Miracle-dz/UniFAR-FTUs huggingface dataset:
The brain-computer interface based robot gives spinal cord injury patients a full-cycle active rehabilitation

The basic requirement of rehabilitation training is active participation. This paper introduces our braincomputer interface based robot gives spinal cord injury (SCI) patients a full-cycle active rehabilitation for the first time. For patients with muscle strength level 0, we use brain-computer interface to recognize active motion intention of brain, which control the running speed of robot. For patients with muscle strength greater than level 0, we combine brain-computer interface and torque sensor to realize assistance and resistance training with the active participation of the brain. Integrated brain-computer interface control and force control, we achieved the active participation of patients from muscle strength 0 to muscle strength 4 throughout the rehabilitation cycle. Two typical SCI patients took part in the training, with an average of more than three-quarters of their 20-minute rehabilitation sessions active. After 2 weeks training, both patients had positive rehabilitation effects.
Evaluation of Q factor of Antennas in Lossy Medium

In this paper, the analytical expressions of Q factor of antennas in lossy medium are derived for two models. In model 1, the lossy medium is regarded as a part of the antenna, to emphasize he radiation loss. In model 2, the antenna is located inside a spherically layered lossy medium, to demonstrate the effect of both radiation loss and conductive loss. The analytical expressions are evaluated numerically and the results are compared with the results presented in previous researches.
Slope Gradient Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons

In recent years, lower limb exoskeletons have gained considerable interest in applications of walking assistance for paraplegic patients. In daily lives, the exoskeleton should have the ability to help the patients to walk over different terrains. For sloped terrains, how to plan the stepping locations on slopes with different gradients and generate stable human-like gaits for patients is a critical issue. In this article, we proposed a slope gradient estimator (SGE) based on the sensor data fusion of the exoskeleton and combined SGE with the capture point theory and dynamic movement primitives (DMP) to construct an adaptive gait planning approach for slopes. After learning from demonstrated gaits sampled from healthy subjects, adaptive gait trajectories can be reproduced online to adapt to slopes with different gradients. The efficiency of the proposed approach was demonstrated on an exoskeleton system named AIDER. Experimental results indicate that the proposed approach can endow exoskeletons with the ability to generate appropriate gaits for different slopes. Note to Practitioners—For lower limb exoskeletons, it is a vital problem to plan the gait for sloped terrains. Considering different gradients among slopes, fixed predefined gait planning cannot cover all cases; thus, a slope gradient adaptive gait planning approach is necessary. The slope gradient estimator proposed in this article provides a possible slope gradient estimation method for exoskeletons or humanoid bipedal robots; it is easy to estimate the slope gradient only based on the local sensor data of the robot. The proposed dynamic gait generator provides lower limb exoskeletons and humanoid bipedal robots a possible adaptive gait planning framework and some flexibility for different slopes. The proposed approach may inspire more extended gait planning strategies for other terrains, such as stairs.
Autoencoder With Emotion Embedding for Speech Emotion Recognition

An important part of the human-computer interaction process is speech emotion recognition (SER), which has been receiving more attention in recent years. However, although a wide diversity of methods has been proposed in SER, these approaches still cannot improve the performance. A key issue in the low performance of the SER system is how to effectively extract emotion-oriented features. In this paper, we propose a novel algorithm, an autoencoder with emotion embedding, to extract deep emotion features. Unlike many previous works, instance normalization, which is a common technique in the style transfer field, is introduced into our model rather than batch normalization. Furthermore, the emotion embedding path in our method can lead the autoencoder to efficiently learn a priori knowledge from the label. It can enable the model to distinguish which features are most related to human emotion. We concatenate the latent representation learned by the autoencoder and acoustic features obtained by the openSMILE toolkit. Finally, the concatenated feature vector is utilized for emotion classification. To improve the generalization of our method, a simple data augmentation approach is applied. Two publicly available and highly popular databases, IEMOCAP and EMODB, are chosen to evaluate our method. Experimental results demonstrate that the proposed model achieves significant performance improvement compared to other speech emotion recognition systems.
High-Stability Quartz Resonant Accelerometer With Micro-Leverages

