Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast Lightweight Spatiotemporal Activity Prediction Method

How to predict spatiotemporal activity from geo-tagged social media is an urgent problem. Existing methods don’t make full use of spatiotemporal information and text sequence features. In view of above problem, we design a Fast Lightweight Spatiotemporal Activity Prediction method(FLSAP) based on Gated Recurrent Unit(GRU) neural network. While GRU structure can extract text sequence features, the model takes up a lot of space due to the numerous parameters. At the same time, due to the long sequence in the text, the convergence speed of GRU is slow. So, we design a novel GRU neuron, GRU with Tiny and Skip(GTS), which can quickly generate a lightweight model with higher accuracy. In GTS, we add a scalar weighted residual connection to stabilize the training. Furthermore, we extend the residual connection to a gate by reusing the parameter matrices to compress the model size. At last, in order to make the model converge faster, we add a binary gate, which determine whether to skip the current state update. According to the experimental results, compared with ReAct [1] in the spatiotemporal activity prediction task, FLSAP improves the accuracy by 3.3%, reduces the model space by 98.79% and accelerates 74.4% of convergence speed.
Experimental Investigation of the Losses and Efficiency of 75 kW Induction Motor Drive System

The frequency converter driven IM can be used as a reference for the efficiency level of a drive system. In here, the losses and efficiency of a 75 kW IE3 -rated modern squirrel-cage induction motor are investigated experimentally. First, the result of IEC loss segregation with sinusoidal supply is used to create a reference point. Secondly, five different commercially available voltage source converters are used to drive the same motor in 30 measurement points up to the rated speed and torque. The losses and efficiencies are analyzed with torque values of 10%, 25%, 50%, 75% and 100% of the rated torque of the motor and supply frequency values of 10%, 25%, 50%, 75%, 90% and 100% of the rated motor frequency. The results are presented as frequency - torque maps and the loss values are given as Table values. Three of these converters show very similar performance from the energy efficiency point of view. The input-output method is used to determine the losses of the motor and converter.
Comfortability Detection for Adaptive Human-Robot Interactions

Recognizing emotional states from nonverbal cues is basic for any kind of social interaction. Extrapolating this capability to robots would definitely attribute them skills which might enhance their interactions with people. This thesis looks to achieve two main goals. The first one is to unravel the Comfortability concept, which we define as the persons internal agreement-acceptance to the situation that arises as a result of an interaction. The second and main goal is to build a robot-embedded system capable of recognizing this internal state, adapting its behavior accordingly. The recognition model will be developed by applying artificial intelligence techniques for temporal modeling data through visual information (body movements and facial expressions). Then, the adaptation model will take into account both the Comfortability perceived, as well as contextual information (concretely, the previous task performed) in order to decide the consecutive action that the robot will perform.
INTRINSIC MUTUAL INFORMATION REWARDS

Learning to discover useful skills without a manually-designed reward function would have many applications, yet is still a challenge for reinforcement learning. In this paper, we propose Mutual Information-based State-Control (MISC), a new self-supervised Reinforcement Learning approach for learning to control states of interest without any external reward function. We formulate the intrinsic objective as rewarding the skills that maximize the mutual information between the context states and the states of interest. For example, in robotic manipulation tasks, the context states are the robot states and the states of interest are the states of an object. We evaluate our approach for different simulated robotic manipulation tasks from OpenAI Gym. We show that our method is able to learn to manipulate the object, such as pushing and picking up, purely based on the intrinsic mutual information rewards. Furthermore, the pre-trained policy and mutual information discriminator can be used to accelerate learning to achieve high task rewards. Our results show that the mutual information between the context states and the states of interest can be an effective ingredient for overcoming challenges in robotic manipulation tasks with sparse rewards. A video showing experimental results is available at https://youtu.be/cLRrkd3Y7vU.
A micro transducer based on contour-extensional vibration mode

The contour-extensional vibration mode is of practical importance in piezoelectric transformer. However, compare with conventional thickness mode transducer, it is difficult to be designed and realized. This paper reports a new type micro piezoelectric transducer based on contour-extensional mode with theoretical simulation analyses and experiments support. A 2.5MHz transducer prototype with dimensions 2*0.6*1 mm is designed by FEM and compared by experiment. The fabricated transducer is judged by performance parameters of measured the impedance curve, received acoustic pulse maximum waveform and acoustic intensity distribution. The main advantages of using contour-extensional are that can get the higher electromechanical coupling and solve miniaturization issues of thickness direction. The contour-extensional transducer has the targeted application in vivo thrombolytic therapy and ultrasound-guide of pedicle screws.
Development of a 1-D Linear Phased Ultrasonic Array for Intravascular Sonoporation

