Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud

FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based DNN accelerators in the cloud run in a time-division multiplexing way for multiple users sharing a single FPGA, and require re-compilation with $\sim$100s overhead. Such designs lead to poor isolation and heavy performance loss for multiple users, which are far away from providing efficient and flexible FPGA virtualization for neither public nor private cloud scenarios. To solve these problems, we introduce a novel virtualization framework for instruction architecture set (ISA) based on DNN accelerators by sharing a single FPGA. We enable the isolation by introducing a two-level instruction dispatch module and a multi-core based hardware resources pool. Such designs provide isolated and runtime-programmable hardware resources, further leading to performance isolation for multiple users. On the other hand, to overcome the heavy re-compilation overheads, we propose a tiling-based instruction frame package design and two-stage static-dynamic compilation. Only the light-weight runtime information is re-compiled with $\sim$1 ms overhead, thus the performance is guaranteed for the private cloud. Our extensive experimental results show that the proposed virtualization design achieves 1.07-1.69x and 1.88-3.12x throughput improvement over previous static designs using the single-core and the multi-core architectures, respectively.
Grating Lobes Reduction Using a Multilayer Frequency Selective Surface on a Dual-Polarized Aperture Array Antenna in Ka-Band

This paper presents a multilayer frequency selective surface for a dual-polarized aperture array antenna in Ka-band. The <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> elements of the array are cylindrical open cavities with a diameter of <inline-formula> <tex-math notation="LaTeX">$0.6\lambda _ {0}$ </tex-math></inline-formula> at 30 GHz, and spaced one wavelength. Due to this separation between elements, which is limiting and not reducible by the architecture of the feeding network and the size of the radiating element, grating lobes appear. Frequency Selective Surfaces (FSS) can be a solution to this problem without modifying the feeder architecture nor the radiating elements. This paper presents the FSS design for reducing grating lobes level, the antenna assembly, and the experimental validation. The full antenna performance demonstrates that FSS operates in a range identical to the feeder (29.5 GHz to 31 GHz) with the added benefit of reducing the grating lobes level more than 10 dB for both polarizations.
Design of Adaptive Fuzzy Controllers for a Class of Fractional Order Nonlinear MIMO Systems With Input Saturation

This paper develops a fractional order adaptive fuzzy backstepping control scheme for an incommensurate fractional order uncertain nonlinear multiple-input multiple-output (MIMO) systems with external disturbance and input saturation. Combined with the adaptive and backstepping technique, the fuzzy logic system is used to approximate the unknown nonlinear uncertainties in each step of the backstepping, and the incommensurate fractional order parameters update laws for fuzzy logic system, unknown parameters as well as the external disturbances are proposed in the controller to compensate the unknown nonlinearities, unknown parameters and disturbances. With the aids of the frequency distributed model of fractional integrator for the fractional order systems in the procedure of controller design, the stability of the closed loop system is established. At last, two simulation examples are demonstrated to verify the robustness and effectiveness of the proposed controller.
An Adaptive Spectrogram and Accelerogram Algorithm for Electronic Warfare Applications

This paper presents a novel adaptive approach to spectrogram and accelerogram calculation based on the signal chirp rate (CR). Such a method may be useful in electronic intelligence (ELINT), electronic warfare (EW) and electronic reconnaissance (ER) or in passive bistatic radar (PBR) systems where waveform parameters are unknown. Precise signal parameter estimation is important to correctly reconstruct and use the received signal for different purposes. However, due to the non-stationary nature of the analyzed waveforms, time-frequency (TF) analysis is often employed. Thanks to numerous types of TF representation techniques, various advantages can be reached. In addition, adaptive approaches allow greater accuracy and resolution to be achieved, which is essential in signal parameter estimation. In this paper, an adaptive spectrogram and accelerogram are presented for which the calculation of the CR estimation in the TF domain was used. An analysis window was changed individually with respect to the obtained instantaneous CR, which ensured optimal resolution over the TF plane. The proposed approach was tested and verified using real-life signals coming from the Air Traffic Control (ATC) radar.
Educational Data Mining Methods: A Survey

Educational Data Mining (EDM) is an emerging inter-disciplinary research area that involves education and computer science. EDM employs data mining tools and techniques, on large datasets related to education, to extract meaningful and useful information. EDM works toward the improvement of educational processes by introducing better and effective learning practices. EDM methods refer to the set of methods that are used for building models/applications. This article presents an extensive literature survey of EDM methods. The article also discusses research trends and challenges in EDM. This insight into EDM attempts to provide useful and valuable information to researchers interested in furthering the field of EDM.
Work-in-Progress: Triple Event Upset Tolerant Area-Efficient FPGA-Based System for Space Applications And Nuclear Plants

