Miracle-dz/UniFAR-FTUs huggingface dataset:
Hospital Receptionist Robot v2: Design for Enhancing Verbal Interaction with Social Skills

This paper presents a new version of robot receptionist system for healthcare facility environment. Our HealthBots consists of three subsystems: a receptionist robot system, a nurse assistant robot system, and a medical server. Our first version of receptionist robot, interacts with human at hospital reception, gives instructions to human verbally, but cannot understand what human says, so it uses a touch screen to get the response from human. In this paper, we design a receptionist robot that recognizes human face as well as speech, which enhances verbal interaction skill of robot. In addition, we design a reaction generation engine to generate appropriate reactive motions and speech. Moreover, we study which social skills are important to a hospital receptionist robot to enhance social interaction, such as friendliness and attention. We implemented perception modules, decision-making modules, and reaction modules to our HealthBots architecture, and did two case studies to find essential social skills for hospital receptionist robots.
Low-Complexity Geometric Inter-Prediction for Versatile Video Coding

Non-rectangular block partitioning is a well-known method for improved inter-picture prediction in video coding, enabling better spatial adaptation to the signal properties. This contribution presents the most recent proposal of geometric inter-prediction (GIP) made to the Versatile Video Coding (VVC) standardization activity led by the Joint Video Experts Team (JVET). Implemented in the latest test model VTM-5.0 and evaluated according to the JVET Common Test Conditions, the proposed low-complexity GIP scheme provides objective luma BD-rate reductions of 0.22 % for random access and 0.44 % for low-delay test cases at 7% encoder runtime increase and negligible decoder runtime increase. The coding gain is provided by non-triangular partitioned blocks and in the presence of multiple other VVC coding tools. Furthermore, BD-rate reductions of 2.58 % and 2.78 % can be achieved specifically for pure screen content by employing an adaptive blending filter.
Convective Initiation Nowcasting Over China From Fengyun-4A Measurements Based on TV-L1 Optical Flow and BP_Adaboost Neural Network Algorithms

Convective initiation (CI) nowcasting over China has a problem of a high false-alarm rate (FAR) due to the local convective processes, most of which do not produce severe weather. In order to focus on those CIs with severe weather, a new 0–2 h CI nowcasting system is developed in this article, named rapidly developed convection monitoring system (RDCMS), using the data from the advanced geosynchronous radiation imager onboard China Fengyun-4A(FY-4A) satellite. The RDCMS is not only used to identify CIs at the early stage, but also to prevent the relative high FAR. The key solutions of RDCMS are to introduce the total variation L1 norm (TV-L1) optical flow method for more tracking efficiency, and to use a supervised learning method named BP_Adaboost neural network for severe convection checking. Case studies show that the RDCMS’ skills have been improved as expected in four regions of China, which are Qinghai-Tibet Plateau, the East China, Northeast China, and South China. In the southern China region, the CI lead time is 17–40 min, and the best probability of detection is as high as 0.80, with the FAR lower than 0.34.
Stochastic Downlink Power Control for Various User Requirements

Power Control (PC) can coordinate mutual interference between cells in heterogeneous cellular networks (HCNs). Most of the existing works focus on real-time PC problems based on instantaneous channel state information (CSI) for all users. However, such scheme may result in low feasible probability and high energy consumption. If the PC problem is frequently infeasible, the users that require low latency communications will fail to get services in time. In this paper, we classify the users into two categories according to their sensitivity to latency: delay-sensitive-users (DSUs) and non-delay-sensitive-users (NDSUs). We use instantaneous signal-to-interference-plus-noise-ratio (SINR) constraints to ensure the success of data transmission per time slot to meet DSUs’ low latency requirements, and the long-term mean data rate constraints to ensure NDSUs’ average data rate requirements. On the one hand, the long-term constraints allow the system to sacrifice NDSUs’ short-term performance to guarantee DSUs’ instantaneous performance when the channel condition is poor. On the other hand, the system will appropriately improve NDSUs’ performance to ensure their target mean data rate when the channel condition is good. Under this scheme, we formulate the PC problems under perfect CSI, bounded CSI error and stochastic CSI error scenarios as a uniform problem, which is a non-convex stochastic constrained problem. The recently proposed constrained stochastic successive convex approximation (CSSCA) technique is utilized to handle this problem. Simulation results show that the proposed scheme can significantly improve the feasible probability of DSUs’ instantaneous constraints and reduce the network’s energy consumption.
A 5.5-dBm, 31.9% Efficiency 915-MHz Transmitter Employing Frequency Tripler and 207- $\mu$ W Synthesizer

