Miracle-dz/UniFAR-FTUs huggingface dataset:
Get Up!: Assessing Postural Activity & Transitions using Bi-Directional Gated Recurrent Units (Bi-GRUs) on Smartphone Motion Data

Many health conditions can affect a person’s mobility. Consequently, a person’s ability to perform transitions between activity states (e.g. sit-to-stand) are accurate measures of their mobility and general health. Mobility impairments can manifest either as discomfort while performing certain activity transitions or a complete inability to perform such transitions. The Timed up and Go (TUG) is an important clinical test that assesses patients’ sit-to-stand abilities. Research into passive methods to assess the quality of patients activity transitions and thus conduct the Timed Up and Go autonomously as they live their lives, have recently become popular. Machine and deep learning analysis of smartphone accelerometer and gyroscope data have demonstrated promising activity and transition recognition results. In this paper, we present Get Up! , a novel deep learning-based method to detect whether a person is performing a certain postural activity or transitioning between activities. Get Up! analyzes data from the accelerometer and gyroscope of the patient’s smartphone using Bi-Directional Gated Recurrent Units (Bi-GRU) neural networks with an attention mechanism. Our method outperforms TAHAR, the current state of the art machine learning method, achieving an error rate of 1.47% for activity classification and an accuracy of 97%. We also achieved an error rate of 0.17% with an accuracy of 93.3% when classifying postural transitions. As Get Up! segments activities and transitions, individual TUG sub-components can be timed to identify sub-components that patients find challenging.
Shared Bottleneck Detection for Multipath Transmission in High Latency Satellite Network

For multipath transmission, the congestion control based on shared bottleneck detection not only improves the throughput in the Non-Shared Bottlenecks(NSB) scenario, but also maintains fairness to other connections in Shared Bottlenecks(SB) scenario. However, due to sharp difference of post-paths' delay(DPPD) in high latency satellite networks, most of existing shared bottleneck detection schemas misused irrelevant data result in poor performance. In this paper, the relationships of OWDs between two subflows are analyzed firstly. Then, we propose a robust schema based on difference estimation of post-paths' delay(DEPPD) to detect shared bottleneck for multipath transmission in high latency satellite networks. Our schema searches for a continuously matching sequence among the elements of two congestion event arrays, and uses the mean intervals of matched congestion events as estimate of DPPD to judge whether the next pair congestion events match. If the tail element of one array can be matched in some way, there is a shared bottleneck between the two subflows. Otherwise there is no shared bottleneck. The schema is implemented in multipath quic(MPQUIC) and simulations demonstrate that DEPPD can achieve higher accuracy in our scenario and remain stable with variations of DPPD, comparing to other techniques.
Simulation and Kinematic Modelling of a Wire-driven Active Interventional Catheter for PCI

Because of the difficulty of traditional interventional catheter operation and limited range of motion and flexibility, we adopt a structure of an active interventional catheter-based on wire driven. A three-dimensional structural model of active interventional catheter was built in CAD software. The kinematics analysis of the catheter-based on the large deflection theory and the circular arc hypothesis. The operational active interventional catheter was simulated and analyzed based on Adams software. Simulation is based on kinematic modelling demonstrates this advantage.
Wideband Cavity-Backed Slot Subarray Fed by Gap Ridge Waveguide for 5G mmWave Base Station

This paper proposes a bandwidth-improved cavity-backed unit cell slot subarray antenna fed by a gap ridge waveguide for Line-of-Sight (LoS) 5G mmWave applications. The proposed subarray is comprised of three layers. The top layer is the antenna layer in a 2 × 2 slots configuration, the cavity layer is in the middle and the ridge gap waveguide feeding is in the bottom layer. We propose to enhance the bandwidth of the unit cell subarray antenna by modifying the coupling slot in the cavity layer. The main advantage of the proposed method is that the bandwidth enhancement can be achieved without adding an extra tuning pin in the cavity layer, which eases mass production. The proposed structure is simulated with the CST software in an infinite array environment. The simulation results show that the S11 is below -10 dB over 24 − 31.5 GHz frequency band covering a 27% bandwidth and the directivity is approximately 33.8 dBi when evaluated as a 16 × 16 element slot array. It is envisaged that the method can be advantageous for bandwidth enhancement of a practical cavity-backed slot array antenna.
An Omnidirectional Circularly Polarized Antenna Based on Substrate Integrated Waveguide