This paper focuses on FPGA-based systems in the context of space applications and nuclear plants which are extremely harsh environments. In such environments, the probability of occurrence of Multiple Event Upsets (MEUs) is not negligible. Conventional fault-tolerant architectures (such as Triple Modular Redundancy) will NOT be able to handle Triple Event Upsets (TEUs) for example. A fault-tolerant architecture with only six identical modules is developed in this paper even though, intuitively, at least seven modules are required for a system to recover from a TEU. It is proven that the proposed architecture can fully recover from any sequence of Single, Double or Triple Event Upsets by using Dynamic Partial Reconfiguration. If a hard fault affects one of the modules, the architecture will lose some of its fault tolerance but may be able to continue operating correctly depending on the nature of the next fault.
A Novel, Graphical Representation of the Classical Radar Range Equation

The classical radar range equation has been around since the beginning of radar. While there are various forms of the equation for different radar types, it has not commonly been expressed in a graphical form until now. Leonardo has developed a novel means of expressing the equation in a form that provides insight into radar performance, particularly with regard to detection of targets with non-uniform radar signatures. In essence, the graphical format splits the contribution to detection into components for the radar parameters and those for the target; both components are scaled precisely in range.
RSSI and Feed Forward Neural Network (FFNN) Based Indoor Localization in WSN

In the advent of Internet of Things (IoT), Wireless Sensor Network (WSN) technologies play an important role in acquisition of different physical quantities for different applications. The Received Signal Strength Indicator (RSSI) based indoor localization is a well-known localization method used in WSN technologies due to its low complexity, availability and low energy consumption. In this research we explore the possibility of applying RSSI value based Feed Forward Neural Network (FFNN) jointly to identify the correct location of a moving object or a person, which is an important requirement of IoT-based Ambient Assisted Living (AAL) applications. We setup an experimental test bed for the acquisition of RSSI data remotely, which contained two types of nodes called beacon node and the mobile node. The ESP 8266 is used as the controller for nodes, which is based on IEEE 802.11 standard. The RSSI values from the beacon nodes will be sent to a remote server via Mosquitto Message Queuing Telemetry Transport (MQTT) broker, and then the RSSI values will be secondhand utilized by the FFNN supervised learning model that we developed at the remote server. Output of FFNN model gives the location of the object or person in two dimensional (2D) space. In the end of the research, the validity is checked by using the statistical assessment models and the results substantiate the significance of using supervised learning method in RSSI based indoor positioning.
Systematic Investigation of Silicon Micromachined Waveguide Structure & Designing Passive Components out of it for THz Application

In this article, a detailed study of Silicon micromachined based rectangular waveguide and various passive components for terahertz (THz) application is reported. Effect of various process parameters on the performance of these components is studied using a commercial EM solver and a comparative analysis is performed with all previously reported models. WR-2.2 based passive devices have been designed for 300 to 400 GHz band. Multiple passive devices require waveguide bends, which directly affects the phase information of signal. Bend design demands highly precise parameter selection at these higher frequencies which has been elaborated in this article. Using this bend optimization, two distinct passive devices, namely, Directional Couplers (7, 10 and 13 dB) and Multi-hole branch line hybrid have been designed for THz band.
Coordinated Fuzzy-Based Low-Voltage Ride-Through Control for PMSG Wind Turbines and Energy Storage Systems

Coordinated control methods involving a wind turbine (WT) and an energy storage system (ESS) have been proposed to meet several objectives, such as smoothing wind power (WP) fluctuations, shaving peaks, enabling power scheduling, and allowing low-voltage ride-through (LVRT). LVRT requirement is defined by grid operators, and it should be satisfied whenever grid faults occur. Several methodologies have been proposed for the LVRT both with or without the use of an ESS. Furthermore, using an ESS is more advantageous for several WP applications. By using an ESS, WTs can be operated in a more economic and reliable way. However, the installation cost of an ESS is high and it has operation range constraints for charging and discharging. Moreover, the WT operation condition and ESS state-of-charge (SoC) can be different when a grid fault occurs. Therefore, it is necessary to coordinate both units, WT and ESS, for reliable and economic operation during a grid fault. Thus, we propose a coordinated fuzzy-based LVRT method that considers the different operation conditions of a WT and an ESS. From the proposed method, the effective reference powers of a WT and an ESS are evaluated by considering the rotor speed and SoC in the fuzzy control algorithm. The effectiveness of the proposed method is validated by considering two case studies on ESS SoC and WT rotor speed violations.
Proof of Concept: Using Reinforcement Learning agent as an adversary in Serious Games

