Miracle-dz/UniFAR-FTUs huggingface dataset:
A User's Steps Considered Motion Control Approach of an Intelligent Walking Training Robot

To guide the patients to follow the designed training trajectory precisely, an omnidirectional intelligent walking training robot (IWTR) is being developed in our laboratory. However, in the clinical experiments, the users usually walk with stumbling gait and at times fall behind the designed walking training trajectory of IWTR due to the incomplete walking ability. If the IWTR keeps tracking original trajectory continuously without considering user's walking steps, it will cause dangers for the users when they stop or fall behind the designed trajectory. To guarantee the patients' safety during the walking rehabilitation, a user's steps considered motion control approach of IWTR was studied in this research. A vision-based measurement of feet motion was proposed to detect user's feet position and orientation. With the measured feet information, the user's body center position and orientation were estimated relative to IWTR. A safe distance was defined to ensure the user's position in the safe walk space of IWTR during the walk training. The experimental results showed the effectiveness of the proposed method.
Analysis, Comparison & Simulation of Three Phase 9 Level Cascaded Multilevel Inverter with Advanced Modulation Control Techniques using Less Switches

The multilevel converter is capable of achieving higher power by using a series of power switches with several low voltage DC sources to perform power conversion by synthesizing a staircase voltage waveform. In the field of power electronics the multilevel inverter permits to reach higher voltages with low harmonics content. Rise in the number of levels, the harmonic level of output voltage will fall gradually. In this paper a three phase 9 level cascaded multilevel inverter is been simulated with minimum switching devices and gate circuitry. The classical three phase MLI utilizes 48 switches whereas the minimum circuit utilizes 33 switches thus eliminating 15 switches in the usage. The output is been verified by extracting gate signals from various control techniques like SPWM POD, Harmonic Injected, Trapezoidal & Triangular Modulation. The analysis and comparison is done with THD, and least THD is been found with triangular modulation with the help of MATLAB/SIMULINK tool.
Optimal Impedance Modulation and Intention Angle of Elbow Assistive Robots: Based on Human Musculoskeletal Model

The assistive rehabilitation devices have been studied for over half-century. However; Assistive as needed (AAN) devices are emergent robots to aid the patients based on their muscle capability. Exposure of the patients to these devices demands more understanding of the human impedance and torque modulation. This paper investigates the impedance modulation of human's elbow joint based on optimal control and Computed Muscle Control (CMC) of the human forearm musculoskeletal systems. The human elbow is modeled as a single degree-of-freedom (DOF) system with an intrinsic passive stiffness and damping. The OpenSim program is exploited to derive the muscle activation and generate the resultant torque of the effective muscles around the elbow joint. Forearm tracks a sinusoidal pattern in the simulation. The consequent torque interpreted by the optimal control solution of the system which opens a new horizon in the assimilation of the human intention angle and elbow impedance in a trade-off for accurate tracking and energy consumption.
LIDAR-based Vehicle Recognition with Global Cylindrical-coordinate Histogram Descriptor

Vehicle Recognition is crucial for an Autonomous Land Vehicle (ALV) navigation in urban environments. In this paper, we proposed a novel Global Cylindrical Coordination Histogram Descriptor(GCCHD) to recognize vehicles in urban environments. This description is created for the centre point of each object. In order to achieve independence with respect to the rotation around $z$ axis, a global reference frame is introduced in GCCHD, and all the three-dimensional points in the cylindrical support region are projected into the three-dimensional histogram according to their three cylindrical coordinates. Experiments on the public Sydney Urban Object dataset and the dataset we prepare and label manually verify the performance of the GCCHD in vehicle recognition.
Empirical Research on a Fuzzy Model of Music Emotion Classification Based on Pleasure-Arousal Model

Affective computing plays a key role in music artificial intelligence, in which a music emotion classification model is indispensable. Both discrete classification model and continuous dimensional model are commonly used for music emotion classification. However, these models are not designed in views of composers, which is insufficient for the perception of music emotion. In this paper, a fuzzy music emotion classification model is proposed by extracting the music expression marks considering the composers emotion. Experiments on subjective evaluation in the feeling of pleasure and arousal according to the change of three selected features (i.e., tempo, register, and dynamic) are conducted by listeners from different subjects. The experimental results show that the correlation between the proposal fuzzy model and the the results from questionnaires reaches 80% on average, which demonstrate the validity of the proposed fuzzy music emotion classification model. The proposal could be applied to music emotion generation conveyed from either composers or players, and to emotion recognition of music as for audiences.
Nonlinear Autopilot Design for Agile Interceptors via Command Filtered Backstepping and Dynamic Control Allocation

