Miracle-dz/UniFAR-FTUs huggingface dataset:
LW-AKA: A Security Protocol for Integrated RFID and IoT Based Smart Home Security System

The smart home is a concept where the user devices and appliances are controlled, monitored, and analyzed with the help of internet and wireless technologies. The Internet-of-Things (IoT) is a technology that monitors the physical status of the objects and captures the significant data using sensors and their associated controllers. Many research works have focused on either sensor or RFID network-based home automation. In this work, we proposed an integrated radio frequency identification (I-RFID) system, which is used for static low-range IoT applications. We proposed a light-weight security protocol for RFID based smart home automation systems. The proposed protocol is simulated and analyzed with the AVISPA and ProVerif tools. The performance analysis is carried out and compared with the existing protocols based on the computational complexity. The simulation results and performances analysis shows that the proposed protocol is safe from the different possible attacks and required less computational functions as compared to their counterpart.
Agent Based Simulation of Bot Disinformation Maneuvers in Twitter

Multiple state and non-state actors have recently used social media to conduct targeted disinformation operations for political effect. Even in the wake of these attacks, researchers struggle to fully understand these operations and more importantly measure their effect. The existing research is complicated by the fact that modeling and measuring a persons beliefs is difficult, and manipulating these beliefs in experimental settings is not morally permissible. Given these constraints, our team designed an Agent Based Model that is designed to allow researchers to explore various disinformation forms of maneuver in a virtual environment. This model mirrors the Twitter Social Media Environment and is grounded in social influence theory. Having built this model, we demonstrate its use in exploring two disinformation forms of maneuver: 1) "backing" key influencers and 2) "bridging" two communities.
Data Driven Approach For Farm Re-Modeling Using Prediction Analytics

The technological advancements in IT has made agriculture industry move towards a sustainable growth. Farming may not sound like an issue we need to care about, but the agriculture industry is in dire need of refinement. Sustaining in agriculture is challenging because of reducing water levels, climate change and shrinking amount of arable land. These after effects can be made approachable using prediction methods. The foremost idea of the prototype is to monitor the large crop area and thereby maintaining it using the sensor enabled IoT technology. The Autopilot drones fly once or twice a week and captured images from the whole farm are also stored. Heat map a feature function of the entire farm which is used to extrapolate the data is analyzed. With this, the data driven approach for re- modeling of the farm is done using Zero- R algorithm and Naive-Bayes algorithm. Visualization and statistical predictions of the data is done using Tableau tool and further analysis using R & Python. The major benefits of providing this real time data analytic prediction help farmers to remodel traditional farming and focus to improve crop quality, potential to increase yields and to overcome uncertainty by this prediction analytic makeover to the agriculture industry.
Reconfigurable Warped Digital Filter Architecture for Hearing Aid

Digital filterbanks in hearing aids employ an array of filters to selectively amplify sound of different frequencies to compensate hearing loss. The use of filterbanks leads to a higher usage of silicon area as there are many filters, each generating a sub-band. Reconfigurable warped digital filter is a Variable Digital Filter (VDF) that can be reconfigured to function as a low-pass, band-pass or high-pass filter with cutoff frequencies that can be varied on-the-go. We propose a system with reconfigurable warped VDF that uses a single prototype filter for generating all the necessary sub-bands. The proposed system automates the generation of the control signals for reconfiguring the filter and pipelines the processing of each sub-band to generate the requisite magnitude response. The proposed architecture is designed to achieve good audiogram matching with minimum matching error.
Bayesian Simulation Optimization with Common Random Numbers

We consider the problem of stochastic simulation optimization with common random numbers over a numerical search domain. We propose the Knowledge Gradient for Common Random Numbers (KG-CRN) sequential sampling algorithm, a simple elegant modification to the Knowledge Gradient that incorporates the use of correlated noise in simulation outputs with Gaussian Process meta-models. We compare this method against the standard Knowledge Gradient and a more recently proposed variation that allows for pairwise sampling. Our method significantly outperforms both baselines under identical laboratory conditions while greatly reducing computational cost compared to pairwise sampling.
Printed Ultra-Wideband Cardioid Monopole Antenna for Energy Harvesting Application

A compact ultra-wideband monopole Cardioid antenna based on fractal geometry for energy harvesting applications is proposed in this paper. The antenna is designed for energy harvesting in WIMAX, WLAN and satellite communications ranges. The performance is achieved using the cardioid shaped radiating element on low-cost FR4 substrate.Simulated results show that antenna have S11 value-10dB in range from 4 GHz to 30 GHz covering almost all SHF band (3-30 GHz). In addition, the gain is up to 5 dBi and efficiency up to 80%. This antenna is very simple for manufacturing, has ultra-wide band, high gain and efficiency and therefore is suitable to use in energy harvesting applications. Proposed antenna performance are simulated in time domain based CST solver.
The Asymptotic Validity of Sequential Stopping Rules for Confidence Interval Construction Using Standardized Time Series

