Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Tracking Control Based on RBF Neural Network of The Lower Limb Rehabilitation Robot

The lower limb rehabilitation robot has been widely applied to the recovery of the patient's limb. For patients without autonomous movement ability, the robot will drive their limbs the planned trajectory to carry out rehabilitation training, and the accuracy of the trajectory tracking effect should be guaranteed. PID control is a conventional method for trajectory tracking. Due to the dynamic model uncertainties and lack of good adjustment ability of PID control method, this paper proposes a control method combining neural network and PID. In this study, Magnetorheological (MR) damper and motor are combined to provide actuation for the robot. The control is simulated in Simulink. By comparing the trajectory tracking errors under PID control and RBF-PID control, it is verified that the RBF-PID control has better anti-interference performance. In the process of rehabilitation robot work, the flexibility of movement and the real-time and stability of track tracking are improved.
Testing and Evaluating Driverless Vehicles' Intelligence: The Tsinghua Lion Case Study

It is crucial to test and evaluate the intelligence capacity of driverless vehicles, which impacts the commercialization process of autonomous cars and trucks. This overview aims to summarize the challenging problems in testing and evaluating the intelligence capacity of driverless vehicles. First, the grading of the behavioral intelligence of driverless vehicles is described. Then, to achieve a quantitative assessment of the integrated intelligence of self-driving vehicles, constructive suggestions for testing and evaluation strategies are introduced. The proposed architecture may provide a valuable reference for developing a practical test and evaluation system for autonomous vehicles.
Novel OMA and Hybrid NOMA Schemes for MEC Offloading

Two novel optimization based schemes for MEC offloading, optimized OMA (O-OMA), and hybrid NOMA with user selection (H-NOMA-S), are introduced in this paper. For O-OMA, we provide an analytical solution to the optimization problem. The energy efficiency results of the proposed schemes are compared with the schemes given in the literature. We show that O-OMA improves the classical OMA, and that H-NOMA-S performs equal to or better than the methods in the literature.
An Accurate Particle Filter Device Free Localization based on Frequency Hopping Method

Device free localization (DFL) is an emerging technique, which could estimate the location of a target by analysing its shadowing effect on surrounding wireless links. This technique neither requires the target to be equipped with any device nor involves privacy concerns, which makes it an attractive and a promising technique for many indoor localization applications. However, state-of-the-art device-free localization algorithm relying on particle filter method does not efficiently consider the multipath interference, which results in low localization accuracy. In this paper, we propose a particle filter method based on frequency hopping to improve the accuracy of DFL. Specifically, we utilize redundancy of different frequency measurement information of each communication link to improve the localization accuracy. Extensive experiments performed in a indoor environment with multipath interference with 24 wireless nodes demonstrate that the particle filter DFL based on frequency hopping could achieve 0. 267m accuracy with a 38.1% improvement, which outperforms sate-of-the-art fixed frequency method.
Factors and Considerations for Modeling Loss of a GaN-based Inverter

The article investigates the impacts of four often-neglected factors on the loss model of a GaN-based full-bridge inverter: parasitic capacitance of the devices, dynamics of junction temperature (Tj) under time-varying power dissipation (Ploss), case temperature estimation, and detailed considerations of the passive components. Procedures to calculate the converter loss considering the above factors are proposed and implemented. A 4.5-kW hard-switching inverter prototype using gallium nitrite (GaN) high-electron-mobility transistors is used to experimentally demonstrate the impact of each factor on the converter loss model. It is found that the accuracy of a converter loss model is mainly affected by the passive components at the light load condition, whereas the thermal and loss models of the active components become the major factors as the output power increases. The results show that after considering the above factors, the converter loss discrepancy between calculation and measurement can be reduced from 30.6 W (28%) to 2.5 W (less than 3%) at heavy load (Po = 4.5 kW), while at the light load condition (Po = 500 W), it is reduced from 3.9 W (28%) to 2.6 W (16%). Furthermore, the difference between simulated and measured case temperature of the GaN devices is within 6 °C.
TCR affinity controls the dynamics but not the functional specification of the Th1 response to mycobacteria

The quality of T cell responses depends on the lymphocytes’ ability to undergo clonal expansion, acquire effector functions and traffic to the site of infection. Although TCR signal strength is thought to dominantly shape the T cell response, by using TCR transgenic CD4+ T cells with different pMHC binding affinity, we reveal that TCR affinity does not control Th1 effector function acquisition nor the functional output of individual effectors following mycobacterial infection. Rather, TCR affinity calibrates the rate of cell division to synchronize the distinct processes of T cell proliferation, differentiation and trafficking. By timing cell division-dependent IL-12R expression, TCR affinity controls when T cells become receptive to Th1-imprinting IL-12 signals, determining the emergence and magnitude of the Th1 effector pool. These findings reveal a distinct yet cooperative role for IL-12 and TCR signalling in Th1 differentiation and suggests that the temporal activation of clones with different TCR affinity is a major strategy to coordinate immune surveillance against persistent pathogens.
Using Extended UTAUT2 Model to Determine Factors Influencing the Use of Shopee E-commerce

