Miracle-dz/UniFAR-FTUs huggingface dataset:
Outage Performance of Multi-Carrier Relay Selection for Full-Duplex OFDM With Index Modulation System

This letter focuses on the multi-carrier relay selection (RS) problem in a two-hop full-duplex relay-assisted orthogonal frequency-division multiplexing with index modulation system which employs a decode-and-forward communication protocol. Specifically, two widely used multi-carrier RS schemes termed as bulk RS and per-subcarrier RS are adopted in the proposed system. The exact expressions and asymptotic expressions of the average outage probabilities over all subcarrier activation patterns are theoretically derived. All the analyses are numerically verified by the Monte Carlo simulation results, which proves that the system can achieve the full diversity gain that equals the number of relays by multi-carrier RS schemes, thus greatly improving the system reliability.
A Review on Segmentation of Plant Maladies and Pathological Parts from the Leaf Images in Agriculture Crop

Each country has a conspicuous place for agriculture production. Hence it needs to have continuous caring in order to have sustainable growth of the country. Disease maladies on the plant are very crucial for sustainable agriculture. So, to overcome the issues here in this paper, we exhibited a concise survey on automatic identification of maladies and pathological parts from the leaf Images of plants inside the farming Crop. Identification and acknowledgment of maladies in plants abuse advanced image technique is incredibly compelling in giving indications of characteristic illnesses at its beginning periods. Plant pathologists will dissect the digital image abuse digital image process for diagnosing of crop maladies. Wellbeing checking and maladies identification on a plant are extremely difficult for supportable agriculture. Physically, it's hard to screen the malaise in a plant. It needs a huge measure of work, expertizes in the plant maladies, and furthermore has need of the exorbitant handling time. Henceforth, image processing technology is introduced to in order to identify and detection of the plant maladies. In this paper, we study the existing detection techniques such as thresholding, clustering based methods, Neural Networks, and backpropagation, etc. But these methods suffer from a few limitations such as more number of iterations, less segmentation accuracy, and boundary leakages. Based on the literature review, In order to overcome the said limitations, changes are made in the pre-processing an image which leads to good segmentation results. Here many hybrid approaches are introduced in the pre-processing such as Optimization methods, deep learning, Illumination Reflectance-based clustering methods etc.to get superior segmentation of plant diseases from agriculture crop leafs images.
Application of Predictive Analytics at Financial Institutions: A Systematic Literature Review

The issues that relate with the automatics and intelligent tracing of the transaction and banking customer become the main issues nowadays. Whereas most of transaction that already moved to digital and online transaction that needed more intelligent features on the Predictive Analyst. The rise of predictive analytics (PA) is one of the biggest disruptions in financial services. In the last years, PA methods grew sharply to give the baseline of data driven decision-making process for forecasting future business situation. PA uses various algorithms to discover different patterns in the big data environment that might create more value for businesses including in financial institutions. The potential implementation of data science in financial industry is still to be explored. In this paper, we summarize work done on PA at financial industry. We also explore the potential application of PA and investigating how data science and big data is going to be used in financial institutions in the future.
An Impedance Based Modeling Towards the Aging Prediction of Lithium-Ion Battery for EV Applications

Modeling of Lithium-Ion Battery (LIB) is essential for studying its behavior under different operating conditions like temperature, load current and state of charge. The parameters of a LIB such as capacity, open circuit voltage, impedance can be characterized by using a suitable model according to the application. This paper has proposed an impedance-based equivalent circuit modeling (ECM) approach for electric vehicles (EV) to estimate the aging of a LIB (LiFePO4) and to prevent the system, where the battery is installed, from failure. The aging has been performed experimentally using a real electric motorcycle load profile and the impedance test results for the aged LIB at different cycles have been fitted and analyzed with a chosen ECM. In addition, the same ECM has been used to analyze and compare the aging for the same battery type with a profile achieved using a hybrid energy storage system (HESS) consisting of LIB and supercapacitors. The ECM for HESS profile showed smaller impedance change and smaller capacity reduction with aging compared with the ECM for the battery profile. Therefore, it validates that HESS has a longer cycle life than battery energy storage system.
Investigating the Possibility to Extend Planar Technology to 10 nm Scale with UTBB DMG SSOI MOSFETs

