Miracle-dz/UniFAR-FTUs huggingface dataset:
Seismic Signal Enhancement and Noise Suppression Using Structure-Adaptive Nonlinear Complex Diffusion

In seismic exploration, effective seismic noise attenuation along with seismic signal preservation is a key problem for seismic signal processing. The seismic data acquired from complex environments usually have a low signal-to-noise ratio (SNR) and nonstationary random noise that makes it difficult to accurately restore seismic signals. For enhancing seismic signals and filtering nonstationary random noise, we propose a structure-adaptive nonlinear complex diffusion method (ANCD) based on the regularized complex shock diffusion (CSD) by exploiting the structure-adaptive diffusion coefficients. The proposed complex diffusion method makes use of the imaginary value which is generated by a complex diffusion process as a directional structure indicator to guide the diffusion coefficients, by cooperating with spatially variant structure factors associated with the eigenvalues and eigenvectors of the structure tensor. In this way, the structural-adaptive diffusion term of ANCD allows the diffusion process along seismic structures, and the resulting imaginary part in turn enables the shock term to enhance the seismic signals with steep dips and rapidly varying slopes. The proposed method is tested on the synthetic seismic data and field seismic data acquired from the forest belt and desert area. The results show that the ANCD achieves a superior tradeoff between suppressing complicated random noise and preserving the seismic structures compared with the state-of-the-art seismic denoising methods.
Inconstant Threshold Keeper & Footer Mirror (ITKFM) Technique for Wide Fan-In Domino Logic Gates

Scant power consumption, high-speed behavior of Domino logic has made it one of the most reliable circuit building logic among all the circuit building logics in VLSI. Though domino logic helps in reduction of power consumption and propagation delay to a certain extent, it fails to reduce both simultaneously. The reduction of either of them increases the other simultaneously. Thus, the paper proposes “Inconstant threshold keeper and footer mirror (ITKFM)” technique for Dominos logic gates which reduces the power consumption without violating the propagation delay of the circuits. Using ITKFM approach the inconstant threshold voltage to keeper transistor is provided by employing body bias generator which further reduces the contention current, the technique also provides a footer mirror transistor for reduction of sub-threshold leakage and prevention of possible short circuits. These two features of ITKFM technique helps in consumption power reduction without deteriorating propagation delay of the domino logic gate. Thus, using ITKFM technique power consumption of about 64% was reduced in comparison with conventional domino circuit. The simulation results were obtained for CMOS 22nm device scaling with a power supply of 1 Volt
Delay-Efficient Offloading for NOMA-MEC with Asynchronous Uploading Completion Awareness

Non-orthogonal multiple access mobile edge computing (NOMA-MEC) is proposed to enhance the connectivity between the edge node and users for low- latency computation offloading. However, it is asynchronous for users to complete the task uploading, which complicates the co-channel interference between NOMA users to affect overall offloading delay. In this paper, we first characterize the impact of this asynchronism in task uploading on interference management in NOMA enabled computation offloading. The optimal power allocation is proposed to coordinate the co-channel interference between both NOMA users. Then, we propose a multi- user offloading scheme to jointly optimize offloading decision and NOMA user pairing, aiming to minimize the users' average delay on executing their tasks. The proposed offloading scheme is designed by formulating a binary nonlinear problem, which is solved by the proposed relaxation method and heuristic algorithm. Simulation results demonstrate that compared with other NOMA based schemes, our proposed scheme can effectively reduce the average delay of users and increase the number of users to perform computation offloading.
Analysis of Magnet Configuration on Electromagnetic Performance of High-Speed Generators Using Post-assembly Magnetization