We establish the asymptotic validity of a class of sequential stopping rules when applying standardized time series (STS) to construct fixed-width confidence intervals (CI). The STS CI construction avoids requiring a consistent variance estimator, which is attractive to a class of steady-state simulation problems in which variance estimation is difficult. We quantify the asymptotic distribution of STS at stopping times as the prescribed half-width of the CI approaches zero. This provides us with the appropriate scaling parameter for the CI in the sequential stopping setting.
Investigations on Space Vector based Bus Clamping PWM Strategies on Speed Control of Induction Motor Drive

This paper investigates on control strategies of speed control of Induction Motor (IM) drive. There are many methods to control the speed of Induction Motor. Among these scalar and vector control are the most prominent methods. By keeping V/f ratio constant we can attain a maximum torque at wide range of speeds in scalar control. Vector control provides highly desirable performance and they are mostly used for IM control. Vector control can provide maximum torque at low speed and independent control of flux and torque can be obtained as in separately excited DC motor. Pulse Width Modulated (PWM) strategies as conventional space vector PWM(CSVPWM) and Bus clamping PWM (BCPWM) fed voltage source inverter (VSI) are applied to IM. The performance of these strategies are analyzed in terms of Total harmonic distortion (THD), steady state and transient performance operated under no load conditions. The proposed system has been presented in MATLAB SIMULINK platform and the obtained results are experimentally validated on a 3 phase,1 HP IM drive.
On Error-Related Potentials During Sensorimotor-Based Brain-Computer Interface: Explorations With a Pseudo-Online Brain-Controlled Speller

Objective: Brain-computer interface (BCI) spelling is a promising communication solution for people in paralysis. Currently, BCIs suffer from imperfect decoding accuracy which calls for methods to handle spelling mistakes. Detecting error-related potentials (ErrPs) has been early identified as a potential remedy. Nevertheless, few works have studied the elicitation of ErrPs during engagement with other BCI tasks, especially when BCI feedback is provided continuously. Methods: Here, we test the possibility of correcting errors during pseudo-online Motor Imagery (MI) BCI spelling through ErrPs, and investigate whether BCI feedback hinders their generation. Ten subjects performed a series of MI spelling tasks with and without observing BCI feedback. Results: The average pseudo-online ErrP detection accuracy was found to be significantly above the chance level in both conditions and did not significantly differ between the two (74% with, and 78% without feedback). Conclusions: Our results support the possibility to detect ErrPs during MI-BCI spelling and suggest the absence of any BCI feedback-related interference.
Gaussian Process Preintegration for Inertial-Aided State Estimation

In this letter, we present Gaussian Process Preintegration, a preintegration theory based on continuous representations of inertial measurements. A novel use of linear operators on Gaussian Process kernels is employed to generate the proposed Gaussian Preintegrated Measurements (GPMs). This formulation allows the analytical integration of inertial signals on any time interval. Consequently, GPMs are especially suited for asynchronous inertial-aided estimation frameworks. Unlike discrete preintegration approaches, the proposed method does not rely on any explicit motion-model and does not suffer from numerical integration noise. Additionally, we provide the analytical derivation of the Jacobians involved in the first-order expansion for postintegration bias and inter-sensor time-shift correction. We benchmarked the proposed method against existing preintegration methods on simulated data. Our experiments show that GPMs produce the most accurate results and their computation time allows close-to-real-time operations. We validated the suitability of GPMs for inertial-aided estimation by integrating them into a lidar-inertial localisation and mapping framework.
Detection and Classification of Microaneurysms and Haemorrhages from Fundus Images for Efficient Grading of Diabetic Retinopathy

Classification of different type of lesions associated with Diabetic Retinopathy (DR) is an essential part of any automated screen system. Lesions share properties with other parts of the retinal such as exudates and optic disc have similar intensity, Microaneurysms (MA) and blood vessels share similar intensity, haemorrhages (HE) share intensity with Microaneurysms and with blood vessels. So, in the proposed work MA and HE are the lesions considered for classification. In proposed methodology the pipelined architecture is presented for detection and classification of Microaneurysms and haemorrhages. The major obstacles for detection of MA and HE is the intensity similarity among them and with blood vessels. In the proposed architecture, first the MA, HE and blood vessels are all detected using the colour intensity (CI). Second, other parts of the retinal are removed by subtracting the CI from original image (I) and stored in the resultant image (MA_HE). Next, the blood vessels are eliminated by simple and efficient morphological dilation on MA_HE by defining appropriate size and shaped structuring element (SE). Finally, the MA and HE are classified based on size. Microaneurysms are usually between 10µu – 100µu in diameter. Using this fact, the MA and HE are classified. The methodology is evaluated by testing on a database of 100 images. The correctness of the pipelined architecture is represented by sensitivity of 84.21% and specificity of 80.95%.
Design and Analysis of Voltage Lift Technique Based Boost DC-DC Converter