Nowadays, ultrathin body-and-BOX (UTBB) and back-gate (BG) biasing are becoming very popular tools in strengthening the implementation of CMOS technology below 22 nm technology node. The present report provides some in-depth design guidelines to optimize the performance of UTBB strained-Si-on-insulator (SSOI) MOSFETs with the help of dual-metal-gate (DMG) accompanied by back-gate (BG) control. The dual-metal-gate (DMG) electrode, on one hand, boosts up the transport efficiency in the channel region and reduces the hot-carrier effects (HCEs) as well. On the other hand, the back-gate biasing competently suppresses short-channel effects (SCEs) with amplified scaling opportunities. The optimization of device parameters is done up to 10 nm channel length with acceptable subthreshold swing, SCEs and drain induced barrier lowering (DIBL) by using the ultra-thin BOX for enhancing back gate controllability with lightly doped drain (LDD). The present appraisal of device characteristics is based on numerical simulations carried out comprehensively on two-dimensional device simulator ATLAS™ by Silvaco Int.
A Model-free Flat Spin Recovery Scheme for Miniature Fixed-wing Unmanned Aerial Vehicle

The present paper proposes a Deep- Reinforcement-Learning-based (DRL-based) model-free flat spin recovery scheme to recover a miniature unmanned aerial vehicle (UAV) back to steady level flight swiftly. Two types of deep reinforcement learning (DRL) are utilized for the two recovery phases to fully exploit DRL’s strengths in model-free situations. In the first phase, the angular rates of UAV are attenuated swiftly by a deep Q-network (DQN); in the second phase, the UAV is continuously regulated with a novel algorithm termed LA-DDPG, which is the deep deterministic policy gradient (DDPG) exploring by learning automata (LA). Simulation tests show that the proposed recovery scheme can recover the UAV from flat spin mode to steady level flight with satisfactory performance without relying on accurate prior models or other control structures. Furthermore, LA-DDPG exhibits better stability and efficiency than DDPG.
Alginate-silk fibroin Bioink : A printable hydrogel for tissue engineering

Bioink design is a big challenge in bioprinting of functional tissues. Bioink serves as extracellular matrix providing structural support and guiding growth and development of tissue. Alginate (SA) and silk fibroin (SF) have been used to create hydrogels due to their excellent biocompatibility and biodegradability. In this study, we investigated printability and cell compatibility of alginate-silk fibroin hydrogels for bioprinting. Different concentrations of SA-SF solution were tested for printability by pushing the mixture through needle and syringe. For Cell compatibility study, osteosarcoma were encapsulated in SA-SF hydrogel, determined cell viability using Live/dead and PrestoBlue™ assay. SA-SF hydrogel containing 1%(w/v) sodium alginate and 2% (w/v) silk fibroin successfully formed uniform strands and supported cell viability. The hydrogel was easy to control mechanical properties by adjusting SA and SF concentrations which could be a tool for studies integrity, printability and mass transport of hydrogels. We demonstrated SA-SF hydrogels has great potential as bioink for use in bioprinter.
Learning from Time Series with Outlier Correction for Malicious Domain Identification

Malicious domain identification is an important task in the field of cyberspace security. However, most of existing work for this task heavily relies on expert experience when constructing machine learning features. What makes matters worse is that these features can be deliberately changed by attackers. As a result, such malicious domain identification methods are easily bypassed by cyber criminals. To solve this problem, in this paper, we propose a novel method for malicious domain identification by effectively learning time series shapelets, the discriminative local patterns of time series. More specifically, our method consists of two main components: 1) modeling user's habits of accessing domains by learning shapelets from domain time series. As the domain time series is generated by the crowd visiting websites, the learned user's habits of accessing domains can potentially reflect what type of service a domain provides, such as pornography, gambling and so on. 2) an outlier correction algorithm designed for a single time series and independent of the model which can enhance the robustness of shapelet initialization. We integrate shapelet learning and outlier correction in our model. Extensive experiments on real-world dataset demonstrates that our proposed method has better performance compared with state-of-the-art methods.
Optical Characterization of ZnO Nanoforest for Hardware Security Applications

Disordered systems or materials with intrinsic variability in some measurable quantity are recently being considered for hardware security applications like physical unclonable functions or PUFs. An inexpensive optical PUF system based on a dense nanoforest of ZnO nanorods is presented here. A relatively concentrated seed solution used in the synthesis process results in these high density nanorods grown mostly in the upright direction with some vertical hollow spots in between neighboring nanorods. This nanorod arrangement grown on optically reflective substrates works as a nano-porous thin film and produces Fabry-Pérot fringes under perpendicularly incident focused white light. The local variations in density, height, diameter, orientation angle, and hexagonal perfection of the ZnO nanorods give rise to unique and reproducible scattering light spectra that vary significantly from one spot on the sample to another. Over 1500 optical measurements are performed to show the strongly variable optical characteristics. These distinct spectra show promise as the raw responses for ZnO-based optical PUFs.
Evaluation of GP-SLAM in real-world environments

