Miracle-dz/UniFAR-FTUs huggingface dataset:
Strained Silicon-on-Insulator Platform for Cointegration of Logic and RF—Part I: Implant-Induced Strain Relaxation

The relaxation of tensile strain in fully depleted (FD) strained silicon-on-insulator (SSOI) by means of ion implantation is experimentally demonstrated in this work. This could enable SiGe p-channel field-effect transistors (pFETs) with high compressive strain (after ion implantation and Ge condensation) to be formed together with Si n-channel field-effect transistors (nFETs) with high tensile strain on the same substrate. From simulations in advanced technology node, 0.8% strain of nFETs and −0.9% strain of pFETs in fin structures can provide saturation drain current and peak <inline-formula> <tex-math notation="LaTeX">${G}_{\text {m}}$ </tex-math></inline-formula> enhancements of ~20%–30%. <inline-formula> <tex-math notation="LaTeX">${f}_{\text {T}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${f}_{\text {max}}$ </tex-math></inline-formula> benefit greatly from strain across the entire range of <inline-formula> <tex-math notation="LaTeX">${V}_{\text {G}}$ </tex-math></inline-formula>, with their peak values increasing significantly, thus allowing SSOI devices to meet 5G targets. In addition, forward back-bias shifts <inline-formula> <tex-math notation="LaTeX">${f}_{\text {T}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${f}_{\text {max}}$ </tex-math></inline-formula> curves toward lower <inline-formula> <tex-math notation="LaTeX">$\boldsymbol \vert {V}_{\text {G}}\boldsymbol \vert $ </tex-math></inline-formula>, enabling reduced power consumption at the same high performance and providing better linearity. Hence, the ability to form highly strained nFETs and pFETs together on a common FD-SSOI substrate paves the way for it to become the ultimate high-performance complementary metal-oxide-semiconductor (CMOS) platform for 5G RF and logic circuits. The relaxation of the tensile strain is demonstrated and documented in Part I. A novel Comb-like device architecture to further enhance the SSOI electrical and RF performance is provided in Part II.
An Efficient Pest Classification In Smart Agriculture Using Transfer Learning

To this day, agriculture still remains very important and plays considerable role to support our daily life and economy in most countries. It is the source of not only food supply, but also providing raw materials for other industries, e.g. plastic, fuel. Currently, farmers are facing the challenge to produce sufficient crops for expanding human population and growing in economy, while maintaining the quality of agriculture products. Pest invasions, however, are a big threat to the growth crops which cause the crop loss and economic consequences. If they are left untreated even in a small area, they can quickly spread out other healthy area or nearby countries. A pest control is therefore crucial to reduce the crop loss. In this paper, we introduce an efficient method basing on deep learning approach to classify pests from images captured from the crops. The proposed method is implemented on various EfficientNet and shown to achieve a considerably high accuracy in a complex dataset, but only a few iterations are required in the training process.
A Single-Stage Dual-Source Seven-Level Inverter for Grid Integrated Photovoltaic Energy Transfer System with MPPT

A new seven-level multilevel inverter (MLI) with dual isolated input for grid-connected photovoltaic energy transfer system (PETS) is proposed in this paper. It has six unidirectional and one bidirectional semiconductor switches for realizing the seven-level output. The MLI finds application in photovoltaic (PV) energy transfer systems, where the requirement of two isolated DC sources is fulfilled by proper solar panel management. The bidirectional switch reduces the control overburden and improves circuit compactness as compared with the cascaded H-bridge MLI. The grid-connected PETS is designed to deliver the available PV power to load, connected in the distribution bus, as well as injects the remaining active power to the grid with a unity power factor (UPF). Moreover, PETS mitigates the reactive and harmonic demand of load irrespective of PV output and maintains the grid power quality within the IEEE-519 standard. The steady-state and dynamic performance of the proposed system is validated by using MATLAB/Simulink model, and the prototype for the MLI is developed and tested in the laboratory.
Enzyme-functionalized electrochemical immunosensor based on electrochemically reduced graphene oxide and polyvinyl alcohol-polydimethylsiloxane for the detection of Salmonella pullorum & Salmonella gallinarum