A 915-MHz binary frequency-shift keying (BFSK) transmitter is proposed in this letter. The proposed transmitter architecture allows relaxing the frequency-tuning requirement of the conventional Internet of Things transceiver by the frequency-tripling signal-path topology. The tuning-range requirement is significantly improved down to 4% with the proposed signaling scheme, and this results in the ultralow-power synthesizer implementation. The proposed frequency tripler provides good spur rejection performance, and the adoption of a class-D switching power amplifier (PA) further improves the efficiency of the BFSK transmitter. Implemented in a 55-nm CMOS technology, the proposed transmitter achieves the output power of 5.5 dBm and 31.9% efficiency with only $207~\mu \text{W}$ of power consumption from the synthesizer.
The Non-Invasive Cardiodiagnosis on a Basis of Computer Processing in Phase Space of Electrography Signals

In medical institutions, the most affordable methods of diagnosis of cardiovascular diseases are based on electrocardiographic examination methods. The subject of the research is the phase trajectories of the electrography signal, which display the dynamic properties of the heart in the space of entropy-parametric potential. Qualitatively new information-probabilistic approach of electrography (ECG) signal processing and analysis is considered in the work. According to it speed properties of change of phase trajectory construction in space of derivative operators are applied. The article gives the formula for a statistical series for approximating probability distributions and choosing the values of an electrocardiocycle. In statistical distributions it is shown that the moments of distributions and their relations can be used as derivatives of ECG in time. The phase trajectories of the ECS allow morphological analysis of the teeth and highlight the temporary epochs of the electrocardiogram. The phase trajectories of the electrocardiography signal allow morphological analysis of the teeth and highlight the temporal epochs of the electrocardiogram. The approximation of the phase trajectory increases the reliability of the allocated time interval of the QRS, P and T wave complex. The use of information and probabilistic characteristics allows you to expand the functionality of the classical analysis of electrocardiograms.
Design and Development of a Virtual Reality System for the Management of Adult Neurogenic Communication Disorders

In this paper we present a Virtual Reality (VR) system (simulated kitchen; Sim:Kitchen) designed and developed for use in the rehabilitation of adults with neurogenic communication disorders. Communication disorders are commonly associated with neurological conditions such as stroke, brain injury, and Parkinson’s disease and require long-term rehabilitation with a speech pathologist. Traditional speech pathology management involves therapy designed to address the communication impairment and to teach strategies to overcome barriers to successful communication. These treatments are delivered within a clinic setting with limited opportunity for practice in realworld situations. VR has the potential to provide a safe, supported lifelike environment in which people undergoing communication rehabilitation could practice therapy tasks and gain confidence before entering actual real-world communication contexts. The aim of this paper is to investigate the perspectives of speech pathologists about the use of VR and the simulated kitchen tool for people with communication disorders as a means to optimise rehabilitation engagement and outcomes.
Power Control and Trajectory Planning Based Interference Management for UAV-Assisted Wireless Sensor Networks

