Miracle-dz/UniFAR-FTUs huggingface dataset:
Attribute-Based Access Control(ABAC) with Decentralized Identifier in the Blockchain-Based Energy Transaction Platform

With the development of ICT technology, it has also been applied to the power grid, but as a target of hackers’ attack, a security solution to protect the power grid is being studied. Attribute-based Access Control (ABAC) is one of the most commonly used security solutions by providing granular and flexible access control, but it threatens privacy because it determines access control based on user attribute values. With user privacy emerging as a new topic, Decentralized Identity(DID) is emerging as a new alternative for protecting user privacy. In this paper, we propose a DID-based ABAC to solve the problem of ABAC’s privacy exposure and apply it to a power transaction platform.
M-Sequence Radar for High Resolution Ranging with Mixed-Signal Radar Receiver Baseband Using 130nm SiGe BiCMOS Technology

An m-sequence radar with a high chip rate of 20 Gcps is presented that makes use of the large bandwidth available in the V-band (40–75 GHz) or at 240 GHz to reduce the detection resolution to 7.5 mm. Measurement results of a mixed-signal radar receiver baseband (BB) integrated circuit designed using 130 nm SiGe BiCMOS technology are presented along with a novel radar ranging concept for the mixed-signal radar BB.
Supply-Modulated PA Performance Enhancement by Joint Optimization of RF Input and Supply Control

This work presents a performance improvement technique for supply-modulated (SM) power amplifiers (PAs). Differently from the typical solutions, the SM PA is treated as a double-input nonlinear dynamic device, for which a dynamic supply shaping function and an RF digital predistorter are jointly identified by means of a gradient-based multi-objective optimization (MOO) technique. Simulation-based tests show that the MOO-based optimal inputs can greatly enhance the SM PA effectively exploring, in an automatic way, the best compromise between linearity and efficiency.
Diode laser spectroscopic measurements of gas temperature in a pulsed dielectric barrier discharge using collisional broadening and shift of 1s3−2p2 and 1s5−2p7 argon transitions

The average gas temperature rise within an argon dielectric barrier discharge operating from 50 to 500 Torr excited by 5 kHz repetition rate fast rise time high voltage pulse has been measured. For these measurements within high pressure discharges, we have employed two novel gas temperature measurement techniques based upon tunable diode laser absorption spectroscopy that take advantage of the nonresonant collision line broadening and collisional frequency shifts from relatively strong argon 1s3-2p2 and 1s5-2p7 transitions from the metastable states. An in situ estimate of collisional broadening coefficients for both transitions have been obtained from 5 to 30 Torr data using an independent estimate of gas temperature from Doppler line width measurements. Our measurements show that the effect of the isolated line assumption inherent within the van der Waals collisional impact approximation limit begins to fail for the 1s3-2p2 and 1s5-2p7 spectral lines when collisional line broadening exceeds ∼23 GHz lin.
A Cost-Effective W-band Slotted Waveguide Antenna

The remarkable progress in millimeter-wavelength radar and communication systems has given a strong motivation for further developing slotted waveguide antennas. In this paper, a novel W-band monopulse antenna is presented. The antenna represents a circular disk with 2044 radiating slots arranged within a 140 mm diameter circle. It is equipped with an integrated dual-plane monopulse comparator. The antenna gain exceeds 39 dBi. The total antenna weight and thickness are as low as 170 g and 4.5 mm, respectively. The design algorithm capable of achieving necessary electrical characteristics and satisfying rigidity requirements and manufacturing limitations is described. CNC machining is used to fabricate the antenna parts which makes serial production of such antennas both cost-effective and well reproducible.
A Charge Pump Phase-Locked Loop with Fast Locking Mechanism for FPGA in 28nm CMOS Technology

Phase-locked loop has been widely applied in FPGA, which is used to provide high-quality clock. In this paper, a charge pump phase-locked loop with a new fast locking mechanism embedded in Field Programmable Gate Array is proposed. Based on the traditional structure of PLL, some new modules are added to reduce the locking time, involving a VCO frequency detection module, a pre-charge module and a configurable filter. The simulation results show that this structure can effectively reduce the locking time of the PLL.
Over 40W, X-Band GaN on SiC MMIC Amplifier

In this paper we present the study and obtained results of a MMIC High Power Amplifier operating in X-band. The device is designed using 250nm GaN HEMT on SiC process. The targeted specifications include pulsed operation from 8 to 10.5GHz. In pulsed mode and 28V of drain voltage, an output power more than 40W with (37-40)% PAE has been achieved throughout the bandwidth, while remarkable 52.4W output power with associated 37% PAE has been recorded at 8.75GHz for a 32V of drain voltage. The HPA stability has been studied by considering the influence of external decoupling components and dedicated testing environment.
EczemaNet: A Deep CNN-based Eczema Diseases Classification

