Miracle-dz/UniFAR-FTUs huggingface dataset:
High Performance Digital to Analog Converter Using CMOS 45nm Technology

This article represents about 8-bit digital to analog converter (DAC). A digital to analog converter (DAC) takes a digital signal and converts it to an analog signal (i.e; continuous form) to drive the interfaces with the real-world such as a speaker in the cell phone or the LED display on your watch. As such, anytime a digital circuit has to interface to a display or a speaker or an antenna or any number of other devices that need to be driven by an analog input and required a digital to analog converter (DAC). Digital to analog converter (DAC) is more efficient and substantially more accurate to do signal processing in the digital domain (i.e language used by computers). This article represents a digital to analog converter (DAC). The DAC was implemented by using 45nm CMOS technology. It also consumes 1.46mW of power from 1.8v supply voltage. The noise margin of the DAC is 9dB.
Comments on “Attribute-Based Data Sharing Scheme Revisited in Cloud Computing”

In this letter, we discuss the security weakness of Wang et al.’s attribute-based data sharing scheme, in IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (TIFS) (DOI: 10.1109/TIFS.2016.2549004). Through designing two concrete attacks, we identify two serious security flaws in their scheme. 1) First, we show that their scheme is insecure because in their scheme any authenticated user can freely tamper with the weight of his own attribute to gain higher level decryption privilege to arbitrarily decrypt the ciphertext belonging to another user with higher weight of attribute. 2) Second, we further demonstrate that their scheme is trivial insecure because in their scheme even any malicious authenticated user’s attribute does not match the access policy of a ciphertext, he/she still has the power to decrypt the ciphertext, i.e., the decryption power is independent of attributes, thus, their scheme is not a rigorous attribute-based scheme. The two weaknesses discovered may hinder their scheme infeasible for practical deployment. Accordingly, we present a remedy solution to the issues while preserving all the security features of the original scheme. We hope that our cryptoanalysis and remedy scheme may contribute to avoiding similar design flaws in future designs.
Reactive Power Compensation using Vehicle-to-Grid enabled Bidirectional Off-Board EV Battery Charger

This paper investigates the application of a grid-connected off-board Electric vehicle (EV) battery charger on the reactive power compensation and simultaneously use as a battery charger (grid-to-vehicle (G2V)) and power generator (vehicle-to-grid (V2G)). The topology of the charger consists of a grid facing front-end AC-DC cascaded H-bridge bidirectional converter, which controls the power flow between the grid and EV battery using a back-end DC-DC bidirectional converter. The charger configuration provides galvanic isolation at the user end from the rest of the system as a safety measure. The proposed control algorithm follows the active power command for G2V and V2G operation along with reactive power command from the utility grid when requested, by controlling EV current and battery current. Furthermore, an adaptive notch filter based controller is designed for system phase estimation and generated reference current synchronization. The proposed control algorithm eliminates phase locked loop (PLL) from the controller design. As a result, the computational complexity of the controller reduces with improved steady-state and trainset performance. Furthermore, a 12.6 kVA off-board charger simulation model is developed in MATLAB/Simulink environment and the performance of the proposed control algorithm is tested during G2V, V2G, and reactive power compensation operation of the EV charger.
Long and Short Term Maneuver Trajectory Prediction of UCAV Based on Deep Learning

Autonomous air combat technology of unmanned combat air vehicles (UCAVs) is a hot issue that is currently being studied by various countries, and maneuvering trajectory prediction is an important part of autonomous air combat research. To address the difficulty of maintaining high prediction accuracy and short prediction time simultaneously in maneuvering trajectory prediction, this paper proposes a maneuvering trajectory prediction method that is based on a layered strategy, which combines long-term maneuvering unit prediction and short-term maneuvering trajectory prediction. In long-term maneuvering unit prediction, the complex trajectory is divided into 21 types of maneuvering units using the four characteristics of maneuvering trajectories, and a maneuvering unit library is established. On the basis of the deep echo state network(DeepESN), to capture multiscale prediction input parameters, autoencoder (AE) technology is incorporated. In addition, to increase the prediction accuracy, adaptive boosting (Ada) learning technology is utilized to build a strong predictor, and seven prediction networks are compared. The results demonstrate that the proposed method realizes the highest prediction accuracy. The single-step prediction time is about 0.002 s, which meets the time requirement. In short-term maneuvering trajectory prediction, the long and short-term memory (LSTM) network is analyzed, and the gaussian random walk strategy particle swarm optimization (GWSPSO) algorithm is used to update the internal weights and biases of the network to overcome the problems of “gradient disappearance” and “gradient explosion”, and a data sharing method is proposed for overcoming the no directionality of optimization algorithms. Compared with four traditional networks, the results demonstrate the method that is proposed in this paper performs better. Compared with the sampling time of 0.3 s, the short-term prediction time of 0.05 s can also meet the requirements. Finally, a long- and short-term layered prediction method is used on a group of complex maneuvering trajectories. The results demonstrate that the prediction accuracy is significantly increased and the real-time requirements are satisfied.
Re-Thinking Analog Integrated Circuits in Digital Terms: A New Design Concept for the IoT Era

