Miracle-dz/UniFAR-FTUs huggingface dataset:
A 78-93 GHz Power Amplifier with 19.6-dBm Psat and 12.8 % PAEpeak in 0.1-µm GaAs pHEMT for Radio Astronomical Receiver System

A W-band power amplifier (PA) for radio astronomical receiver LO chain in 0.1-μm GaAs pHEMT technology is presented in this paper. This PA is a 3-stage common source design using transmission lines to implement matching network. In order to increase the bandwidth, two sections matching networks for output are utilized to maintain the matching loci within the Q = 1 contour of Smith chart. A 90 degrees Lange coupler is employed to enhance the bandwidth and to combine the output power. The chip size with all pads is 2×1.5 mm2. From the measurement results, the proposed PA achieves 11.5-dB small signal gain with 15-GHz 3-dB bandwidth (78-93 GHz), saturated output power (Psat) of 19.6 dBm, output 1-dB compression point (OP1dB) of 18.2 dBm, and maximum power added efficiency (PAEpeak) of 12.8%.
A REVIEW OF LAND USE CHANGE: APPROACHES AND MODELS

the study of land use change is included in territorial planning to inform planners and policy makers of the possible developments they will face. Modelling land use change is a complex task due to different interdisciplinary interactions. Spatiotemporal modelling plays an important role in understanding the complexity and analyzing the processes of current land uses to explore and inform territorial decision-makers of possible future developments. This review provides an introduction to land use change, discusses approaches to model natural and artificial phenomena and we then present two reference models to simulate land use change.
Improving Histopathology Classification using Learnable Preprocessing

A deep learning classifier trained on a source dataset often performs poorly on a target dataset, even for the same classification task, due to the differences in the distributions of the two datasets. In histopathology, the problem of dataset bias is even more severe due to the differences in specific tissue preparation and imaging set ups across patient cohorts. With the objective of improving the generalization across datasets, we propose a set of learnable preprocessing operations – an approach that has not been extensively explored – for a supervised deep learning framework that can be trained separately or together with the rest of the neural network. Through preprocessing, the data from a target domain is transformed before being fed to a classification module trained on the source domain to increase the overlap of the former's distribution with that of the latter. Through an extensive set of experiments on histopathology and face datasets, we show the particular and general utility of the proposed preprocessing operations for domain adaptation and compare it to previous approaches.
Fuzzy Rough Discernibility Matrix Based Feature Subset Selection With MapReduce

Fuzzy-rough set theory (FRST) is a hybridization of fuzzy sets with rough sets with applications to attribute reduction in hybrid decision systems. The existing reduct computation approaches in fuzzy-rough sets are not scalable to large scale decision systems owing to higher space complexity requirements. Iterative MapReduce framework of Apache Spark facilitates the development of scalable distributed algorithms with fault tolerance. This work introduces algorithm MR_FRDM_SBE as one of the first attempts towards scalable fuzzy-rough set based attribute reduction. MR_FRDM_SBE algorithm is a combination of a novel incremental approach for the construction of distributed fuzzy-rough discernibility matrix and Sequential Backward Elimination control strategy based distributed fuzzy-rough attribute reduction using a discernibility matrix. A comparative experimental study conducted using large scale benchmark hybrid decision systems demonstrated the relevance of the proposed approach in scalable attribute reduction and better classification model construction.
A Synonym Extraction Method Based on Intimacy*

This paper proposes a new concept—intimacy for synonyms extraction. Firstly, we find the first n similar word labeled A1.An of the word A, and then, find the first n similar word groups of A1,A2.An respectively, and calculate the intimacy based on whether or not A appears in these word groups and the position A appears to obtain the final intimacy score. Finally, sort the final intimacy score and obtain the first n synonyms of A. In this paper, we test on Wikipedia dataset, and compare with word similarity computing tasks. Experiments show that the synonyms found by this method are higher than other methods both in subjective and objective evaluation.
Intelligent Platform for Visually Impaired Children for Learning Indoor and Outdoor Objects

Using Artificial Intelligence and Computer Vision to assist Visually Impaired personal has been a topic discussed in recent years. Many researchers are focusing on combining several technologies to assist said individuals to perform day to day tasks. Although there are already many technologies being used as platforms to help these individuals, focus put on children who are aged in between 6 – 14 years is considerably less. Therefore; in this research we are focusing on how to use latest advancements of Region Based Convolutional Networks (R-CNN), Recurrent Neural Networks (RNN) and Speech models to provide an interactive learning experience to visually impaired children.
A Deep Neural Network Augmented Approach for Fixed Polarity AND-XOR Network Synthesis

