Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Controlled Medium Access Control Design For Cognitive Radio Ad-Hoc Network

In this work, we propose efficient channel assignment as well as transmission power control (TPC) algorithm to improve the performance of multi-channel single-radio cognitive radio ad-hoc networks (CRAHNs). The channel assignment algorithm aims to assign channels among users in a way that reduces the probability of collision. The TPC aims to tune the transmit power used by nodes. By this mechanism several goals can be achieved, which are: prolonging the battery life, reducing the interference among users, and increasing the frequency spatial reuse. The results show that an enhancement can be achieved when the proposed protocol is integrated to ad-hoc networks, which can be seen through increase in the network’s throughput performance and decrease in access delay. Here, the network simulator 2 (NS2) has been used to verify the theoretical model, which indicates a significant enhancement in network performance.
Efficient Hardware for Generalized Turbo Signal Recovery in Compressed Sensing

Compressed sensing (CS) is becoming a hot topic in recent years for its advantages such as low-power consumption, low memory requirement, and low sampling frequency. However, high-dimensional nonlinear signals will inevitably introduce notable complexity and low efficiency in signal recovery. Generalized turbo signal recovery (G-Turbo-SR) is a cutting-edge method, which efficiently reduces complexity with a partial discrete Fourier transform (DFT) sensing matrix. However, in practical applications, G-Turbo-SR still suffers from high complexity for probability computations and matrix multiplications. This article optimizes the algorithm of G-Turbo-SR in scheduling to reduce the matrix multiplications by half. High-precision numerical approximation method is proposed to replace the complex integral calculation, which efficiently reduces the hardware cost with acceptable performance degradation. Based on the data-flow graph (DFG) analysis, detailed hardware architecture is proposed with module designs. Proper quantization scheme is selected according to the mean square error (MSE) performance. Pipelining, folding, and variable precision quantization (VPQ) scheme are employed for higher hardware efficiency. FPGA implementation on Xilinx 7k325tffg900-2 shows a higher throughput and hardware efficiency compared to existing recovery methods for CS.
Device Identity and Trust in IoT-sphere Forsaking Cryptography

With the exponential growth of the Internet of Things (IoT) ecosystem there is increasing concern regarding how to ensure its security. This is particularly critical because in this ecosystem a significant number of devices are of very low computational capabilities making them particularly vulnerable to attacks. Moreover, cryptographic techniques that are tradition- ally used for establishing trust in entities through identification and authentication also do not appear to be suitable because of computational requirements as well as scalability issues. We present a new vision for security in this ecosystem that does not rely on cryptographic techniques and yet is able to achieve strong device identity. We also present the outlines of a crypto key less trust ecosystem that can be used to implement fine-grained access control in a pragmatic manner.
Enhancing Traceability in Pharmaceutical Supply Chain using Internet of Things (IoT) and Blockchain

This paper proposes an approach to enhance traceability in Pharma supply chain using Internet of Things (IoT) edge devices and Blockchain. Traditional supply chain systems have gaps exploited by anti-social elements to distribute counterfeit medicines and devices. Blockchain ensures source to consumer traceability and tracking which helps to improve efficiency in the industry by ensuring consumer protection, building trust and improving quality of service.
Development of micro-needle array for Tumor vaccine patch applications