Therapeutic efficiency of sonoporation is hampered by ultrasound attenuation, low focusing efficiency due to beam scattering from the variety of tissues in the propagation path, and heating. This paper describes the manufacturing and characterization of miniature 1-3 connectivity 1-D linear phased arrays of the size of an 8 Fr catheter for intracorporeal sonoporation. Their advantage over external transducers is that ultrasound is delivered closer to the target tissue which reduces aberration in the propagation path. Four arrays with resonant frequencies of 1.5 MHz and 3.0 MHz were manufactured using the traditional dice and fill method. The piezoelectric materials were ceramic (PZT-5H) and single crystal (PMN-29%PT). The piezocomposite design was optimised for lowest peak negative pressure (PNP) through a simulation approach using PZFlex (Onscale, CA, US). A comparison between the simulated electrical impedance of individual array elements and the experimental data shows the coupling coefficient (kt) is reduced in the real transducers, but the electrical resonance frequency and modes are well predicted by the simulation. The lower kt and the lack of electrical interconnects in the simulation led to lower measured PNP values than the simulation. However, the normalized beam shapes strongly agree between the two cases.
Domain Decomposed Parallel Implicit Monte Carlo with the Data Server Model

The large computational cost of IMC for radiative transfer is related to two factors: first, using an energy-based source strategy makes it difficult to load balance a dynamic problem. Second, domain decomposed IMC requires a large amount of parallel communication. Currently, that communication is passing particles as they move between spatial sub-domains. A mini-app, BRANSON, has been developed to study both the standard method and an alternative parallel algorithm for IMC that passes mesh and physical information between parallel processes instead of passing particles. This approach handles load balancing by dividing particles evenly between parallel processes and sending mesh data when required. The two methods are compared on simple problems. For load balanced problems, the mesh passing method has slightly improved performance compared to particle passing. For imbalanced problems, the mesh passing method strong scales with 84% efficiency with 2048 processors and simple particle passing cannot scale.
Edge-Boosted U-Net for 2D Medical Image Segmentation

Automatic medical image segmentation has always been a heated study in computer-assisted diagnosis (CAD). It is a quite challenging task due to the diversity and complexity of medical images. In this paper, we propose an Edge-boosted U-Net (EU-Net) to address the problem of medical image segmentation. The architecture is basically a U-shape network, combined with three main parts: Edge Aggregation Path, Feature Fusion Block, and Feature Attention Block. The Edge Aggregation Path is to extract multilevel edge-relevant information. The Feature Fusion Block is designed to fuse features from different paths. And the Feature Attention Block is embedded in the network to generate more informative feature maps. The collaboration of these three parts effectively boosts the performance of the whole network. We verified the importance of each part by conducting several experiments. Meanwhile, we compared the proposed method with other state-of-the-art methods on three different modalities of public medical image datasets. Our method achieves the superiority with IoU and dice coefficient respectively on all the datasets. Notably, it attains 2% accuracy improvement over other methods on the challenging datasets.
Power Switching Noise Removal in Sensitive Doppler Applications

The velocity profile of fluids flowing in industrial pipes can be detected by pulsed-wave Ultrasound Doppler, where bursts of ultrasounds with frequency in the range 300 kHz-10 MHz are transmitted every pulse repetition interval. To detect the weak echoes present in attenuating fluids and/or large pipes highly sensitive front-ends (gain ~50dB) are employed. These are sensitive to the noise produced by power switching regulators, whose working frequency is within the input working bandwidth. EMI filters and spread-spectrum modulation help, but unacceptable artefacts can still be present in the Doppler spectrum. This work proposes an adaptive method that, based on a mathematical model of the noise, is capable of concentrating the switching noise in the low-frequency region of the Doppler spectrum, which is eliminated by the clutter filter together with the echoes from still or slow-moving targets (e.g. pipe walls). Experiments with a Doppler system show the effectiveness of the method.
Vision Information and Laser Module Based UAV Target Tracking

This paper investigates the target tracking mission of an Unmanned Aerial Vehicle (UAV) equipped with a camera and a laser module. Firstly, utilizing Deep Neural Network (DNN) and Kernelized Correlation Filters (KCF), target recognition and location in the pixel coordinate system is achieved based on vision. Furthermore, by combining the laser ranging information and the distance estimation algorithm based on image, the distance between the UAV and the target is well estimated. To ensure the target tracking, a PID controller based on the distance error is applied to the UAV. The effectiveness of the system is verified on an actual UAV target tracking scenario.
High Frame-Rate Ultrasound Imaging Using Deep Learning Beamforming