For agile interceptors with aerodynamic control surfaces and reaction jets, a nonlinear autopilot design method is proposed by using command filtered backstepping and dynamic control allocation algorithm. Since there are two different types of actuators, it is necessary to consider collaboration issues. Therefore, the autopilot involves a nonlinear virtual control law and a dynamic control allocator. To deal with parameter uncertainties and unknown disturbances in the system, a robust virtual control law is designed by command filtered backstepping. Then, a dynamic control allocator is applied by optimal programming to distribute the virtual signals to the aerodynamic control surfaces and reaction jets on the interceptor. Simulation results show that the autopilot control system tracks the acceleration command fast and smoothly. In the tracking process, aerodynamic control surfaces cooperate with reaction jets, verifying the effectiveness of the proposed design method.
Energy Efficient Cloud Control and Pricing in Geographically Distributed Data Centers

It is estimated that data centers constitute 1.5% of global electricity usage. At the same time, to serve increasing user requirements, modern cloud providers are operating multiple geographically distributed data centers. Distributed data center infrastructure changes the rules of cloud control, as energy costs depend on current regional electricity prices and temperatures that we call geotemporal inputs. Furthermore, pricing policies at which cloud providers can offer computational resources depend on the quality of service (QoS). With such pricing schemes and the increasing energy costs in data centres, balancing energy savings with performance and revenue losses is a challenging problem. Existing cloud control methods are suitable only for a single data center or do not consider all the available cloud control actions that can reduce energy costs in geographically distributed data centers. In this thesis, we propose a pervasive cloud control approach consisting of multiple methods for dynamic resource reallocation and hardware configuration adapted to volatile geotemporal inputs. The proposed methods consider the QoS impact of cloud control actions and the data quality limits of time series forecasting methods. We offer a cloud controller design that supports future extensions when new decision support components need to be added. We also propose novel pricing schemes which account for the computational resource availability and costs that arise from our cloud control approach to enable both flexible, energy-aware and high performance cloud computing. We evaluate our methods empirically and in a number of simulations using historical traces of electricity prices, temperatures, workloads and other data. Our results show that significant energy cost savings are possible without harming the QoS or service revenue in geographically distributed cloud computing.
An All-Metallic Reflectarray and Its Element Design: Exploring the Radiation Characteristics of Antennas for Directional Beam Applications

This article presents an investigation into the radiation characteristics of broadband, all-metallic reflectarray antennas with nonresonant convex elements for directional beam applications. The metallic reflecting elements demonstrate advantages over conventional elements that are realized on printed circuit board (PCB)-type dielectric substrates because of the superiority in energy efficiency of these metal elements. The use of the metallic elements results in more radiation power to compensate for the energy loss that occurs during propagation in air. These radiation characteristics cause the antennas to radiate a broader beam with a smaller physical size and with the same gain achievement as that of a PCB-based antenna, resulting in better coverage.
Partial Synchronization for Boolean Network Based on Pinning Control Strategy

In this paper, pinning controller designing for partial synchronization is studied for Boolean Networks in the drive-response structure. The problem can be converted into making the response BN stabilized into certain cycles under the certain switching signals. All limit cycles are found out, which can achieve partial synchronization. Partial synchronization is achieved by changing the transition matrix of the response BN. An algorithm for choosing pinning nodes and pinning controller design is obtained. Finally, a numerical example is given to show the efficiency of the design procedure.
Inkjet-Printed Millimetre-Wave PET-Based Flexible Antenna for 5G Wireless Applications

This paper presents a millimetre-wave (mm-wave) flexible antenna designed on Polyethylene Terephthalate (PET) substrate for the 5th generation (5G) wireless applications. The antenna geometry comprises of a T-shaped patch integrated with symmetrically designed slot arrangements. The defected ground structure (DGS) concept is utilised for bandwidth enhancement and a coplanar waveguide (CPW)-fed slotted monopole antenna is embedded within an aperture cut inside the ground plane. The measurements of the inkjet-printed antenna prototype depict an impedance bandwidth of 26–40 GHz, consistent omnidirectional radiation pattern and a peak gain of 7.44 dBi at 39 GHz. The conformity and flexibility suggest potential applications of the proposed antenna in future 5G applications especially in wearables such as uniform or casual clothing.
[Construction of A Lentiviral Vector Carrying CUEDC1 Gene and Its Effect on the Proliferation and Colony-formating Ability of MOLT-4 Cells].