Conventional DC-DC boost converters are unable to provide high step-up voltage gains due to the effect of power switches, rectifier diodes and the equivalent series resistance of inductors and capacitors. In this paper, the closed loop design and analysis of voltage lift technique based non-isolated dc-dc boost converter is carried out to achieve high step-up voltage gain. In the presented converter two inductors, three capacitors and three power diodes are used. The structure of the presented converter is to be considered a very simple, since it has only two power stages. The voltage and current equations of each element with converter voltage gain under continuous conduction mode (CCM) are extracted. Moreover, the steady state analysis of voltage gains and boundary conditions are discussed in this paper. Finally, the performance verification is carried out with a prototype circuit implemented in the laboratory.
An Improved NSGAII Algorithm Based on Site-Directed Mutagenesis Method for Multi-Objective Optimization

Evolutionary algorithms have been greatly uti-lized in Multi-objective optimization problems. Existing studies on multi-objective evolutional algorithms (MOEAs) rarely consider the evolutionary environment which may influence the selection and mutation of the individuals in the evolutionary process. Therefore, with consideration of the evolutionary environment of human intervention, this paper proposes a novel site-directed mutagenesis method for MOEA to generate offspring based on reinforcement learning. In the evolutionary process, reinforcement learning is utilized to simulate the site-directed mutagenesis of human intervention, where key genes affecting the current status are identified through Q-learning agent in the mutation stage. The mutation stage is further applied in NSGAII and the new algorithms are named as RL-NSGAII. Different benchmark problems are used to verify the performance of the proposed algorithms through a large number of experiments. Compared with NSGAII, RL-NSGAII has advantages in the convergence speed of Pareto front and has outstanding performance in the diversity and stability of the solution set for both the two-objective and multi-objective benchmark problems.
Reinforcement Learning driven Energy Efficient Mobile Communication and Applications

Smart city planning is envisaged as advance technology based independent and autonomous environment enabled by optimal utilisation of resources to meet the short and long run needs of its citizens. It is therefore, preeminent area of research to improve the energy consumption as a potential solution in multi-tier 5G Heterogeneous Networks (HetNets). This article predominantly focuses on energy consumption coupled with CO2 emissions in cellular networks in the context of smart cities. We use Reinforcement Learning (RL) vertical traffic offloading algorithm to optimize energy consumption in Base Stations (BSs) and to reduce carbon footprint by applying widely accepted strategy of cell switching and traffic offloading. The algorithm relies on a macro cell and multiple small cells traffic load information to determine the cell offloading strategy in most energy efficient way while maintaining quality of service demands and fulfilling users applications. Spatio-temporal simulations are performed to determine a cell switch on/off operation and offload strategy using varying traffic conditions in control data separated architecture. The simulation results of the proposed scheme prove to achieve reasonable percentage of energy and CO2 reduction.
Supervised Learning with Small Training Set for Gesture Recognition by Spiking Neural Networks

This paper proposes a novel supervised learning algorithm for spiking neural networks. The algorithm combines Hebbian learning and least mean squares method and it works well for small training datasets and short training cycles. The proposed method is applied in human-robot interaction for recognizing musical hand gestures based on the work of Zoltán Kodaly. The MNIST dataset is also used as a benchmark test to´ verify the proposed algorithm’s capability to outperform shallow ANN architectures. Experiments with the robot also provided promising results by recognizing the human hand signs correctly.
Examination of High- $Q$ -Factor Conductor-Embedded Planar Transformer With Magnetic Flux-Path Control Technology

To improve the power density of switching power supplies, a small transformer with low loss is required. Increasing the drive frequency is an effective strategy for miniaturizing the transformer; however, this strategy increases the losses. In this article, we investigate strategies to reduce the losses arising from conductor-embedded planar transformers using magnetic flux-path control (MPC) technology. The ac resistance of a conductor-embedded planar transformer that uses the MPC technology is less than the ac resistance of a conventional conductor-embedded transformer. In this research, we prove that the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-factor of our proposed transformer is superior to the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-factor of the conventional transformers.
Multi Focus and Multi-Source Image Fusion Based on Deep Learning Model