This study proposes a highly stable differential resonant accelerometer that is monolithically micro-machined from a piece of ultrapure, single z-cut crystal quartz. The overall structure of quartz accelerometer is centrally symmetrical, which comprises two double-ended tuning forks (DETFs), two link beams, two micro-leverages, a proof mass, and a quartz frame. Micro-leverages and DETFs are perpendicular to each other and are located around the chip, which maximizes the utilization of the sensor area and is conducive to the miniaturization of the sensor. The effectiveness of the structure was verified by theoretical analysis, simulation, and experiment. The structure with differential arrangement can eliminate common mode disturbances, such as temperature, to improve sensitivity to acceleration. Within the measurement range of ±100 g, the sensor’s sensitivity, which is measured by experiments, is 17.72 Hz/g, with a velocity random walk of <inline-formula> <tex-math notation="LaTeX">$0.84~\mu \text{g}/\surd $ </tex-math></inline-formula> Hz and bias instability of <inline-formula> <tex-math notation="LaTeX">$3.05~\mu \text{g}$ </tex-math></inline-formula>, which is consistent with the theoretical analysis results. [2020-0309]
Square Enclosed Circle Microstrip Patch Antenna for Microwave Head Imaging

This paper depicts a wideband Square Enclosed Circle (SEC) microstrip patch antenna for the head imaging system. The antenna proposed contains a Square Enclosed Circle central radiating-patch and a slotted partial corner cut ground. Slots are used for improving efficiency, gain and radiation directivity. The optimized electrical dimension at the first resonance frequency of 1.9 GHz is 0.253$\lambda (\times0.190) \lambda (\times0.010) \lambda$. The antenna is installed on FR-4 substrates with 1.5 mm thickness that are inexpensive. $50- \Omega$ impedance matching microstrip-transmission line was being used to feed the antenna. The analyzed findings show that the described prototype has a 1.9 GHz (1.9–3.8 GHz) bandwidth with omnidirectional radiation properties. The antenna mentioned attains a max gain of 3.54 dBi at 3.78 GHz and maximum radiation efficiency of 87.77% over the frequency band. Specific design mechanisms are applied to get much more relevant results. The antenna proposed is altered, developed and evaluated throughout the popular simulator-software CST Microwave Studio 2019.
Smart Parking System Using MobileNet and Single Shot Detection Algorithm and IoT

In today’s world, automobiles are essential part of our day to day lives and people who drive regularly can certainly say that parking is the most difficult and exhausting part of driving. Finding a parking spot in big cities is another strenuous assignment. As this is the era of remarkable technologies, this paper proposes a smart parking system with the help of IoT (Internet of Things) and artificial neural network to ease off the fatigue of the drivers.
Single-Shot Detection Based on Cyclic Attention

The shallow feature map of the single-shot detector (SSD) is not always conducive to enhancing the recognition precision for a small object because of the lack of contextual information. In this research, a single-shot detection algorithm based on cyclic attention (CA-SSD) is proposed to construct a fast and accurate detector that efficiently obtains full-image contextual information. Our network is constructed by integrating ResNet-34 and proposed novel cyclic attention blocks. This type of building block aggregates different transformations, one of which includes an attention module that uses a long but narrow pooling kernel to acquire horizontal and vertical contextual information for each pixel of all pixels. Each pixel eventually captures the full-image dependencies by following an even further cyclic operation. Our design considers the variability of the gradient, which not only improves the reliability of the cyclic attention block but also cuts the number of parameters for computation. Additionally, by exploring the effects of the stem block and its stride on the performance of ResNet-based SSD algorithms, our network retains more detailed information. For an input size of 300 $\times $ 300, CA-SSD attained 82.5% mAP on PASCAL VOC 2007 test, 78.4% mAP on PASCAL VOC 2012 test, and 32.7% mAP on MS COCO. Experimental results achieved with CA-SSD surpass the best results achieved with the traditional SSD and other advanced object detection algorithms while real-time speed is maintained.
Real-time Emotion Recognition for Sales

