Miracle-dz/UniFAR-FTUs huggingface dataset:
The Detection of Non-Cooperative Targets in Space by Using 3D Point Cloud

In the space operations fields, spatial targets that do not have cooperative signals and capture characteristics are called non-cooperative targets. The use of non-cooperative target docking capture and space robot technology is an effective way to carry out the tasks such as satellite unattended maintenance and orbit adjustment. The traditional method of detecting non-cooperative targets using RGBD mainly uses the color and texture information of the objects. However, unstable space light will lead to mismatch or poor robustness of traditional methods. To solve this problem, this paper proposes a non-cooperative target detection and transformation matrix estimation method by 3D point cloud information from the sensor. First, use the depth camera to sample the object at different angles of equidistance to create a template point cloud composed of point cloud models of different poses. Then, the scene is sampled by the sensor and the FPFH feature is extracted. Finally, based on the FPFH feature of the point cloud, the SAC-IA and the ICP algorithm are used to obtain the pose information of non-cooperative targets in the scene. On this basis, the robotic arm can accurately capture non-cooperative targets in space.
In Non-Small Cell Lung Cancer, Can Radiomic Features Predict EGFR Mutations?

Recently, targeted therapy for lung cancer epidermal growth factor receptor (EGFR) mutations has achieved significant clinical results. Therefore, it is very important to identify EGFR mutations for targeted therapy of non-small cell lung cancer(NSCLC). It is dangerous to get the surgery and biopsy samples for EGFR gene detection, in order to overcome this difficulty, we tried a non-invasive method using radiomic features extracted from medical images to predict EGFR mutations.170 patients with NSCLC who received surgery-based treatment were included in this retrospective study. CT scans were gathered and analyzed before invasive surgery. AK (Version V3.2.0.R) software was used to extract lesion features and feature selection. Then, a logistic regression model was established to predict EGFR mutation status. Finally, diagnostic performance of characteristic parameters was analyzed according to the receiver operating characteristic (ROC) curves. Therefore, the radiomic features with higher diagnostic value were screened out. In addition, we used univariate analysis to analyse the association between clinical features and the presence of EGFR mutations. Radiomic features can construct predictive models of EGFR mutations. The area under the receiver operating characteristic curve (AUC) of the training group was 0.766, and the AUC of the testing group was 0.748, both of which at good diagnostic decision point. There is a correlation between clinical features and EGFR mutations, and predictive models can also be established. EGFR mutation status can be predicted well by radiomic features. Predictive models can also be constructed based on clinical features. The combination of clinical features and radiomic features will have higher diagnostic value.
A Novel Bayesian Update Method for Parameter Reconstruction of Remaining Useful Life Prognostics

Remaining useful life (RUL) prediction is a core component for reliability research and condition-based maintenance (CBM). In the existing parameter-reconstruction method, the degradation trajectory of an in-situ unit is reconstructed by the weighted sum of that of historical units. However, this method requires an optimization problem to be solved for each new measurement, which leads to an excessively consumed time and does not satisfy the requirements of online prognostics and decisionmaking. In this paper, these weights are assumed as a set of probabilities, based on which they can be updated via Bayesian estimation, instead of solving the optimization problem at each observation epoch. To verify the proposed approach, a data set developed by a commercial simulation tool for aircraft turbofan engines is involved. In light of the implement situation of the proposed approach on this data set, the absolute error of the prognostics result and the consumed time for computation are significantly reduced compared with the existing approach.
Enhancing Situational Awareness and Performance of Adaptive Cruise Control through Model Predictive Control and Deep Reinforcement Learning

The main objective of this paper is to develop a novel reinforcement learning framework for automated weight tuning for model predictive control (MPC) based Adaptive Cruise Control (ACC) systems. Although MPC based ACC design has been extensively studied over the years, adapting the weights of the controller to a wide variety of driving scenarios remain a challenging task. The main contribution of this work is automating the MPC weight tuning process by formulating this objective as a deep reinforcement learning (RL) problem and training the RL agent on a wide range of simulated traffic scenarios. The trained agent observes state trajectories of both the host and surrounding vehicles and learns to map the driving environment to a suitable set of MPC weights. This approach significantly shortens the exhausting manual weight tuning process and also enhances the situational awareness of the system by anticipating the actions of surrounding drivers. Simulation results show that the developed algorithm outperforms standard ACC approaches in terms of tracking accuracy and passenger comfort.
Design of Microstrip Antenna Array and Analysis of different Beamforming techniques for mm-wave MIMO Systems