In order to realize the image fusion of multi focus and multi-source acquisition, in this paper a method based on deep learning model and DT-CWT image fusion is proposed. Firstly, the base parts and detail features of the image were separated, the base parts was denoised and filtered by DT-CWT, and the fusion base parts were obtained; depth learning model VGG-S was selected to extract the detail features of the image, and then the fusion details were obtained by multi-layer fusion strategy. Finally, the maximum gradient value is used to select the detail features to obtain the fusion details. The fusion base parts and details features were overlapped and reconstructed to obtain the fusion image. The algorithm not only retains the significant features of the image, but also makes the fused image obtain more detailed feature information. In the process, the noise was reduced. The subjective and objective evaluation results are improved obviously.
Relay Probing for Millimeter Wave Multi-Hop D2D Networks

The communications in the millimeter wave (mmWave) band, e.g., WiGig (60 GHz), is considered as one of the main components of 5G and beyond 5G (B5G) networks. However, it is characterized by short range transmissions along with high susceptibility to path blockage, e.g., human shadowing. Thus, mmWave multi-hop relaying using device to device (D2D) connections turns to be an efficient solution to extend its communication range and to route around blockages. In this context, relay probing is essential to discover/explore the candidate multi-hop routes from source-to-destination and then select the best route among them. However, a trade-off exists between relay probing and the required overhead coming from mmWave beamforming training. In this paper, taking advantage of the multi-band $\mu W$ /mmWave relay nodes, an efficient multi-hop relay probing scheme is proposed for mmWave D2D routing. In this scheme, the collected $\mu W$ received signal strengths (RSSs) among the distributed relay nodes are used to estimate the probability of their mmWave signal-to-noise power ratios (SNRs) while considering line-of-sight (LOS) and non-LOS (NLOS) path availabilities. Then, based on a proposed probabilistic metric, a hierarchical search algorithm is proposed to jointly discover the relay nodes along the candidate multi-hop routes and enumerate the number of routes expected to maximize the spectral efficiency of the whole path from source-to-destination. This is done in an offline phase, and only the relay nodes located within the pre-selected multi-hop routes are requested to do online relay probing. Numerical analysis confirms the superiority of the proposed mmWave multi-hop relay probing scheme over the candidate techniques.
A Game-Theoretic Analysis of Shared/Buy-in Computing Systems

High performance computing clusters are increasingly operating under a shared/buy-in paradigm. Under this paradigm, users choose between two tiers of services: shared services and buy-in services. Shared services provide users with access to shared resources for free, while buy-in services allow users to purchase additional buy-in resources in order to shorten job completion time. An important feature of shared/buy-in computing systems consists of making unused buy-in resources available to all other users of the system. Such a feature has been shown to enhance the utilization of resources. Alongside, it creates strategic interactions among users, hence giving rise to a non-cooperative game at the system level. Specifically, each user is faced with the questions of whether to purchase buy-in resources, and if so, how much to pay for them. Under quite general conditions, we establish that a shared/buy-in computing game yields a unique Nash equilibrium, which can be computed in polynomial time. We provide an algorithm for this purpose, which can be implemented in a distributed manner. Moreover, by establishing a connection to the theory of aggregative games, we prove that the game converges to the Nash equilibrium through best response dynamics from any initial state. We justify the underlying game-theoretic assumptions of our model using real data from a computing cluster, and conduct numerical simulations to further explore convergence properties and the influence of system parameters on the Nash equilibrium. In particular, we point out potential unfairness and abuse issues and discuss solution venues.
A Hidden Feature Selection Method based on l2,0-Norm Regularization for Training Single-hidden-layer Neural Networks

Feature selection is an important data preprocessing for machine learning. It can improve the performance of machine learning algorithms by removing redundant and noisy features. Among all the methods, those based on l1-norms or l2,1-norms have received considerable attention due to their good performance. However, these methods cannot produce exact row sparsity to the weight matrix, so the number of selected features cannot be determined automatically without using a threshold. To this end, this paper proposes a feature selection method incorporating the l2,0-norm, which can guarantee exact row sparsity of weight matrix. A method based on iterative hard thresholding (IHT) algorithm is also proposed to solve the l2,0- norm regularized least square problem. For fully using the role of row-sparsity induced by the l2,0-norm, this method acts as network pruning for single-hidden-layer neural networks. This method is conducted on the hidden features and it can achieve node-level pruning rather than the connection-level pruning. The experimental results in several public data sets and three image recognition data sets have shown that this method can not only effectively prune the useless hidden nodes, but also obtain better performance.