This paper investigates and evaluates the correlation between the rotor magnet configurations and the characteristics and performance of a high-speed micro-turbine generator. Four configurations designed and studied include ring, segmented, modular, and asymmetric magnet shapes. The rotor is often manufactured by pre-magnetized permanent magnets (PMs), in which the magnets would be damaged when being assembled into the sleeve due to compressive force and magnetic force in some manufacturing process. Therefore, post assembly magnetization is applied to magnetize the PMs after the rotor being assembled into the sleeve. The effectiveness of this technique for different rotor configurations is firstly investigated. Then, the harmonic spectrums of back EMF, air-gap flux density, and cogging torque are evaluated. The relation among cogging torque, the vibration, noise and mechanical stress in these magnet configurations are analyzed. Finally, the generator performance is evaluated and discussed in the different resistance load conditions. Finite element analysis (FEA) is used to evaluate and validate the analysis results.
A Switched-Capacitor DC-DC Converter with Unequal Duty Cycle for Ripple Reduction and Efficiency Improvement

This work applies unequal duty cycle to switched-capacitor (SC) dc-dc converters to improve its output ripple as well as the power conversion efficiency. By adjusting the duty cycle, the charge and discharge states of the capacitor are reasonably balanced. Therefore, the output ripple is effectively reduced, and the charge redistribution loss is reduced, improving the efficiency. This scheme is verified with an SC converter with voltage conversion ratios (VCRs) of 1/4 and 1/3, and is simulated in a 65nm low-power process. Operating with a 33MHz switching frequency, an input voltage of 2.4V, an output range of 0.5~25mA, and a VCR of 1/4, the peak efficiency is increased by 1.0% and the output ripple is reduced by 43.6% compared to the equal duty cycle mode.
Discovering Interesting Locations in a Geospatial Region using Association Rule Mining

With the help of GPS enabled devices, people can easily share their photos as well as checking in records on the social media. Most of the time users share their experiences and interact with friends through LBSNs through activities such as making online friends, sharing events, checking in, etc. Checking in is an online activity that tells your friends when and where are you and how are you feeling at that place through social media. The paper describes about mining interesting locations using the association rule mining. Here, the meaning of interesting locations is culturally important places, such as frequently visited public areas such as hotels and theatres, etc. With such information users can know about new locations surrounding them and would enable to choose among many different choices. As there is increase in users of social media where people are continuously uploading the data like geo locations, we aim to use this data which can be easily used for best recommendation. In this study, our main goal is to collect data from Foursquare and utilize them as a user reaction on location. Then to use the apriori algorithm for finding interesting locations with the help of user opinions.
Active Gate Control method for Voltage Balancing of Series-Connected SiC MOSFETs

The series connection of silicon carbide (SiC) MOSFETs and the cascaded system allows operating at a higher voltage than the rated voltage of devices. However, the unbalancing voltage problem occurs in the series-connected devices. The main reason for the unbalancing voltage problem is the parasitic capacitance of the device's gate to the ground. There are several methods for balancing the series-connected device voltage. The snubber circuits, active clamp circuits and the active gate driver. In this paper, the active gate driver method is proposed to control the unbalanced device voltage. Also, the device voltage sensing circuit and the feedback controller are designed in detail. Finally, the proposed active gate driver is verified with the simulation.
Phoneme Based Model for Gender Identification and Adult-Child Classification

Speech is the primary way of communicating messages from a speaker to a listener. The listener can identify details about the speaker’s personality, age, gender, accent, emotions from the voiced signal. In this paper, we address the gender identification and adult-child classification using the phoneme-based parametric model. The multi-component amplitude and frequency modulated (AFM) signal model is suitable for the entire duration of a phoneme, and the estimation of model parameters is robust to various recording conditions. The gender identification and adult-child classification are done using the features extracted from the phoneme-based AFM signal model and applying the support vector machines (SVM) with the k-fold cross-validation (Cv). The experimental results have been performed in the various recording conditions and show that the proposed technique is suitable for gender identification and age classification independent of spoken text, emotion of the speaker, environment and microphone set-up. It is demonstrated that the proposed model-based technique performs with a high probability of correct identification/classification.
Evidential Reasoning for Ship Classification: Fusion of Deep Learning Classifiers

