tendent a se developper dans la plupart des outils (comme les cooccurrences, visualisables desormais aisement sous forme de graphes), il semble important de souligner certaines complementarites entre les outils contrastifs ou longitudinaux et ceux qui produisent des cartographies globales sans necessite de partitions intrinseques. Il semble par ailleurs necessaire aujourd’hui de se pencher sur des considerations ergonomiques, souvent peu representees dans les dispositifs que nous connaissons. Enfin, nous formulerons quelques propositions pour de nouvelles fonctionnalites visant a faciliter la visualisation des donnees textuelles et des resultats. (Implementation du mouvement dans les analyses multidimensionnelles, prise en compte du multimedia, navigation au sein des analyses factorielles…)
Fuel Efficient Route Planning Using VANET

Traffic congestion due to overloaded road infrastructure is a key contributing factor of global warming. Dynamic route planning (DRP) of vehicles can be helpful to reduce congestion and provide faster and more fuel-efficient trips for vehicles. VANET (Vehicular Ad Hoc Network) and its associated WAVE (Wireless Access in Vehicular Environment) standards facilitate this process, by allowing vehicles to share accumulated travel data with each other and plan their routes based on the gathered information. Communication and decision making can be carried out using centralized or distributed approach and should consider network usage and scalability. In this paper, we propose a novel and scalable dynamic route planning approach that uses real-time traffic information to update vehicle routes. We introduce the concept of a delay threshold at intersections to identify traffic congestion and propose a hybrid metric that considers average travel time and occupancy rate for each road segment. Our simulation results indicate that the proposed approach can lead to reductions in trip time and carbon emissions.
Research on Data Protection Based on Encrypted Attribute Access Control in Cloud Computing

One of the most promising applications in cloud computing is online data sharing. How to share user data safely and efficiently has become one of the most challenging problems in cloud computing. Encryption attribute access control is a powerful guarantee against unauthorized users from accessing sensitive data in the cloud. However, it only increases the difficulty of illegal data access. It does not know whether data has illegal access in time, nor can it adopt timely optimization and adjustment of access control policies. Based on the encryption attribute access control strategy, this paper provides a data protection model, and provides a data access detection algorithm, forms a closed-loop control, provides timely feedback evidence for continuous optimization of data access control strategies, and improves data protection's integrity.
Automatic Seizure Detection via an Optimized Image-Based Deep Feature Learning

In this paper, our goal is to find an optimized approach that can learn features from multichannel EEG time-series data to perform automatic seizure detection. In general, it is not easy to learn robust features from EEG signals due to the variations in both intra and inter-patient variability. However, to achieve good generalization, we use an algorithm that tries to capture spectral, temporal and spatial information, in contrast to standard EEG analysis techniques that ignore spatial aspects. The first stage of this algorithm is to transform EEG signals into a sequence of topology-preserving multi-spectral and temporal images. After that, these generated images are fed as inputs to a convolutional neural network. By overcoming the lack of data, especially the positive samples, and creating a process to deal with unbalanced datasets and optimizing the complexity of the network, our convolutional neural network learns a general spatially invariant representation of a seizure in a reasonable time and improves sensitivity, specificity and accuracy result comparable to the state-of-the-art results.
Effect of Gamma-Ray Energy on Image Quality in Passive Gamma Emission Tomography of Spent Nuclear Fuel

Gamma-ray images of VVER-440 and SVEA-96 spent nuclear fuel assemblies were reconstructed using the filtered backprojection algorithm from measurements with a passive gamma emission tomography prototype instrument at Finnish nuclear power plants. Image quality evaluation criteria based on line profiles through the reconstructed image are used to evaluate image quality for spent fuel assemblies with different cooling times, and thus different mixtures of gamma-ray emitting isotopes. Image characteristics at the locations of water channels and central fuel pins are compared in two gamma-ray energy windows, 600–700 and >700 keV, for cooling times up to 10 years for SVEA-96 fuel and 24.5 years for VVER-440 fuel. For SVEA-96 fuel, images in the >700-keV gamma-ray energy window present better water-to-fuel contrast for all investigated cooling times. For VVER-440, images in the >700-keV gamma-ray energy window have higher water-to-fuel contrast up to and including a cooling time of 18.5 years, whereas the water-to-fuel contrast of the images taken in the two gamma-ray energy windows is equivalent for a cooling time of 24.5 years. Images reconstructed from higher energy gamma rays such as those in the >700-keV energy window present better water-to-fuel contrast in fuel cooled for up to 20 years and thus have the most potential for missing fuel pin detection.
Design of Clustered Planar Arrays for Microwave Wireless Power Transmission