Eczema is the most common among all types of skin diseases. A solution for this disease is very crucial for patients to have better treatment. Eczema is usually detected manually by doctors or dermatologists. It is tough to distinguish between different types of Eczema because of the similarities in symptoms. In recent years, several attempts have been taken to automate the detection of skin diseases with much accuracy. Many methods such as Image Processing Techniques, Machine Learning algorithms are getting used to execute segmentation and classification of skin diseases. It is found that among all those skin disease detection systems, particularly detection work on eczema disease is rare. There is also insufficiency in eczema disease dataset. In this paper, we propose a novel deep CNN-based approach for classifying five different classes of Eczema with our collected dataset. Data augmentation is used to transform images for better performance. Regularization techniques such as batch normalization and dropout helped to reduce overfitting. Our proposed model achieved an accuracy of 96.2%, which exceeded the performance of the state of the arts.
Research and characterization of blazar candidates among the Fermi/LAT4FGL catalog using Machine Learning

Several machine-learning algorithms were tested to identify blazar candidates among the 4FGL unassociated sources. Using the Scikit-learn toolkit, for a given set of discriminant parameters, methods based on random forests, multilayer perceptions, and support vector machines could reach comparable performance with a very little tuning. We obtain 510 blazar candidates, by combining the results of three classifiers.
Electronic Medical Record Data Sharing Through Authentication and Integrity Management

Electronic Medical Record or EMR are very sensitive data for the patients as well as the hospitals and government organizations. Now a days the data of patients are stored in the medical local storage which are very vulnerable to the invaders and hackers as the data security and privacy threat are high. In addition, these centralized systems lack interoperability and access limitation. So, we propose an EMR system that can control the access management and storage via Blockchain, Ethereum. From IPFS we use the IPFS hash to the transaction into the block of the Blockchain. Together with IPFS, IDS and a Customized Authenticator Service (CAS) we propose a model for authentication and integrity management. The CAS provides two-way authentication for the authorized users making the model more secure. IDS check the signature of the incoming request and notify the system about threats. Security analysis shows that our system works is a secure and effective way for data sharing for EMRs.
A Self-Sovereign Identity Architecture Based on Blockchain and the Utilization of Customer’s Banking Cards : The Case of Bank Scam Calls Prevention

The need to use a wide variety of online services is rising every day in today’s digital age. In order to authenticate and authorize users to access services, identity management is essential. To solve several problems such as fragment identity repositories, aim for identity theft and data breaches and identity correlation, the evolution of identity models across siloes, centralized and presently self-sovereign identity based on distributed ledger is emerging. Self-sovereign identity returns the power to the proprietor of the identity and puts the user at the core of the model. In this paper, we propose a framework to identify scam bank service calls and avoid possible loss of personal accounts. Our framework is based on Hyperledger Indy, a public and permissioned Distributed Ledger Technology. The proposed framework strengthens the Know Your Customer process and enhances the trust of third party agencies delegated by banks to deliver many financial items.
A Novel Demand Response Strategy for Sizing of Hybrid Energy System With Smart Grid Concepts

The sizing problem of the hybrid energy system (HES) is a crucial issue especially in rural communities because any wrong results can mislead the decision makers for building the new HES. Due to the intermittent nature of the renewable energy sources (RES) such as wind and PV, there will be a need for a high storage system and/or a standby diesel engine, which increase the investment, required, and increases the cost of energy (CoE). The use of smart grid concepts like the demand response (DR) using dynamic tariff can improve the system performance, enhance the stability, reduces the size and investments of HES components, reduces the customers’ bills, and increases the energy providers’ profits. The DR strategy will allow the customers to share the responsibility of the HES stability with the energy providers to maintain the stability of the HES. The DR strategies should be selected to ensure the balance between the available RES and the load requirements. In this article, a novel DR strategy is introduced to model the required change in the tariff with the battery state of charge and its charging/discharging power. The novel DR strategy is used in the sizing of the HES based on techno-economic objectives using three different soft computing optimization techniques. This article introduces modeling and simulation of the smart grid integrated with hybrid energy systems to supply a standalone load for a rural site in the north of Saudi Arabia. The sizing of the HES is built based on minimizing the CoE and the loss of load probability. The novel DR strategy introduced in this article reduced the size of the HES compared to the fixed load technique by 20.66%. The results obtained from this novel strategy proved its superiority in the sizing and operation stage of the HES.
Plant Species Recognition using Local Binary and Local Directional Patterns