Unfocused transmit beams such as diverging waves (DW) and coherent compounding are essential in achieving higher volumetric frame rates in 3D ultrasound imaging. However, image quality loss that comes with the use of DW becomes an issue, especially when the number of transmits is small. We propose a deep learning beamforming method for eliminating some of the artifacts associated with DW imaging. We train a convolutional neural network to map the non-linear transformation between the aligned per-channel data from 11 DW transmits, before compounding, to the compounded per-channel data from 51 transmits. We include additional terms in our loss function such as the beamsum value and log-detected image pixel value to guide the learning in the desired direction. The neural network is trained and tested on simulation and in-vivo data. The final network successfully suppresses acoustic artifacts such as side lobe and clutter in the images obtained with 11 DW transmits.
Tracking electromechanical muscle dynamics using ultrafast ultrasound and high-density EMG

Current methods to track the progression and evaluate treatment of muscular dystrophies are scarce. The electromechanical delay (EMD), defined as the time lag from muscle electrical activity to motion onset, has been proposed as a biomarker, but provides only limited insight in the pathophysiol-ogy of muscle function. This work proposes and evaluates a novel method to track the propagation of electromechanical waves in muscles, using high density electromyography and ultrafast ultrasound imaging. Muscle contractions in three healthy subjects were evoked by electrical stimulation, and the subsequent propagating action potentials were successfully tracked in all 90 trials. Contractile waves were detected in 83 recordings. Detection rate varied across muscle depth. Mean (SD) velocities for the action potential were 3.71 (0.08) m/s, 4.73 (0.35) m/s and 3.27 (0.09) m/s for participant 1, 2 and 3 respectively. Velocities for the contractile wave were 3.83 (1.07) m/s, 3.32 (0.78) m/s and 3.41 (0.69) m/s for participant 1, 2 and 3 respectively. In conclusion, our technique can track the fast muscular electromechanical dynamics with high spatiotemporal resolution by combining ultrafast ultrasound imaging and high-density electromyography.
Transmitting CMUT Arrays without a DC Bias

This study focuses on design, simulation, fabrication and measurement of transmitting CMUT arrays with unbiased mode of operation. We presented a design procedure considering the radiation pattern, Rayleigh distance and the parameters of the large signal equivalent circuit, which can be applied for different operation frequencies and applications. Large signal equivalent circuit model is used for lumped-element simulations. Harmonic balance and transient analyses are carried out with Gaussian enveloped tone burst, sinusoidal and pulse width modulation (PWM) signals with different duty cycles. Outputs of these simulations are fed in beamforming toolboxes for further verification. According to the design specifications transmitting CMUT arrays are fabricated. Corresponding experimental impedance measurements are conducted.
SAW 4.3 GHz Delay Line and Reflector Design Results

Surface acoustic wave (SAW) sensors provide wireless and passive operation within small embodiments. With the recent adoption of the 4.2 to 4.4 GHz band for Wireless Avionics Intra-Communication (WAIC), SAW sensors are a promising solution for current and future demands of this application. Although some experiments on SAW sensors have been done at GHz frequencies, very little is known about SAW propagation, important parameters, and properties at these frequencies. No SAW sensors have been demonstrated at WAIC frequencies up to date.This paper presents the design and analysis of SAW delay lines, and reflector devices at 4.3 GHz. Unmatched SAW delay line response having 33 dB loss has been achieved; even though this may seem high – it is acceptable for SAW sensors within near-field ranges expected in air- and space- craft frames. SAW device simulation using the coupling of modes (COM) model [1], device fabrication feasibility, preliminary measurements, and a demonstration of wireless sensor operation at 4.3 GHz will be given. Transducer design topologies explored are shown with validation of transduction coupling at harmonics. Device embodiment and antenna design will also be presented.
Optimising Vehicular System Architectures with Real-time Requirements: An Industrial Case Study

In time-constrained vehicular systems, optimisation of the system architectures with respect to the system resources has a significant impact on the development cost, performance, and reliability of the systems. This paper adapts the state-of-the-art methods to derive optimised system architectures in the vehicular industrial settings, while taking real-time constraints, resource requirements, communication dependencies, and design choices into account. In this regard, the paper describes and evaluates an industrial use case wherein the proposed method is applied to derive an optimised allocation of the system architecture. The paper also presents evidence and validations that suggest the viability of the method and its applicability in the vehicle industry.
Calculating Current Distribution in BAW Resonators from Interferometric Measurements

In this work we are presenting a method to calculate redistribution currents within the electrodes of a BAW resonator, that are caused by the acoustic deformation of the piezo-electric material. The surface deformation data of the resonators has been recorded with an interferometer capable of measuring the amplitude and phase of surface vibrations in the GHz-range. The local deflection is then fed into a discretized model of the resonator electrodes to calculate the redistribution currents and estimate the accompanying ohmic heating.
Overlap-Time Effect Mitigation for Grid-connected Current-Source Rectifiter with Dynamic Overlap-Time Distribution Method