This study aims to discover the factors that influence users in using Shopee e-commerce. One of the ways to know the contributing factor that influences users is the Unified Theory of Acceptance and Usage of Technology 2 (UTAUT2). UTAUT2 is a known model that could determine the factors thoroughly. However, this study uses seven constructs from UTAUT2 with two additional constructs. For this, the study has targeted 160 Indonesian respondents who have done transactions on Shopee e-commerce in Indonesia. Furthermore, data analysis in this study used Partial Least Squares-Structural Equation Model (PLS-SEM) on SmartPLS software due to its ability to analyze data with a small sample size, even if the model used is complex. The results show that Habit and Trust in Interest are significantly influencing Behavioral Intention. In contrast, other factors such as Hedonic Motivation, Effort Expectancy, Facilitating Conditions, Performance Expectancy, Social Influence, Price Value, and Perceived Transaction Risk do not significantly influence Behavioral Intention in using Shopee. Moderating variables like age, gender, and experience do not significantly influence the relationship between independent variables and Behavioral Intention of users. The extension of the original constructs from UTAUT2 with two additional constructs from two previous studies is the novelty contribution in this study.
„The forgotten kingdom“ im Naturschutz

Pilze sind sehr artenreich und spielen eine zentrale Rolle im Stoffhaushalt unserer Waldökosysteme. Durch intensive forstliche Nutzung sind heute viele Arten aus unseren Wäldern verschwunden oder hochgradig bedroht. Im Vergleich zu andere Artengruppen spielen Pilze heute immer noch eine untergeordnete Rolle im Naturschutz. Die Forschungsergebnisse des Nationalparks Bayerischer Wald zeigen, dass sich seltene holzbewohnende Pilzarten und Artengemeinschaften nach Einstellung forstlicher Nutzung wieder erholen können. Es gibt eine Reihe von Arten, die heute deutschlandweit ihren Schwerpunkt in den Zonen ohne Nutzung haben. Zum Erhalt der gesamten holzbewohnenden Pilzdiversität in Deutschland müssten aber auch die Eigentümer von Wirtschaftswäldern ihrer Verantwortung gerecht werden. Hierzu gehören der Erhalt und die Förderung von alten Bäumen (Habitatbäume) sowie die künstliche Schaffung spezifischer Totholzstrukturen. Weiterhin sollten Konzepte zur Wiederansiedelung lokal ausgestorbener Arten entwickelt werden.
Performance Improvement of DC Capacitor Voltage Balancing Control for Cascaded H-Bridge Multilevel Converters

In this article, an improved dc capacitor voltage balancing control is proposed for single-phase cascaded H-bridge (CHB) multilevel converters. Compared with conventional voltage balancing control methods, the proposed scheme has a comprehensive consideration of three aspects while balancing the capacitor voltages among H-bridges: coupling effect elimination, reactive power balancing, and dynamic performance improvement. First, the system mathematical model in the d–q reference frame is derived, and the relationship among duty cycle modifications of H-bridges is revealed to eliminate the coupling effect of voltage balancing control on the current control loop. Then, based on the H-bridge power model, a quantitative solution for reactive power balancing is presented to design the reactive duty cycle modifications. In order to enhance the dynamic performance of voltage balancing control, a dynamic references design method is adopted to evaluate the active duty cycle modifications. Finally, both simulation and experimental results are presented to validate the effectiveness of the proposed scheme.
Obstructive Sleep Apnea Detection Using Sleep Architecture

Obstructive sleep apnea (OSA) is a common disease characterized by repeated episodes of upper airway obstruction that results in cessation of airflow during sleep. Early diagnosis of OSA is essential so that early intervention can reduce the risk of cardiovascular disease, metabolic disorders and neurocognitive dysfunction. Sleep architecture is related to OSA. In this paper, the patient’s sleep stages and their transitions relationship are used as features to propose a machine learning-based OSA detection method. The key parameters are screened through statistical analysis. Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) are used to establish classification models. The whole of results show that XGBoost has a better performance with the area under curve of 0.9128, and find that age, the percentage of N1 sleep stage, the percentage of N3 sleep stage, one-step transition pattern of N2$\rightarrow $N1 and total number of transitions play important roles in identifying OSA patients from normal subjects.
Machine Learning Methods for Internet of Things in Medical Diagnosis