A novel and ultrasensitive electrochemical enzyme immunosensor based on electrochemically reduced graphene oxide (ERGO) and a polyvinyl alcohol (PVA)-multilayer polydimethylsiloxane (PDMS) film modified screen-printed carbon electrode (SPCE) for the rapid detection of Salmonella pullorum (S. pullorum) & Salmonella gallinarum (S. gallinarum) was proposed in this study. The electrochemical characteristics of the stepwise modified electrodes and the determinations of S. pullorum & S. gallinarum were investigated by cyclic voltammetry (CV). PVA-PDMS was characterized by Fourier transform infrared spectroscopy (FTIR). The conductivity of the electrode was promoted by electrochemically reduced graphene oxide, and the sensitivity of the immunosensor was enhanced and strengthened. The binding capacity and biocompatibility of the ERGO modified electrode was elevated considerably by the PVA-PDMS film. Under optimized working conditions, the sensor showed a good performance with a linear response range from 101 CFU mL−1 to 109 CFU mL−1, and the detection limit was 1.61 × 101 CFU mL−1 (S/N = 3). The proposed enzyme immunosensor had high sensitivity, good specificity, acceptable accuracy and reproducibility, and low detection limit characteristics and could be a promising analytical tool in the detection of S. pullorum & S. gallinarum in practical samples.
A Review of High Gain Inverters for Smart-grid Applications

Impedance Source Inverters with high gain are appropriate for applications with low input DC voltage, such as roof-top applications. These inverters are apt for low-power applications in renewable integration to supplement smart grid, owing to the characteristics like high gain and improved reliability due to inherent shoot-through protection. Despite having various advantages and suitable characteristics for micro-inverter applications, high gain ZSIs pose certain challenges. In order to explore their feasibility, a detailed insight into the challenges is highly essential. This paper identifies the issues that curtail the performance of a high gain inverter. Current Fed Switched Inverter (CFSI), a widely accepted topology, under this category is taken into consideration for analyzing the challenges. The issues that are pointed out are verified using a 600 W lab developed prototype of CFSI.
Efficient Revocable Multi-Authority Attribute-Based Encryption for Cloud Storage

As is known, attribute-based encryption (ABE) is usually adopted for cloud storage, both for its achievement of fine-grained access control over data, and for its guarantee of data confidentiality. Nevertheless, single-authority attribute-based encryption (SA-ABE) has its obvious drawback in that only one attribute authority can assign the users’ attributes, enabling the data to be shared only within the management domain of the attribute authority, while rendering multiple attribute authorities unable to share the data. On the other hand, multi-authority attribute-based encryption (MA-ABE) has its advantages over SA-ABE. It can not only satisfy the need for the fine-grained access control and confidentiality of data, but also make the data shared among different multiple attribute authorities. However, existing MA-ABE schemes are unsuitable for the devices with resources-constraint, because these schemes are all based on expensive bilinear pairing. Moreover, the major challenge of MA-ABE scheme is attribute revocation. So far, many solutions in this respect are not efficient enough. In this paper, on the basis of the elliptic curves cryptography, we propose an efficient revocable multi-authority attribute-based encryption (RMA-ABE) scheme for cloud storage. The security analysis indicates that the proposed scheme satisfies indistinguishable under adaptive chosen plaintext attack assuming hardness of the decisional Diffie-Hellman problem. Compared with the other schemes, the proposed scheme gets its advantages in that it is more economical in computation and storage.
Optimal and Economic Cable Connection for 1 GW Offshore Wind Farm Project Near the Gujarat Coast in India