This project work is focusing on the design and simulation of array antenna that operates in the mm wave frequency band and also analyze and implement the beamforming technique suitable for the proposed antenna design. The optimum weights calculated from the LMS Algorithm are used to excite the antenna array in ANSYS HFSS to observe the beamforming and the radiation pattern in the direction of desired signal. The obtained results were analyzed and compared with the theoretical values. The result consists of the measure of various parameters that are required to analyse the performance of an antenna like return loss, gain, directivity and bandwidth. The antennas are implemented in ANSYS HFSS and antenna characteristics are compared based on the Return loss, VSWR, 3D polar plot and radiation pattern which are tabulated. These parameters were also observed for an 8, 16, 32, 64-element antenna array and a significant increase in gain and directivity was observed. All simulations are performed on MATLAB.
Social Force Aggregation Control for Autonomous Driving with Connected Preview

Vehicular social force modeling (SFM) aims at capturing social driving behavior of human drivers in traffic by drawing analogies to models of psychological forces that govern the navigation behavior of humans walking in crowds. In addition, the extended preview afforded by vehicular connectivity can be exploited to bring additional information about downstream traffic to be incorporated in the planning and guidance computations for an autonomous vehicle. This paper outlines a hierarchical vehicular social force control scheme that integrates both ideas. At the upper level, social force aggregation is applied to predictively select the most efficient lane over a long horizon covered by connectivity. This is then passed down to a lower level controller that enforces lane tracking while considering higher fidelity social force resolution and lane-changing dynamics within the shorter horizon captured by the ego vehicle's sensor field of view. The workings and performance of the proposed framework are illustrated via simulations of the connected autonomous vehicle in multi-lane highway scenarios.
Asynchronous Sampling for Decentralized Periodic Event-Triggered Control

Decentralized periodic event-triggered control (DPETC) strategies are an attractive solution for wireless cyber-physical systems where resources such as network bandwidth and sensor power are scarce. This is because these strategies have the advantage of preventing unnecessary data transmissions and therefore reduce bandwidth and energy requirements, however the sensor sampling regime remains synchronous. Typically the action of sampling leads almost immediately to a transmission on an event being detected. If the sampling is synchronous, multiple transmission requests may be raised at the same time which further leads to bursty traffic patterns. Bursty traffic patterns are critical to the DPETC systems performance as the probability of collisions and the amount of requested bandwidth resources become high ultimately causing delays. In this paper, we propose an asynchronous sampling scheme for DPETC. The scheme ensures that at each sampling time, no more than one transmission request can be generated which prevents the occurrence of network traffic collision. At the same time, for the DPETC system with asynchronous sampling a pre-designed global exponential stability and £2-gain performance can still be guaranteed. We illustrate the effectiveness of the approach through a numerical example.
Impact of Emotional Intelligence on Strategic Management of Technology and Organizational Performance in the Banking Sector in Sri Lanka

This study investigates the impact of the emotional intelligence on strategic management of technology and the organizational performance of the banking industry in Sri Lanka. The factors related to this are comprised of technology strategy, technological capability and technological knowledge management forming strategic management of technology (SMT) construct; experiential emotional intelligence, strategic emotional intelligence forming emotional intelligence (EI) construct; learning and growth, internal business process, stakeholder satisfaction forming organizational performance construct. A review of the literature was conducted to investigate and analyze the existing literature on managing the technology strategically, emotional intelligence and the performance of the organization. This resulted in the conceptual model and the hypotheses. A questionnaire-based survey was conducted drawing a sample of 1200 managers of seventeen banks in the Western Province which were registered under the Colombo Stock Exchange in order to test the conceptual model and the hypotheses. The data analysis was performed using SMART PLS based SEM technique. The study findings indicate that the suggested model achieved an acceptable fit with the data and the suggested hypothetical paths were significant. The findings provide a board picture of the influence of the emotional intelligence on the relationship between SMT and the performance of the organization. The study found that there is a moderating effect of emotional intelligence on the relationship between the strategic management of technology and the performance of the organization in the sector. Furthermore, it was evident that EI is more influential on the mentioned relationship than the wellestablished facts in the related literature. Based on the findings, the recommendations have been made in order to increase the performance of the Sri Lankan banking industry.
The Array of Si Nanowires Covered with Ag Nanoparticles by ALD: Fabrication Process and Optical Properties