Microneedles have been given sufficient potential as a patch for transdermal drug delivery. However, very few product on the application of MEMS for the vaccine delivery is reported. The aim of this paper is to develop the vaccine coated solid microneedle patch by the use of micro-electromechanical systems (MEMS) technology, The fabrication of a silicon microneedle arrays made by UV lithography, inductively coupled plasma reactive ion etching (ICP-RIE) and wet chemical etching process. To solve the limitation of low vaccine volume carried, groove and pocket structure will be introduced. Besides, through the control of formulation of surface tension, viscosity and hydrophilic/ hydrophobic properties by optimization of vaccine formulation composition, the vaccine carried volume can be achieved. Moreover, bioactive of vaccine will be investigated by parameter of the osmotic pressure and phase-transformation. Combination of vaccine formulation and microneedle to fabricate the delivery device, the release profile of the device will be evaluated in vitro, and will be further evaluated in the artificial human skin system to observe the immune response. The research will be included two parts: 1. vaccine coated solid microneedle patch device will be established; 2. design a microneedle patch having specific application, such as enterovirus 71 vaccines, and to realize a commercial available vaccine microneedle patch device. Consequently, a microneedle array with biodegradable porous tips was further developed based on the fabricated microneedles patch.
LLC Resonant Converter With Damping Split Inductor Improving Light-Load Regulation Ability

LLC resonant converters are widely used in On-board Power Converter but the pumping voltage of traditional LLC converter is unavoidable under light-load conditions due to the existence of parasitic parameter. In this paper, an LLC resonant converter with damping split inductor is proposed to improve the light-load regulation capability. The original resonant inductor is divided into two parts in series. Then, a simple damping resistor connected on one part in parallel and the other part can be integrated into the transformer. Using damping split inductor, significant regulation improvements could be achieved under light-load conditions with not any change in control strategy and almost zero hardware cost. In addition, based on impedance analysis of the resonant tank, the effects of the proposed method are analyzed including voltage gain, soft switching characteristics, suppression of high frequency interference components, and system power loss. Moreover, the detailed design process and optimization conditions of the damping split inductor parameters are carefully illustrated. Eventually, the experimental results demonstrate the characteristics mentioned above for the 96 W prototype at the expense of a maximum increase in device loss of 0.85 W.
Intrusion detection based on nonsymmetric sparse autoencoder

In order to solve the problem that traditional machine learning intrusion detection methods can not effectively solve the detection of low-frequency attacks in high-dimensional network, an intrusion detection classification method that combines clustering analysis technology with nonsymmetric Stacked Sparse Autoencoder (NSSA) was proposed. Firstly, the NSSA is used to extract data features and reduce dimension of network intrusion data, and then the output results are reconstructed to balance dataset. Then take the reconstructed low-dimensional and equilibrium data as input, and use the clustering analysis technology combining k-means and Mean Shift to extract features. Take the highly abstract feature data as input, and use the shallow learning classifier random forests as the classifier for intrusion detection. The experimental results show that compared with the traditional machine learning intrusion detection method, the detection accuracy of lowfrequency attacks in high-dimensional networks was improved ,and the training time was reduced.
Model-based trajectory prediction approach using an improved dynamic window approach and an interactive behaviour model

The capacity of predicting trajectories of surrounding vehicles for autonomous vehicles is significant as it would improve safety and smoothness. We propose a novel approach which combined an improved dynamic window approach (IDWA) and an interactive behaviour model for vehicle trajectory prediction. The advantages of this approach are its adaptive ability to different scenarios and its high accuracy. Firstly, an improved approach is proposed to take into account the Ackermann steering constraint and thus the approach would be adaptive to predict vehicle trajectories in different scenarios. Secondly, to improve the accuracy of predicted results, an interactive behaviour model of vehicles is constructed. The proposed approach was tested on a real road dataset NGSIM I-80 (I) and an experimental platform PreScan. The results show that our proposed approach performs better than the baseline approach of the constant velocity model approach in NGSIM I-80 (I) and has an excellently adaptive ability in different scenarios validated in PreScan.
Diagnosis and Classification of Epileptic Seizure a Neurological Disorder Using Electroencephalography