Wind generation in India is an up-rising power sector in recent times, which is nearly 10% of its total installed generation capacity. India is also exploring the potential of offshore wind energy near the coastal area of Gujarat. This paper presents an optimal and economic methodology to connect wind turbines (WTs) of 1 GW offshore wind farm (OWF) to be constructed near the Gujarat coast. The OWF consists of 500 WTs, with each having capacity of 2 MW. A segment of the proposed layout (about 25% of the total capacity) of the OWF has been identified for this study. Sweep algorithm is used to determine an appropriate number of WT clusters, followed by optimal cabling by minimizing the length of submarine cables of each WT cluster using Clarke and Wright saving algorithm. Each WT cluster is connected to an offshore substation (OS). The optimal placement of OS for the segment of OWF has been determined considering the lowest capital cost for the submarine cable in this study.
Resilient Security Filtering for Networked Switched Systems With Event-Driven Measurements

This paper is concerned with event-driven filtering for networked switched systems with unreliable links. In order to improve the anti-interference performance of the filter, stochastic cyber-attacks and random parametric perturbations are considered in the filter design. The update of the measurement is determined by a newly proposed intermittent event-driven mechanism, which helps to save limited bandwidth resources. By constructing a new Lyapunov-Krasovskii functional (LKF) without positivity and continuity constraints in a triggering interval, sufficient conditions are obtained such that the filtering error system is exponentially mean-square stable with a weighted $H_{\infty }$ performance. The resilient filter and event-driven mechanism are then co-designed. Finally, the results obtained are applied to a switched circuit system.
Induction of Tumor-Selective Cytotoxicity by Myrtus communis L. Extract and its Bioactive Metabolite 1, 8-cineole

Breast cancer is the most common type of cancer in women. Despite current treatments, new treatments should be investigated. The antioxidant and apoptotic activities of plant metabolites have attracted increasing interest in the molecular pharmacology. Here, we investigated the anti-proliferative and apoptotic potential of Myrtus communis L. extracts, used in traditional medicine. To monitor cancer-selective activities, the extract and its most prominent components were assayed in parallel with human MCF7 breast cancer and normal MCF10A mammary epithelial cells. Indeed, we observed a potential therapeutic window of concentrations with significantly higher toxicity for the tumor cells. GC-MS confirmed the presence of α-pinene, 1,8-cineole, linalool as major components. Analyzing these pure terpenoids revealed exclusive toxicity of 1,8-cineole for MCF7 cells, whereas no toxicity was seen for MCF10A cells. In contrast, both α-pinene and linalool did not exhibit tumor selectivity but were toxic for the tumor and normal cells. These data suggest that the anti-proliferative activity of the oil may be mediated by 1,8-cineole with the other components counteracting this potential therapeutic activity, which apperantely acted preferentially on epithelial cells. To gain mechanistic insight, we tested by RT-qPCR whether the essential oil affected apoptosis mediators. Indeed, BCL2 expression was dramatically down-regulated in MCF7 while it was up regulated in MCF10A. Minor differences were observed for BAX, while APAF1 expression was up-regulated in MCF7 and down-regulated in MCF10A. Our data suggest that in M. communis extracts, 1,8-cineole mediates tumor-selective apoptosis by deregulating BCL2 and APAF1.
Towards Cable-to-Cable Connectors for Flexible Thin-Film Superconducting Transmission Lines

We describe our initial approach and progress towards a reliable and useful technique to connect multiple flexible superconducting cables using a cable-to-cable connector. The test structure comprises multiple components, including flexible thin-film superconducting cables, a bridging connector, a silicon well chip, and a polyimide membranes with Cu pillars. The thin-film superconducting cables used in this work contain 12 parallel Nb lines. A bridging connector which was used to connect two superconducting cables had a stripline structure with 12 parallel superconducting Nb transmission lines with 300 $\mu$m pitch among them. A silicon well chip and a polyimide membrane with Cu pillars have been used in the assembly for high-precision micro-mechanical alignment and to apply uniform pressure on the contact points of cables and the bridging connector. DC resistances of signal traces of connected flexible cables were measured at temperature ranging from 293 K to 4.2 K in an evacuated cryogenic environment. We present the details of the design, fabrication, assembly, and test results, which show that this is a promising cable-to-cable connection approach.
Improved Markov Model for Reliability Assessment of Isolated Multiple-Switch PWM DC-DC Converters