Wireless sensor networks are generally used to assist in collecting and transmitting data where humans cannot directly explore. But in a scenario with complex terrestrial environment, the ground communication links between sensors become so weak to provide reliable and high-speed services. Unmanned aerial vehicles (UAVs) can be used as flying relays to enhance connective reliability of terrestrial wireless sensor networks. However, in a UAV-assisted wireless senor network, if the UAV shares the same spectrum with sensors, the interference degrades the quality of communication links when sensors exist in pairs under co-channel conditions. Motivated thereby, we manage the interference by optimizing the transmit power of all communication nodes and planning the trajectory of UAV to achieve the goal of maximizing the sum throughput of the target sensor. Due to the nonconvexity of the optimization problems, we utilize difference of two convex functions (D.C.) programming and successive convex approximation to obtain the suboptimal solutions. Simulation results prove that the minimum signal-to-interference-plus-noise ratio (SINR) required by sensor pairs, flight altitude and maximum transmit power of the UAV can be carefully selected to maximize the sum throughput of target sensor, when the UAV’s trajectory is pre-planned. The successive trajectory planning algorithm is also employed to significantly improve the sum throughput.
Single-Phase Grid-Tied Transformerless Inverter of Zero Leakage Current for PV System

Multi-level transformerless inverters are widely used in grid-tied PV systems since they characterized by higher efficiency and lower cost. In this context, new topologies, modulation, and control schemes were presented to solve problems of a common-mode voltage and leakage current. This work proposes a transformerless five-level inverter with zero leakage current and ability to reduce the harmonic output content for a grid-tied single-phase PV system. The neutral of the grid links to a common on which the negative and the positive terminals of the DC-link are connected via parasitic capacitors that can eliminate the leakage current. The proposed topology, with its inherent circuit structure, leads to boost the overall efficiency. Simulation and experimental results show almost zero leakage current and a high-quality output when maintaining balanced capacitor voltages on the DC-link input. The experimental results show 1.07% THD and 96.3 % maximum efficiency when injecting a power of 1.1 kW that verify the performance of the proposed inverter with PV sources.
MAP-Coverage: A Novel Coverage Criterion for Testing Thread-Safe Classes

Concurrent programs must be thoroughly tested, as concurrency bugs are notoriously hard to detect. Code coverage criteria can be used to quantify the richness of a test suite (e.g., whether a program has been tested sufficiently) or provide practical guidelines on test case generation (e.g., as objective functions used in program fuzzing engines). Traditional code coverage criteria are, however, designed for sequential programs and thus ineffective for concurrent programs. In this work, we introduce a novel code coverage criterion for testing thread-safe classes called MAP-coverage (short for memory-access patterns). The motivation is that concurrency bugs are often correlated with certain memory-access patterns, and thus it is desirable to comprehensively cover all memory-access patterns. Furthermore, we propose a testing method for maximizing MAP-coverage. Our method has been implemented as a self-contained toolkit, and the experimental results on 20 benchmark programs show that our toolkit outperforms existing testing methods. Lastly, we show empirically that there exists positive correlation between MAP-coverage and the effectiveness of a set of test executions.
RF Receiver Front-End Employing IIP2-Enhanced 25% Duty-Cycle Quadrature Passive Mixer for Advanced Cellular Applications

This paper proposes a second-order input-referred intercept point (IIP2)-enhanced 25% duty-cycle local oscillator (LO) quadrature passive mixer configuration with developing a wideband radio frequency (RF) receiver front-end, suitable for 4G and 5G new radio (NR) sub-6 GHz applications. By combining mixer switches operated by several combinations of quadrature-phase RF and LO signals, the inherent second-order nonlinear characteristics produced by the asymmetric factors of the mixer are greatly improved without any additional calibration process. This second-order linearity improvement is theoretically analyzed in terms of the 25% duty-cycle mixing operations, and is compared to the linearity characteristic observed in the conventional mixer design. To validate the IIP2 performance improvement, a wideband RF receiver front-end with the proposed mixer design is fabricated in a 65-nm CMOS technology. The implemented receiver front-end incorporates a wideband low noise amplifier with a polyphase filter, the proposed quadrature passive mixers, wideband transimpedance amplifiers, and 25% duty-cycle LO circuitry. The performance is characterized mainly in the long-term evolution and 5G NR frequency bands, which range from 1800 MHz to 2700 MHz. The demonstrated design achieves approximately 16 dB higher mean values of the out-of-band (OB) IIP2s compared to a conventional design approach without calibration. The design also attains conversion gains greater than 43 dB, double-sideband noise figures less than 4.4 dB, OB IIP3s greater than −1.52 dBm, and OB input P1dB greater than −23.1 dBm, for all measured frequency bands even in the presence of transmitter blockers. The proposed configuration, excluding the LO circuitry, consumes a bias current of 17.4 mA with a nominal supply of 1.2 V. The active area of the proposed implemented design is 0.6 mm2.
An Efficient Probability Estimation Design for Logarithmic Binary Arithmetic Coding