Positive emotion is a pre-condition to any sales contract. Likewise, the ability to perceive the emotions of a customer impacts sales performance.To support emotional perception in buyer-seller interactions, we propose an audio-visual emotion recognition system that can recognize eight emotions: neutral, calm, sad, happy, angry, fearful, surprised, and disgusted. We reduced noise in audio samples and we applied transfer learning for image feature extraction based on a pre-trained deep neural network VGG16. For emotion recognition, we successfully obtained an audio emotion-recognition accuracy of 62.51% and 68% and video emotion-recognition accuracy of 97.13% and 97.77% on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Surrey Audio-Visual Expressed Emotion (SAVEE) datasets respectively. For the combination of the two models, our proposed merging mechanism without re-training achieved an accuracy of close to 100% on both datasets. Finally, we demonstrated our system for a customer satisfaction use case in a real customer-to-salesperson interaction using audio and video models, achieving an average accuracy of 78%.
Graph visualization of the dark web hyperlink

Various illegal contents such as weapons and drugs are distributed on the dark web. Most of these illegal contents are distributed on anonymous networks that cannot be directly accessed from the World Wide Web. A lot of research has been done on the network structure of the World Wide Web since the advent of the Web. However, even in the dark web, which is connected by HTTP like the World Wide Web, the web learned method can be used. There are many studies on the dark web, but not as many studies on the visualization of the dark web network structure as those done on the World Wide Web, and there are no studies investigating the temporal change of the network structure of the dark web. In this paper, in order to understand the HTML network structure of the dark web, we have created and visualized a graph from the HTML link relations of the Tor network that is popular in the dark web. We analyzed 14,369,621 pages of HTML text files crawled from the Tor network by breadth-first search during the period between June 1 2018 and May 31 2020. Then, we made snapshots from the collected data divided by the time span of half a year, and investigated the time change of the dark web network using a time series graph. As a result, the dark web became visually larger and more complex in the last two years. In each snapshot, the difference between the increasing and decreasing domains was visualized, and it was clarified that there was no bias in domain changes. Then, from the change of the hub node of each snapshot, it was clarified that for the dark web changed from the information retrieval method link-dominated to search-engine-dominated.
Government Religious Preference and Intrastate Conflict

Understanding the causes and consequences of con icts continues to be an important contribution to the economic development literature, particularly the mechanisms that can reduce civilian deaths. We contribute to understanding attacks on civilians and the spillover e ects by analysing the impact of government religious preference on civilian deaths. Using panel data analysis for 113 countries for the period 1989 to 2015, we nd that a higher government preference towards religion causes more civilian deaths for countries experiencing intrastate con ict. Furthermore, we analyse this e ect by the di erent types of con ict and nd that the results are driven by both state-based and non-state-based con icts. Lastly, a regional analysis shows that the negative impact of a strong preference towards religion from the government is particularly notable for countries in Africa and Asia.
A Novel NLOS Error Compensation Method Based IMU for UWB Indoor Positioning System

Electromagnetic wave is susceptible to the multipath effect, especially in complex indoor environment. As transmission of non-line of sight (NLOS), it leads to bigger measurement error, which makes positioning accuracy decline. In the paper, we aim to compensate the measurement error mainly affected by the NLOS to enhance the positioning accuracy. Conceptions of “virtual inertial point” and “environmental factor” are introduced, and moreover acceleration data of inertial measurement unit (IMU) are adopted. A novel and simple NLOS error compensation method for high accuracy indoor positioning system based UWB is proposed. Based on the principle of inertia, IMU can be used to fix motion trend of tag and to get coordinate of virtual inertial point. The measurement coordinate can be acquired by UWB system. We can calculate the distances between virtual inertial point and real measurement in same area, respectively under line of sight (LOS) and NLOS in advance. The ratio of two different distances can be defined as environmental factor, which is mainly used to reflect the influence of NLOS on positioning accuracy quantitatively relative to LOS in the paper. Environmental factor, as the weighted coefficient, is adopted to merge coordinates of the virtual inertial point and measurement to obtain the coordinate compensated under NLOS. It is a process of scaling measurement down to user-defined area for positioning accuracy requirement to reduce the impact of NLOS. At last, Kalman Filter is used to smooth the set of coordinate compensated under NLOS to further improve positioning accuracy. Experimental results show that our method can effectively compensate the NLOS error in the indoor positioning system based ultra-wide band (UWB) technology. The positioning accuracy is improved nearby 80% in NLOS area.
A Portable Electromagnetic Head Imaging System Using Metamaterial Loaded Compact Directional 3D Antenna

