Miracle-dz/UniFAR-FTUs huggingface dataset:
Triptych: Multi-objective Optimisation of Service Deployment Costs, Application Delay and Bandwidth Usage

Advanced Internet services increasingly rely on many components to implement their functionality. These composite services have three important features: they are expensive to deploy, components need to be placed intelligently close to the users to improve quality of experience and they will potentially consume significant amounts of bandwidth. This paper presents Triptych, a multi-objective optimisation framework that tries to optimise according these three dimensions to help the three main stakeholders in the Internet ecosystem: users, application providers and network providers. Triptych implements evolutionary computation approaches for this complex problem, which simultaneously optimise service deployment costs, latency-based user utility and network congestion. These algorithms provide possible operating points, bringing important tools for network managements and resource allocation. A large set of simulations under different scenarios are provided to validate the algorithms.
Parametric Analysis for Robust Force/Torque Tracking Control of a Virtual Stiffness-Damping System in Aeroelastic Instability Testing

The force/torque tracking control of a virtual stiffness-damping system (VSDS) for aeroelastic instability testing (AIT) is studied in this paper. Existing test-beds rely on elastic elements or structures to set airfoil elasticity in tests, which can be costly and inconvenient in cases of frequent stiffness adjustment across a wide range. A possible alternative is the VSDS that utilizes electric drives to simulate the effects of structural elasticity and damping, as seen in non-AIT fields. However, adaptation of existing VSDSs for use in AIT results in an increased sensitivity to transmission power-loss caused by generally unknown inputs including friction as well as other unmodeled dynamics and disturbances, due to different operation principle and conditions compared with other existing VSDSs. This is a critical problem that can result in inaccurate virtual stiffness and damping. In this paper we tackle this problem by treating power loss as an unknown input and employing the linear-quadratic-Gaussian (LQG) tracking control enhanced by unknown-input estimation (UIE). A systematic procedure based on numerical study is proposed to investigate the effects of DIE-related parameters on system sensitivity and stability robustness. To confront uncertainties in parametric analysis, a stability robustness index is proposed. Findings from the proposed parametric analysis not only assist effective controller design but also correct and supplement the existing knowledge in literature. Wind-tunnel experiments were conducted with comparisons between standard LQG tracking control and the DIE-LQG scheme. Superior performance of the VSDS under the systematically synthesized DIE-LQG control was confirmed.
Dynamic Modeling and Optimizing Analysis of Rigid-Flexible Coupling for Bundling Manipulator Based on ADAMS

Aiming at the new type of binding manipulator, the flexible body model of steel belt is established in ADAMS and SolidWorks, the flexible body model of steel belt is established by screw theory, and then the rigid-flexible coupling dynamic model of the manipulator is established by Lagrange equation method. The mechanical model of the steel belt and the manipulator is superimposed to obtain the overall dynamic model of the binding manipulator, which is finally applied in ADAMS. The model is validated. Then the parameters of the manipulator can be modeled in ADAMS using its design and calculation module, and the optimal design of the new type of steel tube binding manipulator can be completed finally.
Theoretical and Experimental Analysis of Spiral Tiled Combined Wireless Power Supply for Track Based on Electromagnetic Induction

In order to achieve the efficient wireless power transmission, and ensure the continuous stable power supply between the toy car and track, a spiral tiled combined wireless power supply system is designed based on electromagnetic induction. This paper introduces the hardware circuit modules of the spiral tile combined wireless power supply system. With the combination of the transmitting and receiving coils characteristics, such as magnetic induction intensity, mutual inductance and coupling coefficients, which are affecting the efficiency of wireless power transmission, constructs the transformer equivalent model and deduce the theory formula of transmission efficiency of spiral tiled wireless power supply system. Then with the method of control variables, the experiments have gotten the influence of input alternating current (AC) frequency, relative height and diameter ratio of transmitting and receiving coils, and output load impedance on receiving coil voltage. The experiment analysis results show that the output power is more than 1. 5W, and can run FF-ISOSH type toy car when the input AC frequency, relative height and diameter ratio of transmitting and receiving coils, and output load impedance are 1.95*105Hz, 10mm, 0.94, 100Ω, respectively.
A New Multilevel Circuit Partitioning Algorithm Based on the Improved KL Algorithm

