Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Speaker Recognition Using Deep Belief Network Gender Classification Voices

Recently, the algorithm of machine learning are used to able to train, enhance, characterize and anticipate the data results accurately. In a way to training, the process on the algorithm can be able to produce an appropriate model based on that data; it’s like supervised and unsupervised data. In this paper, we tried to trace the gender (male and female) from acoustic data i.e., pitch, median, frequency etc. The gender that would like to implement is classified on the basis of the intensity of their utterances. To analyze the utterances, the voice intensity measuring by the hamming window to make a normalize curve to obtain the peaks of the utterances where peaks are found from each frame of speech utterance when it is divided into frames of the length of 20 milliseconds. At certain amplitude levels it can be considered to find a peak. As well as making decisions about gender use a thresholds that are adapted are adjusted. If the area of an utterance is above the threshold the gender type is a female otherwise male. After that, we handle the feature learning from the utterance into deep belief network as a machine learning tool to predict single speech by gender classification voices with optimization (taboo search) to train several neurons in the initial weight vector for the accuracy of female and male voices 75.67% and 80.83% precisely.
WalkingBot: Modular Interactive Legged Robot with Automated Structure Sensing and Motion Planning

This paper presents WalkingBot, a modular robot system that allows non-expert users to build a multi-legged robot in various morphologies using a set of building blocks with sensors and actuators embedded. The kinematic model of the built robot is interpreted automatically and revealed in a customized GUI through an integrated hardware and software design, so that users can understand, control, and program the robot easily. A Model Predictive Control (MPC) scheme is introduced to generate a control policy for various motions (e.g. moving forward, turning left) corresponding to the sensed robot structure, affording rich robot motions right after assembling. Targeting different levels of programming skill, two programming methods, visual block programming and events programming, are also presented to enable users to create their own interactive legged robot.
Graphene Fiber-Based Strain-Insensitive Wearable Temperature Sensor

A strain-insensitive wearable temperature sensor based on graphene fibers is proposed to solve the temperature measurement strain interference of wearable temperature sensors. The stress, strain, and conduction characteristics of the sensor are discussed based on finite element simulations and the principle of graphene conduction. The wearable temperature sensor showed excellent negative temperature coefficient characteristics. The responsivity of the sensor is 0.85% °C−1, and the sensing resolution is 0.1 °C. The sensor shows good antiinterference below a tensile strain of 90% or a curved surface with a curvature of 0.5. When the sensor is attached to the human body, it reliably detects skin temperature changes at different curved surfaces.
Direction-of-Arrival Estimation for High-Frequency Radar on a Floating Platform

For flexibility and wider working area, it would be a good idea to mount high-frequency radar on a floating platform. But under the condition, new problems will appear. When the platform is rotating on sea surface, the direction-of-arrival to the receiving array keeps shifting which causes the uncertainty of arrival direction estimation. And in the paper, we try to improve the precision of direction-of-arrival estimation. A description of the method could be fixed-beams minimum-mean-square error beam forming. The method consists of two ideas: 1) the idea to convert data form rotating antenna channels into fixed beam channels; 2) the constraint of minimum-mean-square-error based on antenna patterns to adaptively generate the weight for the conversion. For uniform circular array and two kinds of rotation, the simulations have been conducted. The azimuth searching spectrum around the set direction gets much sharp. And accuracy for estimated direction of the set object is greatly improved.
Energy Efficient ADC Bit Allocation for Massive MIMO: A Deep-Learning Approach

It is known that adopting Variable-Resolution (VR) ADCs in millimeter-wave (mmWave) Massive Multiple-Input Multiple-Output (MaMIMO) receivers improves Energy Efficiency (EE). However, the effect of imperfect channel state information (CSI) at the receiver is detrimental in achieving the EE. None of the previous works consider imperfect CSI for designing ADC Bit Allocation (BA) for MaMIMO receivers. We propose a deep learning based framework to achieve a near-optimal EE for MaMIMO receivers. The contributions of this paper include a machine learning approach to arrive at a BA that achieves near-optimal EE by training the framework for a combination of perfect and imperfect channels using the conditions derived for capacity maximization. Using simulations, we show that the EE obtained using our proposed approach is very close to that of the brute force both for perfect and imperfect channels. Also, through simulations, we claim a computational complexity advantage using the proposed approach compared to brute force after sufficient learning of the channels presented to the system.
State of AI-Based Monitoring in Smart Manufacturing and Introduction to Focused Section

Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area.
Unscented Kalman Filter for Indoor Bluetooth Voice Transmission Interference over Non-Gaussian Fading Channel

This study proposes a novel signal detection and modulation optimization scheme using the unscented Kalman filter (UKF) for the Bluetooth voice communication system for the indoor noise environment at Oakland University. It is designed for filtering received signals at the front-end of the receiver stage and established to be robust due to the usage of UKF. The UKF model is capable of handling modulated waveforms represented by the state-space model to reduce the non-Gaussian noise over a multipath fading channel. Moreover, this novel work addresses the characterization of the fading channel with typical non-Gaussian noise, which can be applicable in Bluetooth systems with 802.11b (Wi-Fi) standard interference. The simulation results are used to calculate the bit error rate and frame error rate.
5G Network Performance Experiments for Automated Car Functions

This article discusses the results of supporting transition towards fully automated driving with remote operator support via the novel V2X channels. Automated passenger cars are equipped with multiple sensors (radars, cameras, LiDARs, inertia, GNSS, etc.), the operation of which is limited by weather, detection range, processing power and resolution. The study explores the use of a dedicated network for supporting automated driving needs. The MEC server latencies and bandwidths are compared between the Tampere, Finland test network and studies conducted in China to support remote passenger car operation. In China the main aim is to evaluate the network latencies in different communication planes, whereas the European focus is more on associated driving applications, thus making the two studies mutually complementary.5G revolutionizes connected driving, providing new avenues due to having lower and less latency variation and higher bandwidths. However, due to higher operating frequencies, network coverage is a challenge and one base station is limited to a few hundred meters and thus they deployed mainly to cities with a high population density. Therefore, the transport solutions are lacking so-called C-V2X (one form of 5G RAT) to enable data exchanges between vehicles (V2V) and also between vehicles and the digital infrastructure (V2I). The results of this study indicate that new edge-computing services do not cause a significant increase in latencies $(\lt 100$ ms), but that latency variation (11 - 192 ms) remains a problem in the first new network configurations.
Fail-Operational Shock Detection and Correction of MEMS-based Micro-Scanning LiDAR Systems

Highly automated or autonomous vehicles will be dependent on systems that have to perceive the environment to make valid decisions during the driving cycle. One of the key enablers for autonomous and highly automated vehicles will be Light Detection And Ranging (LiDAR) technology. A Micro-Electro-Mechanical System (MEMS) based Micro-Scanning LiDAR is able to detect obstacles in a predefined Field-of-View (FoV). The point cloud stability of the scanned FoV is mandatory to be able to make a valid point where the obstacle is located in the scenery. Due to the fact that massive shocks can occur arbitrarily to the system, it is necessary to be able to detect and correct them as fast as possible that point cloud stability can be recovered as fast as possible. In this paper, we introduce a novel system architecture that enables a fast shock detection and correction of phase and frequency for MEMS-based Micro-Scanning LiDAR Systems. Our novel introduced fail-operational detection and correction system architecture was implemented in a 1D MEMS-based Micro-Scanning LiDAR FPGA platform to prove its feasibility and for performance evaluation.
Joint Learning of Token Context and Span Feature for Span-Based Nested NER

Nested named entity recognition (NER) is a linguistic phenomenon that has received increasing attention in the NLP community. In this article, we propose a novel joint learning network of the token context, and span feature (TCSF) for nested NER. Our model is a combination of token context network (TCN) for token learning, and deep residual convolutional neural network (CNN), and span relation network (SRN) for span learning. Span features are represented at both the token level, and span relation level. The span relation representation of SRN is trained on the similarity of span, and its positional features by attention weights. The <inline-formula><tex-math notation="LaTeX">$IoU$</tex-math></inline-formula> metric is employed for span filtering, and analysis of the overlapping adjacent spans. Moreover, we propose a novel head-inner-tail (HI<inline-formula><tex-math notation="LaTeX">$^{P}$</tex-math></inline-formula>T) operation to extend the inner features in spans for a fixed-length representation. TCSF is a fully end-to-end entity recognition model. We perform a set of comprehensive experiments, including an ablation study on the ACE, and GENIA datasets, and show state-of-the-art performance without, and with the pretrained language models.
Enabling IEEE 802.15.4-2015 TSCH based Wireless Network for Electric Vehicle Battery Management