The design of microwave wireless power transmission systems sometimes requires the synthesis of large phased arrays, and the objective is to obtain the maximum beam collection efficiency (BCE). To reduce the feeding network complexity as well as system costs, subarray partition is usually indispensable, which is especially essential for large arrays in the context of long-range and high-power transmission applications. Through minimizing the difference between the subarrayed BCE and the reference one, the subarray partition problem can be reduced to an excitation matching one. Toward this end, a clustering method named k-means algorithm is adopted to determine the optimal subarray configuration. To assess the effectiveness and efficiency of the proposed method, representative examples on different sizes and shapes of transmitting arrays and receiving regions are presented. Moreover, comparisons with the state-of-the-art technique are reported as well.
IEEE-754 Half-Precision Floating-Point Low-Latency Reciprocal Square Root IP-Core

In different matrix-decomposition techniques for wireless-communication systems, the reciprocal square root (RSR) is a fundamental and recurrent operation, as well in gaming and signal processing systems computation of the RSR is required. Most reported RSR architectures are focused on accelerating high-precision floating-point (FP) units. The IEEE 754–2008 half-precision FP standard offers larger dynamic range than fixed-point systems, fewer hardware resources than single-precision FP and enough precision for some applications. This article reports the FPGA implementation of a low-latency, half-precision floating-point RSR unit. The implementation results show that the proposed design exhibits lower latency and better throughput than Intel and Xilinx RSR IP cores.
A Novel Cross-shaped Bandpass Filter with Reconfigurable Notch Band

In this brief, a cross shaped resonator (CSR) with variable capacitor and parallel coupled three line (PCTL) feeding is analyzed and used to design wideband bandpass filter (BPF) with reconfigurable notch band. The capacitor of the CSR can be used to implement the notch tuning, while the PCTL is formed to provide sufficiently strong coupling degree in the desired wideband. The proposed BPF achieves the reconfigurable function with only one tuning element. A prototype BPF centered at 6 GHz with 91.7% fractional bandwidth (FBW) reports that the notch band frequency can be reconfigured from 4.1-5.5 GHz with 1.4 GHz reconfigurable range.
A Crowdsourcing Semi-Supervised LSTM Training Approach to Identify Novel Items in Emerging Artificial Intelligent Environments

Nowadays always new kinds of cuisines appear on the market. Even though main cuisines such as French, Italian, Japanese, Chinese and Indian are always appreciated, they are not anymore the most popular. The new trend is fusion cuisine. A fusion cuisine is a combination of different main cuisines, this combination makes this cuisine new. The opening of a new restaurant proposing a new kind of cuisine produces a lot of excitement and people feel the need to try it and be part of this new culture. Yelp is a platform which publishes crowd-sourced reviews about different businesses, in particular, restaurants. Yelp allows the possibility to declare for each restaurant the kind of cuisine. Unfortunately, since the restaurants in the Yelp database are not often generated by the owners but by the users creating the reviews, there is no much information about the kind of cuisine, especially for restaurants providing fusion ones. In this paper, we address the problem of identifying restaurants proposing new kinds of cuisines by using their Yelp reviews. These new cuisines can be completely new or fusion cuisines. Discriminating between main cuisines and fusion cuisines is very difficult because fusion cuisines are similar to the main ones even if they are conceptually different. We propose 4Phase, a semi-supervised procedure that trains Long Short-Term Memory with only the text reviews of the restaurants providing main cuisines. The trained LSTM is ultimately used as a feature generator in combination with a standard novelty detection model (e.g., Gaussian Mixture Models). We perform experiments on Yelp to separate restaurants providing main cuisines from the ones providing completely new cuisines or fusion ones. In this experiments, our 4Phase procedure outperforms all the baselines (term frequency, Doc2Vec, autoencoder LSTM, etc.) and reaches 0.91 of both AUROC and MAP.
Numerical Methods for Retrieval and Adaptation in Nagao’s EBMT model

We build an example-based machine translation system. It is an instance of case-based reasoning for machine translation. We introduce numerical methods instead of symbolic methods in two steps: retrieval and adaptation. For retrieval, we test three different approaches to define similarity between sentences. For adaptation, we use neural networks to solve analogies between sentences across languages. Oracle experiments allow to identify the best retrieval technique and to estimate the possibilities of such an approach. The system could place itself between a statistical and a neural machine translation systems on a task with not so large data.
A Low Power 9T SRAM Cell Design for both Active and Passive Mode

A low power 9T SRAM architecture for both active and passive (sleep) modes has been suggested in the present paper. The design contains voltage source which is connected to the output nodes of the cell. At the time of switching these sources minimizes the required swing voltage. This minimization of swing voltage reduces the dynamic (active) power utilization. To reduce the subthreshold current in passive mode a sleep transistor attached to the ground path. This sleep transistor produces the stack effect and minimizes the static power consumption. 45nm CMOS libraries are used for simulation.
Using Social Reasoning Framework to Guide Normative Behaviour of Intelligent Virtual Agents