An improved overlap-time distribution method is proposed for the grid-connected current source rectifier (CSR) in this paper. Due to the existence of the overlapping period during current commutations, input PWM currents and input voltages of CSR as well as grid currents become distorted with a number of low-order harmonics (mainly 5th and 7th harmonics), which seriously degrades the performance of the power system. To effectively eliminate the low-order harmonics introduced by the overlapping period, the dynamic distribution method of overlap-time has been presented for the grid-connected CSR. The overlap-time effect under different rectifier input voltages is analyzed, and the working principle of the proposed method is designed and given in details. Finally, the experimental results on a low power prototype are presented to verify the proposed method.
Supervised Autonomous Electrosurgery via Biocompatible Near-Infrared Tissue Tracking Techniques

Autonomous robotic surgery systems aim to improve patient outcomes by leveraging the repeatability and consistency of automation and also reducing human induced errors. However, intraoperative autonomous soft tissue tracking and robot control still remains a challenge due to the lack of structure, and high deformability of such tissues. In this paper, we take advantage of biocompatible Near-Infrared (NIR) marking methods and develop a supervised autonomous 3D path planning, filtering, and control strategy for our Smart Tissue Autonomous Robot (STAR) to enable precise and consistent incisions on complex 3D soft tissues. Our experimental results on cadaver porcine tongue samples indicate that the proposed strategy reduces surface incision error and depth incision error by 40.03% and 51.5%, respectively, compared to a teleoperation strategy via da Vinci. Furthermore, compared to an autonomous path planning method with linear interpolation between the NIR markers, the proposed strategy reduces the incision depth error by 48.58% by taking advantage of 3D tissue surface information.
A Novel Configurations of Modified CUK Converter Using Multiple VLSI Modules for High Voltage Renewable Energy Application

In this paper, new modified configurations of CUK converter are proposed for high-voltage/low-current applications with the employment of multiple Voltage Lift Switched Inductor (VLSI) modules. Proposed CUK converter configurations are designed based on the placement of VLSI modules in modified CUK converter named as MCCVLSI-XYL, MCCVLSI-LYZ, MCCVLSI-XLZ, and MCCVLSI-XYZ. The mathematical evaluation of each configuration is done to find out the voltage conversion ratio. The outstanding qualities of the proposed CUK configurations are discussed in the paper. Also, the operating modes of MCCVLSI-XYZ configuration are discussed. Moreover, the proposed configurations are compared with non-inverting converters in terms of voltage gain and to each other in terms of reactive components, and semiconductor devices. The functionalities and performance of the proposed converters are verified by MatLab (R2016a) simulation results and which are always show a good agreement with the theoretical analysis.
Switching Position-Torque Control for Series Elastic Actuators with Disturbance Observer

This paper proposes a novel switch control for the series elastic actuator (SEA). This switch control provides more robust position control along with better impulse dissipation performance. SEA is an actuator technology commonly used in robotic applications that require safe and precise interaction control. The SEA has built-in spring elements to inherently measure or estimate the transmitted force. It is often used for the application where the relevant action between motion and force is needed. The switching problem between position and torque control is one of the major issues, especially in situations where both position and torque information are critical, such as collisions. To deal with this problem, we discuss the conventional switching method and suggest a new method. Besides, this paper describes the criteria for comparing and analyzing them and presents a new approach that utilizes the concept of power to evaluate the performance of switching algorithms.
Static edge voting models

Modelling human dynamics is an emerging field, where researchers try to develop mathematical and computational models about social phenomenon. However, human society is extremely complex, therefore if we would like to obtain a mathematically tractable model, then we have to cut down the complexity. It is a common approach to modelling only a particular aspect of the complex behaviour. We are primarily interested in how networks of the human society come into being, because the processes that shape our society operate on these networks. In this paper we highlight one aspect, and try to model the following situation: there is a closed population of agents, where each agent has a level of extrovert behaviour: every agent knows the desired number of connections that she or he would like to maintain. Here we discuss very simple models, where each agent votes on the connections according to their preferences, and the connections are based on the votes.
Learning Articulated Constraints From a One-Shot Demonstration for Robot Manipulation Planning

Robots manipulating in domestic environments generally need to interact with articulated objects, such as doors, drawers, laptops and swivel chairs. The rigid bodies that make up these objects are connected by a revolute pair or a prismatic pair. Robots are expected to learn and understand the objects’ articulated constraints with a simple interaction method. In this way, the autonomy of robot manipulation will be greatly improved in an environment with unstructured constraints. In this paper, a method is proposed to obtain the articulated objects’ constraint model by learning from a one-shot continuous visual demonstration which contains multistep movements, and this enables human teacher to continuously demonstrate several tasks at once without manual segmentation. At the end of this paper, a six-degree-of-freedom robot uses the constraint model obtained by demonstration learning to achieve manipulation planning of various tasks based on the AG-CBiRRT algorithm.