Very Large Scale Integration (VLSI) includes circuit partition, layout planning, layout, overall routing, detailed routing and packaging. How to design a reasonable circuit partition to reduce the complexity of VLSI design is a crucial step in the physical design of VLSI. Combining with the idea of the improved KL algorithm and the multilevel partitioning algorithm, a multilevel circuit partitioning algorithm based on the improved KL algorithm is proposed. The algorithm is based on the theory of the balanced bipartition theory of graphs, which is improved by giving a reasonable initial partition. And the coarsening phase uses the improved HEM algorithm to shrink the matching vertices to form a series of smaller graphs. When the scale of the graph is small enough, the improved KL algorithm is applied to the initial partition; then the initial partition of smaller graphs is mapped to the partition of the upper detailed graph in turn. The experimental results show that the algorithm has lower time complexity and better robustness while ensuring the accuracy of the solution.
Energy Efficiency Optimization for Wireless Power Transfer Enabled Massive MIMO Systems With Hardware Impairments

This paper investigates the energy efficiency (EE) optimization for massive multiple-input multiple-output (MIMO) systems powered by wireless power transfer (WPT) with hardware impairments at sensor nodes (SNs). In the considered system, the SNs are first powered by the WPT from power beacon (PB). Then, the SNs use the harvested energy to transmit data to the base station (BS) with large scale of multiple antennas. Finally, the BS employs maximal-ratio combining (MRC) to detect the data symbols transmitted by the SNs. As the EE optimization problem is a non-convex problem which is difficult to solve directly. A lower bound approximation and variable substitution method are used to transform the EE maximization problem into a concave-linear fractional programming. Then, an energy efficient resource allocation algorithm that combines time and power allocation is proposed by fractional programming to maximize the EE of the system. Finally, simulation results are presented to show the effectiveness of the proposed algorithm and the impact of the hardware impairments on the system performance.
Service Function Chain Composition and Mapping in NFV-Enabled Networks

Network Function Virtualization (NFV) is a new network paradigm that decouples network functions from dedicated hardware. Network services in NFV are deployed as service chains, also known as Service Function Chains (SFCs). SFC consists of an ordered set of Virtual Network Functions (VNFs). One of the main challenge when deploying SFC is to efficiently make use of the resource. In this paper, we focus on the SFC composition and mapping considering resource optimization. We formulate the SFC composition and mapping problem as a weighted graph matching problem. Then we propose a Hungarian based algorithm to solve the SFC composition and mapping problem in a coordinated way.
Locally Power-optimal Nonlinear Model Predictive Control for Fixed-wing Airborne Wind Energy

Airborne Wind Energy (AWE) is a new way of harvesting the wind's power via tethered kite systems which has enormous potential, but poses many challenges in practice. One particularly challenging aspect is the control of the kite, or similarly a tethered fixed-wing vehicle. Tethered flight is a highly nonlinear, constrained, and fast dynamic system, requiring careful control design for optimal power producing results. This paper formulates the AWE problem as a practical, high-level Nonlinear Model Predictive Control scheme, balancing an abstracted control augmented modeling approach with the tight computational constraints on board small fixed-wing systems for real-time, long-horizon predictive control. A power objective is developed which trades off tracking performance of a given nominal path with the expected power generation resulting from the aircraft's trajectory. An analysis of the performance gains in various wind conditions are elaborated per tuning of the power objective, and the controller is validated in simulation before deployment on a small development platform. The control system is demonstrated in practice in an exemplary tethered flight experiment.
A Deep-Learning Approach for Parking Slot Detection on Surround-View Images

Being able to automatically detect parking slots during navigation is an important part of an autonomous driving system. Most of the current parking slot detectors either work on images coming from static cameras or are based on hand-designed low-level visual features, limiting the domains of application of such systems considerably. Learning the most suitable features for the task directly from data would allow the system to function in a broader range of situations and be more robust to noise and different observation conditions. This paper presents an end-to-end deep neural network trained to perform automatic parking slot detection and classification on surround-view images, generated by fusing the views of four different cameras positioned on a vehicle. The network architecture is based on the Faster R-CNN baseline. To account for the fact that parking slots can be of different shapes and can be observed at different angles, the predicted bounding boxes are generic quadrilaterals rather than image-aligned rectangles. Moreover, instead of computing offsets wrt anchor boxes, the RPN regresses the region proposals directly. In order to train the network, a small training set comprised of a few hundred images has been manually annotated. The network shows good performance and generalization capability on new examples containing parking slots of the same kinds as those in the training set, suggesting that a wider array of parking slot types could easily be integrated into the system by expanding the dataset.
Use of 18F-fluorodeoxyglucose positron emission tomography-computed tomography in patients affected by polymyalgia rheumatica and persistent increase of acute phase reactants

