Cryptocurrencies have been a massive surge since last decade leads to blockchain technology steps in epical milestone. Blockchain is a distributed database that is shared among the nodes of a computer network. Bitcoin, as a critical role in cryptocurrency system is known as a typical blockchain, maintaining a secure and decentralized record of transactions. The value and advantage of blockchain technology has been acknowledged by the public and applied in difference areas. The defect of blockchain technology is gradually revealed. Excessively wasting electricity have severely affect the utilities usage in many countries. Therefore, DRD Memory Mining is developed to solve the defects. This article illustrates the algorithm of DRD Memory Mining and how the memory consensus mechanism works.

A document by Kuan-Jung Chiang . Click on the document to view its contents.

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of high sampling rates of PMUs. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

There exist several approaches to design the optimal control strategy to harvest wave energy with a point absorber. However they are generally based on the assumption that the WEC and the PTO dynamics are well-known. In the practical WEC control implementation, this is generally not the case. The objective of this paper is to design a robust optimal control strategy that can take into account the uncertain WEC and PTO dynamics. Our choice is a robust adaptive PI control law. The proposed controller is validated and compared through simulation for irregular sea states.

this article is aimed to design two impedances matching network to let the source degeneration and common gate amplifier to achieve the ideal characteristic. In the source degeneration case as the resistance is so small, so we just use down converting matching network. As the source degeneration gives us a high resistance , we just match with a up converting network.

We propose a neuromimetic architecture that can perform always-on pattern recognition. To achieve this, we have extended an existing event-based algorithm (Lagorce et al., 2017), which introduced novel spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built from asynchronous events captured by a neuromorphic camera, these time surfaces allow to encode the local dynamics of a visual scene and to create an efficient event-based pattern recognition architecture. Inspired by neuroscience, we have extended this method to improve its performance. First, we add a homeostatic gain control on the activity of neurons to improve the learning of spatio-temporal patterns (Grimaldi et al., 2021). We also provide a new mathematical formalism that allows an analogy to be drawn between the HOTS algorithm and Spiking Neural Networks (SNN). Following this analogy, we transform the offline pattern categorization method into an online and event-driven layer. This classifier uses the spiking output of the network to define new time surfaces and we then perform the online classification with a neuromimetic implementation of a multinomial logistic regression. These improvements not only consistently increase the performance of the network, but also bring this event-driven pattern recognition algorithm fully online. The results have been validated on different datasets: PokerDVS (Serrano-Gotarredona and Linares-Barranco, 2015), N-MNIST (Orchard et al., 2015a) and DVS Gesture (Amir et al., 2017). This demonstrates the efficiency of this bio-realistic SNN for ultra-fast object categorization through an event-by-event decision making process.

The paper presents algorithms for parallel and vectorized full-word addition of big unsigned integers with carry propagation. Because of the propagation, software parallelization and vectorization of non-polynomial addition of big integers have long been considered impractical due to data dependencies between digits of the operands. The presented algorithms are based upon parallel and vectorized detection of carry origins within elements of vector operands, masking bits which correspond to those elements and subsequent scalar addition of the resulting integers. The acquired bits can consequently be taken into account to adjust the sum using the Kogge-Stone method. Essentially, the paper formalizes and experimentally verifies parallel and vectorized implementation of carry-lookahead adders applied at arbitrary granularity of data. This approach is noticeably beneficial for manycore, CUDA and vectorized implementation using AVX-512 with masked instructions.