In this paper, we propose an omnidirectional circularly polarized antenna based on substrate integrated waveguide. The proposed CP antenna has a simple structure with a single feed. The antenna can be considered as a superimposition of two circular cavities. Two orthogonal TM20 modes are then generated to achieve an omnidirectional CP radiation pattern. Parasitical strips are used for impedance matching. The antenna has a 10-dB impedance fractional bandwidth range from 4.29 GHz to 4.38 GHz. The axial ratio (AR) of the proposed antenna is 0.6 dB at 4.31 GHz.
LMVI-SLAM: Robust Low-Light Monocular Visual-Inertial Simultaneous Localization and Mapping

Visual-inertial simultaneous localization and mapping (SLAM) shows significant progress in recent years due to the complementary nature of the visual and inertial sensor but still, challenges remain for low-light environments. Recent visual-inertial SLAMs often drift or even fail in low-light conditions due to insufficient 3D-2D correspondences for bundle adjustment. To address the issue, this paper performs image preprocessing firstly with a united image enhancement method involving adaptive gamma correction and contrast limited adaptive histogram equalization, which could ameliorate the brightness and contrast of the image greatly. Moreover, we track features using optical flow for adequate point correspondences in dim-light environments, and supplement the corresponding map points continually by insert keyframe and triangulation to keep tracking. Finally, we construct a tightly-coupled nonlinear optimization model, which combines a feature reprojection error on point correspondences and IMU measurement by pre-integration to constrain and compensate each other for more accurate pose estimation. We validate the performance of our algorithm on public dataset and real-word experiments with a mobile robot, including dark laboratory, etc., and compare against existing state-of-the-art visual-inertial algorithms. Experimental results indicate our algorithm outperforms other state-of-the-art SLAMs in accuracy and robustness, and works reliably well for both general and low-light environments.
Holographic Antenna with Slotted Yagi Array Antenna as Surface Wave Launcher

In this paper, the holographic antenna with 4 elements of slotted Yagi array antenna as surface wave launcher is proposed to generate the plane wave holographic pattern within the small size of the antenna. The type of the feeder antenna is chosen to the slotted Yagi antenna for mode which radiates uni-directionally. The main radiator of the feeder antenna is modified to reduce mutual coupling. By arraying 4 elements of the feed antennas, they are able to generate plane wave just in front of themselves. The proposed holographic antenna with 12 metal strips is designed on the substrate of 24.25 x70 mm2 and analyzed the characteristics of the gain and the radiation efficiency.
All-ECG: A Least-number of Leads ECG Monitor for Standard 12-lead ECG Tracking during Motion*

As a leading cause of death, cardiac diseases are taking away lives from over a half million US people each year. Standard 12-lead electrocardiogram (ECG) signals are gold-standard cardiac vital signs, and have been widely used in clinics and hospitals. However, it is still not readily available in our daily lives, due to its inconvenient and uncomfortable setting, as well as large signal quality degradation during our daily motions. In this research, a novel ECG monitor called, All-ECG, is proposed, which is expected to, at the same time, provide a convenient setting and enable motion-tolerant 12-lead ECG tracking. To achieve the first goal – convenience, a least-number of leads are selected to reconstruct the remaining leads. To achieve the second goal – robustness, a deep learning framework based on the long short-term memory is developed to reconstruct high quality ECG leads from noisy ECG leads. Evaluated on patient ECG data, the proposed deep learning framework can effectively reconstruct standard 12-lead ECG only from noisy 3-lead ECG during daily motions, with a correlation coefficient of as high as 0.82 and a root mean square error of 0.073 mV. To the best of our knowledge, this is the first study on 12-lead ECG reconstruction from a least-number of noisy leads, and is expected to greatly advance long-term daily heart health management.
Human-like gait generation from a reduced set of tasks using the hierarchical control framework from robotics