Social norms have a potential to contribute to advances in social intelligence. One approach to take this advantage in the design of virtual agents is the use of institutional models - a social reasoning framework which brings about social norms - in conjunction with classical AI techniques, to achieve the appropriate recognition of complex situations and provide guidance on the subsequent choice of adequate action(s) with norms. In this paper, we aim to show that the combination of an institution providing social reasoning and BDI agents providing individual reasoning, establishes a framework for socially intelligent behaviour by the interplay between: (i) the institution and Intelligent Virtual Agents (IVAs), and (ii) norms maintained by the institution and the mental states of IVAs. From an engineering point of view, the framework provides a separation of concerns because the BDI agent is augmented with the capacity to process social obligations, while the specification and verification of social structure resides in the institutional models. We illustrate our approach with two scenarios: one on queuing and another on inter-personal distance theory.
30-Targets Hybrid BNCI Speller Based on SSVEP and EMG

Brain-Computer Interface (BCI) interprets brain signals, which are measured by an electroencephalogram (EEG), allowing people to communicate without the need for any muscular movement. One of the most commonly discussed applications in literature is BCI speller as a communication modality for people with disabilities. In Brain-Neural Computer Interface (BNCI) other biosignals are merged with the brain signals to combine the advantages of both. Consequently, the here-studied hybrid BCI system discusses the parallel usage of an Steady-State Visual Evoked Potential (SSVEP) BCI and surface-electromyographic (sEMG) activity to accomplish a faster performance for a more accurate spelling application. sEMG signal was used for the activation of target selection for an SSVEP-based speller. Eight participants carried out three copy-spelling tasks for each system (six in total) to compare between both paradigms: SSVEP alone and the hybrid speller. Results showed that the hybrid speller can achieve faster performances without affecting the accuracy (mean accuracy 92.37%, 93.75%, and 100%, mean ITR 37.37, 33.41, and 31.05 bits/min), verifying our hypothesis. In addition, participants who experienced difficulties controlling the SSVEP speller were able to control the hybrid system.
Real-Time Healthcare Monitoring System using Smartphones

Monitoring and tracking healthcare conditions of crowds is a challenging mission. Although, there are several existing techniques to monitor and track crowds, these techniques suffer a number of serious issues such as inaccurate identification, high energy consumption and high infrastructure cost. In this paper, we propose to use a low-cost solution without the need to build any infrastructure by exploiting the availability of Bluetooth and Wi-Fi interfaces in smartphones. We design a Smart Real-Time Healthcare Monitoring and Tracking System based on mobile clustering. This application can be used in large public events such as the annual Muslim pilgrimage (Hajj), festivals, airports, train stations, etc. The monitored crowd are grouped autonomously into small clusters (i.e. piconets). Each cluster collects its members' data via Bluetooth and then delivers it using WiFi/3G/4G. We have evaluated the proposed approach via simulation and a prototype using our developed API under different scenarios. Our approach demonstrates 74% drop in energy consumption compared to a contemporary approach and its ability to detect 96.4% of participants.
Approximate Kalman Filtering Using Robustified Dynamic Stochastic Approximation Method

The problem of making the Kalman filter robust in a case of undetermined noise statistic has been considered in the paper. Firstly, the equivalence between the Kalman filter and a specific linear dynamic stochastic approximation recursive algorithm is established. In addition, the dynamic stochastic approximation algorithm is considered as a regression problem, and the regression problem is solved in a robust manner using the M-robust statistical approach. In order to improve the convergence rate, the gain matrix of the proposed state estimator is derived from the minimization of the particular criterion. A real-life example of maneuvering target tracking is considered to illustrate the usefulness of the proposed robustified Kalman filtering technique.
Analysis of TCP Traffic in Smart Grid Using SDN Based QoS

Smart Grid (SG) communication infrastructure is expected to be scalable, efficient and adaptable to support inevitable grid growth. Software Defined Network (SDN) is a technology that has so far proven it usage in scalable distributed systems especially for traffic prioritization management and is thus a prime candidate for the SG network traffic management that is capable of reaching 100% utilization. In this paper, SG communication network is emulated on a physical testbed and the performance of the Automatic Generation Control (AGC) Smart Grid application performance under high network load when using traditional and SDN networks is compared. The AGC network traffic performance is analyzed w.r. t. duplicate acknowledgement, packet retransmission, packet loss and congestion windows size. Congestion window size was evaluated for four algorithms: Cubic, Westwood, Veno and Scalable. The results show that SDN based QoS management significantly improves overall performance of the critical SG application.