Maritime Situational Awareness (MSA) is becoming critical in a world dominated by complex maritime civilian or military activities, diplomatic and ecological challenges for all coastal countries. Ship tracking, detection and classification through automated surveillance systems help improve the MSA. This paper proposes a new approach to ship classification from an airborne surveillance platform, such as the Aurora CP140. The proposed approach is based on the combination of several deep learning classifiers, using evidential reasoning (Dempster-Shafer theory) to better take into account the uncertainty at the last layer of the classifier. The traditional softmax layer is replaced with more adequate layers to model the uncertainty. Such layers are based on the min-max or ReLu scalings, jointly with additional modeling of the uncertainty. Results obtained from maritime observation videos are compared: the evidential fusion approach provides better classification results than the initial Bayesian classifier.
LoRa-MAB: Toward an Intelligent Resource Allocation Approach for LoRaWAN

For a seamless deployment of the Internet of Things (IoT), self- managing solutions are needed to overcome the challenges of IoT, including massively dense networks and careful management of constrained resources in terms of calculation, memory, and battery. Leveraging on artificial intelligence will enable IoT devices to operate autonomously by using inherently distributed learning techniques. Fully distributed resource management will free devices from draining their limited energy by constantly communicating with a centralized controller. The present work is devoted to a specific IoT context, that of LoRaWAN, where devices communicate with the access network via ALOHA-type access and spread spectrum technology. Concurrent transmissions on different spreading factors increase the network capacity. However, the bottleneck is inevitable with the expected massive deployment of LoRa devices. To address this issue, we resort to the popular EXP3 (Exponential Weights for Exploration and Exploitation) algorithm to steer autonomously the decision of LoRa devices towards the least solicited spreading factors. Furthermore, the spreading factor selection is cast as a proportional fair optimization problem used as a benchmark for the learning-based algorithm. Extensive simulations were run in a realistic environment taking into account physical phenomena in LoRaWAN such as the capture effect and inter- spreading factor collision, as well as non- uniform device distribution. In such a realistic setting, we evaluate the performances of the EXP3.S algorithm, an efficient variant of the EXP3 algorithm, and show its relevance against the fair centralized solution and basic heuristics.
Optimal DG Allocation in Radial Distribution Networks Using a Combined Approach Consisting Particle Swarm optimization9 Grey Wolf optimizer and Loss Sensitivity Factor

In the radial distribution system (RDS), the load demand of this network increases gradually and it is considering a normal process, this lead to increase in the active and reactive power losses, poor voltage profile, and the thermal limit for the cables isn’t be in the acceptable limit. So, it must find solutions to get clear of this problem to deal with the annual increase in the load demand and increase the capacity of the network feeder section, while satisfying the system constraints. In this paper, a Combined optimization Technique (COT) is used based on the hybrid formation of Particle Swarm optimization (PSO) with Grey Wolf optimizer (GWO) called HPSOGWO and loss sensitivity factor (LSF) is proposed to determine the optimal allocation of Distributed Generations (DGs) in radial distribution networks (RDNs). HPSOGWO is considered an efficient hybrid optimization algorithm. However, the COT is depended on two-stage, in the first stage, the loss sensitivity factor (LSF) is employed to choose the high potential buses for reducing the search space and the computational time. In the second stage, the HPSOGWO is decided to select the optimal locations and size of DGs. the COT is examined on standard IEEE 33-bus at load growth.
Polarization Enhanced GaN Avalanche Photodiodes With p-type In0.05Ga0.95N Layer