This paper shows that controlling only a small set of adequately selected tasks is sufficient to closely reproduce the human gait kinematics. To this aim, a hierarchical controller is applied to a whole-body model including 42 degrees of freedom with 3 hierarchical tasks. The analysis of the simulated gaits shows the emergence of significant human-like properties in walking. In order to validate our results, a comparison between joint rotations in the simulated motion and in human reference motions is conducted. Finally, a discussion is given to illustrate the interest of the approach in view of related works.
Pseudo-Continuous Flow System for Dopamine and Ascorbic Acid Detection Based on FTIR-Spectrometery

In this work a new protocol based on FTIR-microspectrometry and solvent evaporation for neurotransmitter detection is presented. In addition, a new automated sample handle support is developed and coupled to the microscope-FTIR in order to perform Dopamine and Ascorbic Acid sensing in aqueous solutions. Results show that these molecules can be detected in homogeneous and mixture samples with different ratios of concentrations (Dopamine/Ascorbic Acid) varying from 4 to 0.25. Achieved limits of detection was 3 µM and 5 µM for Dopamine and Ascorbic Acid, respectively, with this system configuration.
A Kinematic Equivalence Trajectory Planning Method of Hybrid Active and Passive Cable-Driven Segmented Hyper-Redundant Manipulator*

A hybrid active and passive cable-driven segmented hyper-redundant manipulator is very flexible and dexterous to conduct tasks in highly cluttered environment. However, computation load of inverse kinematics and trajectory planning are also very large. In the paper, a kinematic equivalence method is proposed for the hybrid active and passive cable-driven segmented hyper-redundant manipulator to overcome the above challenge when the position and direction of end-effector are considered. The kinematic equivalence method is an effective way to solve the inverse kinematics and trajectory planning by simplifying and rearranging joints of each segment. The mechanism and joint layout of the manipulator are first analyzed. Then, the kinematics model is established by both traditional DH method and kinematic equivalence method. The calculated amount is decreased by reducing the number of rotation axis that needs to be processed in each segment. Furthermore, the desired trajectory is generated for the end effector of the arm to approach the target point. Finally, the proposed method is applied to a practical prototype, which has five segments and each segment consists of six subsegments. Simulation results verified the proposed method.
Simultaneous Multi-task Determination And Manipulation With Vision-based Self-reasoning Network

It remains a great challenge for robots to simultaneously determine the task objective and schedule the corresponding manipulation, which requires robots to autonomously perceive and understand the environments, think up a response strategy and plan a set of trajectories or actions. This paper proposes a muti-task self-reasoning neural network (MSRnet), which is an end-to-end imitation learning framework consisting of a self-reasoning module and a control module. The self-reasoning module supports the task objective inference, while the control module predicts the robot motor angles for manipulation. With multi-task learning, MSRnet enables robots to accomplish multiple tasks with one model without updating parameters. This paper takes three tasks as an example, involving pouring water into a cup of coffee powder, taking a spoon and stirring coffee with the spoon, which simulate the application scene of making coffee. We employ a low-cost robotic arm(less than $300) to evaluate our MSRnet on coffee maker(CM) dataset which is collected by ourselves. MSRnet with 640*480 resolution inputs can achieve 83.3% success rate on multi-task test, 76.7% success rate on complex environment test. The result verifies that MSRnet can accurately infer the task objective and generate corresponding task actions simultaneously.
Dynamic Analysis and Control Design for a Tiltable Quadrotor

