Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Upgrade/Downgrade of WDM Link Capacity in SDN-enabled WDM VNTs over SDM Networks

We present a WDM over SDM control system that detects overloaded/underloaded WDM links between ROADMs and provisions/removes virtual WDM links in response to dynamic traffic changes in SDN-enabled WDM VNTs over SDM Networks. We have experimentally validated the architecture in a WDM over SDM testbed.
Graceful ECC-uncorrectable Error Handling in the Operating System Kernel

Memory errors that can be detected but cannot be fixed by ECC modules, called ECC-uncorrectable errors, have a serious impact on the operating system (OS) kernel, the primary software layer to control hardware and applications. When an ECC-uncorrectable error corrupts the OS kernel's memory contents and causes the OS kernel to stop, all of the running applications also stop because they are no longer scheduled. The standard means of recovery from ECC-uncorrectable errors in the OS kernel is to reboot the kernel itself. Such a reboot involves rebooting all the running applications whose memory contents are not damaged; in turn, this triggers restoration of their running states, which is non-trivial in modern big-memory applications. This paper presents Ev6, an OS kernel that survives ECC-uncorrectable errors in the kernel memory space. Ev6 prunes its in-memory objects damaged by ECC-uncorrectable errors and reconstructs its internal structures to keep the OS kernel running as long as possible. We prototyped Ev6 on xv6 and conducted several experiments. The experimental results show that Ev6 gracefully handles our fault injections with trivial runtime and memory space overheads.
A Multi-GPU Python Solver for Low-Temperature Non-Equilibrium Plasmas

The collisional Boltzmann kinetic equations for low-temperature plasmas find important applications in industry, for example semiconductor processing. Particle-in-cell (PIC) methods are the state-of-the art solvers for such problems, but they can be quite expensive. We present GPU acceleration of PIC codes and ways to increase programming productivity for rapid prototyping and algorithmic exploration. First, we present algorithms that minimize data movement and take advantage of modern GPU architectures. Second, we discuss their HPC implementation using Python-based productivity tools: CuPy, Numba, and PyKokkos. We analyze their performance, interoperability, portability, and overheads. We present performance analysis, comparing different algorithms for the main computational kernels. On a single GPU we observe 1.4ns/particle/time step. We also report scaling results on up to 16 NVIDIA Volta V100 GPUs using MPI.
A Multi-modal Feature Layer Fusion Model for Assessment of Depression Based on Attention Mechanisms

Depression is a common psychiatric disorder that can lead to depressed moods and even suicidal behavior. Intelligent assessment of depression from multiple physiological and behavioral data and breaking the limitations of traditional methods is the focus of this research area. In this paper, a multi-modal fusion depression assessment model based on attention mechanisms is proposed to predict the severity of depression from visual, acoustic and text modalities. By training and testing on an improved dataset, the proposed multi-modal feature layer fusion model based on attention mechanisms (MFF-Att) is validated to be superior to unimodal prediction models and achieved good results in depression assessment. The root mean square error (RMSE) and mean absolute error (MAE) of the proposed model on the development set are 4.03 and 3.05, respectively, which are better than the baseline and state-of-the-art results.
Survey on Vision based Fake News Detection and its Impact Analysis

Fake news is a post containing multimedia content and misrepresents the event that it is covering. A large number of fake news detection(FND) techniques rely on text analysis and very little attention has been paid to visual content-based detection. Deep learning-based generative models have increased the complexity by creating ultra-realistic phony media content and most detection techniques fail when dealing with such synthesized media. Therefore, the paper surveys the vision-based FND and improves our understanding of the role of visual content in detection. At the same time, it is important to study propagation characteristics and find an impact analysis of fake news. The existing review papers do not combine two parts - detection and impact analysis. Therefore, the proposed survey paper focuses on FND with face manipulation and also performs its impact analysis.
I Feel Stressed Out: A Mandarin Speech Stress Dataset with New Paradigm