Polymyalgia rheumatica (PMR) is an inflammatory disease affecting older adults characterized by aching pain and morning stiffness of the shoulder and pelvic girdles. Moreover, PMR can be associated with giant cell arteritis (GCA). Generally, PMR is highly responsive to steroids, reaching complete remission in the majority of cases. However, the possibility of occult diseases, including extra-cranial GCA, should be excluded. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) is able to detect the presence of peri-/articular or vascular inflammation, which may be both present in PMR, thus representing a useful diagnostic tool, mainly in presence of extra-cranial GCA. We retrospectively evaluated all consecutive patients who received the diagnosis of PMR in our rheumatology clinic, classified according to the 2012 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) criteria, in the period between April 2020 and May 2022. Among this case series, we selected the patients who underwent 18F-FDG-positron emission tomography (PET) because of the persistent increase of acute phase reactants (APR) besides the steroid therapy. Eighty patients were diagnosed with PMR. Nine out of them also presented arthritis of the wrists during the follow-up, whereas none showed signs of cranial GCA at the diagnosis. Seventeen out of eighty subjects (mean age 71.5 ± 7.5 years; M/F 2/15) presented persistent increase of erythrocyte sedimentation rate (mean ESR 44.2 ± 20.8 mm/h) and/or C-reactive protein (mean CRP 25.1 ± 17 mg/l), thus they underwent total body 18F-FDG-PET/CT. Large vessel 18F-FDG uptake indicating an occult GCA was found in 5/17 (29.4%) cases. Twelve out of seventeen (70.6%) patients showed persistence of peri-/articular inflammation, suggesting a scarce control of PMR or the presence of chronic arthritis. Finally, in 2 cases, other inflammatory disorders were found, namely an acute thyroiditis and a hip prosthesis occult infection. 18F-FDG-PET/CT in PMR patients with persistent increase of APR is a useful diagnostic technique in order to detect occult GCA, persistence of active PMR or other misdiagnosed inflammatory diseases.
16nm CMOS Technology based Fast Ring Oscillator for Fast Computing and Mathematical Applications

Oscillators play an important role as a major component of all digital, optical devices and the communication systems. The invention of integrated circuit has reduced the size of the electronic devices. On the other hand, one of the most widely manufactured integrated circuit is always a ring oscillator. A ring oscillator circuit can be defined as a closed loop circuit which is made up of inverter stages in odd number and that uses a positive feedback mechanism. It is to be noted that the ring oscillators output always swings around two voltages levels namely HIGH and LOW. Even though ring oscillators are used by the foundries on every wafers of the semiconductor in order of priority to examine the delay associated with the gate, but they have a vital use in the construction of Phase Locked Loops and Signal Generators. In this paper, 5-stage CMOS Ring oscillator is implemented using spice tools with Predictive Technology Model 16nm technology to obtain the temperature effects.
Analyse and Design a micro strip spiral monopole antenna with and without indicator

In this work we portray the investigation and structure of configurable planar radio wire that can work in the WIFI of 2400 MHz and in the WLAN of 6400 MHz that is likewise in understanding with the expected parameters which is to be utilized for Cognitive applications. The radio wire was structured as a plane square winding that utilizes small scale strip innovation and the reconfigurability was made along with an inductor. Reconfigurability can be accomplished by utilizing PIN diode switches or by utilizing varactor diode / MEMS switches. A broad investigation of a Planer Inverted-Field radio wire (PIFA)was displayed to structure three resounding frequencies to cover GSM, DCS and DMB applications and it was accounted for that two collapsed parts, which can be added to the principle PIFA to make the two groups, here we are utilizing Inductor to accomplish the Re configurability.
An Octave Analysis Approach Based on Vibrational Data for Early Detection of Multiple Faults in Rolling Bearing

Industry 4.0 is focusing on modern maintenance techniques to minimize the downtime so that the production can be increased. In order to implement Industry 4.0, the maintenance department needs automatic condition monitoring and fault diagnostics techniques. In this study, octave analysis has been proposed for early detection of bearing faults. In order to show the effectiveness of the proposed methodology, detailed experimentations have been performed on a Machinery Fault Simulator (MFS). Octave analysis has been implemented on the vibrational data acquired through the accelerometers placed on the inboard and outboard bearing housing. The vibrational data of a healthy bearing has been compared with a faulty bearing at four different rotational speeds under two types of loading conditions: no load applied and a constant load applied near outboard bearing housing. The standard deviation has also been calculated for healthy and faulty bearings. The experimental results revealed that octave plots have high peaks for all rotational speeds and both loading conditions. The values of the standard deviation of faulty bearings were found high as compared to a healthy bearing. This research article proposes a simple approach that does not require signal processing. The results proved that octave analysis along with standard deviation can be used for early detection of rolling bearing faults.
Scalable Multi-Agent Learning for Situationally-Aware Multiple-Access and Grant-Free Transmissions