An epileptic seizure is a neurological disorder which is result of sudden excessive electrical discharge from neurons which may cause loss of consciousness. The brain signals can be measured by using Electroencephalography (EEG). In this paper we analyze the EEG signal in time frequency domain and classify the signal as seizure and non-seizure. The available standard online database is used which is acquired by International standard 10–20 EEG placement system. The signal is then preprocessed to remove power noise and eye blink artifact. The features such as mean, standard deviation, variance, skewness and kurtosis are found, which are classified by classifier such as Support Vector Machine, K-Nearest Neighbor algorithm and Probabilistic Neural Network. The performances of above classifier are evaluated on the bases of sensitivity, specificity and accuracy.
Optimal UAV Route in Wireless Charging Sensor Networks

An unmanned aerial vehicle (UAV) can be utilized as a flying data collector and wireless power source in wireless charging sensor networks (WCSNs). Different from conventional studies that focused on maximizing the efficiency of wireless power transfer (WPT) for UAV route design, in this article, we maximize the lifetime of WCSNs by considering sensor energy consumption and energy harvesting simultaneously. We consider simultaneous wireless information and power transfer (SWIFT), where data collection capability guarantees power leftover for UAV to complete its round-trip flight. Our objective is to jointly optimize the UAV hovering location and duration to maximize the minimum energy of sensors after data transmission and energy harvesting under data collection and UAV energy consumption constraints. To tackle this nonconvex optimization problem, we first assume that the UAV hovering location for each sensor is fixed and optimize UAV hovering duration by the Lagrange multiplier method. Next, for each UAV hovering location, we propose a geometry-based update algorithm, which can be used to find initial feasible UAV routes to the problem. Last, a near-optimal UAV route is determined by adjusting the initial feasible UAV route iteratively, where UAV hovering locations and duration are updated at each iteration. The numerical results are provided to validate the performance of our proposed algorithm.
Circularly Polarized Antenna with Low Profile and High Gain

A low profile and high gain circularly polarized (CP) antenna is investigated in this paper. The CP antenna consists of two orthogonal metallic strips and periodic shorting slices on their edges. The strips are fed with 90-degree phase difference at the corner by a single coaxial probe to realize circular polarization. Then four metallic strips are arrayed in sequential rotation and fed by a compact 3/4 ring. The simulated 10-dB impedance bandwidth and 3-dB axial ratio bandwidth are 30.9% (6.76–9.23 GHz) and 18.8% (6.75–8.15 GHz), respectively. The CP antenna with a low profile of $\sim 0.06 \lambda_{0}$ has a peak gain of 13.3dBi.
Fuzzy Cruise Control for Connected Vehicles Based on Vehicle Networking

Vehicle networking cruise system is a complex system with large inertia and nonlinearity. It is difficult to establish an accurate model of the system because of the complex and changeable road conditions. To deal with this problem, a fuzzy cruise control method is proposed in this paper. The cruise system consists of two fuzzy inputs and one fuzzy output. The fuzzy inputs are the speed of the vehicle in the fleet and the distance of the adjacent vehicles, the output is the acceleration of the auto-driving vehicle. Based on the experience, 25 rules are formulated. The information between vehicles transmitted by vehicle to vehicle (V2V) communication. Under the premise of ensuring safety, the optimal tracking is ensured to the maximum extent and the utilization rate of road is improved. This paper relies on MATLAB/Simulink to simulate the vehicle movement in the simulation environment, and verifies that the fuzzy cruise control can achieve good result in the vehicle networking cruise system.
Design of Linear Quadratic Regulator Controller for Sepic Converter

In this paper design of Linear Quadratic Regulator (LQR) controller with integral action for Sepic converter is presented. Small signal model of Sepic converter is derived by applying linearization to average state space model of Sepic converter at steady state operating point. LQR controller is designed by selecting the weighting factors for the states using the energy considerations. Simulation is done in MATLAB/Simulink. Performance of the controller is tested by analyzing the controller response to sudden change in input voltage, reference voltage and load. Detailed simulation study of the controller is presented. Results show the robust performance of the controller.
Circularly Polarized Reconfigurable Patch Antenna with an Annular Slot on the Ground