Stress recognition is useful for mental health diag-nosis and prevention of mental illness. Due to non-invasiveness, less expenditure and privacy preservation, speech stress recognition(SSR) is the way forward in this field. However, the influences of individual differences are distinct and the number of subjects in existing speech stress datasets is inadequate, which lead to poor generalization of the existing models. In this paper, we design a new experimental paradigm that induces reference information, which is more suitable for practical application. Based on this paradigm, we present a challenging dataset, I Feel Stressed Out(IFSO), which has data collected from 87 subjects. Moreover, we propose a baseline model Stress Siamese ResNet(SSResNet) that is capable of regularly stress detection. Results show that this paradigm design and model improve the performance in the SSR task.
Characterization of Dielectric Elastomer Actuators: A Design of Experiments Approach

Dielectric Elastomer Actuators (DEAs) are recent type of smart materials, which transform electric energy into mechanical work. They show impressive performance as soft actuators. Capable of multidirectional actuation, large strain and high energy density, they become new references when developing soft artificial muscles. Even though challenges such as low generated force and high actuation voltage requirement still exist, this technology shows a great potential. Several factors influence the DEA performance, such as the material selection, prestretch, preload, actuator configuration (layers, dielectric thickness, geometry). In this paper, a model and its experimental validation are presented in order to identify and evaluate the main factors, their interactions, as well as significance for DEA performance. The proposed study is based on a design of experiment (DOE) approach to model the different behaviours and interactions between those factors. Three main factors having a significant interaction have been highlighted and a discussion to optimize the DEA performance is presented. This study may provide guidance for the design of high performance DEAs.
An Optical Guidance Method for Robotic Intramuscular Injection System

Intramuscular (IM) injection is mainly performed manually at present. Large-scale COVID-19 vaccination has exposed various problems of manual IM injection. In addition, the clinical success rate of manual IM injection is also unsatisfactory. Using robotic intramuscular injection system (RIMIS) is expected to realize automated vaccination and improve the success rate of IM injection. The existing robotic needle insertion system based on image guidance is not a practical option for IM injection because of the time-consuming medical imaging process. In this paper, an optical guidance method for RIMIS is proposed, which uses near-infrared optical tracking system and retro-reflective patch to achieve rapid acquisition of surface normal vector. A closed loop formed by six coordinate systems is used to realize the accurate control of the injection angle and depth. Experimental results show that the RIMIS based on the proposed method can complete the simulated IM injection operation without image guidance and possess accurate control of the injection angle and depth.
Reluctance and Magductance Calculation of Laminated Core Under Different Frequency for Electrical Machines

It is well known that the magnetic circuit theory can directly reflect the real change of magnetic circuit parameters. In this paper, a new magnetic component - magductance, together with reluctance, is proposed to accurately describe the effect of operating frequency on the parameters of the magnetic circuit. According to the distribution of magnetic flux in the laminated core, the expressions of reluctance and magductance are derived by combining the electromagnetic theory and the vector magnetic circuit theory. The validity of these expressions is demonstrated by an experimental platform with Epstein frame.
A Compact Filtenna for 5G Millimeter Wave Applications

In this paper, a compact high-selectivity filter antenna (filtenna) for $5\mathrm{G}$ millimeter wave (mmWave) applications is introduced. It comprises two circular patches, two annular patches and a circular substrate integrated waveguide(SIW) cavity. Through the special slot design of the upper and lower radiating patches, two radiating nulls are introduced at the upper and lower edges of the passband, which enhances the filtering selectivity of the antenna. The finite element simulation software HFSS is used to simulate and optimize the filtenna model. The measurement results show that the impedance bandwidth of the filter antenna covers 24.23-29.41GHz. In the 5G frequency band N257 and N258, its S11 is less than -10dB, the average gain of the antenna is 6.12dBi, and the impedance bandwidth is 19.31%. Therefore, this filtenna could be a good candidate for millimeter wave 5G applications.
Discrete-time Adaptive SMO based Sensorless Fixed-Switching-Frequency MPC of Three-Level NPC-fed PMSM Drives