A non-invasive, low-powered, and portable electromagnetic (EM) head imaging system is presented using metamaterial (MTM) loaded compact directional 3D antenna. The antenna consists of two slotted dipole elements with $2\times 3$ and $3\times 3$ finite MTM array elements in top and ground, respectively, and folded parasitic elements that operate within the frequency range of 1.12 GHz to 2.5 GHz. The MTM array elements are optimized to enhance the overall performance regarding antenna bandwidth, realized gain, efficiency, and directionality in both free space and proximity to the head model. The mathematical modelling is also analyzed to justify the integration of MTM unit cells to the top and ground side of the antenna. The impact of MTM on SAR analysis is also performed. A tissue-mimicking 3D head phantom is fabricated and measured to validate the antenna performance. A nine-antenna portable setup is used with the fabricated phantom to measure and collect the scattering parameters that are later analyzed to detect and reconstruct the haemorrhage images by applying the updated IC-CF-DMAS algorithm. The overall performance demonstrates the feasibility of the proposed system as a portable platform to successfully detect, locate and monitor the haemorrhages inside the head in EM imaging system.
EEG Driving Fatigue Detection With PDC-Based Brain Functional Network

In EEG based driving fatigue detection, the redundant EEG channels would increase the probability of introducing noise and heaving calculation burden. Aiming at this point, we built up an EEG dataset with thirty electrodes from every brain lobe, proposed a driving fatigue detection approach based on brain functional network. And provide a possible way for the key EEG electrode and rhythm investigation with these brain networks. First, a simulation driving experiment was set up to obtain EEG of multiple subjects during a long-term monotonous cognitive task. The feasibility of the collected data was verified with PSD. For brain functional network construction, partial directed coherence (PDC) is used to calculate the correlation between EEG channels. Then, four graph related features (i.e., node degree, clustering coefficient, characteristic path length and local efficiency) are extracted for fatigue detection with SVM as classifier. After that, we get the key electrodes with the feature of betweenness centrality. The results show that the low frequency rhythms are more distinguishable than that of high frequency. The best classification accuracy of 87.16% is achieved in delta, which is 18.9% higher than that of the baseline method with PSD. At last, we verified the effectiveness of the selected key electrodes, and drew the conclusion that the key EEG rhythm corresponding to different features may not be consistent.
Microwave Reflectometry to Characterize the Time-Varying Plasma Generated in the Shock Tube

To investigate the behavior of plasma generated in the shock tube, microwave reflectometry is proposed to extract the permittivity <inline-formula> <tex-math notation="LaTeX">$\epsilon _{r}$ </tex-math></inline-formula> of plasma. To remove the influence of parasitic reflections caused by the surroundings, a calibration process is introduced and the unknown calibration coefficients are determined by utilizing microwave interferometry as the reference technique. The shock tube is modeled as a three-layered medium to calculate the reflection coefficient. A time-dependent reconstruction algorithm is applied and theoretically validated to eliminate the multiple solutions in the inverse problem. By comparing the permittivities extracted with microwave reflectometry and interferometry, the effects of plasma diffusion are demonstrated with a modified analytical model in the beginning time region of experiments. In addition, the nonuniform flow in the generated plasma located near the end time region is also observed. The determination of the effective time region for electron density <inline-formula> <tex-math notation="LaTeX">$N_{e}$ </tex-math></inline-formula> and collision frequency <inline-formula> <tex-math notation="LaTeX">$v_{e}$ </tex-math></inline-formula> extraction is discussed as well. Finally, the differences between microwave reflectometry and interferometry in terms of averaged <inline-formula> <tex-math notation="LaTeX">$N_{e}$ </tex-math></inline-formula> < <inline-formula> <tex-math notation="LaTeX">$1{\times }10^{17} m^{-3}$ </tex-math></inline-formula> and averaged <inline-formula> <tex-math notation="LaTeX">$v_{e}$ </tex-math></inline-formula> < <inline-formula> <tex-math notation="LaTeX">$1.5{\times }10^{9} s^{-1}$ </tex-math></inline-formula> are investigated in the effective time region
Pressure Sensor Embedded Inductive Coil Toward a Wireless Pressure Sensing Stent