In this work, an introduction to a novel laser-based 2D Simultaneous Localization and Mapping (SLAM) approach, named as GP-SLAM, is made. The proposed method is based on a new type of map representation utilizing a regionalized Gaussian process map reconstruction algorithm. Two real-time experiments are conducted in indoor environments in order to evaluate the performance of this approach in real-world environments. In both experiments, a mobile robot equipped with a 2D laser scanner obtained its trajectories and learn the map of the environments simultaneously. The proposed method is compared with the widely-used Gmapping method in the identical scenarios. The results of those experiments testify the efficiency, accuracy, robustness and capacity of our approach to meet the requirements of real-time tasks.
Research on Personal Credit Risk Assessment Index System Based on Internet Social Big Data

The traditional personal credit risk assessment index system is difficult to adapt to the needs of today's Internet era, and Internet social big data can easily and efficiently collect personal behavior data on the Internet and provide important information for discriminating personal credit. This paper discusses the advantages of Internet social big data and the scientific and rationality of introducing it into the personal credit risk assessment index system. It establishes 19 evaluation indicators, uses the AHP method to assign weights to each indicator, and quantifies the indicators. Finally, a personal credit risk assessment index system based on Internet social big data was formed. The TOPSIS method was used to analyze the simulation data and verify the rationality of the index system.
Discussion on Regular Hexagon Array Parameter Optimization in Microwave Power Transmission

Regular hexagon array is provided with good symmetrical characteristic, plane expansibility and array validity. A weight structure of regular hexagon array symmetrical distribution is proposed in this paper, and the array pattern expression of that structure is provided, which simplifies the amount of calculation of parameter optimization. The optimal matching relation among transmitting antenna array aperture, receiving plane aperture, distance between transmitter and receiver and array main lobe width is analyzed with a new perspective, a series of beam parameter within optimal weight distribution of different aperture is numerically calculated under matching relation, and the characteristic of the optimal beam is analyzed.
Identification of Walker Identity Using Smartphone Sensors: An Experiment Using Ensemble Learning

Nowadays MEMS sensors, like accelerometers, gyroscopes, and magnetometers, are spreading in a wide range of applications, because of their small size, cheapness and increasing performance. For instance, smartphones are currently equipped with this kind of sensors, which could be used to improve the user experience of the phone itself or the navigation functionalities. In this work, accelerometers, gyros, and orientation measurements are exploited to provide advanced information about the walker bringing the phone. In particular, smartphone sensors outputs are used to recognize the identity of the walker and the pose of the device during the walk. The aforesaid information, if known, could be used to improve specific smartphone functionalities. For instance, the recognition of walker identity can be used for theft protection or the device pose can be used to improve the performance of the pedestrian navigation. Machine learning algorithms have been effectively adopted in several fields to solve problems involving classification, time series prediction, pattern recognition, and object detection. Herein, a novel hierarchical approach for classification is applied to data produced by smartphone sensors in order to recognize the previously described contexts, obtaining effective results.
A Novel LTCC-based 3D Packaging for Ultrawideband RF Applications

This paper realized high-density stereo packaging based on LTCC by researching the 3D integration method of integrated devices in double-sided cavities on an independent LTCC substrate, which satisfied the requirements of integration and airtightness of multiple chips, solved the problem of high-density stacking integration of chips in broadband RF system, and increases the integration density of functional units by more than doubled. The stack planning and interconnection structure design of the board solve the contradiction between ultrawideband (0.1-40GHz), low insertion loss, high density and low cost. Finally, the design and optimization of the multi-temperature gradient assembly process ensured the engineering realization of the package and completed a small batch trial production, the size of an individual product is 30mm×30mm×5.6mm, and the weight is 16g.
Version Detection in Spreadsheets Based on Headers Similarity

In almost every company, institution, business, or even for personal use people employ spreadsheets to perform a variety of tasks. Spreadsheet programming is the most commonly used end-user programming language. Spreadsheets users create new spreadsheets based on existing ones and reuse data layout and computational logic, so there are multiple versions of spreadsheets. End-users have no background of professional programming skills. That is why they are not well aware of the importance of version control and keeping the record of previous documents. So, the version information is usually missing, and it creates a lot of mess when some error is found in one spreadsheet that needs to be resolved in all of its other versions, and it becomes almost impossible when users deal with a very large number of spreadsheets. In this paper, we propose an automatic approach to cluster spreadsheets and find different versions of spreadsheets based on the similarity of headers present in spreadsheets. The header detection is not done in a conventional way that is present in literature, but we use font attributes in order to detect headers. We applied our approach on 40 spreadsheets randomly selected from spreadsheet corpora like Enron, FUSE, and EUSES. The evaluation results showed that the proposed approach could detect versions of spreadsheets with higher precision and recall rates of 82.24% and 78.48% respectively. The F1-Measure achieved is 80.08%.
Benchmarking Hand and Grasp Resilience to Dynamic Loads