This paper proposes an efficient probability estimation design based on the Logarithmic Binary Arithmetic Coding (LBAC) in the second generation of Audio Video coding Standard (AVS2). The probability estimation process in LBAC is not stable enough in case of violent fluctuation and lack of flexibility in updating speed. The proposed design introduces two regulatory factors to mitigate the violent fluctuation and adjust the probability fluctuation all along the line. It can achieve a higher-precision and more stable probability estimation process with a hardware-friendly architecture. Experiments are carried out on JVET and AVS2 reference softwares, JEM-7.1 and RD-19.2. The results demonstrate that the proposed design can obtain a higher compression performance, up to 0.36% and 0.68% respectively.
A Resonant CO2 Sensor Functionalized with a Polymerized Ionic Liquid

Carbon dioxide (CO2) is a commonly monitored gas; however, few commercially available sensors exist that can accurately measure CO2 concentrations with low cost and low power consumption. This work presents initial assessments of microelectromechanical sensors based upon small-scale resonators embedded in an oscillator circuit for CO2 gas concentration measurement. Devices were functionalized with a polymerized ionic liquid via an inkjet printing technique. An imidazolium cation functionality with a hexafluorophosphate anion chemistry (poly(1-methyl-3-(4-vinylbenzyl)-1H-imidazol-3-ium) hexafluorophosphate [PMVBI-PF6]) was added to a polystyrene backbone motif, as this combination has previously shown significant CO2 interactions when utilized as a sorbent material for the capture of carbon dioxide from the atmosphere. Assessments were performed in a bench-top environmental test chamber by introducing a known amount of CO2 balanced with nitrogen, down to 100 ppm. The oscillation frequency was measured using a custom digital logic-based frequency counter circuit implemented in a field-programmable gate array (FPGA). The results show that the proposed approach, with additional development, could serve as an alternative to current commercially available CO2 sensing technologies.
Live Demonstration: Multiple-Path Depth Imaging with Time-of-Flight Sensors

By considering the speed of light to be finite, the so-called Time-of-Flight (ToF) sensors encode the information in delays which allows for imaging a scene in new ways. This is very different from traditional imaging systems in that existing imaging modalities work on the premise that there is a one–to–one mapping between the scene and the pixels. In this way, all of the conventional imaging sensors are agnostic to the time information. Said differently, the image-formation process is insensitive to the potentially distinct times that the photons spend traveling between their sources and the detector.
Time-Delay Estimation of Off-grid Targets for One-bit Compressed Sampling Radar via Polar Interpolation

Sparse reconstruction-based time-delay estimation for one-bit compressed sampling radar usually requires targets to be located on discrete time-delay grids. For the off-grid targets, the estimation performance significantly degrades due to the model error resulting from the discretized sparse representation dictionary. In this paper, we propose a binary iterative hard thresholding with polar interpolation (Polar-BIHT) method to estimate the time-delays of off-grid targets. The method expresses radar echoes by polar interpolation approximation, and thus transforms the time-delay estimation into a one-bit sparse reconstruction sub-problem and a one-bit nonlinear optimization sub-problem. By solving the two sub-problems efficiently, it can accurately estimate the time-delays. The simulation results show Polar-BIHT is superior to the binary iterative hard thresholding method. Moreover, Polar-BIHT still has low computational cost, which is significant in practical application.
A Multi-Trans Matrix Factorization Model With Improved Time Weight in Temporal Recommender Systems

