Miracle-dz/UniFAR-FTUs huggingface dataset:
DymGPU: Dynamic Memory Management for Sharing GPUs in Virtualized Clouds

gVirt is a full GPU virtualization technique for Intel's integrated GPUs that alleviates the problems of other GPU virtualization techniques such as API remoting and direct pass-through. The original gVirt is known to have an inherent scalability limitation on the number of simultaneous virtual machines (VM). gScale solved this problem by allowing each VM to share a global graphics memory space and copy the entries in a private graphics translation table (GTT) to a physical GTT along with a GPU context switch. However, it still suffers from a large overhead of copying entries between private GTT and physical GTT, which becomes worse when the global graphics memory space allocated for each VM is overlapped. In this paper, we identify that the copy overhead caused by GPU context switch is the major bottleneck in performance improvement and propose a dynamic memory management scheme, called DymGPU, that provides two memory allocation algorithms such as size-based and utilization-based algorithms. While the size-based algorithm allocates memory space based on the memory size required by each VM, the utilization-based algorithm considers GPU utilization of each VM to allocate the memory space. DymGPU is also dynamic in the sense that the global graphics memory space used by each VM is rearranged at runtime by periodically checking idle VMs and GPU utilization of each runnable VM. We have implemented our proposed approach in gVirt and confirmed that the proposed scheme reduces GPU context switch time by up to 53% and improved the overall performance of various GPU applications by up to 39%.
Fast Solution of Volume Integral Equations Based on Meshless Discretization

Electromagnetic problems with inhomogeneous or anisotropic penetrable media require using volume integral equations (VIEs) to describe in the integral equation approach. The VIEs are usually solved by the method of moments (MoM) with the Schaubert-Wilton-Glisson (SWG) basis function, but the implementation may not be convenient because conforming meshes are required to define the basis function. In this paper, we propose a novel meshless method to discretize the VIEs. The method uses the Green-Gauss theorem to transform a volume integral into a boundary integral so that discretizing volume domains can be removed, resulting in a truly meshless method. For electrically large problems, we incorporate the method with multilevel fast multipole algorithm (MLFMA) to accelerate the solving process. A numerical example is presented to demonstrate the approach and its effectiveness and robustness have been validated.
Hybrid Satellite-Terrestrial Cooperative Communication with Mobile Terrestrial Nodes

This work investigates the performance of hybrid satellite-terrestrial systems over time-selective fading links arising due to the node mobility with multiple relay based selective decode-and-forward cooperation. The aerial satellite-to-relay and satellite-to-destination links are non-identical time-selective shadowed Rician fading in nature, whose parameters depend on the elevation angle of the satellite, whereas the terrestrial relay-destination links are assumed to be non-identical time-selective generalized Nakagami faded. Closed form expressions are derived for the per-frame average symbol error rate (SER) and asymptotic SER floor considering the transmission of Mary PSK modulated symbols. It is observed that the time-varying nature of the links significantly degrades the system performance. Further, the impact of the satellite elevation angles at the terrestrial nodes is explicitly demonstrated through simulations, along with the effect of preamble versus midamble for channel estimation. The error rate of the system is seen to reduce significantly with increasing satellite elevation angle at the relay when the satellite-destination link experiences frequent heavy shadowing (FHS) and the relay-destination links are relatively strong. However, for other scenarios when the relay-destination links are relatively weak and satellite-relay links experience FHS, significant performance improvement can be seen by increasing the satellite elevation angle at the destination UE.
A Comparison Between Sliding Mode Control and Feedback Linearization

in this paper we take a 2- link robotic Manipulator system, which is nonlinear in nature. First we convert the nonlinear system into linear system. To control the system torque is derived with linear approximation. SMC and Feedback Linearization is applied to track the desired trajectory. The desired trajectories is taken with different frequencies and the controlling of two scheme with different trajectories is evaluated with the help of Simulink. A comparison between SMC and Feedback linearization is detailed with regard different frequencies of desired trajectory.
Uncover Product Review Patterns via Weighted Motifs