In this work, we investigate the behavior of artificial hands under impulsive load conditions. Resilience to impacts has been seldom considered in grasp and manipulation literature and benchmarks, although it is one of the most relevant issues in a number of applications involving physical interactions with unstructured environments, in prosthetic as well as in robotic manipulation. We focus on two research questions: the capability of hands to withstand impacts before losing the grasp on a object (Grasp Resilience) and before being damaged (Hand Resilience). To these aims, we introduce an evaluation framework, including a precisely defined experimental setup and test procedure. The proposed methodology, metrics, and test variables are discussed through analytical evaluation and with experimental data extracted from the testing of three different hand designs.
Reliability and Variability of 1S1R OxRAM-OTS for High Density Crossbar Integration

HfO2 OxRAM was co-integrated with an optimized OTS back-end selector in 1S1R memory arrays showing outstanding performances. Up to 3 decades of current window margin and 5 decades of selectivity were achieved. More than 106 programming, 108 reading and 109 read disturb cycles were demonstrated. Ultra low OTS leakage current is compatible with 100Mb-1Gb bank size. Semi-analytical model of OTS was developed to analyze stochastic switching behavior. Variability of voltage margin, selectivity and current margin were deeply investigated in a statistical way for the 1st time to identify the most critical features for high-density crossbar arrays.
Comparative Analysis of Modified Modulation Scheme for Three-phase Voltage fed QZS Inverters

Single-stage converters are one promising solution in different field of applications. In particular, quasi-Z-Source VSI topology structure playsan important role because it presents the same advantages of VSI and qZS converters. In addition, it allows increasing the inverter reliability. Moreover, modulation schemes and control strategy are still being developed. For this reason, aim of this paper is to introduce a modified modulation scheme based on the Maximum Constant Boost Control (MCBC) concept and Switching Frequency Optimal (SFO)as reference signals that represents the novelty issue of this work. The analysis has been carried out by comparing the performance of the converter in terms of qZS network requirements, voltage stress and AC current harmonics between MCBC and SFO MCBC modulation schemes.
Deep Feature Extraction From the Vocal Vectors Using Sparse Autoencoders for Parkinson’s Classification

Parkinson’s disease (PD) is a chronic, neurodegenerative disorder that mainly affects the motor system of the body. The progression rate is very slow but, as the condition worsens it stimulates the non-motor characteristics, especially mood disorders, hallucinations, delusions, and other observable cognitive changes. Genetic and environmental factors greatly influence the risk of Parkinson’s development. The early diagnosis of PD improves the condition through proper treatment. During the initial stage of PD, vocal impairments become more common among the affected individuals. Advanced studies on vocal disorders give more assistance for precise PD detection. In this paper, the vocal features of PD affected individuals are analyzed with sophisticated computational models. Initially, the samples are pre-processed, as it contains more missing values. Then the predictor candidate subset is identified from the processed vocal features using the Adaptive Grey Wolf Optimization Algorithm, a meta-heuristic global search optimization technique. Furthermore, the latent representation of the candidate features is extracted through sparse autoencoders for effective discrimination between the PD affected and control cases. For classification, six supervised machine learning algorithms were employed. The proposed model is trained with the data, divided through 10-fold cross-validation scheme and the performance is evaluated based on validation metrics. The PD dataset is accessed from University of California (UCI), Irvine Machine Learning repository to conduct the experimental analysis. The result shows that the proposed algorithm outperformed the benchmarked models, exhibits its efficacy in distinguishing the affected and healthy samples of PD. The outcome of this study emphasizes the significance of intelligent learning models in complex disease diagnosis.
Embedded RTOS for a Smart RFID Reader

Location information systems are widespread and the demand for them is growing due to the increasing spread of mobile devices. The information can be used by context-aware systems to provide the user with relevant data corresponding to current actions. In a museum chamber the user can receive detailed information of the surrounding exponents, or when changing the rooms during working hours the office phone can be redirected to the current location. A couple of different location aware infrastructures are available in order to collect position data. Mobile phone towers, GPS satellites, video cameras, badge proximity detectors are just a few of the possible solutions. Through the mentioned sensors, position or proximity information can be obtained. In this paper a system concept based on smart Radio-Frequency Identification (RFID) readers will be presented. Through the proposed system location properties will be provided to the context-aware system. Details of the embedded real time operating system of the smart reader will be provided in order to manage the complex protocol of communicating through RFID technology.