Internet of Things is increasingly getting popular in our daily life and also in many other fields. It can be caused by incredibly rising numbers of the devices connected to the Internet, like smartphones, tablets or laptops. Machine learning enables getting computers to act without being explicitly programmed. Furthermore, both of these fields are getting popular in the enormously essential field, which is medicine. The paper presents the analysis of chosen machine learning methods for cardiac arrhythmia diagnosis and choice of the best one with the highest accuracy as well as the construction of the device for ECG data gathering. Three machine learning methods were chosen for the analysis: Naive Bayes, K Nearest Neighbours and J48 decision tree. These methods were evaluated with the selected features. For this purpose, the MIT-BIH Arrhythmia Database was analysed and used features were elaborated.
A Dynamic Resonant Period Control Technique for Fast and Zero Voltage Switching in GaN-Based Active Clamp Flyback Converters

Compared to Si FETs, when the main GaN FET is in full zero voltage switching condition, maximum switching loss reduction can be achieved. Therefore, in this article, an active clamp flyback converter with GaN devices that implements fast ZVS by using the proposed dynamic resonant period control (DRPC) technique can reduce large transformer leakage inductance energy loss on the active resonant circuit. As the loading changes, the DRPC technique can dynamically adjust the on-time in auxiliary switch to prevent large voltage stress on the components in the primary side. In full load condition, the leakage energy loss can be reduced, thereby transferring more energy to the secondary side with maximum efficiency up to 94%.
Data Direct I/O Characterization for Future I/O System Exploration

I/O performance plays a critical role in the overall performance of modern servers. The emergence of ultra high-speed I/O devices makes the data movement between processors, main memory, and devices a major performance bottleneck. Conventionally, the main memory is used as an intermediate buffer between the processor and I/O devices and I/O devices cannot directly access processor side caches. Data Direct I/O (DDIO) technology aims to reduce the memory bandwidth utilization by enabling the I/O devices to leverage Last Level Cache (LLC) as the intermediate buffer. Our experimental results show that DDIO can completely eliminate memory bandwidth utilization while running network-or storage-intensive applications. However, when modeling the I/O subsystem using architectural simulators, DDIO is often ignored, which can result in inaccurate assessments about the I/O and memory sub system of emerging and future large-scale computer systems. In this paper, we provide a detailed background on DDIO technology in Intel server processors. Then we present our cycle-accurate I/O subsystem model in gem5 simulator that can be configured to model DDIO. We verify our model against baseline gem5 and validate it by comparing its results against a physical computer system.
LDO With Improved Common Gate Class-AB OTA Handles any Load Capacitors and Provides Fast Response to Load Transients

This article proposes an LDO with fast response to load transients that can handle any practical capacitive loads. These features are mainly due to a novel frequency compensation circuit tailored for its error amplifier, which is based on an improved version of the popular common gate amplifier. A simple yet effective approach to the small-signal analysis of LDO with multiple feedback loops is employed to analyse intuitively the LDO and derive key design constraints. Simulation and measurement results performed on a test chip implemented in standard 130nm CMOS process validated the proposed LDO. It requires only $0.7\mu \text{A}$ quiescent current but exhibits an excellent response to load transients: when the load current jumps from 0A to 100mA in $1\mu \text{s}$ the output voltage presents an undershoot of 76mV and an overshoot of 198mV, without decoupling capacitors. It compares well against seven LDOs designed with common gate error amplifiers for similar levels of supply voltage, output voltage and current and against seven fast LDOs employing different error amplifiers. A figure-of-merit that considers the quiescent current, the maximum load current and capacitance, as well as the output voltage deviation, yielded a value for our LDO 39.8 times better than for the nearer competitor that employs common gate amplifier and 6 times better than the one employing a different error amplifier. When considering edge time and process scaling the performance of the proposed LDO is 4.8, respectively 4.5, times better than the second best in both comparisons.
Design and Development Of A Flexible Robotic Operative Microscope For Neurosurgical Applications

The neurosurgical operative microscope is primarily designed with an aim to enhance the range of view for neurosurgeons during surgical practices. The existing surgical tools do not allow an angular field of view, thereby limiting depth information, which is critical for neurosurgical analysis and any further surgical actions. The proposed neurosurgical setup offers 4 degrees of freedom, two translational and two rotational motions, enabling enhanced depth information of the specimen under investigation. The flexible probe with an endoscopy camera was built in the form of a continuum of 3D printed plastic hollow structures that rest on top of each other. The hollow structures were configured to a hemispherical shape at the top, to allow easy sliding against the other homogeneous entity forming a probe. A set of three cables connecting each individual structure offer confined angular movement in 3D space, and is repeated for all structures, to offer flexibility in the entire sequence of probe. The cables were connected to stepper motors to complete the pulley system design and offer controlled probe curvature ability to the device. A micro-controller was used for controlling the pulley systems and an additional power supply unit was integrated to operate motors and their drivers. The developed microscopy tool was utilized to view 15 human cadaver brain dissections and report first-hand information on the usage of the flexible robotic operating microscope tool and the significance of the curvature scope. An interface to move the probe in different directions was developed and introduced to the investigator for performing brain dissection. In addition, angular re-positioning of the probe to regain old probe view on a specific region was also designed in the novel operating microscope device. The novel real-time microscope design is considered a step forward to precision robotic surgery, which is regarded to immensely benefit mankind.
Power Coupling Mechanism Analysis and Improved Decoupling Control for Virtual Synchronous Generator