A steady trend towards the design of mostly-digital and digital-friendly analog circuits, suitable to integration in mainstream nanoscale CMOS by a highly automated design flow, has been observed in the last years to address the requirements of the emerging Internet of Things (IoT) applications. In this context, this tutorial brief presents an overview of concepts and design methodologies that emerged in the last decade, aimed to the implementation of analog circuits like Operational Transconductance Amplifiers, Voltage References and Data Converters by digital circuits. The current design challenges and application scenarios as well as the future perspectives and opportunities in the field of digital-based analog processing are finally discussed.
A Framework for Context-Aware Query Processing

Using natural language processing (NLP) and artificial intelligence techniques to handle database queries can make query results more useful and accurate, especially in context-aware applications, where their query results depend on the user and the surrounding environment settings. The existing research work [1] can be extended by adding context-aware services to the Location Based Services (LBSS) server of each cell. A new framework has been proposed for handling context-aware queries based on analysis of a user's query text, and a General Query Format (GQF) has been suggested to exchange queries between different database systems (Relational, NoSQL). A prototype has been implemented by using Python 3.6. 10 (Anaconda package) and NLTK 3.5 library to analyze the dependency between text words of a user's query and convert the query text into various formats. The main contributions of this paper are summarized as follows: Selecting a cell location-based service server to provide context-aware services to users in its cell. Suggesting to unify query formats among database systems. Using natural language techniques to analyze the user's query text written in the user's natural language to determine which database will be accessed, and convert it into a standard query format that can be executed by database systems, and a technique for handling the context-aware quires.
OPNN: Optimized Probabilistic Neural Network based Automatic Detection of Maize Plant Disease Detection

Agriculture is one of the important ant sectors of the Indian economy and also an important source of income for Indian people. Approximately 50% of employees worked in the Indian agricultural sector. Many crops such as rice, maize, cotton, and wheat are grown in India. Of these, maize is India's most important food crop and plays a crucial role in food production. The major problem in food production is diseases in plants. The plant diseases are identified using a leaf. Diseases in plants affect the quality and quantity of crops in agricultural production. To avoid the problem, in this study; an efficient automated diagnosis of maize plants has been developed. The proposed methodology is consists of four stages namely, preprocessing, feature extraction, classification, and segmentation. Initially, the images are converted into RGB format and the images present in the noises are removed. Then, the R band is given to the feature extraction stage. Then, the selected attributes are fed to the classifier to classify an image as normal or abnormal. For classification, an optimized probabilistic neural network (OPNN) is utilized. The PNN classifier is improved by using artificial jelly optimization (AJO) algorithm. Finally, the Northern leaf blight disease leaf images are fed to the segmentation stage to separate the affected portion of a leaf. The effectiveness of the proposed lead disease classification is investigated base on the various quality metrics namely, accuracy, sensitivity, and specificity.
AmpleDroid Recovering Large Object Files from Android Application Memory

Analysis of app-specific behavior has become an increasingly important capability in the fields of digital forensics and incident response. The ability to determine the precise actions performed by a user, such as URLs visited, files downloaded, messages sent and received, images and video viewed, and personal files accessed can be the difference between a successful analysis and one that fails to meet its goals. Unfortunately, proper analysis of volatile app-specific evidence, especially the recovery of large objects such as multimedia and large text files stored in memory has not been explored. This is mainly because the allocation function in the various Android memory management algorithms handles large objects differently and in separate memory regions than small objects. Thus, in this paper our effort is focused on developing an app-agnostic memory analysis tool capable of recovering and reconstructing large objects from process memory captures. We present AmpleDroid, a tool that identifies and extracts large objects loaded in an application memory space. Our methodology involves the inspection of the process image to identify vital Android runtime data structures utilized during large object allocation. AmpleDroid is evaluated on a number of apps and the results shows the recovery of almost 91% of the allocated large objects from process memory
Model approximation of an arm of the NAO™ robot using system identification

This paper proposes a methodology to obtain a model approximation for the SoftBank NAO™ robot. It is worth to stress that the NAO™ robot is a closed commercial platform, namely, most of the parameter values are not available. Then, to improve the knowledge about dynamic behavior and to carry out further analysis, a mathematical model is necessary. The identification is achieved by basic knowledge form undergraduate control engineering courses, such as system identification, classic control, state space and robot dynamics. Model identification allow us to obtain unknown parameters of a first-order approximation of the mechanical dynamics for the NAO™ robotic arm. Additionally, a theoretical dynamic model is obtained by means of the Euler-Lagrange approach. Both viewpoints are complemented in order to compute the missing model parameters and to obtain a satisfactory approach. This demonstrates that even simple models can replicate without significant error the system dynamics
Functional and physicochemical properties of flours of six Mucuna species