In this work, we proposed a method for creating an Ag/Si composite structure consisting of an array of vertical silicon nanowires (SiNWs) decorated with silver nanoparticles (AgNPs). A two-stage metal-assisted chemical etching of Si was used to obtain the SiNW array, and atomic layer deposition was used to fabricate the AgNPs. A uniform distribution of AgNPs along the SiNW height was achieved. The measured characteristics by spectroscopic ellipsometry directly established the presence of AgNPs deposited on the SiNWs. The height of the sublayers and the fractions of Si and Ag in them were determined using the multilayer model and the effective Bruggeman medium approximation in the interpretation of the experimental data. For AgNP layers deposited on an Si wafer surface, the thickness (from 2.3 to 7.8 nm) and complex dielectric functions were verified within the framework of the Drude–Lorentz model. The optical properties of Ag/SiNW structures with complex spatial geometry were simulated in the COMSOL Multiphysics software. The expected localization of the electric field on the surface and near the AgNP was observed as a result of the plasmon resonance excitation. The calculated enhancement factor reached 1010, which indicates the possibility of using such structures as substrates for surface-enhanced Raman scattering.
Microscopic driving behavior modelling at highway entrances using Bayesian network

Entrances into highway represent critical sections, as entering vehicles are bound to merge with the existing traffic at a fixed junction point almost independently from the traffic conditions-in some sense a “constrained” cut-in manoeuver. Advanced driver-assistance systems (ADAS) need to perform correctly under these conditions as well, but could also be used to facilitate merging and reducing risks. In particular, vehicles on the main road could adapt their speeds-or even change lane-provided an estimate of the entering vehicle's time to merge is available, exactly as a human driver would do. This paper is concerned with providing such an estimate. To this end, we observe that the speed profile on the entering ramps is rather well predictable if the acceleration of the entering vehicles is described as a function of their actual distance from the junction point. Still, uncertainties remain and to cope with them, we use a stochastic prediction model based on Dynamic Bayesian Network. The result are probability distribution functions of the time to merge based on the observation of speed and distance of the entering vehicle once they become visible to the traffic on the main road. Experimental data are used to illustrate the model structure and the parameter determination. The model quality is then assessed by comparing statistics from simulations with the one recorded on road. The possible use of the model as a tool for traffic prediction algorithm embedded in ADAS and an extension to existing highway stochastic traffic models are shortly discussed at the end of the paper.
High-Performance Robust Controller Design of Plug-In Hybrid Electric Vehicle for Frequency Regulation of Smart Grid Using Linear Matrix Inequality Approach

This paper proposes a high-performance and robust linear quadratic regulator-proportional integral derivative (LQR-PID) controller for frequency regulation in a two-area interconnected smart grid with a population of plug-in hybrid electric vehicles. Controller robustness is achieved using a linear matrix inequality approach. The proposed control framework is tested in a simulated two-area interconnected smart grid integrated with plug-in hybrid electric vehicles under load disturbances and wind power fluctuations. The performance of the proposed controller is simulated using Matlab and compared with that of a conventional linear quadratic regulator controller. Simulation results show that the proposed controller provides reliable smart grid frequency control.
Pitch Motion Control of a Soft Bionic Robot Fish Based on Centroid Adjustment

This paper discussed the pitch motion control problem of a 3D soft bionic robot fish with Body/Caudal Fin propulsion mode. The fish has a polyurethane resin made body and is powered by two pieces of Macro Fiber Composites (MFC) smart materials inside it. The straight and turning motion of the fish is realized by applying symmetric and asymmetric driving signals to the MFC materials. To regulate the depth of the fish, a new centroid adjustment mechanism is designed and installed inside the fish. By changing the centroid of the fish while it is swimming, the fish could realize the floating upward and diving motion. A simple feedback PID controller is also designed to stabilize the pitch angle of the fish during its floating upward and diving motion. Experiments are carried out to test and certify the successful design of the fish.
A Novel Master Manipulator with Force Feedback for Robot-Assisted Natural Orifice Transluminal Endoscopic Surgery

The robot-assisted Natural Orifice Transluminal Endoscopic Surgery (NOTES) enables to perform complex and delicate operations along anatomical tortuous paths through natural orifices. Such emerging techniques also pose difficulties for the master manipulator design. Based on the requirement analysis and ergonomics for robotic NOTES systems that rely on a master-slave configuration, this work proposes a new type of master manipulator in a combined parallel and serial configuration, providing advantages in terms of improved stiffness and accuracy, large workspace, low damping ratio, easy implementation of force feedback. It has three degrees of freedom (DoFs) with one driving unit for each DoF, including translation and two rotation forms, and supports both passive mode and active mode, permitting force feedback in all the three DoFs. A simplified and improved parallel mechanism with a planar six-linkage configuration is proposed to achieve translational motion, and it adopts a rotational form to replace the traditional approach using a linear guide rail. The singularity of the mechanism is analyzed, and the design has been improved to avoid reaching singular positions. The master arm platform has been fabricated and assembled, and it possesses a large workspace of $300 \times 300 \times 173.2$ mm3. Both simulation and experiments have been performed to validate its effectiveness and advantages, and the proposed design can be applied as a generalized master platform to target NOTES surgery.
FAVE: Bandwidth-Aware Failover in Virtualized SDN for Clouds

