Miracle-dz/UniFAR-FTUs huggingface dataset:
${L}_{2}$-Stability Criterion for Systems with Decentralized Asynchronous Controllers

This paper deals with the stability analysis of decentralized sampled-data Linear Time Invariant (LTI) control systems with asynchronous sensors and actuators. We consider the case where each controller in the decentralized setting has its own sampling and actuation frequency which translates to asynchrony between sensors and actuators. The errors induced due to sampling and asynchronicity are modelled using two different operator approaches, leading to simple $\mathcal{L}_{2}$-stability criteria for the overall decentralized control system. The simplicity of the obtained criteria is illustrated by an example and simulation results exhibit the effectiveness of the approach.
Wideband Accurate-Out-of-Phase-Fed Circularly Polarized Array Based on Penta-Mode Aperture Antenna Element With Irregular Cavity

This communication presents a wideband circularly polarized (CP) aperture antenna array with a specially designed feeding network. First, an irregular-cavity-backed aperture antenna element is introduced. Due to its penta-mode operation, a 72% CP bandwidth (5.3–11.2 GHz) is obtained, which is much wider than many existing unidirectional aperture-/slot-based CP antennas. In order to solve the element beam tilting problem, a novel four-element array with rotationally symmetric array arrangement and accurate out-of-phase feeding network is then proposed, which has advantages such as compact feeding structure, less gain variation, and symmetric radiation patterns. Measured results indicate that the four-element array achieves a CP bandwidth of 70.6% (5.48–11.46 GHz), a peak gain of 12.8 dBic, a 3 dB gain variation bandwidth of 69%, and averaged front-to-back ratio of 18 dB.
Towards More Accessible Physiological Data for Assessment of Cognitive Load - A Validation Study

Cognitive load is an often-discussed important topic with regards to human performance. Currently, many psychophysiological measures are used to quantify the level of perceived cognitive load under different tasks and environments. Heart rate (HR) is reported in literature as one of the physiological parameters that is influenced by varying cognitive load levels. Electrocardiography (ECG) is the gold-standard measure of HR measurement, however the use of traditional ECG measurement systems limits the applicability of the system to a lab environment. Recent advancements in wearable devices have provided a step towards bringing the physiological signal based human performance measuring system into real-world applications. In this study we are investigating the usability of the Polar OH1, a HR monitoring device initially used for the purpose of physical activity monitoring to use in an arithmetic cognitive load task. With a study carried out with a dataset of 10 subjects, we are able to conclude that the Polar OH1 can be used in place of ECG monitored HR, at varying cognitive load levels.
A Highly Linear Ring Oscillator for VCO-based ADCs in 65-nm CMOS

We present a novel ring oscillator circuit that improves the linearity of actual circuits by mixing two kinds of delay cells. One group of delay cells is connected to a reference supply voltage while other group of cells is connected to a modulated current bias. By interleaving both groups of cells in a single ring oscillator, we obtain a highly linear variation of the oscillation frequency with respect to the analog input signal. Additionally, the delay cells are still implemented in a mostly digital way, which makes them very suitable for deep-submicron processes. The proposed structure is of special interest for the implementation of voltage-controlled oscillator based analog-to-digital converters (VCO-based ADCs), specially with open-loop configurations where the VCO nonlinear transfer function might strongly degrade the performance. Also, the fixed level cells help to sample the oscillation without residual amplitude modulation. The new structure was designed in a 65-nm CMOS process and validated through simulations in a simple VCO-based ADC architecture. The resulting third harmonic distortion (HD3) for a 600 mVpp-amplitude differential sinusoidal input signal was equal to −79 dB, which is well-suited for medium resolution and medium-high bandwidth applications.
RGB-D Fall Detection via Deep Residual Convolutional LSTM Networks