This paper proposes a comprehensive Markov model to study the reliability performance of the conventional isolated multiple-switch pulse width modulation DC-DC (IMSDC-DC) converters including full-bridge, half-bridge and push-pull DC-DC topologies. The suggested model helps to achieve more precise outcomes with a better reflection of the real-world operation characteristics by (i) relying on self-embedded fault tolerant capability of the IMSDC-DC converters, (ii) considering the probability of both short and open circuit (SC and OC) faults in each component, (iii) assessing the effect of semiconductors’ SC/OC probability on the converters’ reliability, (iv) analyzing the continuous and discontinuous conduction modes, (v) updating the operational characteristics of the converters after each fault occurrence, and (vi) evaluating control of the converters to have more durable power transfer in some post-fault cases. Then, the model is applied to assess the effects of duty cycle, output power, voltage gain and transformer turns ratio on the overall reliability and mean time to failure. Practical analytic conclusions are followed by some experimental results to verify the accuracy of the proposed model.
A Three-Phase Five-Level Active-Neutral-Point-Clamped Z-Source Inverter

This paper presents a three-phase five-level active-neutral-point-clamped Z-source inverter which combines a modified Z-source network with a three-phase active-neutral-point-clamped 5-level (ANPC-5L) topology. The proposed inverter has high boost factor and improved performance even at low power factor loads. Switching state redundancies of the topology allows easy balancing of capacitor voltages. Operation modes at high and low power factors, voltage relationships, modulation technique and control signal generation for the active switches are described in detail. Simple boost control with carrier-based SVPWM method is used for modulation. MATLAB simulation of the proposed topology, on a 5.4HP, 400V three-phase V/f controlled induction motor drive, is used to verify the performance of the inverter circuit.
Native Resolution Detection for 4K-UHD Videos

There are many resolution enhancement methods to produce 4K-UHD videos including interpolation and deep learning based Super-Resolution, but lack of details, aliasing and unnatural transition are still inevitable and affect viewer’s immersive experience. Therefore, it is necessary to detect authenticity and native resolution of 4K-UHD videos. In this paper, we propose a new native resolution detection method for 4K-UHD videos. The method is based on extracting feature from the DCT frequency domain and integrating this feature in all frames. Our method can determine authenticity of 4K-UHD videos as well as native resolution, no matter which kind of SR model is applied for upscaling.
An unsupervised learning approach toward automatic selection of recognition parameters for mobile robot navigation in less structured environments

Autonomous mobile robots are expected to act in less structured environment such as outdoor environment, specifically aiming for agricultural use. In such environments, it is desired that the recognition of environments for detecting pathway for the robot can be prepared with less processing cost of an engineer’s hand-tuning. This paper presents an unsupervised learning approach to support the acquisition of features required for automatic selection of sensor features and parameters for sensor information processing. As an example of sensor feature, lines are detected from a 2D image obtained by a depth camera, their corresponding 3D configurations are calculated using depth information, and the line segments in 3D space can be used as cues to detect pathways for a mobile robot. Resolution of the image is one parameter that requires adjustment depending on the environment. The proposed method can obtain the necessary features for automatic selection of appropriate resolution parameters by performing mean shift clustering of 3D line configuration for different resolution parameters. The proposed unsupervised learning approach has been tested in multiple environments with diverse appearances and suggested that it is an effective method for automatic selection of appropriate parameters depending on the environment.
A Comparative Evaluation of Top-N Recommendation Algorithms: Case Study with Total Customers