Network virtualization based on SDN has gained attention in cloud networking. However, existing studies have not provided any failover technique in the event of physical link failure. We propose FAVE, which provides seamless failover and bandwidth-aware protection. FAVE carefully allocates backup routes to handle both failure and interference between tenants. Evaluation shows that FAVE is effective. To our knowledge, FAVE is the first attempt to address failover in virtualized SDN environments.
Robustified Distributed Model Predictive Control for Coherence and Energy Efficiency-Aware Platooning

Platooning has become one of the most appealing formations for intelligent vehicles safety enhancement and traffic regulation. Besides the traditional control algorithms, which are required to enforce at least local and string stability, more complex control schemes can be designed to cope with advanced requirements. In this paper, a suitable Distributed Model Predictive Control (DMPC) scheme, robustified with a second -order Integral Sliding Mode (ISM) correction term, is proposed to enforce and maintain coherence during cruising, while considering energy efficiency during acceleration/deceler-ation phases. While the former aspect has a complex impact on traffic regulation, especially when a large number of vehicles is considered, the latter is of primary importance in an increasingly eco-friendly transportation systems design. The proposed approach is well suited for real-world implementation, and can constitute a valid basis for more advanced control architectures. Simulation results highlight the effectiveness of the proposed architecture in maintaining the formation while guaranteeing a robust achievement of the required performance.
Iterative Learning Control Based on Nesterov Accelerated Gradient Method

Based on Nesterov accelerated gradient method, the problem of iterative learning control for a class of linear discrete-time systems is considered in this paper. Firstly, the iterative learning control problem of linear discrete-time systems is transformed into an iterative least-squares problem. Then, the Nesterov accelerated gradient method is introduced into the iterative learning control framework. Note that the Nesterov accelerated gradient learning algorithm has the capability of fast convergence. It is shown that the algorithm presented in this paper can guarantee the output tracking error converges to zero with rate <inline-formula> <tex-math notation="LaTeX">$O(1/k)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is the iteration counter. Moreover, the monotonic convergence of the Nesterov accelerated gradient learning algorithm is analyzed and discussed. Finally, the effectiveness of the proposed method is verified by two simulation examples.
Distributed tracking of multiple under-actuated Lagrangian systems with uncertain parameters and actuator faults

A distributed adaptive controller is proposed for a class of under-actuated Lagrangian systems under a directed communication graph to control the actuated variables to track a dynamic leader and keep unactuated ones bounded. A finite-time observer is introduced to estimate the leader's velocity. Based on the two sliding variables defined for the actuated and unactuated channels, the adaptive controllers are designed for the under-actuated Lagrangian systems with or without actuator faults. The convergence of the proposed controllers is proved based on the separation principle between the estimation and control. Finally, experiments are conducted to verify the effectiveness of the proposed controllers.
Investigation of Methods for Ensuring Electromagnetic Compatibility of Atmospheric Radiosonde Systems

Upper-air radiosondes, which transmit coordinate-telemetric information on the main meteorological parameters to the ground station, contain a transmitter and, in some cases, a receiver of GLONASS / GPS satellite navigation systems. The known methods providing electromagnetic compatibility of transmitting and receiving systems cannot be fully applied in this case, taking into account the specifics of the radio sounding system. This paper is devoted to the development and study of methods for reducing the influence of out-of-band emissions from a radiosonde transmitter on navigation signal receivers and other communication systems operating in close frequency bands.
The Cost of Software-Defining Things: A Scalability Study of Software-Defined Sensor Networks

Software-Defined Networking is a promising paradigm for providing flexibility and programmability to computer networks. Our goal is to assess the performance of this paradigm applied to Wireless Sensor Networks. Previous evaluations are not complete, since they study small networks, do not explore crucial performance metrics, or solely examine light traffic conditions. For this, we execute simulations and a testbed experiment. The testbed shows Software-Defined Networking successfully operates in a real network. We study simulated networks up to 289 data-transmitting nodes, while assessing all the main networks metrics: data delivery, delay, control overhead, and energy consumption. We investigate important parameters for Software-Defined Wireless Sensor Networks, such as controller positioning, radio duty cycling, number of data sinks, and use of source routed control messages. The results indicate that Software-Defined Networking is feasible for Wireless Sensor Networks, presenting competitive data delivery ratio while saving energy in comparison to RPL, the Routing Protocol for Low-power and lossy networks.