Miracle-dz/UniFAR-FTUs huggingface dataset:
A Ka-Band Stacked Power Amplifier with 24.8-dBm Output Power and 24.3% PAE in 65-nm CMOS Technology

This paper presents a fully integrated one-stage three-stack Ka-band power amplifier (PA) with neutralization technique in 65-nm CMOS process for 5G applications. A transformer-based power combiner is adopted to combine two differential PA cells to increase the output power. Four small size stacked sets are combined together as one differential PA cell for increasing efficiency. A shunt feedback drain-source capacitor is utilized to divide the output voltage equally between drain and source of each individual transistor in three-stack PA. The proposed PA achieves the measured saturated output power (Psat) of 24.8 dBm with 24.3% peak power added efficiency (PAE), output 1-dB compression point (OP1dB) of 21.7 dBm and 17.5-dB power gain at 38 GHz. The chip size without pads is 0.146 mm2. To the author’s best knowledge, this stacked PA demonstrates the best power performance compared with the reported CMOS PAs around 38 GHz.
Reading and the Response Towards Unknown Single Words and Formulaic Sequences by English Second Language Learners

With advancements in technology, reading task can take place on a computer, where a gloss is only a click away. A gloss can be consulted to find the meaning of any single word (SW) or formulaic sequence (FS). So how does this influence the L2 reader? In an attempt to understand the L2 reader, this study will use a within subject design to look at clicking behaviors, reading comprehension, and characteristics of the individual L2 readers as they complete the task of reading on the computer. This study focuses on 20 targeted lexical items equally distributed between single words (SW) and formulaic sequences (FS). In addition, 50% of these targets take the form of underlined, blue text to consider the properties of typographical saliency. One reading passage, embedded with hyperlinks for single words (SW) and formulaic sequences (FS), was given to 107 participants to read on the computer along with a multiple choice reading comprehension paper test of 20 questions. Statistical analysis surprisingly finds similarities and differences between single words (SW) and formulaic sequences (FS) in both clicking behaviors and reading comprehension scores. These results, demonstrates a need for further evaluation on how L2 readers perform in a reading task, involving single words (SW) and formulaic sequences (FS).
Going global: professionals and the micro-foundations of institutional change

This study links theories of relationality and institutional change to deepen understanding of professionals’ role in globalization. In previous institutional research, it has been conventional to treat professionals as agents of firms or transnational organizations, and institutional change as the result of planned, strategic ‘professional projects’. By bringing a relational analysis to bear on the problem of institutional change, this study reasserts the theoretical significance of individual agency and everyday interactions between professionals and their clients, peers, and organizational environment. It also broadens the model of agency to include invention and improvisation by individual professionals, as a counterpart to collective strategic action. The argument is based on data from a 16-nation study exploring the emergence of a particular ‘globalized localism’: the transformation of the asset-holding trust from a tool of medieval English landowners into a mainstay of contemporary international finance. Drawing on interviews with 61 wealth management professionals in Europe, Asia, Africa, and the Americas, this article uses their accounts of the diffusion and deployment of trusts to specify a new, more detailed model of the ways local practices and ideas develop into global institutions.
Modeling and Performance Evaluation of Generative adversarial network for image denoising

During transmission and reception the images are degraded by noise. Also Images captured using different low quality devices adversely affect the visual quality of images. The presence of the noise results in loss of visibility, gives a mottled, grainy, textured, or snowy appearance. Thus making the image visually unpleasing which drastically affects the human vision. In addition during image processing, presence of noise makes it hard for further processing (impulse, Gaussian white, salt and pepper, adversarial etc.,). Existing methods use conventional filters and Neural network models for image denoising where they compromise with the visibility of image after rigorous iterations of denoising algorithms. In this paper we implement CGANs for image denoising and evaluate the performance of CGAN with different Neural network models viz., CNN ,GAN for single or multiple image denoising problem. The qualitative performance of de-noised image/images is measured using PSNR and confusion matrix.
Novelty-assisted Interactive Evolution Of Control Behaviors