Legume flours were prepared from six species of mucuna bean, M. Veracruz mottle, M. rajada, M. cochinchinensis, M. deerigeana, M. pruriens and M. veracruz white. Physicochemical and functional characteristics were carried out on full fat and defatted flours. Bulk density of the flours increased following defatting. Isoelectric point of the proteins lies between 4 and 5. Generally, solubility reduced as the pH increases until it reached isoelectric point, followed by progressive increase in solubility with further increase in pH. Defatted flours have higher water and oil absorption capacities compared with full fat samples and M. veracruz white recorded the lowest value (1.40 g/g) while M. veracruz mottle had the highest value (2.20 g/g). Gelation studies revealed that M. veracruz mottle and M. rajada recorded the highest values (20%) while M. veracruz white and M. deerigeana had the lowest value (14%). The foaming capacity in full fat flours ranged between 9.6% in M. veracruz white and 19.23% in M. pruriens while the foaming capacity in defatted flours ranged from 50.0% in both M. pruriens and M. veracruz white and 84.30% in M. veracruz mottle. In addition, foaming capacities in full fat flours are lower than those of defatted flours. Emulsion capacity ranged between 78-90% in full fat flours and 56-68% in defatted flours.
Non-Technical Losses Detection in Smart Grids: An Ensemble Data-Driven Approach

Non technical losses (NTL) detection plays a crucial role in protecting the security of smart grids. Employing massive energy consumption data and advanced artificial intelligence (AI) techniques for NTL detection are helpful. However, there are concerns regarding the effectiveness of existing AI-based detectors against covert attack methods. In particular, the tampered metering data with normal consumption patterns may result in low detection rate. Motivated by this, we propose a hybrid data-driven detection framework. In particular, we introduce a wide & deep convolutional neural networks (CNN) model to capture the global and periodic features of consumption data. We also leverage the maximal information coefficient algorithm to analysis and detect those covert abnormal measurements. Our extensive experiments under different attack scenarios demonstrate the effectiveness of the proposed method.
Trend Analysis and Countermeasure Research of DDoS Attack Under 5G Network

DDoS has been terrorizing network operators since its birth in the late 20th century, and people are constantly researching new methods and techniques to mitigate or solve DDoS attacks. However, with the network technology update iteration, DDoS attacks also emerge in endlessly. When we exclaims the high speed experience brought by 5G, we should not forget that the network is a double-edged sword, and the security problem of 5G network is also not to be underestimated. As traditional DDoS defenses continue to fail, researchers who have grown more aggressive in recent years have seen some promising, low-cost technologies emerge. This paper first analyzes the trend of DDoS attacks in the past two years, then analyzes some DDoS defense mitigation schemes based on SDN, NFV and MTD, and points out some challenges faced by using new technologies to defend DDoS attacks.
AAM: An Advanced Attention Module in Convolutional Neural Networks

With the development of convolutional neural networks, attention has been successfully applied to object detection, object classification, and other fields. Meanwhile, it has significantly improved the performance in these scenes. To fully find the attention’s ability out, this paper uses the FaceBoxes as a baseline and studies some famous attention modules’effect for face detection, like SE, CBAM, and ECA. According to the results, an advanced attention module (AAM) based on CBAM is proposed and tested on the validation dataset of the WIDER FACE. The performance is better than the above attention modules. Compared with the baseline, it is increased by 2.0%, 3.0%, and 2.8%, respectively, and compared with the CBAM by 0.1%, 0.9%, and 1.1%, which fully proves the effectiveness of AAM, and it does not increase model size.
Analysis & Control Design of Different Level VSI Based DSTATCOM

This research work presents, different levels of voltage source inverter (VSI) based distributed static compensator (DSTATCOM) in the operation of 3-phase low voltage distribution system. It examines the harmonic distortion of traditional two-level VSI (6-pulses), three-level VSI (12-pulses), five-level VSI (24-pulses) and seven-level VSI (36- pulses). This DSTATCOM is attached at the point of common coupling (PCC) of the electrical utility system (EUS) for mitigating the power quality (PQ) related problems. The main function of the used icosɸ control technique to provide appropriate switching pulses for the semiconductor devices of VSIs, which will help the EUS to eradicate harmonics as per the IEEE-S19-2014 grid code prescribed total harmonic distortion (THD) values. The motto of this work is to select the suitable level of VSI for better performance of EUS.
Factorization Machine based Music Recommendation Approach