An annular slot coupled reconfigurable patch antenna with circular polarization (CP) is proposed. By loading two controllable PIN switches and three capacitors across the annular slot, a square radiating patch can be switched between the left-hand CP (LHCP) and right-hand CP (RHCP). The simulated −10dB impedance bandwidth of proposed antenna is 15.4% with 3-dB axial ratio (AR) bandwidths of 3.7%; broadside radiation patterns were observed in both LHCP and RHCP with maximum gains of 8.9 dBic. The proposed design exhibits simple DC bias that can be easily extended to array applications.
Comparative Analysis of Vehicle Make and Model Recognition Using Deep Learning Techniques

Fine-grain vehicle make and model recognition plays a vital role in Intelligent Transport System (ITS). Vehicle Make and Model Recognition has large number of applications in ITS including electronic toll collection, providing visual information for law authority, for various security and authentication purpose. Vision-based fine grained vehicle model recognition is an extremely tough task because of intra-class appearance variation among subordinate-level vehicle classes, with which the fine-grained demonstrate can't be effectively perceived even by a human without domain knowledge. In this way, fine-grained vehicle model recognition needs progressively ground-breaking and discriminative features to classify intra-class objects. This indulges a challenge in this intra-class variety domain, prompting few works of fine-grained vehicle recognition in ITS. Various deep learning strategies can be adequately utilized to defeat this issue of identifying vehicle make and model. Various CNN models with pre-trained weights can be utilized to generate a model which effectively detects if the input image is the front or rear view of the vehicle as well as identify the vehicle make and model utilizing images from both the front as well as rear view.
Research on Smart Health Services Based on Wearable Devices

With the continuous development of medical level, people have higher and higher requirements to medical services. Personal daily health information is of great significance to the real-time monitoring of personal vital signs and health conditions, which can help people understand their physical conditions and detect symptoms early. After consulting a flood of relevant literature in the fields of WIT120, sensing and communication, a set of wearable mobile user health information-aware system was designed, and the application scenarios and future development prospects of the system were systematically analyzed.
Movie Frame Prediction Using Convolutional Long Short Term Memory

Video frame prediction involving predicting the future frames given some input frames has been in the limelight in the recent years. However, robust models have not been developed and tested which could handle the videos involving sequence of frames having characters and objects having high temporal variations. And more significantly there are no models which embrace the sequence of frames which are extracted from movies (Example: Cartoon movies). The focus of this paper is to explore a model which could learn the spatio-temporal consistency amongst the frames which are sampled from a cartoon movie. To this end, we propose a Convolutional Long Short Term Memory based architecture wherein the model harnesses the ability of conventional Long Short Term Memory (LSTM) and convolutional neural network (CNN) to learn the time series and spatial features of the given video frame sequences.
A Clock Synthesis Algorithm for Hierarchical Chains of Homogeneous Clover-Leaves Clock Networks for Single Flux Quantum Logic Circuits

Single Flux Quantum (SFQ) is a promising option for high performance and low power supercomputing platforms. Nevertheless, timing uncertainty represents an obstacle to the design of high-frequency clock distribution networks. The hierarchical chains of homogeneous clover-leaves clocking, $(\mathrm{HC})^{2}\mathrm{LC}$. was proposed as an innovative solution to this challenge. This paper presents a novel algorithm for the physical implementation of $(\mathrm{HC})^{2}\mathrm{LC}$ networks. The proposed method models the (HC)2LC network as a directed graph with multiple cycles representing the synchronizing feedback signals. This graph is then transformed to a directed acyclic graph (DAG) by eliminating feedback edges. The physical location of the nodes in the generated DAG (such as splitters and C-junctions) in the Manhattan plane is calculated using a zero-skew clock embedding algorithm. Additionally, a novel mixed integer linear programming (MILP) based approach minimizes the maximum clock skew among the sinks of the clock network and the sum of the delay of the edges in feedback loops, simultaneously. Experimental results show that using the proposed approach, the average clock skew for five benchmark circuits is 4.6ps.
Implementation of Rectenna using Dielectric Resonator Antenna for harvesting RF energy