This paper presents a discrete-time sliding-mode observer (SMO) based sensorless model predictive control (MPC) method for three-level neutral-point-clamped (NPC) inverter-fed permanent magnet synchronous motor (PMSM) drives. The fixed-switching-frequency model predictive current control (MPCC) features higher dynamics, lower dv/dt as well as superior harmonic spectra, which is suitable for high-performance drive applications. To achieve superior rotor position estimation accuracy and dynamic with sensorless MPC, a discrete-time domain designed adaptive SMO is proposed to avoid approximation. The optimized voltage references regulated by the MPCC process can be directly used for EMF estimation. Furthermore, the adaptive filter by adopting multiple second-order generalized integrator (SOGI) structures is adopted to filter out the low harmonics in estimated EMF waveforms. Therefore, the inevitable estimation error and delay caused by conventional low pass filter (LPF) can be eliminated. The simulation and experimental results are provided to verify the effectiveness of the proposed method.
An Empirical Study of Training Mixture Generation Strategies on Speech Separation: Dynamic Mixing and Augmentation

Deep learning has dramatically advanced speech separation (SS) in the past decade. Although advances in model architectures play an essential role in improving the separation performance, an efficient training strategy is also important. In this study, we investigate various strategies for training mixture generation in SS, considering that such strategies are likely essential in improving the generalization abilities of the trained models. More specifically, instead of using the vanilla training mixtures pre-generated by a given dataset, we remix clean source signals to generate more mixtures by using dynamic mixing (DM), which is an on-the-fly speech mixing strategy for model training. In addition, we combine DM with other data augmentation methods to further improve the separation performance. We analyze the effects of training data generation strategies for training sets at different scales and with various diversities. Evaluation results on multiple public datasets suggest that increasing the number of speech mixtures using DM with data augmentations is a very effective strategy for SS, especially for training sets with a limited number of clean sources.
A Compact 58-65 GHz 6-bit Phase Shifter with 0.7deg/0.35dB RMS Phase/Gain Error in 40nm CMOS Technology

This paper presents a 58-65GHz 6-bit active vector synthesis phase shifter (PS) in a 40nm CMOS. To generate I/Q signals, a compact differential capacitor-free quadrature generator circuit (QGC) based on folded transformer is proposed. Two additional transformer-based series peaking are placed between the second-stage amplifier and the digital-to-analog converter (DAC) to obtain wider bandwidth. The post-layout simulation results show that the proposed 6-bit PS achieves a 3-dB band-width of 58 ~ 65GHz. The simulated root-mean-square (RMS) phase error is 0.7° ~ 2.1° and the RMS gain error is 0.35 ~ 0.5dB within the 3-dB bandwidth. The core area of the PS is only $240\text{um}\times 570\text{um}$.
Direction-aware target speaker extraction with a dual-channel system based on conditional variational autoencoders under underdetermined conditions

In this paper, we deal with a dual-channel target speaker extraction (TSE) problem under underdetermined con-ditions. For the dual-channel system, the generalized sidelobe canceller (GSC) is a commonly used structure for estimating a blocking matrix (BM) to generate interference, and geometric source separation (GSS) can be used as an implementation of BM estimation utilizing directional information. However, the performance of the conventional GSS methods is limited under underdetermined conditions because of the lack of a powerful source model. In this paper, we propose a dual-channel TSE method that combines the ability of target selection based on geometric constraints, more powerful source modeling, and nonlinear postprocessing. The target directional information is used as a geometric constraint, and two conditional variational auto encoders (CVAEs) are used to model a single speaker's speech and interference mixture speech. For the postprocessing, an ideal ratio Time-Frequency (T-F) mask estimated from the separated interference mixture speech is used to extract the target speaker's speech. The experimental results demonstrate that the proposed method achieves 6.24 dB and 8.37 dB improvements compared with the baseline method in terms of signal-to-distortions ratio (SDR) and source-to-interferences ratio (SIR) respectively under strong reverberation for 470 ms.
Real-Time Estimation of ESR and Capacitance in the DC-Link Capacitors of AC Machine Drives