OBJECTIVE To construct a lentiviral vector carrying human CUEDC1 gene, to establish leukemic cell line MOLT-4 stably expressing recombinant plasmid, to analyze the expression of CUEDC1 in MOLT-4 cells and to investigate its effect on the proliferation of MOLT-4 cells. METHODS The CUEDC1 gene was amplified by RT-PCR, and then was subcloned into the lentiviral vector pCDH to generate a lentiviral vector pCDH-CUEDC1. Recombinant lentivirus was generated by co-transfection of 3 plasmids, and transfected into MOLT-4 cells. The Real-time PCR and Western blot were respectively applied to detect the expression of CUEDC1 mRNA and protein, the CCK-8 and colony formation assay were used to evaluate the effect of CUEDC1 on proliferation of MOLT-4 cells. RESULTS The recombinant lentiviral vector pCDH-CUEDC1 had been constructed successfully. After infection of MOLT-4 cells with the lentivirus, the recombinant plasmid could stably up-regulate the expression of CUEDC1 and protein. The CCK-8 detection and colony formation assay showed that exogenous CUEDC1 could significantly promote cell growth and the colony formation of MOLT-4 cells. CONCLUSION The recombinant lentiviral vector carrying human CUEDC1 has been successfully constructed, exogenous CUEDC1 can significantly promote cell growth and the colony formation of MOLT-4 cells.
Microwave Acoustic Wave Devices: Recent Advances on Architectures, Modeling, Materials, and Packaging

This paper reviews applications of acoustic wave devices in mobile communication. After a general and historical introduction to bulk acoustic wave (BAW) and surface acoustic wave (SAW) devices, a review is given on the architectures where acoustic wave devices are applied driving the requirements on the SAW and BAW components. Following this, we discuss the progress in technology important materials. Next, an overview on the modeling and characterization of SAW and BAW at high power levels is given. Finally an overview of packaging technologies and an outlook to future developments is provided. Finally, an outlook to future developments is provided.
Human Activity Recognition based on Real Life Scenarios

In Active and Assisted Living (AAL) systems, a major task is to support old people who suffer from diseases such as Dementia or Alzheimer. To provide required support, it is essential to know their Activities of Daily Living (ADL) and support them accordingly. Thus, the accurate recognition of human activities is the foremost task of such an AAL system, especially when non-video/audio sensors are used. It is common that one or more sensors could share or represent a unique activity, and consequently, finding out the most optimal window size among them to represent such an activity is challenging. This paper proposes a Recurrent Neural Networks (RNN) based on a windowing approach for subject-independent human activity recognition. The proposed RNN model is trained based on dynamic systems perspectives on weight initialization process. In order to check the overall performance, this approach was tested using the popular CASAS dataset and the newly collected HBMS dataset. The results show high performance based on different evaluation metrics.
Prevalence and factors associated with self-reported chronic obstructive pulmonary disease among adults aged 40-79: the National Health and Nutrition Examination Survey (NHANES) 2007-2012.