In this letter, novel heterostructure p-i-n GaN avalanche photodiodes (APDs) with p-type In<sub>0.05</sub>Ga<sub>0.95</sub>N layer are proposed and analyzed through Silvaco Atlas simulations. The introduction of the In<sub>0.05</sub>Ga<sub>0.95</sub>N layer generates a negative polarization charge at the p-In<sub>0.05</sub>Ga<sub>0.95</sub>N/i-GaN heterojunction interface via the piezoelectric polarization effect. Three times higher hole concentration in the p-In<sub>0.05</sub>Ga<sub>0.95</sub>N layer is obtained due to the smaller activation energy of the Mg dopant. Induced polarization charge and increased hole concentration work together to reduce the voltage drop in the p-In<sub>0.05</sub>Ga<sub>0.95</sub>N layer and enhance the electric field intensity in the i-GaN layer. The calculated results show that the heterostructure APD demonstrates a reduced operating voltage of more than 26 V and an improved multiplication gain by an order of magnitude in comparison to the conventional one.
Process-Based Intrusion Detection Method for IoT System with MQTT Protocol

Due to the scalability of MQTT network, the increasing number of devices and network complexity cause the detection of anomalies to be less accurate. In this paper, we proposed a detection method to detect anomalies (i.e. attack and fault) that are hard to detect due to its minor deviation from the normal behavior. We model the network behavior with a process tree, then compare the similarity with the behavior of the observed network events. We showed that by partially checking the behavior of network to the depth of events and its execution relations, we can effectively detect minor deviations.
A Visual Relocation Algorithm With Illumination Changing Robustness

The visual slam algorithm is mainly used for estimating the state of the camera and building a map for the scene, including tracking, mapping, loop closure detection and relocation. The relocation algorithm is to obtain the position and posture of the current camera in the constructed global map. One of the disadvantages of visual slam is that it is susceptible to lighting changes of the scene. Severe lighting changes in the scene can cause locating failure easily. In most practical work, the lighting conditions of the current momentary environment are inconsistent with the lighting conditions at the time of map construction, which may lead to relocation failure when we use visual SLAM. Therefore, a visual relocation algorithm is proposed to improve the success rate of relocation under different lighting conditions in this paper.
Selfish Avoidance Payoff Allocation in Mobile Ad Hoc Network

Every mobile node must provide services in a mobile ad hoc network for sustaining the network. However, servicing others consumes resources. Because of independent and rational nature, a mobile node may refuse to provide services. This behavior leads an inefficient network. In this report, we focus on providing the solution to the problem using game theory. Various methods have been proposed using game theory to address the issue. Here ideas from non-cooperative and cooperative game theory are used for modeling the behavior of the mobile nodes. Non-cooperative game theory determined the best strategy that a mobile node shall choose in the model. Cooperative game theory explored the way to form a coalition that optimizes the benefit of each mobile node. A theoretical model is built to avoid selfish nature of the mobile nodes. The proposed model forces the mobile nodes to converge to a desirable equilibrium point by avoiding selfish nature.
AdaptaBLE: Data Rate and Transmission Power Adaptation for Bluetooth Low Energy

The use of Bluetooth Low Energy (BLE) has been steadily increased since Bluetooth Special Interest Group (SIG) standardized BLE in 2010, and BLE performance has been improved owing to various studies. Bluetooth specification 4.2 defined only one data rate, i.e., 1 Mb/s. However, the specification 5 released in 2016 specifies 2 Mb/s and coded PHYs for higher throughput and more stable transmission, respectively. This version also allows the transmission power to be varied from -20 dBm to 20 dBm. Therefore we are enabled to use various data rates and transmission powers in BLE. However, no research has studied how to adapt these yet. In this paper, we propose an algorithm, termed $AdaptaBLE$, that selects data rate and transmission power of BLE by considering link stability and energy consumption. We implement $AdaptaBLE$ on real devices and measure its performance. As a result, we confirm that $AdaptaBLE$ successfully selects an appropriate TX power and data rate while keeping the BLE link stable.
Reinforcement-Learning-Based Optimization for Content Delivery Policy in Cache-Enabled HetNets