This article focuses on simple rehabilitation video-game called Flying with Friends. The rehabilitation industry has been experiencing a boom in recent years, coupled with the growing popularity of virtual reality technology, a drop in prices for these technologies and the expansion entertainment industry in the form of computer games. The goal of this experiment was to provide a proof that such systems are viable option when it comes to artificial intelligence systems in serious video-games, but not limited to only serious ones. The solution described in this article, in cooperation with experts, is going to be deployed in a real rehabilitation environment.
Closed loop control of a braided-structure continuum manipulator with hybrid actuation based on learning models

Design of continuum manipulators have always been restricted to simplified structures due to the modeling complexity involved. However with the success of machine learning based models, there are potentially more structures and actuation strategies that could be investigated. This paper presents a novel continuum manipulator with variable diameter and a general control strategy for closed loop task space control. The controller constitutes of an inverse kinematics based feedback component, a forward kinematics based feedforward component and a low level velocity controller. Our findings indicate how such an approach can deal with singularities and provide smooth and accurate motion.
An Effective and Efficient Dynamic eMBMS Multicast Grouping Scheduling Algorithm in MBSFNs for Public Safety Scenarios

The LTE (Long-Term Evolution) eMBMS (evolved Multimedia Broadcast Multicast Service) technology employed in MBSFNs (Multicast Broadcast Single Frequency Networks) has been shown to be capable of considerably increasing the capacity of serving public safety users under group communication scenarios. However, due to its own limitations, a more fine-tuned scheduling approach is needed in order to fully utilize the strengths of eMBMS multicast. In this work, we first identify and analyze several overlooked challenges for scheduling multicast traffic in MBSFNs. Then we develop an effective and efficient dynamic scheduling algorithm for eMBMS multicast in time and frequency varying channel. The proposed algorithm leverages the advantages of both multicast and unicast schemes via user grouping; and through extensive simulations, is shown to be capable of significantly enhancing the mission critical performances under both best effort and guaranteed bit rate delivery models. We also show the resiliency of our proposed algorithm by applying it onto various network and user deployment scenarios. Our consistent results further prove that LTE eMBMS in MBSFN is a key solution in overcoming limitations in near future public safety networks.
Automatic Isolated Kannada Speech Recognition System under Degraded Conditions

In this paper, a spoken query system is developed for accessing the real time commodity price information in Kannada dialects. The speech data or corpus is collected under uncontrolled environment from the real farmers of Karnataka state. The language and acoustic models are built by using Kaldi. The least word error rate (WER) models are used to deploy and develop the spoken dialogue system. The performance, on line and off line accuracies of speech recognition is tested and verified by farmers of Karnataka under different noisy conditions.
Enfolded Textile Actuator for Soft Wearable Robots

The usage of soft textile materials in wearable robotics shows several advantages compared to rigid robotic parts. We describe a soft pneumatic bending actuator composed of one folded textile tube (flexor) surrounded by another straightening textile tube (extensor). Each actuator is made of two parts: an inflatable tube and housing. Tubes are made of a bladder wrapped by textile. A soft spiral structure was inserted inside the bladder to prevent air jamming. We describe the design and fabrication of the actuator, provide an analytical model of the torque, and characterize the actuator performance. The actuator is made with different sizes to be used in a large range of applications such as robotic hands, robotic arms, and wearable soft exosuits. Five actuators were attached to a commercial glove fingers and form a wearable glove, the glove shows different gestures and it is able to hold 9 kg weights. With a larger scale, the bending actuator was able to hold 20 kg. In addition, a preliminary experiment shows a reduction of 50% in EMG signals reflecting muscle activities during 9 kg weight bar grasping and releasing stages.
Balancing Power and Rate Control for Improved Congestion Control in Cellular V2X Communication Environments

The Society of Automotive Engineers (SAE) J2945/1 standard prescribes a wireless channel congestion control algorithm for periodic Basic Safety Messages (BSMs) in vehicular safety communication. The algorithm is designed to work with the Dedicated Short Range Communication (DSRC) technology as the radio interface, and utilizes transmit (Tx) power control and rate control elements for the periodic BSM transmissions, which are supposed to work in a complementary manner. An equivalent standard is being made for the cellular vehicle-to-everything (C-V2X) communication environment, and it is expected that the J2945/1 congestion control algorithm will be reused. Unfortunately, we find that when transplanted to the C-V2X communication environment, the J2945/1 algorithm exhibits a poor balance between the two control elements under congestion. Specifically, the Tx rate control is pushed to its limit much earlier than the Tx power control. We believe that this is due to the new relation formed between the respective inputs to the control elements, channel busy ratio (CBR) and the vehicle density (VD), induced by the different radio technology used in the C-V2X communication. The consequence of the unbalanced control dynamics is the degraded update delay of neighboring vehicles’ kinematics due to the over-exerted rate control, which potentially undermines the driving safety. In this paper, we propose to adjust the rate control function to react to congestion in a more relaxed manner so that the power control contributes more actively to the overall congestion control. With the re-balancing between the control elements, we show that the update delay improves, where larger improvements are obtained in closer, more safety-critical distances from the BSM transmitter. We believe that in future efforts to define the standard congestion control algorithm for the C-V2X environment, the balanced control aspect as explored in this paper should be considered in the design phase.
Solving Permutation Flowshop Problem with Deep Reinforcement Learning