A virtual synchronous generator (VSG) control-based grid-connected converter (GCC) is an attractive solution to improve the stability of a more renewable-energy-integrated power system. Unfortunately, the inherent power coupling (i.e., the interaction between the active power loop and the reactive power loop) defect of VSG control severely restricts the power delivery capacity and the grid support capability of the GCC. The virtual inductor is commonly used to reduce coupling, but its decoupling capability is very limited. In addition, the power coupling mechanism and its limiting factors are not clear. For this issue, the nature of power coupling in the VSG system is investigated first. The decoupling capability of the virtual inductor is studied, and the reason for decoupling effectiveness is revealed. It indicates that the effectiveness of decoupling results from the proper voltage compensation, but this kind of positive effect is limited by the d-axis voltage drop across the virtual inductor. Then, a q-axis voltage-drop-based power decoupling control (QVPDC) is proposed to further reduce the power coupling, which does not consider the d-axis voltage drop when applying the virtual inductor. Compared with the virtual-inductor-based decoupling method, the decoupling performance of QVPDC is better, and the computation burden is reduced by half. Finally, the analysis and the proposed method are validated by simulation and experiment.
Virtual Passive Bipedal walking on the Irregular Ground using Kinetic Energy Tracking Control

This paper extends our previous study investigating a closed-loop kinetic energy tracking control algorithm for virtual passive bipedal walking. Two kinds of irregular grounds: a sloped ground composed of a series of changing slopes and a stepped ground are designed and adopted to test the stability of the control algorithm by numerical simulations. It is found that the control algorithm can adapt to the sloped ground very well, it enables robots to perform complex walking gaits like upward slope walking, level ground walking and downward slope walking. However, for the stepped ground, only the step height difference is less than 0.53% of the robot leg length can the virtual passive bipedal walking be stable. The two road conditions designed in our study represent the external step disturbance and impulse disturbance respectively. The reliability of our control algorithm is well verified through numerical experiments.
Combining texture analysis and deep learning in renal tumour classification task

Current advances in Computer Vision allowed solving the vast majority of tasks in this field. However, the analysis of medical imaging still requires specialized algorithms, usually employing a lot of expert knowledge. This research aims to create an automated computer system able to differentiate 8 types of renal cancer types. Such a system can contribute to earlier diagnosis and better prioritization of patients waiting for medical consultation. The multi-feature ensemble system integrates 2 distinct groups of feature generation and classification: texture analysis and deep learning. In the first approach, three independent methods of feature generation: GLCM, SFTA, and Unser algorithm have been used. Their results are then passed to the SVM classifier. The second group utilizes transfer learning applied to pretrained deep models, developed within the ILSVR challenge. Combining both individual approaches with weighted voting yields results reaching 93.6 % of weighted F1-score evaluated in 10-fold cross validation mode.
A 470-nA Quiescent Current and 92.7%/94.7% Efficiency DCT/PWM Control Buck Converter With Seamless Mode Selection for IoT Application

An ultra-low quiescent current dual-mode buck converter system is designed for IoT application, which includes a double clock time (DCT) and a pulse-width-modulation (PWM) control modes. The proposed DCT mode can reduce the conversion loss over a wide loading range from nA-to-mA and achieve seamless mode transition from DCT to PWM control. Implemented in a <inline-formula> <tex-math notation="LaTeX">$0.18\mu \text{m}$ </tex-math></inline-formula> CMOS, this converter achieves a peak efficiency of 92.7%/94.7% in DCT/PWM and >80% efficiency from <inline-formula> <tex-math notation="LaTeX">$10~\mu $ </tex-math></inline-formula> A to 50 mA (5000x), with a wide input voltage from 2 V to 5 V. A quiescent current of 470 nA including bandgap voltage reference and internal oscillator is achieved. The DCT-to-PWM mode selection mechanism achieves an undershoot of 80 mV at <inline-formula> <tex-math notation="LaTeX">$11~\mu \text{s}$ </tex-math></inline-formula> recovery time when load current jumps from <inline-formula> <tex-math notation="LaTeX">$6.67~\mu \text{A}$ </tex-math></inline-formula> to 50 mA.