Automatic plant species recognition is an intriguing and challenging problem which has vital applications in important real-world areas such as determining plant health, identifying new species and conserving biodiversity. Therefore, deriving an effective plant species representation from images is vital for successful species recognition. In this article the robustness of computer vision texture-based recognition techniques, such as Local Binary and Local Directional Pattern implementations, are evaluated for capturing discriminant textural features of leaf images. Furthermore, classification is done using both K-Nearest Neighbour and Support Vector Machine classifiers on the Swedish and Flavia leaf datasets. The results show that both these local patterns are successful at encoding dominant textural characteristics of leaf images to uniquely identify different plant species. The highest result obtained with the proposed methodology on the Flavia and Swedish leaf datasets are 96.94 % and 98.22 % identification accuracy respectively.
Empirical Model For the Propagation of Shockwaves Produced by Underwater Pulsed Current Discharge

The propagation characteristics of shockwaves produced by underwater pulsed current discharge (UPCD) and used in industrial applications have received considerable attention. However, the direct measurement of shockwave intensity is challenging, especially in the near-field area. The high electromagnetic field and strong mechanical impact in the near-field area may destroy the pressure sensor and digital recording terminal. The shockwave pressure is obtained based on the UPCD and Schlieren observation system. The calculation model of the shockwave is established based on the Tait and Rankine–Hugoniot equations; the pressure shockwave in the near field is obtained from the Schlieren images. The empirical model of the propagation characteristic of shockwave pressure induced by UPCD is obtained and verified through a comparison of the calculated and measured pressure intensities. The results show that the empirical model can be used to obtain the propagation characteristic of shockwaves induced by UPCD, including pressure in the near.
A Novel Hash Function Based on a Chaotic Sponge and DNA Sequence

Many chaos-based hash functions have convoluted designs that are not based on proper design principles, complicating the verification of security claims. We address this problem by proposing a hash function based on a chaotic sponge construction and DNA sequence. DNA sequence is used to design state transition rules of a deterministic chaotic finite-state automata (DCFSA), a chaotic structure that enhances the chaoticity of digital chaotic maps. We use a DCFSA configuration consisting of four states associated with logistic maps. Analysis shows that the DCFSA configuration is efficient and has high chaotic complexity. Both DCFSA and DNA are used to design the sponge-based hash function. An input message is transformed into a DNA sequence and divided into fixed-length blocks. Each block is absorbed into a sponge structure via DNA-XOR. The sponge state is then updated with a new DNA sequence by iterating DCFSA as a compression function. Statistical evaluations indicate that the proposed hash function has near-ideal diffusion, confusion, collision resistance and distribution properties. The proposed hash can also extend the provably secure notions of the sponge construction, specifically the indifferentiability from random oracles. Furthermore, the hash function has a high level of performance as compared to other chaos-based hash functions.
A Radar Target Simulator for Generating Synthesised and Measured micro-Doppler-Signatures of Vulnerable Road Users

In this contribution, a radar target simulator for generating time-varying Doppler shifts to simulate micro-Doppler signatures of vulnerable road users, is presented. Using a digital-to-analogue converter, directly measured data as well as synthesised data can be used as a micro-Doppler input signal. With a maximum bandwidth of 1.5 GHz within in the frequency range of 76 to 81 Ghz it is well suited for the automotive industry.
Voltage-Source Control of DFIG in Standalone Wind Power-Based Microgrids

As the penetration of wind power increases, there is a growing operational demand on Wind Turbine (WT) systems to participate in microgrids frequency regulation. Traditional grid-connected Doubly-Fed Induction Generator (DFIG) control strategies are basically dependent on the power grid which is strong enough through the phase-locked loop (PLL). Traditional control strategies are not applicable to the weak grid with a relatively large short-circuit ratio (SCR). A new method must be introduced to let the wind farm actively respond to system frequency changes. This paper presents a control strategy which enables DFIG to behave as a voltage source. The droop curve is designed to realize the power sharing between DFIGs and battery energy storage system (BESS). Also, the grid frequency and phase angle required for coordinated transformation are no longer obtained through the phase-locked loop, but generated through the droop controller. Simulation results under various operation conditions are presented to verify the effectiveness of the proposed control strategy.
Disturbance Observer-Based Control for Three-Phase Grid-tied Inverter With LCL Filter

This paper presents the design and performance evaluation of disturbance observer-based control for grid-tied inverter with LCL filter. The proposed controller consists of combining state feedback control with disturbance observer. Specifically, the composite controller is developed to ensure stable and accurate regulation of the current injected to the grid despite the variation in the system parameters. Such variations can be compensated by the disturbance observer, while the state feedback control takes on the responsibility to ensure the stability of the closed-loop system. The developed controller is designed in the αβ stationary frame with a view to cope with both balanced and unbalanced grid voltages. In such a reference frame, the disturbance observer is established to compensate for sinusoidal disturbance with a view to ensure asymptotic tracking of sinusoidal current reference. This mandates using a disturbance observer that has a resonant property to provide accurate estimate of the sinusoidal disturbance. However, under control saturation, the resonator buildup can cause the transient response to deteriorate by exhibiting a large overshoot. This problem is overcome in this work by considering control saturation in the design process. Simulation tests were conducted to evaluate the performance of the controller under different operating conditions. The obtained results show that the proposed controller is capable of achieving a perfect tracking of sinusoidal reference with a good transient response even during control saturation.
Short-time Series Load Forecasting By Seq2seq-LSTM Model