This paper presents a tiltable quadrotor that can independently control position and attitude. Firstly, the dynamic modeling of the system is carried out. We adopted a nonlinear separation strategy for the nonlinear strong input problem existing in the system. We separated the nonlinear system into a linear dynamic link and a nonlinear static link by constructing an intermediate control quantity. Then, linear quadratic regulator with small calculation and easy hardware implementation was designed only for a linear dynamic link while real control, including tilting angle and motor speed, was mapped from intermediate control by calculating the input nonlinear static link. The simulation shows that the designed nonlinear controller based on nonlinear separation and LQR can stably control the tilting quadrotor and can independently track the position and attitude expectations.
Study of Mutual Coupling Reduction for PIFA MIMO Antenna with Pattern Diversity for 5G Applications

In this paper, three different methods of spatial and pattern diversity have been studied to reduce mutual coupling and improve the overall performance of a PIFA MIMO. The proposed antenna consists of parasitic wall elements embedded at the edge of the ground plane with U-slot in the middle of the ground plane is fabricated using copper sheet with thickness of 0.45 mm and air as the dielectric substrates. The performance of the proposed antenna is simulated using CST Microwave Studio Software. The proposed antenna is measured to validate the simulation results. The best isolation of greater than 20 dB were achieved in the both simulated and measured antenna. Thus this proposed PIFA MIMO offers a promising solution for future 5G wireless communication systems.
Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery

Fault diagnosis of rotating machinery plays a significant role in the industrial production and engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as heavily dependence on human knowledge and professional experience, intelligent fault diagnosis based on deep learning (DL) has aroused the interest of researchers. DL achieves the desirable automatic feature learning and fault classification. Therefore, in this review, DL and DL-based intelligent fault diagnosis techniques are overviewed. DL-based fault diagnosis approaches for rotating machinery are summarized and discussed, primarily including bearing, gear/gearbox and pumps. Finally, with respect to modern intelligent fault diagnosis, the existing challenges and possible future research orientations are prospected and analyzed.
Bandwidth Enhancement of 5G Millimeter-wave Phased Array Antenna for Mobile Communications

This paper presents a study on the bandwidth enhancement of the 5G millimeter-wave (mmWave) phased array antenna. A reference millimeter-wave phased array antenna is designed first without the parasitic strips, copper frame and shorting vias, which works at 26.59 GHz operating frequency. The reference antenna has impedance bandwidth of 13.6%. For the case of proposed phased array antenna, parasitic strips, copper frame and shorting vias technique are integrated. Analysis shows that the parasitic strips, copper frame and shorting vias enhance the operational bandwidth of the proposed antenna with a good isolation performance as compared to the reference antenna. The impedance bandwidth of the reference mmWave antenna is 13.6 % GHz which got enhanced to 18.3 % at 25.72 GHz frequency of with parasitic strips. The impedance bandwidth is suitable for the 5G millimeter-wave phased array Mobile Communication applications.
4G LTE Detection using NEMO Handy for PFSR Support System

4G is the fourth technology of broadband cellular network technology. In year 2016, 4G Long Term Evolution (LTE) has become one of the successful mobile wireless broadband technology and conduct a wide range of applications. Signal strength for 4G LTE is most important to maintain the data access demand used by various services. The demand for strong 4G LTE signal coverage for mobile services is increasing around the world. This paper presents a study on signal strength of 4G LTE network that focuses on Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) parameter that support the passive forward scattering radar (PFSR) system. In this study, Nemo Handy was used to detect signal strength, as well as support the result of human detection based on passive forward scattering radar system. Besides that, this study used a system called passive forward scattering radar that identifies any object that passes through between commercial transmitter and receiver that produces Doppler signature. For further study, this research was conducted at three different places, which are Dungun, Port Dickson and Taman Suria using 1.8GHz LTE frequency. Then, the data received by the receiver was collected and analysed by using MATLAB software. Conclusively, this detection of strong and poor signal cellular network may help telecommunication companies in Malaysia to provide better cellular network service.
Coded Spread-Sprectrum for Low-Latency Underwater Acoustic Communications