In today’s e-commercial websites, product reviews written by genuine users are commonly seen, which play a crucial role as customer feedbacks and planting seeds to trigger much more transactions. However, motivated by profits, fake reviews crafted by spammers are inevitable to promote or demote product reputations whereas misguiding potential buyers to make bad decisions. Until recently, the problem how to distinguish whether a review is fraudulent or a reviewer is a spammer has long been studied, but the question of general review pattern mining is still open. In this paper, we model online product review systems into bipartite networks and adopt a network technique, called the weighted motif to uncover underlying reviewing patterns. Experiments on Amazon’s review dataset show that, our system is feasible and effective.
Multi Band Antenna with Multi Band Notch 2.5/3.7/6.4/8.1/12.4/14.4GHz Characteristics

In this paper an UWB antenna has been proposed. This antenna consists of a circular patch with crescent shaped slot, in which different slots has been introduced to enhance the performance and operate the antenna for the multi-band operation, also window shaped parasitic patch has been used to introduce notches at different frequency bands. Band notched characteristics of the scheduled antenna is obtained to prevent the potential interference between the ultra wideband transmission systems and the existing limited band transmission system. The scheduled antenna is fabricated on a FR-4 Epoxy substrate; and has compact size of dimensions 36 × 32 mm2. According to the measured and simulated results of scheduled antenna, antenna has impedance bandwidth of 2.3 to 15 GHz with VSWR ≤ 2. The proposed antenna is suitable for the Bluetooth (2.4GHz), WiMax(2.5/3.5GHz), WLAN(2.4/4.9/5.8-GHz, and Ku-band satellite communication. The proposed antenna has been fabricated and the measured result is found very close to simulated results. The scheduled antenna shows high impedance bandwidth, stable gain, good antenna radiation pattern and high efficiency across the entire frequency range spectrum used for suitable UWB application.
Load Balancing Approach for Finding Best Path in SDN

Software Defined Networking (SDN) is a new network model, and it eliminates the current limits of network facilities. SDN has the advantage of taking global view of overall network. In large networks, a single server is often unable to handle client requests with the fast growth of internet users and applications. Our network should be able to cope with large volume of traffic without introducing unnecessary delay. One solution to this problem is load balancing for network scalability and service availability. Load Balancer is a device that distributes the traffic among number of servers. But traditional vendor specific Load Balancer is a device that is much more expensive. Internet user can not change the functionality of that device i.e. Traditional Load Balancer are non-programmable. In a network single Load Balancer becomes a single point of failure or sometimes in large network it becomes bottleneck. Software defined networking is a promising solution to all these disadvantages. SDN improved the performance of transmission using six features like bandwidth ratio, hop count, latency, packet overhead, trust and packet loss. Find load on each node using this feature. Select least loaded path for transmission as a new incoming data flow in a real time.
Manner of Articulation based Split Lattices for Phoneme Recognition

Phoneme lattices have been shown to be a good choice to encode in a compact way alternative decoding hypotheses from a speech recognition system. However the optimal phoneme sequence is produced by tracing all the phoneme identities in the lattice. This not only makes the search space of the decoder huge but also the final phoneme sequence may be prone to have false substitutions or insertion errors. In this paper, we introduce the split lattice structures that is generated by splitting the speech frames based on the manner of articulation. Spectral flatness measure (SFM) is exploited to detect the two broad manner of articulation sonorants and non-sonorants. The manner of sonorants includes broadly the vowels, the semivowels and the nasals whereas the fricatives, stop consonants and closures belong to non-sonorants. The conventional way of speech decoder produces one lattice for one test utterance. In our work, we split the speech frames into sonorants and non-sonorants based on SFM knowledge and generate split lattices. The split lattice generated are modified according to the manner of articulation in each split so as to remove the irrelevant phoneme identities in the lattice. For instance, the sonorant lattice is forced to exclude the non-sonorant phoneme identities and hence minimizing false substitutions or insertion errors. The proposed split lattice structure based on sonority detection decreased the phone error rates by nearly 0.9 % when evaluated on core TIMIT test corpus as compared to the conventional decoding involved in the state-of-the-art Deep Neural Networks (DNN).
A Novel Architecture of Scheduling System for Big Data Framework