It is very important for RF energy harvesting es to optimize output power and to achieve sustainable energy. Here the design of Rectenna is proposed where Dielectric Resonator Antenna is used as antenna to receive RF signal. A microstrip fed Triangular Dielectric Resonator Antenna (TDRA) for ISM frequency is proposed here. It is observed that Triangular DRA exhibits a radiated power of 0.9033W with respect to 1W incident power and maximum gain of 5.3866 dBi. Tha AC signal received by TDRA is given to a rectifier & through proper impedance matching circuit. The rectified pulsating DC signal is given to filter to produce pure dc current. Here the range of 1.5 mA to 2.5 mA is achieved for finite input power.
Adaptive feed-forward control for seeker based on the disturbance observer

In order to improve the tracking and stabilization performance, a control method combining disturbance observer and adaptive feed-forward control using parameterization is proposed for seeker stabilized platforms. The system identification algorithm is used to identify dynamic model of the true plant. Then the discrete-time disturbance observer is designed. The feedback loop controller is designed using pole-placement techniques. The recursive least square (RLS) adaptive filter is used to build the inverse model of the feedback control loop and in this way, the feed-forward control is constructed. The adaptive filter and the feedback loop constitute a control scheme with two degrees-of-freedom (DOF). Tracking experiments conducted on velocity command inputs verify the validity of the adaptive feed-forward controller. Experimental results show that this method responses quickly to commands. For a sinusoidal signal at the frequency of 30Hz, the adaptive feed-forward controller can obtain an amplitude response without attenuation and the phase lag is reduced to 11° in contrast with the feedback control whose phase lag is 60°. This method significantly improves the transient performance and is superior to the feedback control alone.
Cancelable Iris Recognition with DPL

The security of users may be one of the challenges that need to be addressed in the practical applications of iris recognition. In order to prevent the theft of iris patterns, non-invertible transformations are desired to be adopted. Combining random projections (RP) with dictionary pair learning (DPL) mode, a cancelable iris recognition scheme is proposed in this paper. Using the framework of DPL, dictionary pair of synthesis dictionary and analysis dictionary can be learnt jointly for iris pattern classification, which greatly reduce the computation complexity without sacrificing match accuracy. And cancelable iris templates are created with random projections. It is very difficult to extract useful information from the transformed iris patterns, which can enhance the related security. The suggested algorithm of cancelable iris recognition with DPL is also robust with the sectored random projections. Experiment results on public datasets have shown that a robust and accurate cancelable iris recognition can be obtained.
A 7.0–8.0 GHz High Efficiency Medium Power Amplifier Design and 3-D Modeling for FEA

This paper presents a 7.0 GHz to 8.0 GHz medium power amplifier (PA) using a 0.15-μm GaAs pHEMT process. To achieve the wideband high gain and high efficiency, the proposed PA applies a two-stage topology and two series LC output matching network targeting the optimum efficiency impedances over the desired bandwidth. To implement the finite element analysis for this design, a manual modeling method is proposed to generate the 3-D model of the PA in ANSYS WORKBENCH. Measurement results show that the PA achieves a small-signal gain of 26-27 dB, a saturated output power of about 25 dBm with PAE of 47% from 7.0 GHz to 8.0 GHz.
UAV Energy Utility Maximization Algorithm for Cellular UAV-to-Device Communications

In this paper, we consider a cellular unmanned aerial vehicles (UAVs) system where the collected sensory data can be transmitted to the base station (BS) by cellular communications, or to the mobile devices (MDs) directly by overlaying UAV-to-Device (U2D) communications. We optimize the UAV transmit power to maximize the energy utility (EU) of the UAVs, and we solve this problem by convex optimization and extremum principles. Simulation results show that our proposed algorithm can efficiently improve the EU performance of the system.