Industrial applications of recommendation systems aim at recommending top-N products that are the most appealing to their customers, often focusing on those products that customers are likely to purchase in the near future. In this experiments and analyses paper, we present an extensive experimental evaluation of various top-N collaborative filtering recommendation algorithms based on a real-world dataset of customer’s purchase history provided by our business partners at TOTAL. Our study aims to compare representative collaborative filtering approaches in practice and study the ones yielding the highest recommendation accuracy, with respect to well-established evaluation measures. These experiments are part of the development of a promotional offers campaign for TOTAL customers owning a loyalty card. We show how different settings for training and applying the selected algorithms influence their absolute and relative performances. The results are valuable to our TOTAL partners as they constitute the first large-scale analysis of recommendation algorithms in the context of their datasets. In particular, the study of the impact of recency in the training set and the role of customer activity and of context in recommendation shed light on a finer design of promotional product campaigns.
Stability Analysis of Neuro-Adaptive SMC-Based Current Sharing Technique for Multiphase Converters in Battery Charging Applications

In the present scenario, battery plays a vital role in most of the domestic and industrial applications as an uninterrupted power source for sensitive loads, storage systems, electric vehicles, etc. Conventional single port chargers are upgraded to multiport circuits in most of the battery charger applications. However, it faces the following problems; 1. Insufficient control over the voltage regulated and current shared among the multiport converters, 2. Transient perturbation and stability control. This paper presents a new hybrid control strategy for an integrated renewable energy load bus. It comprises of Photovoltaic (PV) and Fuel Cell (FC) based source side converters delivering power to the battery chargers connected on the load side. The hybrid control strategy includes; 1. Neural Network (NN) based self-adapting Proportional-Integral (PI) tuner for adjusting the droop resistance values in multiport network and current sharing among the converters, 2. Sliding Mode Controller (SMC) based PWM control for increased voltage regulation with constant current control as well. The hand in hand process of Neuro Adaptive Droop (NAD) - SMC takes care of the proper load sharing, battery charging and a rapid response control system with wide stable region. The proposed self-adapting and self-tracking technique is evaluated in Matlab/Simulink platform and the results were validated in real time prototypical hardware by implementing the control law using dSPACE-MicrolabBox-1202.
An Evaluation of Pairs Trading in Commodity Futures Markets

Pairs trading is an algorithm to trade two cointegrated assets in order to get neutral profits theoretically. This paper is aimed to demonstrate the cointegration concepts and pairs trading algorithms. Also, we need to take reality into account, especially margins and commission fees. We use grid search methods to find ideal combinations of trading signals. It turns out that this trading algorithm can achieve great performances.
Classifying and Solving Arithmetic Math Word Problems—An Intelligent Math Solver

Solving <italic>mathematical</italic> (<italic>math</italic>) <italic>word problems</italic> (MWP) automatically is a challenging research problem in <italic>natural language processing</italic>, <italic>machine learning</italic>, and <italic>education (learning) technology</italic> domains, which has gained momentum in the recent years. Applications of solving varieties of MWPs can increase the efficacy of teaching–learning systems, such as <italic>e-learning systems</italic>, <italic>intelligent tutoring systems</italic>, etc., to help improve learning (or teaching) to solve word problems by providing interactive computer support for peer math tutoring. This article is specifically intended to benefit such teaching–learning systems on arithmetic word problems solving by adding an interactive and intelligent word problem solver to assess an individual's learning outcome. This article presents <italic>arithmetic mathematical word problems solver</italic> (AMWPS), an educational software application for solving arithmetic word problems involving <italic>single equation</italic> with <italic>single operation</italic>. This article is based on a combination of a machine learning based (classification) approach and a rule-based approach. We start with classification of arithmetic word problems into four categories (<italic>Change</italic>, <italic>Compare</italic>, <italic>Combine</italic>, and <italic>Division–Multiplication</italic>) along with their subcategories, followed by the classification of operations (+, –, *, and /) related to different subcategories. Our system processes an input arithmetic word problem, predicts the category and subcategory, predicts the operation, identifies and retrieves the relevant quantities within the problem with respect to answer generation, and formulates and evaluates the mathematical expression to generate the final answer. AMWPS outperformed similar systems on the standard <italic>AddSub</italic> and <italic>SingleOp</italic> datasets and produced new state-of-the-art result (94.22% accuracy).