This paper presents a study of a coded, single-user, direct sequence spread spectrum (DSSS) based system for low-latency underwater acoustic communications using a receiver equipped with a single-hydrophone. Low-latency is achieved by utilizing a short turbo product code (TPC) type of forward error correction (FEC) with a Chase II soft decoder in conjunction with a Quadrature Phase shift Keying (QPSK) modulation scheme. Furthermore, we introduce a novel adaptive chip-level decision feedback equalizer (DFE) receiver with explicit chip-level Doppler, frequency-offset and symbol-timing correction. Experimental results indicate that the proposed receiver can overcome Doppler spreads up to 6 knots and multipath delay spreads in the excess of 180 chips with a processing gain of as low as 7. The achieved effective bit rates of the proposed receiver were 774, 364 and 132 bps for the 7, 15, and 31-chip codes, respectively, over a 4 kHz bandlimited channel.
Small-Size coupled-fed loop antenna for the LTE Smartphone

A small coupled loop antenna for LTE smart phones is proposed. The antenna is placed in the corner of the system plane of the smart phone, with a thickness of 6 mm, which meets the profile requirements of most mobile phones. The main part of the antenna is designed as a coupling feed ring structure. After introducing the lumped element matching circuit, the antenna can provide two wide working bands to cover LTE low band (730 - 960 MHz) and LTE middle band (1710 - 2690 MHz). In this study, the bandwidth is extended by introducing high-pass matching circuit. The simulation efficiency of the antenna is given in this paper.
Real-Time Chinese Sign Language Recognition Based on Artificial Neural Networks*

Sign language is the main method for the deaf-mute to communicate with each other. However, normal people cannot understand the meaning of specific gestures without training. In this study, a real-time Chinese Sign Language (CSL) recognition model based on surface electromyographic (sEMG) signals and Artificial Neural Networks (ANN) is proposed. The model achieves an average accuracy at 88.7% on 15 CSL gestures with average response time around 300ms (timing at movement begins). In our approach, the MYO armband is used for acquiring raw sEMG signals. Signal preprocessing includes rectification, filter and extracting the muscle activation section. We apply a sliding window and a muscle detection approach to segment the signals and extract features. A test method is used to recognize the gesture when the totality of same label, which returned by ANN classifier, reach the activation times threshold.
A CRLH-TL Inspired Multi-Functional Metamaterial and Its Application in Reconfigurable Antennas

A Composite Right/Left Handed Transmission Line (CRLH-TL) based multi-functional metamaterial is designed, which combines both Right Handed Transmission Line (RH-TL) parts and CRLH-TL parts. Two different functional reconfigurable antennas are proposed based on the same metamaterial structure. By loading PIN diodes to switch the dispersion properties of the proposed metamaterial structure, the antenna 1 has the ability to obtain pattern diversity in two bands. By using varactor diodes to continuously tune the dispersion properties, the antenna 2 can achieve dual band frequency tuning.
Development of A Robot System Performing Maintenance Tasks on High-Voltage Power Transmission Lines

Performing maintenance tasks on high-voltage Power Transmission Lines (PTLs) at high altitude is dangerous and tedious, hence demands robots to take place of human being. In this paper, a robot system is developed to fulfill three of typical maintenance tasks. These tasks are adjusting vibration dampers, cleaning the current deflectors and assembling cotter pin. In general, different tasks require different end-effectors, and work at height demands the system simple and light-weighted. To this end, three special end-effectors are designed by modular method so that they can be connected to a manipulator and changed for the corresponding tasks, similar to a CNC machining center. The whole robotic system consists of three end-effector modules, a tool exchange device, a light-weight 5-DoF modular manipulator, and a desk-top level modular master robot for remote control of the manipulator. The architecture and integration of the system are presented in detail, while experiments are carried out to verify the feasibility and effectiveness of the proposed setup. It is shown that such a system will bring new robotic applications in the working at height of power industry.