In this paper, we present Situationally-aware Multiple-Access and gRant-free Transmissions (SMART) to address the low-latency multiple access issue in wireless uplinks with massive machine-type communications (mMTC). SMART is smart in a distributed manner, as terminals are trained to be situationally aware. The solution is based on a distributed reinforcement learning framework which is capable of dealing with diversified quality-of-service requirements. As such, terminals can make informed transmission decisions by themselves, e.g., by taking into account the urgency of their packets and system load. In this way, the effective number of concurrent access terminals is significantly reduced while maintaining the system performance. Compared with conventional contention-based random access schemes, SMART has significant advantages. Building upon our previous work [1], this work presents the first SMART algorithm that is scalable to massive terminals (hundreds) and stable with near-optimal performance.
Wireless-Powered Mobile Edge Computing with Cooperated UAV

In this paper, we study a wireless-powered mobile edge computing (MEC) system, where the access point (AP) cooperates with an unmanned aerial vehicle (UAV). The AP broadcasts energy to the UAV, while the UAV broadcasts part of its harvested energy to the UEs and helps the UEs compute their offloaded tasks or further offload to the AP for computing. The weighted sum completed task-input bits (WSCTB) of UEs is maximized by optimizing the task allocation, the UAV's energy transmit power and trajectory, under the information-causality constraints, the energy-causality constraints, and the UAV's trajectory constraints. The formulated WSCTB maximization problem is non-convex, and a block coordinate descending algorithm is proposed to solve it iteratively. In the simulation results, the UAV's trajectory and the achieved performance are given to verify the effectiveness of the proposed algorithm in comparison with some practical baselines.
Loss Function Selection in a Problem of Satellite Image Segmentation Using Convolutional Neural Network

Results of training a convolutional neural network for the satellite image segmentation are presented. Input images use four channels: Red, Green, Blue and Near-infrared. The convolutional neural network was trained to mark areas containing buildings and facilities. U-Net architecture was used for the task. For learning procedure supercomputer NVIDIA DGX-1 was used. The process of data augmentation is described. Results of training with different loss functions are compared. Network evaluation results for different types of residential areas are presented.
Cooperative Adaptive Cruise Control with Fuel Efficiency Using PMP Technique

In recent years automated driving have attracted more attention, as many autonomous features are evolving to a more intelligent stage. Cooperative adaptive cruise control (CACC) an important and challenging control problem. The motivation for developing intelligent system is to increase safety during driving. Most of the research on this topic considers kinematic model of vehicle with PID controller used for comfort and safety. The current work addresses CACC problem considering Engine Model with fuel parameter to improve fuel efficiency and maintaining desired headway. Safe distance is maintained from preceding vehicle by the help of different sensors. System measures distance form preceding vehicle through sensor and take information of position, velocity and acceleration. Pontryagin's Minimum Principle (PMP) is used to optimize the cost function, numerical algorithm is used to solve the optimization problem. Pontryagin's Minimum Principle (PMP) produced better results than PID with improved fuel efficiency and accurate distance between vehicles.
A Proposed Methodology for the Fusion of Microwave and Optical Remote Sensing Data

The merging of images obtained by satellites through remote sensing has evolved into an established protocol. In popular parlance such blending is known as image fusion. This is done chiefly because it gives myriad advantages. Image fusion comes in extremely useful in the observation, study and analysis of diverse fields, including environment, agriculture and other related areas. In essence, what happens in image fusion is that the needed data or information is gleaned from numerous images. These images then are coalesced to form fewer pictures. The ideal, of course, is the blending of them into a lone picture. This is highly sought-after because the image thus intermingled is said to contain all relevant data and, moreover, is more suitable and error-free than an image secured from one single source. Needless to say, it also incorporates all the information that is needed. Besides this, there are other benefits. For one, it curtails the volume of data. For another, it produces images that are pertinent and apt. This paper’s chief objective is to proffer a suggested methodology on the fusion between the capabilities of optical and microwave satellite images and to improve the visible quality of Landsat image.
Designing pneumatically actuated deformable mirrors: control of circular plates with varying thickness

This contribution focuses on the optimal design and pneumatic deformation control of thin circular plates with spatially-varying thickness. The work is motivated by pneumatically actuated deformable mirrors, which are used to compensate for wavefront disturbances in adaptive optics. To achieve the required deformation profiles with high precision, the equilibrium deformation is optimized with respect to the plate's thickness. For designing an asymptotic tracking control, the finite relative degree of the distributed parameter system is exploited. Since the nonlinear pressure dynamics actuate the governing partial differential equation (PDE) in a distributed way, finite-order feedback linearization can be applied. In addition, static nonlinear effects in the pneumatic valve and infinite-dimensional internal dynamics have to be taken into account. Finally, we demonstrate the controller's performance by means of simulations.