With the development of intelligent, flexible, interactive, and renewable energy power generation systems with higher penetration rates, the short-term load forecasting of individual power users plays an increasingly important role in future grid planning and operation scheduling. Due to the high volatility and uncertainty of the power load sequence, the power load prediction of individual energy users is quite challenging. Considering the impact of historical information and future information that affects the current state on the prediction results, this paper proposes a short-term load forecasting model by Seq2seq-LSTM based on Long-short-term Memory (LSTM) recurrent neural network to solve this difficult problem. In this framework, residual LSTM and Scaled Dot-Product (SDP) Attention are integrated to improve the accuracy of short-term forecasting of single-user power consumption. In order to verify the effectiveness and efficiency of the method, the actual measured single-user load data is used as an example and the model is compared with three state of the art. Experimental results show that the model proposed in this paper is superior to other models in terms of prediction accuracy and algorithm robustness.
A 24-27GHz Phased Array Transceiver with 6bit Phase Shifter and 31.5dB Gain Control for 5G Communication in 65nm CMOS

A 24-27 GHz phased array transceiver with 6bit phase shifter (PS) and 31.5dB gain control is presented for 5G communication. The transceiver consists of 6bit PS based on the passive vector modulation topology, and two digitally controlled variable gain amplifiers (VGA) with variable gain control range of 31.5dB and 0.5dB step. LNA and PA with embedded T/R switch are optimally co-designed to achieve excellent performance of the 5.2dB NF and 14.5dBm OP-1dB. The transceiver achieves $2^{\circ}- 3.6^{\circ}$ RMS phase error, 0.2-0.4dB RMS gain error over 24-27GHz. The core area is only 1mm2, and the power consumption is 160mW/250mW in the RX/TX mode.
Edge-magic total labeling algorithm and its application

Let G be a simple connected graph with p vertices and q edges, there exists a bijective function f from $V(G)UE(G)$ to the integers 1,2,…, $p+q$, such that the sum of any edge label and its incident vertex labels is equal to a constant k, f is called an edge-magic total labeling(EMTL for short) while k is magic constant. In this paper, an edge-magic total labeling algorithm (EMTL algorithm for short) is designed, which has a lot of graphics processing capabilities. Several complex research problems in some open literatures can be solved easily and effectively.
Integrating Stacked Sparse Auto-Encoder Into Matrix Factorization for Rating Prediction

Currently, collaborative filtering technology has been widely used in personalized recommender systems. The problem of data sparsity is a severe challenge faced by traditional collaborative filtering methods based on matrix factorization techniques. A lot of improved collaborative filtering methods have been proposed to alleviate the data sparsity problem; However, due to the sparsity of the user rating matrix, the latent factor learned by these improved methods may be not efficient. In this paper, we propose a novel recommendation algorithm named SSAERec by integrating stacked sparse auto-encoder into matrix factorization for rating prediction, which can learn effective representation from user-item rating matrix. Extensive experiments on three real-world datasets demonstrate the proposed method outperforms other baselines in the rating prediction task.
A low phase noise W-band MMIC GaN HEMT oscillator

This paper presents a fundamental mode W-band MMIC balanced Colpitts oscillator implemented in an advanced 60 nm gallium nitride (GaN) high electron mobility transistor (HEMT) process from OMMIC foundry. The oscillator operates around 85 GHz with measured peak output power of nearly 0 dBm and phase noise at 10 MHz offset of -120 dBc/Hz. To the best authors’ knowledge, the phase noise is state-of-the-art value for W-band monolithic microwave integrated circuit (MMIC) GaN HEMT oscillators.
A circuit-algorithm codesign method to reduce the accuracy drop of RRAM based computing-in-memory chip

Deep neural networks have exhibited superior performance in many areas such as image classification and so on. Due to the “memory bottleneck the energy efficiency of traditional chips is limited. The RRAM based computing-in-memory chip offers a new computing paradigm to remove data transfer between computing units and memory units. However, the nonidealities of RRAM device and analog circuits will reduce the on-chip accuracy. Here, we propose a novel codesign method to reduce the accuracy drop in the post-mapping phase of computing-in-memory system. First, we introduce the architecture of computing-in-memory chip and develop the compact model of the device and peripheral circuits. To reduce the accuracy drop, we present a novel codesign method by add nonidealities of device and circuits in the training phase of neural network. Finally, we found the post-mapping accuracy could recover more than 20% accuracy after device and circuit aware training.