The development of smart healthcare environments has witnessed impressive advancements exploiting the recent technological capabilities. Since falls are considered a major health concern especially among older adults, low-cost fall detection systems have become an indispensable component in these environments. This paper proposes an integrable, privacy preserving and efficient fall detection system from depth images acquired using a Kinect RGB-D sensor. The proposed system uses an end-to-end deep learning architecture composed of convolutional and recurrent neural networks to detect fall events. The deep convolutional network (ConvNet) analyses the human body and extracts visual features from input sequence frames. Fall events are detected via modeling complex temporal dependencies between subsequent frame features using Long-Shot-Term-Memory (LSTM) recurrent neural networks. Both models are combined and jointly trained in an end-to-end ConvLSTM architecture. This allows the model to learn visual representations and complex temporal dynamics of fall motions simultaneously. The proposed method has been validated on the public URFD fall detection dataset and compared with different approaches, including accelerometer based methods. We achieved a near unity sensitivity and specificity rates in detecting fall events.
IoT for Next-Generation Racket Sports Training

We propose an Internet of Things (IoT) framework for next-generation racket sports training. To validate its performance, a wireless wearable sensing device (WSD) based on microelectromechanical systems motion sensors was used to recognize different badminton strokes and classify skill levels from different badminton players. The system includes a customized sensor node for data collection, a mobile app, and a cloud-based data processing unit. The WSD developed is low-cost, easy-to-use, and computationally efficient compared to video-based methods for analyzing badminton strokes. It offers the advantage of dynamic monitoring of multiple players in indoor and outdoor environments. In this paper, we present the hardware design, mobile software implementation, and data processing algorithms of the system. Twelve right-handed male subjects wore the WSD on their wrists while each performed 30 trials of different strokes in a real badminton court. The results show that our system is capable of recognizing three different actions, i.e., smashes, clears, and drops, with an accuracy rate of 97%. The skill assessment function can differentiate between professional, subelite, and amateur players from their stroke performance. This IoT framework aims to change the way of racket sports training from experience-driven (subjective) to data-driven (objective), and which can be easily extended to analyze the motions and skill levels of players in other racket sports (e.g., tennis, table tennis, and squash) for training and/or practice.
Study of Insulation Material Properties Subjected to Nonlinear AC–DC Composite Electric Field for Converter Transformer

The nonlinear ac–dc composite electric field in the winding area of the converter transformer was studied by considering the frequency and temperature dependence of the insulation materials in this paper. First, the relative permittivity and conductivity of vegetable oils, mineral oils, and insulation papers were measured at different frequencies and temperatures. Then, the oil-paper insulation structure model of one 500 kV converter transformer valve-side winding was established. The nonlinear composite electric field of the converter transformer insulation with vegetable oils and mineral oils was analyzed. The maximum voltage of the converter transformer with mineral oils is 524.9 kV, at −20 °C and that with vegetable oils is 497.1 kV. The results show that the calculation method proposed in this paper is more accurate than the traditional superposition method.
2×192 Pixel CMOS SPAD-Based Flash LiDAR Sensor with Adjustable Background Rejection

In this paper we present a $2\times 192$ pixel SPAD-based sensor for LiDAR applications fabricated in a $0.35\ \boldsymbol{\mu}\mathbf{m}$ standard CMOS process. Distance measurement is performed using the direct time-of-flight technique. For time acquisition a DLL-based pixel-parallel TDC with 312.5 ps resolution and a $1.28\ \boldsymbol{\mu}\mathbf{m}$ range is integrated. Employing four single SPADs in each pixel enables background light suppression by detecting temporal correlated photons. Moreover, by using adjustable correlation parameters the dynamic range is increased significantly allowing a constant range at varying target reflectivity.
A mean field route choice game model

We study a route choice game model, where a large number of drivers are circulating on a road network. Given an origin-destination pair, each driver tries to pick the shortest least congested route to minimize his/her travel time. We develop a mean field game based algorithm that generates the drivers' optimal choices and anticipates the evolution of their probability distribution on the network. The optimal choices, which constitute a Nash equilibrium in the limit of an infinite number of drivers, guide a generic driver to his/her destination with the most efficient road. Moreover, they define a maximum likelihood function that can be used to estimate the model's parameters. Our algorithm takes only the drivers' initial distribution as an input, which is typically provided to the drivers by navigation applications. Finally, we illustrate via a numerical scheme how the model can also be used to evaluate the performance of different network configurations. An example shows how adding a road link to an existing network might not improve the expected travel time of the drivers.
Fast Design of Reliable, Flexible and High-Speed AWGN architectures with High Level Synthesis