Since, people are listening to different songs nowadays, selecting an appropriate recommendation algorithms is remaining as a bottleneck in several music fields. In this scenario, the proposed research work has the tendency to aim and assess the chance in which a user can hear the song repeatedly once the primary apparent listening expertise begins within the timeframe. If the user encompasses a continual sense modality prevalence among a month following the primary apparent listening event, its goal is one and otherwise zero. The proposed research work has used the approaches like content-based filtering, collaborative filtering, and factoring machines approach. Finally, the proposed factorization machine approach has obtained the highest accuracy.
Forsterite powder prepared from water‐soluble hybrid precursor

Partially polycondensed y-aminopropylsilanetriol (APSTOL) was used as the silica precursor to prepare magnesium orthosilicate in the form of crystalline forsterite. Aqueous solutions of APSTOL and magnesium acetate were mixed and dried forming a water-soluble product that was converted into forsterite by heating to 1,000°C. The thermal evolution of the system was studied using TGA, FTIR, UV-VIS spectrophotometry, and XRD techniques, especially for the acetate decomposition and transformation of CSiO 3 -tetrahedra into the orthosilicate anions. The combustion of organics and the crystallization of forsterite proceed at temperatures 350 and 870°C, respectively. At intermediate temperatures, the system contains amorphous silicates with oligomeric and polymeric anions, which gradually depolymerize as temperature increases. The resulting crystalline product contains less impurities (periclase and enstatite) than samples prepared using other wet-chemistry techniques.
Optimization of Energy and Area of a Randshift: Fault-Tolerant Technique using FPGA design flow

Stuck-at faults occur in secure non-volatile memories due to endurance issues. In non-volatile memories data is available to access for many years even after the power shut down, which enables unauthorized access to an attacker. Here, we use Advanced Encryption Standard (AES) algorithm which is powerful encryption algorithm in recent years to provide security for confidential data in cloud computing systems. We are also using random characteristics of the AES algorithm along with rotational shift operation that helps in fixing the faults present in main memories. The implementation of Randshift technique in FPGA design flow by using simulation analysis shows less energy consumption and area-efficient parameters compared to recently proposed fault-tolerant techniques.
Polar optic phonon and Γ→L intervalley scattering times in GaAs from steady‐state hot‐electron luminescence spectroscopy

We deduce the Γ→L intervalley and polar optic phonon scattering times of hot electrons in bulk GaAs from cw hot (e,A0) luminescence spectra at low excitation densities and their dependence on electron kinetic energy. We obtain the lifetime broadening due to these two processes from comparison with line shape calculations using a 16×16 k⋅p Hamiltonian, a full integration over k space, and a dipole model for the optical matrix elements. We find for the LO‐phonon emission time τLO=(132±10)fs. The threshold for Γ→L scattering is determined as 330±10 meV, above which a distinct decrease in total lifetime is observed. Γ→L scattering times of 150–200 fs are deduced, and we discuss a corresponding deformation potential.
Dropwise Condensation on Micro- and Nanostructured Surfaces

In this review we cover recent developments in the area of surface-enhanced dropwise condensation against the background of earlier work. The development of fabrication techniques to create surface structures at the micro- and nanoscale using both bottom-up and top-down approaches has led to increased study of complex interfacial phenomena. In the heat transfer community, researchers have been extensively exploring the use of advanced surface structuring techniques to enhance phase-change heat transfer processes. In particular, the field of vapor-to-liquid condensation and especially that of water condensation has experienced a renaissance due to the promise of further optimizing this process at the micro- and nanoscale by exploiting advances in surface engineering developed over the last several decades.
Thematising online food risks: Comparison of a manual tagging procedure and topic modelling

One of the main needs to face in front of a huge amount of contents is to classify them in themes. The present study compares a manual tagging with an automatic procedure implemented in the context of Machine Learning applied to food risk issues. For a year, web sources have been monitored through the web monitoring application Web-Live®, developed by the company Extreme s.r. l. (http://www.web-live.it) and 12,163 contents were collected. Subsequently, the items were in parallel labelled according to two procedures: a manual (Elo & Kyngäs, 2008) and an automatic one (cf. Tuzzi, 2003), that is the Latent Dirichlet Allocation (LDA) (Blei, Ng, & Jordan, 2003) implemented in the “topicmodels” package (Grün & Hornik, 2011) available in R. Discrepancies and overlapping of the labelling and the classification have been observed using the data visualisation software Qlik Sense®. Both procedures highlighted mostly the same contents as regards the labelling goal, and return a similar classification regarding the overlapping topics. The analysis of both outputs showed that the automatic procedure preferably returned precise and detailed topics, whereas the manual procedure enabled more levels of tagging. Results have been further discussed highlighting the criticality and potential of the approaches addressed, to inform any additional application.