Permutation flowshop problem is a classic problem in combinatorial optimization. In this paper, we propose a deep reinforcement learning (DRL) model with heterogeneous network according to the different task characteristics of actor and critic in actor-critic model. The actor, acting as the policy network, is mainly responsible for strategy search and composed of LSTMs; the critic, acting as the value network, is mainly responsible for strategy evaluation and formed by the attention network. In order to increase the exploration ability of the model, we adopt the ε-greedy strategy to further improve the effectiveness of the model. The experimental results on multiple data sets show that our model achieves better performance on permutation flowshop problem.
Positioning Based on Noise-Limited Censored Path Loss Data

Positioning is considered one of the most important features and enabler of various novel industry verticals in future radio systems. Since path loss or received signal strength-based measurements are widely available and accessible in the majority of wireless standards, path loss-based positioning has an important role among other positioning technologies. Conventionally path loss-based positioning has two phases; i) fitting a path loss model to training data, if such training data is available, and ii) determining link distance estimates based on the path loss model and calculating the position estimate. However, in both phases, the maximum measurable path loss is limited by measurement noise. Such immeasurable samples are called censored path loss data and such noisy data is commonly neglected in both the model fitting and in the positioning phase. In the case of censored path loss, the loss is known to be above a known threshold level and that information can be used in model fitting as well as in the positioning phase. In this paper, we examine and propose how to use censored path loss data in path loss model-based positioning and demonstrate with simulations the potential of the proposed approach for considerable improvements (over 30%) in positioning accuracy.
Classification techniques for Disease detection using Big-data

This paper focuses on prediction of diabetes disease with the help of classification algorithms in distributed environment. The tool used to carry out the classification is Apache Spark which can handle big data applications efficiently. We have also explained differences between Hadoop and Spark with respect to fault tolerance, execution and support for parallel operations. The paper shows that Spark is better than Hadoop in many aspects which helps organizations to develop fast and efficient big data solutions in comparatively less time than Hadoop. The classification is based on some major parameters like Blood pressure, Serum insulin level, Body Mass Index (BMI), Diabetes pedigree function etc. The aim of this paper is to compare the results of different classification algorithms in distributed environment. The results of the algorithms are compared based on some critical parameters like Accuracy, Precision, and Recall etc. We have also included effects of handing missing values on these parameters. We have explained details of confusion matrix which is used it to find quality of binary classifier. We have explained details of classification algorithms like neural network, support vector machine etc. in depth. The details about Apache Spark and classification algorithms with their results are included in this paper.
Study on the Quality of life of the Internet’s active population in China from the perspective of big data

In this study, the data of quality of life (QOL) of Internet’s active population in six provinces and two big cities of China was collected by big data, using the World Health organization Quality of Life Questionnaire (WHOQOL-BREF). On the basis of the data, the relationship of quality of life (QOL) and its influencing factors of this kind of person was set up and all the process was analyzed using Partial Least Squares (PLS) approach via Smart PLS software 3.0. The results show that traditional factors such as physiological factors, psychological factors, and environmental factors have a positive relationship on the quality of life (QOL) of the Internet’s active population but social relationships do not since the demand and dependence of people on traditional social network is weakening under the background of big data era.
Improved Covariance Matrix Estimation using Riemannian Geometry for Beamforming Applications

The estimation of interference plus noise covariance (INC) matrix for beamforming applications is considered from a Riemannian space perspective. A new INC estimation technique based on regularized Burg algorithm (RBA), Riemannian mean and Riemannian distance is proposed to maintain a stable performance in presence of angle of arrival mismatch and small sample size with high and low signal to interference plus noise ratio (SINR). The RBA is exploited to generate Toeplitz Hermitian positive definite (THPD) covariance matrices from the estimates of the reflection coefficients for each radar snapshot. The estimated INC is formulated as a linear combination of THPD covariance matrices of the interference plus noise excluding potential target snapshots. The weights of the linear combination operation are based on the Riemannian distance between the Riemannian mean and each THPD covariance matrix. The largest distance (potential target) will have zero weight and the smallest distance will have maximum weight. Simulation results demonstrate the performance of the proposed technique in comparison with sample covariance and Riemannian mean covariance under steering vector mismatch and small sample size in presence of high and low SINR.