The field of evolutionary computation is inspired by the achievements of natural evolution, in which there is no final objective. Yet the pursuit of objectives is ubiquitous in simulated evolution because evolutionary algorithms that can consistently achieve established benchmarks are lauded as successful, thus reinforcing this paradigm. A significant problem is that such objective approaches assume that intermediate stepping stones will increasingly resemble the final objective when in fact they often do not. The consequence is that while solutions may exist, searching for such objectives may not discover them. This problem with objectives is demonstrated through an experiment in this dissertation that compares how images discovered serendipitously during interactive evolution in an online system called Picbreeder cannot be rediscovered when they become the final objective of the very same algorithm that originally evolved them. This negative result demonstrates that pursuing an objective limits evolution by selecting offspring only based on the final objective. Furthermore, even when high fitness is achieved, the experimental results suggest that the resulting solutions are typically brittle, piecewise representations that only perform well by exploiting idiosyncratic features in the target. In response to this problem, the dissertation next highlights the importance of leveraging human insight during search as an alternative to articulating explicit objectives. In particular, a new approach called novelty-assisted interactive evolutionary computation (NA-IEC) combines human intuition with a method called novelty search for the first time to facilitate the serendipitous discovery of agent behaviors.
An Integrated WPCA-ANN-GA Approach to Solve Multiresponse Optimization Problems

Many Industrial processes have multiple quality characteristics to be optimized. However, among them some are conflicting with each other and optimizing one response causes loss in the other response. Such a problem is called Multiresponse optimization (MRO) problem which involves optimizing conflicting multiple responses simultaneously. Unifying approaches are the most common approaches to solve MRO problem, theses approaches convert a MRO problem into a single response optimization problem by combining the multiple responses into one index, which is used for optimization. Principal Component Analysis (PCA) is one of the tool used to unify the responses. The major advantage of PCA is it takes consideration of correlation among responses. However, there are certain shortcomings as well, this paper address those shortcomings and proposes an Weighted PCA- Artificial Neural Network- Genetic Algorithm (WPCA-ANN-GA ) approach to perform multi response optimization. A case study using data from published literature is presented at the end to demonstrate the WPCA-ANN-GA approach.
Prioritization of Mobile IoT Data Transmission Based on Data Importance Extracted From Machine Learning Model

Predicting real-time spatial information from data collected by the mobile Internet of Things (IoT) devices is one solution to the social problems related to road traffic. The mobile IoT devices for real-time spatial information prediction generate an extremely high volume of data, making it impossible to collect all of it through mobile networks. Although some previous works have reduced the volume of transmitted data, the prediction accuracy of real-time spatial information is still not ensured. Therefore, this paper proposes an IoT device control system that reduces the amount of transmitted data used as input for real-time prediction while maintaining the prediction accuracy. The main contribution of this paper is that the proposed system controls data transmission from the mobile IoT devices based on the importance of data extracted from the machine learning model used for the prediction. Feature selection has been widely used for extracting the importance of data from the machine learning model. Feature selection methods were also used to reduce communication overhead in distributed learning. Unlike the conventional usage of feature selection methods, the proposed system uses them to control the data transmission of the mobile IoT devices with priority. In this paper, the proposed system is evaluated with a real-world vehicle mobility dataset in two practical scenarios using the random forest model, which is an extensively used machine learning model. The evaluation results show that the proposed system reduces the amount of transmitted input data for real-time prediction while achieving the same level of prediction accuracy as benchmark methods.
Deadtime Impact on the small-signal output impedance of Single-Phase Power Electronic Converters

The deadtime is an important factor in the design of power-electronic converters in order to prevent shoot-through faults. The deadtime may also cause a voltage error and an undesired damping effect which, in turn, affect the converter stability. As most effects caused by the deadtime are highly nonlinear, conventional modeling techniques to analyze these effects cannot be straightforwardly applied. This paper proposes a novel frequency-domain approach to model the damping effect caused by the deadtime in single-phase half-bridge inverters. Hardware-in-the-loop (HIL) simulations and laboratory measurements are presented and used to demonstrate the effectiveness of the proposed method.
Distributed Representation of Words in Vector Space for Kannada Language

An objective of neural language modelling is to take in the joint probability function of sequence of words in a language. This is characteristically dif• cult due to the huge computation requirement and curse of dimensionality. A word sequence, the model will encounter during testing is probably going to be not quite the same as all the word sequence seen amid training. Recent works in learning word vector representation are successful in capturing semantic and syntactic relationship between words of a language. These word embeddings are proven to be very efficient in various Natural Language Processing (NLP) tasks like Machine Translation, Question Answering, Text summarization etc. Training word embeddings with neural networks has been prevalent among NLP researchers. Two major models, Continuous Bag of Words (CBOW) and Skip-gram have not only improved the accuracy but also reduced the training time. However, the vector space representation can still be improved using some existing techniques which are rarely used together like subword model, where a word is represented as a weighted average of n-gram representation. Pre-trained word vectors are key requirements in any NLP tasks, generating word vectors for Indian languages has drawn very less attention. This paper proposes a distributed representation for Kannada words using an optimal neural network model and combining various known techniques.
Influence of Ground Impedance on Unbalanced Analysis of Grid