The Battery Management System (BMS) of an Electric Vehicle (EV) is a system designed to ensure safe operation of the battery pack, and reporting its state to other systems. It is a complex and distributed system. In today’s BMS implementations, the communication is performed through wire buses. In this paper, we study the opportunity to use IEEE Std 802.15. 4 Time Slotted Channel Hopping (TSCH), a standardized Internet of Things (IoT) approach. We first describe the real- world experiments we did to measure the link quality at Medium Access Control (MAC) layer for wireless nodes placed inside an EV battery pack. Then, we propose a topology management and TSCH scheduling strategy using Linear Programming, based on the results obtained in the experiments.
A Novel Emission Test Method for Multiple Monopole Source Stirred Reverberation Chambers

This letter presents a novel method for predicting the electromagnetic field radiated by an equipment under test (EUT) in free space. The method is based on the knowledge of the electric field values close to the metallic walls of a reverberation chamber where the device is placed. From these field samples, a set of equivalent sources is retrieved. The equivalent sources likewise produce in free space the same field radiated by the EUT.
The Understanding and Forecast of AS-Level Anycast Path Inflation

Anycast, as a network layer solution for providing faster and stabler services to end-users, is actively deployed on the Internet today. A common argument is that the underpinning routing system will automatically direct users to the closest site among the set of anycast sites. However, anycast path inflations are observed, where users are unexpectedly directed to a site farther away. In this paper, we study a specific kind of anycast path inflation called AS-level Anycast Path Inflation (AAPI). AAPI means, after the deployment of an anycast site in a different Autonomous System (AS), the number of ASes that traffic passes through is larger than that before, so that users may experience increased latencies or be exposed to higher inter-domain security risks. We discuss AAPI’s causes, analyse its characteristics, and propose deployment guidance. In particular, we classify AAPI in two basic forms, i.e. Route Suppression (RS) and Route Promotion (RP), and present their various characteristics as well as their possible coupling. We propose Conflict Point (CP), a topological feature which represents the intrinsic conflict between routing policies and AS path length at these nodes, to further study the necessary and sufficient conditions for AAPI. And based on the properties of CP, we give some suggestions on anycast deployment strategy to avoid AAPI and verify them by simulation.
Operation Identification by Shared Tactile Perception Based on Skin Vibration

Tactile sensation contributes to sensory-motor control and facilitates skillful manipulation of objects. If the tactile sensation can be shared between two partners, the state of each partner can be partially observed by the other, and the possibilities for cooperative work may be expanded. Recently, examples of utilizing tactile sensation in human-robot cooperative work have been proposed. For cooperative work between people, it is necessary to investigate the basic ability of humans to identify other person's motions and operations by tactile presentation. To avoid hindering work performed with the hands and fingertips, the sensation must be presented elsewhere. This study investigates the possibility of identifying other person's tool operations by presenting tactile information induced on his/her finger with a wearable vibrator on the arm. A wearable skin vibration sensor was employed to acquire tactile information during an experiment in which five different tool operations were tested. This sensor measures skin vibration while directly touching the target. We proposed a non-linear signal processing function to adjust the intensity of the skin vibration to within the range of human sensitivity for tactile presentation. We compared vibrotactile stimulation between the non-linear and linear corrections, and then conducted experiments on identifying operations. The results showed that the non-linear correction increased small signals and enhanced the variance of large signals, and that operations were significantly identified by tactile presentation to the arm.
A 3-Bit DAC With Gray Coding for 100-Gbit/s PAM Signal Generation

An integrated generator for 3-bit pulse amplitude modulation (PAM-8) is presented, supporting symbol rates above 30-GBaud. To optimize the generated pulses for a transmission, the digital input word is mapped to the binary-weighted segments of a digital-to-analog converter using the binary reflected Gray code, which minimizes the total bit error probability. The mapping is realized using full-rate digital logic based on exclusive-or gates. For validation, the circuit was fabricated in a 130-nm SiGe BiCMOS technology and measured on a PCB in combination with three pseudo-random bit sequence generators. At a symbol rate of 33.4-GBaud, the circuit can generate Gray-coded signals at 100.2-Gbit/s with PAM-8 and 66.8-Gbit/s with PAM-4.
Explaining the Different Time Constants Extracted from Low Frequency Y22 and $I_{DS}$-DLTS on GaN HEMTs