The Terahertz (THz) band (0.1-3.0 THz) spans a great portion of the Radio Frequency (RF) spectrum that is mostly unoccupied and unregulated. It is a potential candidate for application in Sixth-Generation (6G) wireless networks as it has the capabilities of satisfying the high data rate and capacity requirements of future wireless communication systems. Profound knowledge of the propagation channel is crucial in communication systems design which nonetheless, is still at its infancy as channel modeling at THz frequencies has been mostly limited to characterizing fixed Point-to-Point (P2P) scenarios up to 300 GHz. Provided the technology matures enough and models adapt to the distinctive characteristics of the THz wave, future wireless communications systems will enable a plethora of new use cases and applications to be realized in addition to delivering higher spectral efficiencies that would ultimately enhance the Quality-of-Service (QoS) to the end user. In this paper, we provide an insight into THz channel propagation characteristics, measurement capabilities and modeling methods along with recommendations that will aid in the development of future models in the THz band. We survey the most recent and important measurement campaigns and modeling efforts found in literature based on the use cases and system requirements identified. Finally, we discuss the challenges and limitations of measurements and modeling at such high frequencies and contemplate the future research outlook toward realizing the 6G vision.

We investigated the effects of mechanical sensor cover damages like scratches, cracks, and holes on point clouds of automotive lidar.

selected active device is essential to design an RF power amplifier for optimum gain and power added efficiency. As they are obtained, these impedances may not be realizable network functions over the desired frequency band to yield the input and the output matching networks for the amplifier. Therefore, in this paper, first, we introduce a new method to test if a given impedance is realizable. Then, a novel “Real Frequency Line Segment Technique” based numerical procedure is introduced to assess the gain-bandwidth limitations of the given source and load impedances, which in turn results in the ultimate RF-power intake/ delivering performance of the amplifier. During the numerical performance assessments process, a robust tool called “Virtual Gain Optimization” is presented. Finally, a new definition called “Power-Performance-Product” is introduced to measure the quality of an active device. Examples are presented to test the realizability of the given source/load pull data and to assess the gain-bandwidth limitations of the given source/load pull impedances for a 45W-GaN power transistor, namely “Cree CG2H40045”, over 0.8 -3.8 GHz bandwidth.

In this paper, we comprehensively present and derive two INDI principle based guidance laws for fixed wing aircrafts. More specifically, two control methods are mathematically derived in detail, where the first decouples the lateral and the longitudinal channels while the second takes the interactions into account. The cumbersome mathematical operations involved in the derivation process aim at reaching a more concise control method and also at providing the community with clearer physical concepts behind this formula. The reason for manipulating transformation matrices is to find a univariate function and to isolate the variable as a virtual input. Efficient and modular guidance control law is then permitted. Lastly, the proposed guidance methods are applied to a 6 dof nonlinear platform under various flight modes to demonstrate the feasibility and advantages.

In this paper, we comprehensively present new advancements towards the working-principle, algorithm, prototype setup and experimental characterization and feasibility demonstration of a new aircraft helmet mounted display aiming system. More specifically, we describe detailed design methodology and algorithm of an eye tracking based HMD aiming system. We also present a proof-of-concept prototype and conduct experiment tests in lab environment to characterize the performance of the proposed system. Lastly, through a comparison with related works, we demonstrate the advantages of the proposed HMD system for free head movement and no additional light source required.

The analysis of intercepted multi-function radar (MFR) signals has gained considerable attention in the field of cognitive electronic reconnaissance. With the rapid development of MFR, the switch between different work modes is becoming more flexible, increasing the agility of pulse parameters. Most of the existing approaches for recognizing MFR behaviors heavily depend on prior information, which can hardly be obtained in a non-cooperative way. This study develops a novel hierarchical contrastive self-supervise-based method for segmenting and clustering MFR pulse sequences. First, a convolutional neural network (CNN) with a limited receptive field is trained in a contrastive way to distinguish between pulse descriptor words (PDW) in the original order and the samples created by random permutations to detect the boundary between each radar word and perform segmentation. Afterward, the K-means++ algorithm with cosine distances is established to cluster the segmented PDWs according to the output vectors of the CNN’s last layer for radar words extraction. This segmenting and clustering process continues to go in the extracted radar word sequence, radar phase sequence, and so on, finishing the automatic extraction of MFR behavior states in the MFR hierarchical model. Simulation results show that without using any labeled data, the proposed method can effectively mine distinguishable patterns in the sequentially arriving PDWs and recognize the MFR behavior states under corrupted, overlapped pulse parameters.

Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is online and adaptive to different data distributions by adaptively determining symmetric or asymmetric probability proportion pairs for quantiles of PIs’ bounds. It relies on the online learning ability of reinforcement learning (RL) to integrate the two online tasks, i.e., the adaptive selection of probability proportion pairs and quantile predictions, both of which are modeled by neural networks. As such, the quality of quantiles-formed PI can guide the selection process of optimal probability proportion pairs, which forms a closed loop to improve PIs’ quality. Furthermore, to improve the learning efficiency of quantile forecasts, a prioritized experience replay (PER) strategy is proposed for online quantile regression processes. Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method. Compared with offline-trained methods, it obtains PIs with better quality and is more robust against concept drift.

Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual's face image with high accuracy. Many techniques have been proposed to ensure user privacy, such as visible distortions to the images, manipulation of the original image with new face attributes, face swapping etc. Though these techniques achieve the goal of user privacy by fooling face recognition models, they don't help the user when they want to upload original images without visible distortions or manipulation. The objective of this work is to implement techniques to ensure the privacy of user's sensitive or personal data in face images by creating minimum pixel level distortions using white-box and black-box perturbation algorithms to fool AI models while maintaining the integrity of the image, so as to appear the same to a human eye.

This manuscript is about frequency characteristic of a uniformly rotating laser gyro

Edge/fog computing is a key enabling technology in 5G and beyond for fulfilling the tight latency requirements of emerging vehicle applications, such as cooperative and autonomous driving. Vehicular Fog Computing (VFC) is a cost-efficient deployment option that complements stationary fog nodes with mobile ones carried by moving vehicles. To plan the deployment and manage the VFC resources in the real world, it is essential to take into account the spatio-temporal variations in both demand and supply of fog computing capacity and the trade-offs between achievable Quality-of-Services and potential deployment and operating costs. Concerning the complexity and the economic load of real-world measurements, simulation becomes a better option at the early research phase to validate capacity and resource management solutions in various urban environments. The existing simulation platforms cannot provide a realistic techno-economic investigation to analyze the implications of VFC deployment options, due to the simplified network models in use, the lack of support for fog node mobility, and limited testing scenarios. In this paper, we propose an open-source simulator VFogSim that allows real-world data as input for simulating the supply and demand of VFC in urban areas. It follows a modular design to evaluate the performance and cost-efficiency of different deployment scenarios under various vehicular traffic models, and the effectiveness of the diverse network and computation schedulers and prioritization mechanisms under user-defined scenarios. Compared with the existing edge/fog computing simulators, such as IFogSim, IoTSim, and EdgeCloudSim, to the best of our knowledge, our platform is the first one that supports the mobility of fog nodes and provides realistic modeling of V2X in 5G and beyond networks in the urban environment. Furthermore, we validate the accuracy of the platform using a real-world 5G measurement and demonstrate the functionality of the platform taking VFC capacity planning as an example.

In this paper, we develop a Verilog-A implementable compact model for the dynamic switching of ferroelectric Fin-FETs (Fe-FinFETs) for asymmetric non-periodic input signals. We use the multi-domain Preisach Model to capture the saturated P-E loop of the ferroelectric capacitors. In addition to the saturation loop, we model the history dependent minor loop paths in the P-E by tracing input signals’ turning points. To capture the input signals’ turning points, we propose an R-C circuit based approach in this work. We calibrate our proposed model with the experimental data, and it accurately captures the history effect and minor loop paths of the ferroelectric capacitor. Furthermore, the elimination of storage of each turning point makes the proposed model computationally efficient compared with the previous implementations. We also demonstrate the unique electrical characteristics of Fe-FinFETs by integrating the developed compact model of Fe-Cap with the BSIM-CMG model of 7nm FinFET.