Caching popular contents at radio access networks is a promising approach to improve the content delivery efficiency. Most of the existing content delivery schemes focus on the perspective of content providers, paying less attention to the service demand of content requesters. In this paper, we investigate the content delivery policy of a mobile device with service delay constraint in a cache- enabled heterogeneous network (HetNet), where a macro base station (MBS) is overlaid with some small base stations (SBS) with caches. In the considered network, the mobile device needs to make content delivery decisions based on the time, cache state, and signal-to-interference-plus-noise ratio (SINR) state. The problem of solving an optimal content delivery policy is modeled as a Markov decision process (MDP), where the objective is to minimize the delivery cost of the mobile device under the constraint of content service deadline. In order to address this problem, we propose a reinforcement learning (RL) algorithm to learn the optimal policy. The simulation results demonstrate that our proposed RL-based policy achieves a significant improvement in content delivery cost compared with other benchmark solutions.
Spectral Models of Bigmoon: Experimental Results

Spectral models of the moon are important for the lunar space studies and applications. This preliminary research and experimental work examines spectral studies of the lunar eclipse images during Super moon and studying the variation of spectrum in space domain and attempt to derive a spectral model of the eclipsed moon. The preliminary experimental investigation reveals that spectral properties of the moon are characterized and helps in deriving a spectral model of the moon during eclipse and compare it with the normal moon. From our spectral analysis and studies of lunar images during Bigmoon helps in deriving a spectral model of the moon using power spectral density (PSD) and FFT techniques. The preliminary studies during eclipse helped to derive moon spectral model in terms of power spectra. From the magnitude spectrum studies for is may be observed that, for the normal moon is about 110 Hz/dB whereas 170 Hz/dB for the blood moon and we can conclude that the big moon appears about 28% brighter than the normal moon.
Cooperative Downlink Interference Transmission and Cancellation for Cellular-Connected UAV

The line-of-sight (LoS) dominant air-ground channels have posed critical interference issues in cellular-connected unmanned aerial vehicle (UAV) communications. In this paper, we propose a new base station (BS) cooperative beamforming (CB) technique for the cellular downlink to mitigate the strong interference caused by the co-channel terrestrial transmissions to the UAV. Besides the conventional CB by cooperatively transmitting the UAV's message, the serving BSs of the UAV exploit a novel CB-based {\it \textbf{interference transmission}} scheme to effectively suppress the terrestrial interference to the UAV. Specifically, the co-channel terrestrial users' messages are shared with the UAV's serving BSs and transmitted via CB so as to cancel their resultant interference at the UAV receiver. To optimally balance between the CB gains for UAV signal enhancement and terrestrial interference cancellation, we formulate a new problem to maximize the UAV's receive signal-to-interference- plus-noise ratio (SINR) by jointly optimizing the power allocations at all of its serving BSs for transmitting the UAV's and co-channel terrestrial users' messages. First, we derive the closed-form optimal solution to this problem in the special case of one serving BS for the UAV and draw useful insights. Then, we propose an algorithm to solve the problem optimally in the general case. Simulation results show that the proposed CB scheme with interference transmission and cancellation (ITC) significantly improves the UAV's downlink SINR as compared to the conventional CB without applying ITC.
Early-Late Protocol for Coordinated Beam Scheduling in mmWave Cellular Networks

As a benefit of using highly directional beams in millimeter wave systems, the downlink inter-cell interference (ICI) imposed on the users can be avoided, provided that the beams of neighbor cells do not point towards the user. We exploit this by designing a protocol for network-coordinated time- domain beam scheduling. Specifically, every pair of two neighbor cells maintains a beam collision table for recording the beam pairs that may inflict ICI upon each other. Then, to avoid beam-collision, the two neighbor cells exchange the necessary information to avoid the simultaneous activation of two beams recorded in one pair. More explicitly, our protocol supports a distributed cell coordination method without requiring any information exchanged between the user and the base station, once the beam collision table has been established. Furthermore, our theoretical analysis and numerical simulations demonstrate that the proposed protocol is capable of efficiently mitigating the ICI between the adjacent cells and hence improves the overall network performance.