In this paper, rapid prototyping of reliable, flexible and high-speed AWGN hardware architectures are presented. To do so, different methods to generate high precision Gaussian noise are discussed. These methods are compared on an algorithmic level and then implemented from a High Level Synthesis (HLS) tool. Unlike previous works that have focused on area-efficient but time-consuming hand-made architectures, HLS tools enable fast and reliable design of architectures. This work proposes reliable architectures in terms of Gaussian noise quality for a minimum of design effort. Designed architectures are compliant with the IEEE-754 standard for floating-point arithmetic. The architectures are implemented onto field-programmable gate array (FPGA) Virtex-7 device. Comparing to hand-made architectures, the synthesized architectures are similar in terms of performance with a reasonable hardware resources overcost.
Routing in Fog-Enabled IoT Platforms: A Survey and an SDN-Based Solution

Fog computing is a promising technology that helps overcome to the difficulties of handling a huge amount of Internet of Things (IoT) data by distributing its applications and services closer to the network edge. In fog computing, distributed fog servers’ proximity to the end devices enables upcoming and outgoing data to be forwarded efficiently. However, as the size of fog-enabled IoT platforms increases, efficient routing mechanisms are required in fog computing to forward the ubiquitous IoT data to related servers with low latency, low bandwidth, and/or high security. Therefore, software defined networking (SDN) can be employed to optimize the routing process in fog-enabled IoT platforms. In this paper, we first identify the routing requirements of fog computing. Then, we present an extensive survey of routing in fog-enabled IoT platforms. Lastly, we propose a hierarchical SDN-based fog computing architecture for routing in fog-enabled IoT platforms where fog controllers take actions locally for frequent events while the cloud controller takes actions globally for rare events. The proposed framework is evaluated by varying the number of controllers. The initial results show that the proposed hierarchical SDN-based framework has the potential to reduce routing delay and data transmission overhead. Also, throughput is increased as the number of controllers increases.
Non-parallel Voice Conversion Using Generative Adversarial Networks

Considering the ease of data collection, it is desirable to build a voice conversion system (VCS) from non-parallel voice data, with different sentences read by the source and target speakers. Previous non-parallel VCSs have used either a mel-cepstrum or spectral envelope as an input feature. However, these features have different acoustic characteristics that play important roles in speaker recognition. Thus, we propose a non-parallel VCS that efficiently uses both mel-cepstrum and spectral envelopes as input features. Our method is based on three key strategies: 1) we use generative adversarial networks for voice conversion; 2) we add noise to facilitate the training of the formant part; and 3) we integrate the acoustic features to generate high-quality converted voices. Subjective evaluations in the Voice Conversion Challenge 2016 (VCC 2016) revealed that our model outperformed the previous approaches in terms of the naturalness and similarity of the converted voice.
First Demonstration of 3D stacked Finfets at a 45nm fin pitch and 110nm gate pitch technology on 300mm wafers

3 Dstacking using a sequential integration approach is demonstrated for finfet devices on 300mm wafers at a 45nm fin pitch and 110nm poly pitch technology. This demonstrates the compatibility of the 3D sequential approach for aggressive device density stacking at advanced nodes thanks to the tight alignment precision of the first processed top layer to the last processed bottom layer through the top silicon channel and bonding stack during 193nm immersion lithography. The top devices are junction-less devices fabricated at low temperature $(\mathrm{T}\leq 525^{\circ}\mathrm{C})$ in a top Si layer transferred by wafer-to-wafer bonding with a bonding dielectric stack down to 170nm. The top devices offer similar performance as the high temperature bulk finfet technology for LSTP applications. The use of TiN/TiA1/TiN/HfO2 gate stack provides the proper threshold voltage adjustment while the insertion of the LaSiOx dipole improves device performance and brings the BTI reliability within specification at low temperature.
Hybrid Weight Factorization Recommendation System