This paper presents method to estimated ESR (Equivalent Series Resistance) and capacitance in three phase inverters for motor drives. The dc-link voltage and output current of the inverter are measured in online condition while motor in running state. The recursive least squares (RLS) algorithm is applied to the ac-ripple signal on the voltage and the current to estimate process. A simulation model in MATLAB/Simulink software package and an experiment with DSP-controlled, 1$/_{2}$ Hp of induction motor drives based on SVPWM inverter system are used to verify the proposed method. Analytical, simulated, and experimental results agree well with each other. Experimental waveforms confirm the effectiveness of the presented algorithm to estimate both capacitance and ESR of the electrolytic capacitor on the common dc-link. The results show that the error between the actual value and estimated value of ESR and capacitance, which are quite acceptable value for capacitor conditioning monitoring in online during the motor operation to predict the lifetime of the capacitor by using the simple technique without additional sensors.
A Low Power Sub-GHz PLL with Optimized Class-C Voltage-controlled Oscillator

A wideband fractional-N phase-lock loop (PLL) is designed for sub-GHz wireless communications. Class-C voltage-controlled oscillator (VCO) circuit is optimized with low power consumption, followed by all-digital programmable counter to achieve flexibility in operating frequency. Implemented with a commercial 0.11-μm CMOS process, the proposed PLL has a working range of 680MHz - 998MHz with 2.69m W power consumption, including all the buffers from a 1.5V supply. It occupies a silicon area of 0.12mm2.
A Dual-Band Magneto-Electric Monopole Antenna Fed by a Self-Reconfigurable Waveguide

A dual-band magneto-electric (ME) monopole fed by a self-reconfigurable waveguide is proposed. By loading periodic structures with a stopband, the waveguide owns two operating bands supporting the single quasi TE10 mode. The antenna is developed based on the combination of an open-ended self-reconfigurable waveguide and electric monopoles that separately operate in microwave and millimeter-wave bands. Stable radiation characteristics are achieved within the low-frequency band from 10.6 to 11.7 GHz and the high-frequency band from 30.9 to 41 GHz, respectively.
A Unity-Gain Buffer with 1.6GHz Bandwidth and $1900\mathrm{V}/\mu \mathrm{s}$ Slew Rate based on an OPA Adopting Cascaded Class-AB Structure

A wideband and high slew rate (SR) unity-gain buffer (UGB) with the capability to drive a heavy load is proposed in this brief. An operational amplifier (OPA) is configured as unity gain negative feedback to improve the bandwidth and gain accuracy. A complementary input stage and cascaded class-AB output stage are adopted to enhance the small-signal bandwidth and SR of the OPA. The UGB is implemented in the $0.18-\mu\mathrm{m}$ SiGe BiCMOS technology and achieves a −3 dB bandwidth of 1.6 GHz, and an average SR of $1900\ \mathrm{V}/\mu\mathrm{s}$ with 5 V supply voltage and $100\ \Omega$ load resistance. The quiescent current of the core amplifier is 3.8 mA.
TRANSVERSE FLUCTUATIONS IN THE INTERPLANETARY MAGNETIC FIELD: A REQUISITE FOR GEOMAGNETIC VARIABILITY.