This paper reports the development of a wireless pressure sensing coil system by embedding a capacitive micro sensor onto a wire. The coil system, consisting of an inductor (coil itself) and multiple capacitors (sensors), formed a passive LC telemetry configuration for wireless sensing. The capacitive sensor, utilizing the movements of a liquid in a sealed chamber for hydraulic amplification, enabled high capacitance changes and amplified signal outputs. The capacitive sensor also produced digitized signal outputs as the liquid movement crossed a finite number of the interdigitated electrodes. Such characteristics were modeled and confirmed by establishing an equivalent fluidic circuit. The capacitive sensor was fabricated by grafting and polishing a copper wire piece on a silicon wafer, then performing standard microfabrication on the polished wire surface and filling a liquid into a sensor chamber. The fabricated pressure sensing coil system demonstrated a pressure sensitivity of 11.8 kHz/mmHg under a clinical pressure range of 0~206.9 mmHg in an <italic>ex vivo</italic> test with a water flowing tube, meeting the requirements for wireless detection of vessel restenosis. The sensitivity corresponded to the capacitance changes of 0.052 fF/mmHg and liquid displacement of <inline-formula> <tex-math notation="LaTeX">$0.39~\mu \text{m}$ </tex-math></inline-formula>/mmHg. The fabricated coil system held a dimension of 2.5 mm in diameter and 7.2 mm in length in the form of a wound wire (copper, <inline-formula> <tex-math notation="LaTeX">$260~\mu \text{m}$ </tex-math></inline-formula> in diameter). The embedded capacitive sensor consisted of a hydraulic chamber of <inline-formula> <tex-math notation="LaTeX">$400\times 100\times 1 2\,\,\mu \text{m}^{3}$ </tex-math></inline-formula> and a microchannel of <inline-formula> <tex-math notation="LaTeX">$500\times 20\times 1 2\,\,\mu \text{m}^{3}$ </tex-math></inline-formula>. [2020-0364]
Fixed-Point and Objective Convergence of Plug-and-Play Algorithms

A standard model for image reconstruction involves the minimization of a data-fidelity term along with a regularizer, where the optimization is performed using proximal algorithms such as ISTA and ADMM. In plug-and-play (PnP) regularization, the proximal operator (associated with the regularizer) in ISTA and ADMM is replaced by a powerful image denoiser. Although PnP regularization works surprisingly well in practice, its theoretical convergence—whether convergence of the PnP iterates is guaranteed and if they minimize some objective function—is not completely understood even for simple linear denoisers such as nonlocal means. In particular, while there are works where either iterate or objective convergence is established separately, a simultaneous guarantee on iterate and objective convergence is not available for any denoiser to our knowledge. In this paper, we establish both forms of convergence for a special class of linear denoisers. Notably, unlike existing works where the focus is on symmetric denoisers, our analysis covers non-symmetric denoisers such as nonlocal means and almost any convex data-fidelity. The novelty in this regard is that we make use of the convergence theory of averaged operators and we work with a special inner product (and norm) derived from the linear denoiser; the latter requires us to appropriately define the gradient and proximal operators associated with the data-fidelity term. We validate our convergence results using image reconstruction experiments.