Renewable energy utilization and projects are being used as green energy initiatives worldwide. There are smallest possible renewable energy sources of the order of few KW to large wind mill and PV farms of the capacity of several 100 MW in India. Current systems are hybrid systems having combination of AC, DC, single phase, 3 phase, grounded and ungrounded systems with significant converters for interface with AC grid. Under these conditions the assumption that the transmission, distribution and loads are balanced is perhaps not acceptable. In this respect the influence of ground impedance modeling or more specifically the intentional impedance grounding of the neutral of the 3 phase network must be considered in the analysis. A common assumption made is “Ground potential is always at zero (0)” and neutral voltage can be eliminated in the formulation of analysis. IEEE standards however specify that ground resistance is neither zero nor uniform. Mathematical models are needed that consider the ground impedances and non zero neutral potentials of star connected windings. In this article hypothetical grounding impedances are considered and its influence on power system balanced and unbalanced operation are computed for static performance and analyzed. The outcome of the analysis shows that it is not correct to ignore ground impedance for unbalanced analysis, nor it is correct to assume neutral voltage as ‘0’ in star connected systems.
ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network

The classification of electrocardiogram (ECG) signals is very important for the automatic diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of feature extraction and the step of pattern classification. Owing to recent advances in artificial intelligence, it has been demonstrated that deep neural network, which trained on a huge amount of data, can carry out the task of feature extraction directly from the data and recognize cardiac arrhythmias better than professional cardiologists. This paper proposes an ECG arrhythmia classification method using two-dimensional (2D) deep convolutional neural network (CNN). The time domain signals of ECG, belonging to five heart beat types including normal beat (NOR), left bundle branch block beat (LBB), right bundle branch block beat (RBB), premature ventricular contraction beat (PVC), and atrial premature contraction beat (APC), were first transformed into time-frequency spectrograms by short-time Fourier transform. Subsequently, the spectrograms of the five arrhythmia types were utilized as input to the 2D-CNN such that the ECG arrhythmia types were identified and classified finally. Using ECG recordings from the MIT-BIH arrhythmia database as the training and testing data, the classification results show that the proposed 2D-CNN model can reach an averaged accuracy of 99.00%. On the other hand, in order to achieve optimal classification performances, the model parameter optimization was investigated. It was found when the learning rate is 0.001 and the batch size parameter is 2500, the classifier achieved the highest accuracy and the lowest loss. We also compared the proposed 2D-CNN model with a conventional one-dimensional CNN model. Comparison results show that the 1D-CNN classifier can achieve an averaged accuracy of 90.93%. Therefore, it is validated that the proposed CNN classifier using ECG spectrograms as input can achieve improved classification accuracy without additional manual pre-processing of the ECG signals.
Capacitive Analysis of Hetero Material Gate PNIN-DG-TFET Over Diverge Temperature Range for Superior RF/Microwave Performance

Tunnel FETs overcome the limitations of conventional MOSFETs and offer wide scope for high speed switching analog/RF applications. In most of the research papers, the main focus is to improve the ION current and decrease the ambipolar current in TFETs. However, the parasitic capacitances and ambient temperature also play a crucial role in the performance analysis of the device. Here, in this research work, a Hetero-Material gate, n+ source pocket, dual-gate, TFET (HMG-PNIN-DG-TFET)is proposed for superior switching ratio and its CV analysis is presented for distinct temperature range. The parasitic capacitances have been analyzed with respect to gate and drain biasing for temperature range from 200K to 400K. In addition to it, the influence of temperature variations on transconductance (gm), cut-off frequency (fT)and intrinsic delay (τ)has also been evaluated. Analysis gives a huge prospect to realize efficient analog and RF circuitry with the device proposed (i.e. HMG-PNIN-DG-TFET)in this paper.
Fault prognosis of Engineered Systems: A Deep Learning Perspective

In modern industry, engineered systems are generally required to work under complex operational conditions to complete specific missions. But most of existing data-driven prognostic methods still lack an effective model that can utilize operational conditions data to predict remaining useful life (RUL) of engineered systems. To fill these practical gaps, this paper develops a novel prognostic method based on Bi-Directional Long Short-Term Memory (BLSTM) network. The proposed method can effectively integrate multiple sensors data with operational conditions data for RUL prediction of engineered systems. In the proposed prognostic framework, multiple raw sensors data, operational conditions data, and inspection time epoch are preprocessed to form the desired sequences data with fixed length, and then are taken as main inputs and auxiliary inputs to fed into proposed BLSTM based network. And the labeled RUL are used as actual output for training the proposed model. Different from other deep learning (DL) based prognostic methods, the developed method can simultaneously model both sensors data and operational conditions data in a consolidated framework. The proposed approach is validated through a case study on aircraft turbofan engine, and comparisons with other existing state-of-the-art methods are also included.
An Exact SOCP Formulation for AC Optimal Power Flow