This paper presents an explanation for the observed differences on the trapping time constants extracted from low frequency $Y_{22}$ and $I_{DS}$-Deep-Level Transient Spectroscopy (DLTS) on GaN high electron mobility transistors (HEMTs). It is shown that the trapping time constant, not being a physical identity but a parameter of a model, can vary according to the conditions of extraction. This dependence is thus clearly seen in $Y_{22}$ and $I_{DS}$-DL TS measurements, as they are made respectively under small-signal steady-state and large-signal transient conditions. A theoretical explanation supported by the Shockley-Read-Hall (SRH) model is proposed, showing how time-constants extracted from $I_{DS}$-DLTS, i.e. under charge carrier emission, must be longer than the ones obtained from $Y_{22}$, i.e. under equilibrium and thus where both emission and much faster capture processes play a role.
The CCC framework: Training and inference of a simple MLP for the XOR problem

In this work we consider a simple multilayer perceptron (MLP) for the 3-input XOR problem. We design and implement both training and inference stages from scratch, with hardware implementation in mind. We give our algorithm details, verify its correct functionality in GNU Ada and proceed with high-level synthesis, via our Custom Coprocessor Compilations (CCC) framework tool. Results validate our motivation for accelerated performance and device portability.
The Effects of Robot’s Facial Expressions on Children’s First Impressions of Trustworthiness

Facial expressions of emotions influence the perception of robots in first encounters. People can judge trustworthiness, likability, and aggressiveness in a few milliseconds by simply observing other individuals’ faces. While first impressions have been extensively studied in adult-robot interaction, they have been addressed in child-robot interaction only rarely. This knowledge is crucial, as the first impression children build of robots might influence their willingness to interact with them over extended periods of time, for example in applications where robots play the role of companions or tutors. The present study focuses on investigating the effects of facial expressions of emotions on children’s perceptions of trust towards robots during first encounters. We constructed a set of facial expressions of happiness and anger varying in terms of intensity. We implemented these facial expressions onto a Furhat robot that was either male-like or female-like. 129 children were exposed to the robot’s expressions for a few seconds. We asked them to evaluate the robot in terms of trustworthiness, likability, and competence and investigated how emotion type, emotion intensity, and gender-likeness affected the perception of the robot. Results showed that a few seconds are enough for children to make a trait inference based on the robot’s emotion. We observed that emotion type, emotion intensity, and gender-likeness did not directly affect trust, but the perception of likability and competence of the robot served as facilitator to judge trustworthiness.
Use of the Preisach and L2P Models Together for Greater Confidence in Hysteresis Curves at Different Frequencies

This work proposes a new approach for the observation of frequency hysteresis curves. The idea developed is based on the joint use of two hysteresis models to observe the response and compare it. Thus, Preisach and L2P (Limiting Loop Proximity) hysteresis models were simulated, comparing their responses at different frequency values. The results presented show slight discrepancies due to its form of assessment, which in the classic Preisach model is totally irreversible and the L2P model has a small portion that causes it to have a percentage of reversibility. An approximation is made so that both can diminish their differences so that with the same field applied the results obtained have a better precision allowing to obtain hysteresis curves with greater reliability without the need to carry out other studies other than the application of the two models.
A Multi-Standard 15-57 GHz 4-Channel Receive Beamformer with 4.8 dB Midband NF for 5G Applications

This paper presents a 4-channel 15-57 GHz receive phased-array front-end designed in a SiGe BiCMOS SBC18S5 process. A wideband architecture employing 3-stage resistive-feedback low-noise amplifier (LNA) followed by an active phase shiftier results in an ultra wideband operation with high gain and low noise figure. Each channel has a 5-bit phase control and 18 dB gain control. The outputs of the four channels are combined using a wideband two-stage Wilkinson network. The measured chip electronic gain is 27.2 dB with a 4.8 dB noise figure (NF) and a -28 dBm input P1dB at the midband frequency of 35 GHz. To author's knowledge, this work achieves the widest bandwidth covering multiple 5G bands and enables the construction of multi-standard wideband phased arrays.
The Changeable Region of Wind Power Considering Its Uncertainty

The rapid development of wind power generation imposes unprecedented challenges on power system operations due to stochastic variations. This paper seeks to compute how much nodal injected wind power variation a power system can handle. Two corrective control strategies—the fixed and the fully adaptive participation factor—are considered. To speed up the computation for fully adaptive participation factor strategy, a pairwise convex hull is proposed to build a more compact initial set, and a local admissible region is characterized instead of the whole region. To reduce the required computation, a novel method is proposed to decrease the number of inequalities. Finally, the effectiveness and efficiency of the proposed methodology are verified on a modified 5-bus and 118-bus systems.