For the first few months of the Mariner 4 flight, the probe-sun-earth angle remained small. The magnetometer data obtained during this time are analyzed to determine the relationship between properties of the interplanetary field and geomagnetic variability. It is shown that, although there is a good correlation between geomagnetic variability and the interplanetary field magnitude, there is still higher correlation between geomagnetic variability and fluctuations of the interplanetary field in the plane normal to the earth-sun line. The nature of these fluctuations is illustrated for the two sudden commencement events that occurred during the period.
Current Pre-Estimation-based Delay Compensation for Sensorless FCS-MPCC Used in PMSM Drives Over High-Speed Range

This paper proposes a current pre-estimation method to solve the delay issues arising from the analog current signal processing circuit (SPC) and the digital execution process, improving the control performance of the sensorless finite control set model predictive current control (FCS-MPCC) strategies used in permanent magnet synchronous motor (PMSM) drives. First, the PMSM model used for sensorless FCS-MPCC is established. Then, the mechanisms of the two kinds of delays are detailed. Third, the proposed compensation is presented. By using the pre-estimated currents, the sensorless FCS-MPCC strategy with the delays compensated is achieved. Finally, simulation is conducted to verify the effectiveness of the proposed compensation and control strategies.
Demonstration of Turbulence-Resilient Self-Homodyne 12-Gbit/s 16-QAM Free-Space Optical Communications using a Transmitted Pilot Tone

We experimentally demonstrate a turbulence-resilient 12-Gbit/s 16-QAM FSO link using pilot-assisted self-homodyne (rather than heterodyne) detection. Results show link resilience under 400 random turbulence realizations and up to ~20-dB improvement of optical-to-electrical mixing efficiency compared to conventional LO-based coherent detection.
ARCards: Marker-Based Augmented Reality Recognition for Business Cards

Augmented Reality (AR) is an advanced technology that can improve human life. It provides the users an interactive experience where the real world objects are combined with the computer-generated virtual elements such as sound, video, graphics, GPS overlays and more. This paper surveys on the existing AR applications and also introduces ARCards which is an AR mobile application for business cards using marker-based recognition approach. ARCards is designed to enhance a person's insight view of reality. It changes the users' perception by analyzing and implementing a new way of business card sharing where additional features and functions are well equipped by recognizing the markers on the business card. This could deliver an invaluable experience through AR when introducing a person or a company detail. More valuable contents can be included and displayed as virtual digital information and this is hardly to be fitted into a normal business card. AR markers, are trained to identify as a trigger to output the visual components stored digitally in a database. The main camera in the scene is replaced by ARCamera from Vuforia Engine to recognize the feature markers on a business card and automatically revokes the digital contents from the database to be rendered on the phone scene. Extended tracking is enabled so the users are not required to always place the business card in front of the camera for constantly tracking. The functionalities are combined with the physical world and instantly offer realtime feedback to the users. Recognition process takes less than a second to identify the important features. With ARCards, one can easily stand out from others. ARCards also received 86.7% of users' full satisfaction.
Novel Smart Sectoring and Beam Designs in mmWave Broadcast Channels

This work proposes a smart sectoring scheme for the mm Wave broadcast systems to enhance the throughput by overcoming the inefficient power consumption due to transmit-ting power to undesired user directions in traditional sectoring systems. We optimize the beam pattern of the proposed scheme so that multiple users can be simultaneously served by only one RF chain. As a result, the hardware complexity can be greatly reduced. Simulation results show that the proposed sectoring scheme significantly outperforms the traditional ones under the same numbers of RF chains and antennas. In addition, the advantage of the proposed scheme is also revealed in the complexity. That is, even with only one RF chain, the proposed system can still achieve the close performance of the traditional systems with multiple RF chains (benchmark).
Design Guidelines to Allow Bifurcation Operation of Wireless Battery Charger with Primary Side Controller

In wireless power transfer (WPT) system for battery charger, the mutual inductance is mainly influenced to the secondary voltage, current, and power. These variables cannot be directly measured from the primary without the additional wireless sensors. This paper presents the design guidelines to achieve the coupling independent operation compatible with wireless battery charger. The current gain is independent from mutual inductance and depended only on the ratio of two coil inductances. Thus, the secondary current can be estimated by the primary current. The charging current and battery voltage are also controlled by the primary side controller. Finally, the experimental results show that the charging current is equal to the primary current with efficiency of 80%.