Various recommendation systems have been introduced to filter, prioritize and efficiently deliver relevant information to users of e-commerce sites. But each of these approaches contain disadvantages which are unique to them in performing recommendations. To cope up with this issue hybrid recommendation systems are introduced. This study introduces a novel weight factorization model to integrate multiple recommender models to overcome disadvantages such as data sparsity and cold start. The proposed hybrid model is evaluated against multiple recommendation models using Root-Mean-Squared-Error. Based on the experimental results that the proposed model performs better compared to content-based filtering, collaborative filtering and latent factor recommendation models.
A Survey of Deep Learning and Traditional Approaches for EEG Signal Processing and Classification

Processing of Electroencephalography (EEG) signals help to detect the specific patterns in a person's ongoing brain activity and then translate these patterns into control command. Brain Computer Interfacing (BCI) is a communication system which uses these commands to interact with outside world. BCI's have extensive applications such as to classify motor imagery tasks, and to detect abnormalities and loss of functions of brain. Selection and optimization of the efficient processing technique to extract discriminative features from the EEG signals is a key to develop high performance and robust system. The main purpose of the feature selection and reduction process is threefold: by extracting relevant features computational complexity can be reduced, it improve performance by avoiding over fitting which may be caused due to irrelevant features, selected features will take less time to process. Some novel methods such as time-domain analysis, power spectral estimation, and wavelet transform have been widely used for the evaluation of the selected subset of features. In this paper first, we survey comparative evaluation of various recent techniques for feature selection and classification applied for BCI at each stage. Second, the use of deep learning techniques for brain signal classification is explored.
A Fully Adaptive 19–58-Gb/s PAM-4 and 9.5–29-Gb/s NRZ Wireline Transceiver With Configurable ADC in 16-nm FinFET

The design of a dual-mode, 19–58-Gb/s four-level pulse-amplitude modulation (PAM-4) and 9.5–29-Gb/s nonreturn to zero (NRZ), transceiver in 16-nm FinFET is presented. The fully adaptive receiver consists of a multi-stage continuous time linear equalizer (CTLE), a configurable 32-way time-interleaved 3-to-7-bit successive-approximation-register (SAR) analog to digital converter (ADC), a 14-tap feed forward equalizer (FFE)/one-tap decision feedback equalizer (DFE) digital signal processing (DSP), and a baud-rate clock and data recovery (CDR). A four-tap voltage mode transmitter with two pre-cursor taps and one post-cursor tap, incorporates impedance control loop to meet return loss requirements while maintaining good PAM-4 level mismatch ratio of 0.98. At 56 Gb/s, the transceiver achieves the <1e−12 bit error rate (BER) without explicitly added crosstalk, <1e−6 BER with 2 mVrms (crest-factor of 10) additional crosstalk, over a 32-dB channel while consuming 9.7 pJ/bit with 7-bit ADC. The transceiver equalizes channel losses up to 46 dB with pre-forward error correction BER <1e−5 at 58 Gb/s without additional crosstalk. With 3-bit ADC, it achieves 6.4 pJ/bit over a clean 7.4-dB channel.
pplying hybrid “CD-CNN-HMM” model for keywords spotting in continuous speech

Recently, there has been an increased interest in Keywords Spotting (KWS) systems. They have been widely used in various applications, like, spoken data retrieval, speech data mining, spoken term detection, telephone routing, etc. These applications require low computational cost and high precision. To meet these requirements, we propose in this work a systematic approach of keywords spotting (KWS) in continuous speech. This system performs on two-stage, in first one the continuous speech is decoded into phonetic flow using a Context Dependent-Convolutional Neural Network-Hidden Markov Model (CD-CNN-HMM) built with the open source speech recognition toolkit Kaldi, and in the second stage the keywords will be identified and detected from this phones sequence using the Classification and Regression Tree (CART) implemented with the software MATLAB. The work will be conducted on TIMIT data set.
Exploring MAS to a High Level Abstration NoC Simulation Environment