The calculation of massive data has always been an significant research topic, and the data calculation job scheduling system is an important platform to deal with this problem. There are many scheduling systems in use, however, their functions are not perfect and cannot meet the needs of users. This paper designs a novel architecture of scheduling system for big data framework. The system has basic scheduling functions, such as algorithm job assignment, job priority scheduling, job result management. It supports three algorithms to schedule jobs: scheduling-in-order algorithm, scheduling-by-priority algorithm, and scheduling-by-resource algorithm, meanwhile implementing dynamic switching of these algorithms. The system has a graphical interface which greatly facilitates interaction between the system and the user.
The evaluation of parametric and non-parametric models for total forest biomass estimation using UAS-LiDAR

Forest biomass estimation has drawn substantial attention due to its major impact on climate change and sustainable forest management. In this study, we assessed the performance of distributional and calibrated intensity metrics derived from the Unmanned Aerial System–Light Detection and Ranging (UAS-LiDAR) data to estimate total forest biomass individually and in combination over a ginkgo (Ginkgo biloba L.) planted forest in southeast China. First, the importance of these metrics were investigated and the optimal UAS- LiDAR metrics were selected by the "all-subsets" models. Then, the parametric (multivariate linear model (MLR)) and non-parametric (random forest (RF)) models were investigated for total forest biomass estimation. The results showed that, in general, the combo models (based on distributional and intensity metrics) (CV-R2=0.92-0.94, rRMSE=7.13%-7.62%) performed better than the separated models (CV-R2=0.90-0.92, rRMSE=8.05%-9.53%). Secondly, the estimation accuracy obtained from RF (CV-R2=0.92-0.94, rRMSE=7.13%-8.05%) was relatively higher than the MLR (CV-R2=0.88-0.90, rRMSE=7.62%-9.53%). This study demonstrated that compared with the MLR model, RF has stronger potential to enhance the performance of biomass estimation by using UAS-LiDAR- derived metrics, and the implementation of calibrated intensity metrics has shown marked contributions to total forest biomass estimation in planted forest.
MIMO Radar Waveform Design with Practical Constraints: A Low-Complexity Approach

In this letter, we consider the multiple-input multiple-output (MIMO) radar waveform design in the presence of signal-dependent clutters and additive white Gaussian noise. By imposing the constant modulus constraint (CMC) and waveform similarity constraint (SC), the signal-to-interference-plus-noise ratio (SINR) maximization problem is non-convex and NP-hard in general, which can be transformed into a sequence of convex quadratically constrained quadratic programming (QCQP) subproblems. Aiming at solving each subproblem efficiently, we propose a low-complexity method termed Accelerated Gradient Projection (AGP). In contrast to the conventional IPM based method, our proposed algorithm achieves the same performance in terms of the receive SINR and the beampattern, while notably reduces computational complexity.
Constrained Control of Autonomous Underwater Vehicles Based on Command Optimization and Disturbance Estimation

In this paper, a method is presented for antidisturbance constrained control of autonomous underwater vehicles subject to uncertainties and constraints. The uncertainties stem from uncertain hydrodynamic parameters, modeling errors, and unknown forces due to the ocean currents in an underwater environment. An antidisturbance constrained controller is developed by designing a command governor and a disturbance observer. Specifically, the disturbance observer is developed to estimate the lumped disturbance composed of parametric model uncertainties, modeling errors, and unknown environmental forces. The command governor is designed for optimizing command signals in the receding horizon within the state and input constraints. The command governor is formulated as a quadratically constrained quadratic programming problem. To facilitate online implementations, a neurodynamic optimization method based on a one-layer recurrent neural network is employed for solving the quadratic optimization problem subject to inequality constraints with finite-time convergence. The efficacy of the proposed antidisturbance constrained control method for autonomous underwater vehicles is substantiated via simulations and comparisons.
Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