In real-world recommender systems, users’ interest and products’ characteristics tend to go through a distinct series of changes over time. Thus, designing a recommender system that can simultaneously track the temporal dynamics of both drifts becomes a significant task. However, most of the existing temporal recommender systems only focus on users’ dynamics, ignoring changes in products’ characteristics. In this study, we propose a Multi-Trans matrix factorization (MTMF) model with improved time weight to capture temporal dynamics. Firstly, we introduce a personalized time weight that combines the forgetting curve and item similarity to reduce the impact of outdated information and retain the influence of users’ stable preferences. Then, we model user and item dynamics by learning the multiple transitions at the user-factor and factor-item latent space between the ongoing time period and all past time periods. Accordingly, we formulate a joint objective function and take a gradient-based alternating optimization algorithm to solve this joint problem. Experimental results on historical datasets MovieLens show that the recommendation accuracy of MTMF with improved time weight is superior to the existing temporal recommendation methods.
Deep Neural Networks for Predicting Vehicle Travel Times

This paper focuses on prediction if vehicle travel time. An established open dataset of taxi trips in New York City is used. We first perform statistical analysis on the data in order to determine the informative features that can be used for the problem at hand. Successive to detailed analysis of the data and features, we develop a deep neural network for travel time prediction. We show that our model performs with high accuracy, and outperforms a number of baseline techniques.
Convolutional Subspace Clustering Network With Block Diagonal Prior

Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points. However, the real data in raw form are usually not well aligned with the linear subspace model. Therefore, it is crucial to obtain a proper feature space for performing high quality subspace clustering. Inspired by the success of Convolutional Neural Networks (CNN) for extraction powerful features from visual data and the block diagonal prior for learning a good affinity matrix from self-expression coefficients, in this paper, we propose a jointly trainable feature extraction and affinity learning framework with the block diagonal prior, termed as Convolutional Subspace Clustering Network with Block Diagonal prior (ConvSCN-BD), in which we solve the joint optimization problem in ConvSCN-BD via an alternating minimization algorithm, which updates the parameters in the convolutional modules and the self-expression coefficients with stochastic gradients descent and updates other variables with close-form solutions alternatingly. In addition, we derive the connection between the block diagonal prior and the subspace structured norm, and reveal that using the block diagonal prior on the affinity matrix is essentially incorporating the feedback information from spectral clustering. Experiments on three benchmark datasets demonstrated the effectiveness of our proposal.
A J Viterbi Error Bounds For Convolutional Codes And An Asymptotically Optimal Decoding Algorithm

Viterbi decoding algorithm was developed by Andrew J. Viterbi in 1967.It is used as a A.J. Viterbi (1967) “Error bounds for Convolutional Coding and an asymptotically optimum decoding algorithm ”, IEEE Transactions on Inform. Theory. algorithm and BCJR algorithm can be used to decode convolutional codes If we use an exhaustive search for the optimum code word , the computational (10) A. J. Viterbi, “Error Bounds for Convolutional Codes and an Asymptotically.
Applying Model Checking in the Verification of a Clock Masking Unit

As the number of microelectronic circuits populating the world is increasing, so is the number of critical applications relying on their well-function. Nevertheless, ensuring that a circuit complies with the specifications is still a challenge for design and verification engineers. While simulation-based verification stands out as the most widely adopted strategy, it can only offer limited compliance guarantees since it lacks exhaustiveness. Alternatively, formal verification methodologies and, in particular, model checking, provide the means to prove functional correctness. In this work, we walk trough the verification of a clock masking unit using the NuSMV model-checking tool. Finally, we discuss the applicability of this methodology to larger and more complex designs.