This work presents the development of a Network-on-Chip environment based on multiagent systems. This simulator aims to provide high level of abstraction to quickly simulate, compare and choose routing algorithms. The simulator calculates routers occupancy rate, number of messages that passed through each router and the number of messages lost. As a case study, the XY and West-First routing algorithms were modeled and analyzed. Simulation results indicated a better use of spare routers by the West-First algorithm when compared to the XY algorithm.
Transferring Human Tutor's Style to Pedagogical Agent: A Possible Way by Leveraging Variety of Artificial Intelligence Achievements

Pedagogical agents (P A) are lifelike characters presented on a computer screen that guide users through multimedia learning environments. Evidences show that P A has effect on promoting learning. However, P A field still faces common problems to be solved, such as how to promote the trust relationship among P A and learners. Such questions relate to how to make P A behaves more like human. This paper argues that solving these problems requires at least P A to simulate humans better in appearance, speech and motion, a possible solution is to transfer a human tutor's style to P A. Traditional P A production methods rely on the authoring tool, such as Microsoft agent, this type of methods are highly depending on manual production, so it is difficult to transfer the style of human tutor to P A well. Meanwhile, the production cycle is long, the cost is high, and the adaptability is not ideal. Based on the analysis of the achievements of interdisciplinary literature, this paper points out that based on various machine learning technologies, the above problems can be solved to a great extent. The related technologies are more likely to enhance the human-like of PA, such as the establishment of teacher strategy and behavior prediction model.
The Shape of the Game

The development of multimedia and network technologies strongly increase the interest on Internet broadcasting or streaming services, especially for soccer games. An example is the 2014 World Cup soccer tournament that registered record-breaking audiences worldwide, providing attractive alternatives to traditional TV viewing. The confluence of video streaming and computational resources opens up many possibilities for applications such as the online detection of interesting events, strategy analysis, or statistics comparisons. While much research targets algorithms to detect match statistics, strategy, retrieval, and indexing, the problem of presenting such information to users is much less studied. This paper proposes a simple but effective visual metaphor to help users browse and get insight into sports matches, with a focus on soccer games. We extract video segments, based on audio and metadata, identifying the main events of a game. Next, we use such events to define a visual representation that preserves the time-order of the video sequence, highlighting the most important events. Our visual representation enables the quick finding of the main events, allowing users to improve navigation when exploring a match, and also provides a way to evaluate the quality of a game or entire tournaments. We demonstrate our approach by applying it to several matches of 2014 World Cup, analyzing its knockout stage and comparing the final match in six different languages.
Cloud Factory Environment Monitoring Using Energy-Harvesting Wireless Sensor Networks

This study proposes a piezoelectric energy-harvesting module (PEHM) for sensor nodes, which can provide energy by using motor vibration signals. Moreover, a prototype PEHM is developed in this study, which is used by sensor nodes in an environmental monitoring system. The monitoring system consists of a front-end sensing platform, a back-end server, and a cloud database. The monitoring data are collected by the sensor nodes and then transmitted to the back-end server using a layered multi-hop scheme. The goals of the monitoring system are to detect motor in the factory. The information of motor faults are displayed on a webpage in real time, after the monitoring data are processed and analyzed to find motor faults. In addition, all monitoring data and analysis results are uploaded to the cloud database, so remote monitoring can be achieved. Also, the proposed PEHM is field tested where motors operate continuously. In the test, the nodes transmit a heavy-load packets for 10 minutes and then enter the sleep mode. The PEHM operating at the rated motor speed of 1800rpm, charging for 24.3 hours is observed to reach the node power self-sufficiency. The test results show that the proposed PEHM is capably of stably suppling energy to sensor nodes and building up a cloud factory monitoring system by using the wireless sensor network with PEHM.