AC Optimal Power Flow (ACOPF) formulation, in general, is nonlinear and non-convex in nature and hence its application to problems like robust system operation, optimal transmission switching, etc. is a herculean task. Convexification of OPF is desired in order to reduce the complexity of solving such problems. Existing convex formulations such as Semi-Definite Programming (SDP), conventional Second Order Conic Programming (SOCP), and so on and their variants result either in significant optimality gap (OG) or reduced OG, albeit with the presence of non-convexity or approximations which bridge the gap between convexity and non-convexity. Due to these challenges, the current practice in the electricity sector is still to use the DCOPF (approximation of ACOPF). Hence in this paper, an exact and novel SOCP formulation of actual ACOPF has been proposed which aims for getting an equivalent convex formulation similar to the actual nonlinear, non-convex ACOPF. This also ensures attaining zero OG for mesh as well as radial networks and hence, the possibility of easily integrating this formulation in OPFs with data uncertainty. The proposed methodology has been tested on a 3 bus, IEEE 14 bus and IEEE 118 bus test systems on GAMS platform. The results thus obtained, have been compared with those obtained from DCOPF, nonlinear, non-convex ACOPF, conventional SOCP formulation for radial networks (SOCP-I) and conventional SOCP formulation for mesh networks (SOCP-II). The results ensure zero OG of the proposed SOCP formulation.
Investigation into Using Compensation for the Nonlinear Effects of the Output of LEDs in Visible Light Communication Systems

This paper investigate the effects of the nonlinear output power to the input current transfer function of a light emitting diode (LED) used as the transmitter in visible light communication systems, and proposes a compensation technique to mitigate the non-linearity. Using an off the shelf red, amber, blue, green (RAGB) LED the nonlinear transfer function is measured and then compensated for. The results show that, for the green LED, which has the highest degree of nonlinearity, an improvement in the received error vector magnitude (EVM)of almost 7 dB can be achieved. We also show that, the improvement in EVM over the uncompensated scheme decreases with increased LED linearity.
An Optimal Lubrication Oil Replacement Method Based on Selected Oil Field Data

The regular replacement of lubricating oil plays a key role in improving machine reliability and reducing unexpected failures of an oil lubricated system. This paper proposes a condition-based maintenance problem with selected oil field data to determine the optimal time of the lubricating oil replacement. The selected oil field data contain health information about the lubricating oil, so the degradation state of the oil can be predicted and the future health condition can be evaluated. The proposed lubricating oil replacement problem is modeled with the evaluated oil health condition in a Markov decision process framework and then, a method for constructing a health index for the lubricating oil is proposed based on information theory to fuse the multiple oil field data and build a degradation progression prediction model. Finally, the proposed method for condition-based lubricating oil replacement is illustrated in a practical case study. The possible applications of an optimal policy for lubricating oil replacement are much wider. For instance, the method can be used as an input to optimize an operational plan and further reduce the maintenance costs.
Trust-Aware Service Chain Embedding

Network function virtualization (NFV) decouples network functions (NFs) from dedicated hardware, leading to significant cost reduction in network service provisioning. With NFV, a network service is represented by a series of inter-connected virtual network functions (VNFs), forming a service function chain (SFC). The problem of placing the VNFs on the NFV infrastructure (NFVI) and establishing the routing paths between them, according to the service chain template, is termed as SFC embedding. The objectives and constraints for the optimization problem formulation of SFC embedding may vary depending on the corresponding network service. We introduce the notion of trustworthiness as a measure of security in SFC embedding and thus network service deployment. We formulate the resulting trust-aware SFC embedding problem as a Mixed Integer Linear Program (MILP). We relax the integer constraints to reduce the time complexity of the MILP formulation, and obtain a Linear Program (LP). We investigate the trade-offs among the two formulations, seeking to strike a balance between results accuracy and time complexity.
Remote photovoltaic outdoor solar lab

This paper presents a photovoltaic (PV) lab with the following key characteristics: (i) it is remote controllable, (ii) it is designed for outdoor use and (iii) it is a low cost solution. In fact, incorporating Internet and remote labs into the practical work setting makes student learning experiences more dynamic, interactive, and interesting. In addition, as the experimentation system operates under real weather conditions, students can move from theory to practice and become capable of assessing the real PV performances, comparing them to those supplied by the manufacturer and evaluating a panel’s performance under real environmental conditions. Finally the low cost solution makes the series of very valuable PV-experiments that are presented in this paper everywhere easily available.