Recently, mobility has gathered tremendous interest as the users’ desire for consecutive connections and better quality of service has increased. An accurate prediction of user mobility in mobile networks provides efficient resource and handover management, which can avoid unacceptable degradation of the perceived quality. Therefore, mobility prediction in wireless networks is of great importance and many works have been dedicated to this issue. In this paper, the necessity of mobility prediction, together with its intrinsic characteristics in terms of movement predictability, prediction outputs, and performance metrics is discussed. Moreover, the learning perspective of solutions to mobility prediction has been studied. Specifically, an overview of the state-of-the-art approaches is provided, including Markov chain, hidden Markov model, artificial neural network, Bayesian network, and data mining based on different kinds of knowledge. At last, this paper also explores the open research challenges due to the advent of the fifth-generation mobile system and puts forward some potential trends in the near future.
Comparative Analysis of Buried-Gate GaN OPFET Models for UV Photodetector Applications

We estimate the UV (Ultraviolet) characteristics of the buried-gate GaN OPFET (Optical Field Effect Transistor) models and analyze the results based on the photoconductive and the photovoltaic effects. The various detector parameters are evaluated. Two distinct illumination models are considered for the present work (the front-illuminated and the back-illuminated buried-gate OPFETs). The device is biased in the saturation region of operation with the gate shorted to the source and is studied under different radiation flux densities. Both the illumination models exhibit significantly high UV response with the front-illuminated OPFET showing superior performance at the lower flux densities whereas the back-illuminated model showing better response at the higher flux density with a compromise in the bandwidth. The results demonstrate that the devices under study exhibit greater performance potential than many of the photo detectors reported in the literature under review. The detectors shall greatly contribute towards UV communication and other related applications.
Effects of VM Placement Constraints in Heterogeneous Virtual Clusters

Virtualized systems consist of a large number of machines that are configured with different hardware and software, and execute a large number of virtual machines (VMs) for diverse applications. There can be various constraint conditions of placing VMs in such systems due to the concerns on security, availability, performance, etc. However, VM placement constraints can limit the choice of hosts for VMs, affecting the performance of the systems negatively. In this paper, we study constraint-aware VM placement in heterogeneous computing clusters. We first present a model of VM placement constraints that supports all types of constraints between VMs, and between VMs and hosts. Second, we discuss constraint-aware VM placement algorithms which optimize the performance for either energy saving or load balancing. Third, using simulations, we analyze the effects of different types of VM placement constraints on VM placement, and evaluate the performance of the algorithms over various settings. Our extensive simulation results demonstrate that the effects of VM placement constraints vary, depending on the optimization goal, the types of the constraints, and the system configurations.
An Improved Least Trimmed Square Hausdorff Distance Finger Vein Recognition

In order to solve the problem of poor accuracy and matching speed in finger vein recognition, an improved Least Trimmed Square Hausdorff Distance (LTS-HD) finger vein recognition based on weighted matching point algorithm is proposed in this paper. The incremental neighborhood search method is used to achieve matching acceleration for Least Trimmed Square Hausdorff Distance algorithm firstly, and the optimal matching weights for different types of matching points is searched by Particle Swarm Optimization, at the same time, the particle box exclusion mechanism is applied to avoid premature convergence so as to achieve global particle optimization. On this basis, the directional matching points which can effectively characterize the vein pattern information are extracted, and the optimal weights of vein matching point is calculated after introducing the weights and model optimization. At last, the improved Least Trimmed Square Hausdorff Distance algorithm is used to achieve finger vein recognition by introducing the optimal weights. Compared with other algorithms, the results show that the proposed algorithm has a significant improvement in the objective indicators such as matching speed and accuracy.
Temperature Coefficient of Frequency in Silicon-Based Cross-Sectional Quasi Lam e; Mode Resonators