Introduction Chronic obstructive pulmonary disease (COPD) is a respiratory disease that often goes undiagnosed, particularly in its early stages. Objective To examine sociodemographic, general health, and COPD specific factors, including severity of lung obstruction, that are associated with healthcare provider-diagnosed COPD among U.S. adults. Methods NHANES cross-sectional data collected from 2007-2012 on adults aged 40-79 years (n=10,219) were analyzed. The primary outcome was self-reported COPD diagnosis with predictive factors analyzed via chi-square and logistic regression analyses. Results During 2007-2012, 5.2% of US adults aged 40-79 reported being diagnosed with COPD. Among those diagnosed, 50.8% reported fair or poor health, 47.1% were currently smoking cigarettes, 49.1% were taking prescription respiratory medication, 36.4% had moderate or worse lung obstruction, and nearly 90% had one or more respiratory symptoms. Logistic regression revealed positive associations between receiving a COPD diagnosis and the following: being white (AOR: 3.08, 95% CI: 1.82-5.20); being aged 60-79 years (AOR: 1.65, 95% CI: 1.07-2.53); reporting fair/poor health (AOR: 2.91, CI: 1.55-5.46); having 4-9 (AOR: 3.5, CI: 1.3-9.4) or 10 or more healthcare visits in prior year (AOR: 5.06, CI: 1.62-15.77); being a former (AOR: 1.75, CI: 1.2-2.5) or current smoker (AOR: 1.70, CI: 1.17-2.48); having more severe lung obstruction (AOR: 4.90, CI: 3.28-7.32); having 3 or more respiratory symptoms (AOR: 22.07, CI: 12.03-40.49). Conclusions Multiple factors are associated with self-reported COPD diagnosis with number of reported respiratory symptoms having the strongest association. After controlling for other factors, having mild lung obstruction was not associated with being diagnosed.
LEACH protocol based on Clustering and Multi-leader Selecting in Wireless Sensor Network

In this paper, we consider the energy consumption problem of LEACH protocol in Wireless Sensor Network. Based on the network clustering analysis and multi-leader selection strategy, we propose applying a simple augmentation to the original protocol in order to solve the energy consumption problem. Finally, some comparative simulations reveal that the proposed approach has good performance in LEACH routing protocol, and can greatly improve the network life cycle and throughput.
Networked Quadrotor Predictive Control Based on Identified Models

In order to achieve good performance of controlling quadrotors with networked delays, this paper firstly proposes a novel experimental architecture aiming to boost the whole process of the experiment and save expenses. Secondly, different from traditional methods of mathematical modelling, the models adopted in this paper are identified rather than derived or calculated using measured systematic parameters. Thirdly, the dynamic models of the system need to be verified before implemented into further experiments. Finally, based on the classic scheme of a proportional-integral-derivative (PID) control algorithm, a predictive PID controller is designed for quadrotor trajectory tracking, and the results show that the designed controller can fulfill the task of tracking with time delays.
Effects of the Rewiring of Promoter Activity States on Gene Expression

Gene transcription takes place often in a bursty manner. The gene promoters contain multiple activity states which compose complex patterns via chromatin remodeling. An entirely unexplored question is how the rewiring of promoter activity states affects gene expression. Here, we systematically analyze and compare two representative models of stochastic transcription: the one is that promoter activity states form a chain-type structure and the other is that they form a star-type structure. After the theoretical analysis and the numerical simulation, some interesting phenomena have been found. Specifically, both the increase in the mean level of mRNA and the decrease in the expression noise are quicker in the chain -type structure than in the star-type structure with the enhanced recruitment mechanism. The recruitment mechanism strength has unchanged the change rate of mean burst frequency in the chain-type structure but reduced it in the star-type structure. The star-type structure has shorter first passage time but more fluctuations than the chain-type structure. These results indicate that the transcription outcome is of promoter architecture-specific.
Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use.
Sustainable Service Allocation Using a Metaheuristic Technique in a Fog Server for Industrial Applications

Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or healthcare industry that require a rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in a fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a nondeterministic polynomial-time hard problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a tradeoff is maintained between the energy consumption and makespan. Specifically, this paper proposes a metaheuristic-based service allocation framework using three metaheuristic techniques, such as particle swarm optimization (PSO), binary PSO, and bat algorithm. These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic-based service allocation algorithms by conducting a set of rigorous evaluations.
Neural Network Adaptive Control of Flexible Robotic with Unknown Time-delay

In this paper, an adaptive dynamic surface control(DSC) method is proposed for the state time-delay flexible robotic system with unmodeled dynamics and time-varying output constraints. The dynamic disturbances are effectively dealt with by introducing a dynamic signal. The unknown continuous functions are approximated by using radial basis function neural networks (RBFNNs). An asymmetric time-varying barrier Lyapunov function (BLF) is employed to ensure constraint satisfaction. The Lyapunov-Krasovskii function is used to deal with the unknown time-delay. By theoretical analysis, the closed-loop control system is shown to be semi-globally uniformly ultimately bounded. Simulation results are provided to illustrate the effectiveness of the proposed approach.