Temperature coefficient of frequency (TCF) is studied in silicon-based cross-sectional quasi Lamé modes (CQLMs). Such modes are demonstrated in thin-film piezoelectric-on-silicon (TPoS) resonators and the TCF curves are modeled using eigenfrequency analysis in COMSOL for highly n-type doped silicon. It is shown that the ratio between the finger-pitch and the resonator thickness affects the turnover temperature of these resonators which could be predicted using this model. The CQLM-TPoS resonators fabricated on a $40\mu\mathbf{m}$ thick SOI substrate, are characterized and the measured TCF values are confirmed to be in close agreement with the prediction. A relatively high turnover temperature of >100°C is reported for a third-order CQLM-TPoS resonator aligned to <100> silicon plane while a turnover temperature of <20°C is recorded for the <110> counterpart.
Enablers of Adversarial Attacks in Machine Learning

The proliferation of machine learning (ML) and artificial intelligence (AI) systems for military and security applications creates substantial challenges for designing and deploying such mechanisms that would learn, adapt, reason and act with Dinky, Dirty, Dynamic, Deceptive, Distributed (D5) data. While Dinky and Dirty challenges have been extensively explored in ML theory, the Dynamic challenge has been a persistent problem in ML applications (when the statistical distribution of training data differs from that of test data). The most recent Deceptive challenge is a malicious distribution shift between training and test data that amplifies the effects of the Dynamic challenge to the complete breakdown of the ML algorithms. Using the MNIST dataset as a simple calibration example, we explore the following two questions: (1) What geometric and statistical characteristics of data distribution can be exploited by an adversary with a given magnitude of the attack? (2) What counter-measures can be used to protect the constructed decision rule (at the cost of somewhat decreased performance) against malicious distribution shift within a given magnitude of the attack? While not offering a complete solution to the problem, we collect and interpret obtained observations in a way that provides practical guidance for making more adversary-resistant choices in the design of ML algorithms.
Joint Social, Energy and Buffer to Select Relay in Relay-Aided D2D Communications

Device-to-device (D2D) communications is conceived as an effective technique to improve the performance on overall throughput and enhance system capacity in cellular networks. However, it is hard to establish a reliable direct transmission link when there is a long distance between source user device (SUD) and destination user device (DUD). Thus, it is necessary to take advantage of Relay-assisted D2D communications to solve the problem. In this paper, a relay selection method is proposed to improve the performance of D2D communications. Firstly, the success probability of transmission and the average transmission delay between SUD and DUD in one-hop D2D communications are derived. Secondly, a relay selection mathematical utility model is formed which jointly considers the interest similarity among users, the surplus energy, buffer state of candidate relay users, and the channel condition in each communication phase. Finally, a relay selection strategy is proposed which selects the link with the maximum utility for data transmission. Simulation results demonstrate the effectiveness of the proposed scheme.
Towards 3D Human Action Recognition Using a Distilled CNN Model

Motivated by the remarkable performance achieved using deep learning strategies in solving action recognition tasks, an effective, yet simple method is proposed for encoding the spatiotemporal information of skeleton sequences into color texture images, referred to as Skeletal Optical Flows (SOFs). SOFs collectively represent the kinetic energy, predefined angles and pair-wise displacements between joints over consecutive frames of skeleton data, as color variations to capture meaningful temporal information and make them highly interpretable. A novel Convolutional Neural Network with Correctness-Vigilant Regularizer (CVR-CNN) is then employed to exploit the discriminative features of SOFs for human action recognition. Empirical results show that the efficiency of the proposed method is superior in terms of the generalizability of the generated model, the training convergence speed, and the resulting classification accuracy on commonly used action recognition datasets, such as MHAD, HDM05 and NTU RGB+D.