11This work is partially supported by the research project sponsored by the Synopsys Inc., USAWith the recent advancements of FPGA (Field Programmable Gate Array), circuits in AND-XOR plane gets its fair share of advantages due to the high testability feature of the AND-XOR networks and independence of the logic-gate area as well as delays on FPGA. Minimization of the product terms for such networks is an NP-hard problem. In this paper, we have proposed a Binary Particle Swarm Optimization (BPSO) based technique to solve the optimization problem accurately and accelerate the same using a Deep Neural Network. With the proposed technique, after testing it against various MCNC benchmark circuits, the results were very promising in terms of product terms, while utilizing significantly lesser CPU-time.
A Multimode Teleoperation Framework for Humanoid Loco-Manipulation: An Application for the iCub Robot

Over the years, there have been many improvements in job-related safety standards and working conditions, but there are still many situations and environments where human lives are put at risk, such as in search and rescue situations, construction sites, and chemical plants. We envision a world where robots can act as physical avatars and effectively replace humans in those hazardous scenarios through teleoperation.
An Efficient Quadratic Constrained Least Squares Localization Method for Narrow Space With Ranging Measurement

The localization algorithm for mobile robots working in narrow space needs to handle the scenario that the geometric shape of reference nodes tends to a line, which results in the matrix of least squares localization approaches ill-conditioned. Estimator bias becomes an important factor that can degrade the localization performance. In this paper, we present a fast unbiased range-based localization algorithm to resist the ill-conditioned problem. The main strategy is to augment objective function in the resultant optimization formulations via introducing a measurement distance into the locating model, which forms a least squares problem with cone constrained. The proposed model decouples the measurement distances from the matrix of least squares, which avoids the ill-conditioned problem when the target is around the geometric center. The closed-form expression of locating position ensures that the proposed algorithm is unbiased and low computation burden in the presence of zero-mean disturbance. Moreover, the robustness improvement of the augmented objective function is analyzed. Numerical simulations are used to corroborate the analytic results which demonstrate the good performance, robustness, and fastness of the proposed method.
Design of Low-Power Reconfigurable Low-Noise Amplifier with Enhanced Linearity

In this paper a dual-band low-power reconfigurable low-noise amplifier (LNA) for GSM bands namely DCS-1800 and 802.11b/g applications is proposed. Gain enhancement, low noise figure and dual-band operation are obtained by employing a gain-boosting technique using transformer coupled feedback and positive feedback using source follower. Current reuse and forward-body-bias techniques were employed to reduce power dissipation of the LNA functioning at a reduced drain bias voltage of 0.63 V. Designed using UMC 180 nm CMOS process, post layout simulation results showed that the reconfigurability of LNA is from 1.86 GHz to 2.4 GHz. It achieves a power gain from 9.68 dB to 6.87 dB at 1.86 GHz and from 10.17 dB to 3.96 dB at 2.4 GHz, minimum noise figure of 3.77 dB, DC power dissipation of 1.61 mW and maximum IIP3 of −3.53 dBm.
Toward Optimal Selection of Information Retrieval Models for Software Engineering Tasks

Information Retrieval (IR) plays a pivotal role in diverse Software Engineering (SE) tasks, e.g., bug localization and triaging, bug report routing, code retrieval, requirements analysis, etc. SE tasks operate on diverse types of documents including code, text, stack-traces, and structured, semi-structured and unstructured meta-data that often contain specialized vocabularies. As the performance of any IR-based tool critically depends on the underlying document types, and given the diversity of SE corpora, it is essential to understand which models work best for which types of SE documents and tasks. We empirically investigate the interaction between IR models and document types for two representative SE tasks (bug localization and relevant project search), carefully chosen as they require a diverse set of SE artifacts (mixtures of code and text), and confirm that the models' performance varies significantly with mix of document types. Leveraging this insight, we propose a generalized framework, SRCH, to automatically select the most favorable IR model(s) for a given SE task. We evaluate SRCH w.r. t. these two tasks and confirm its effectiveness. Our preliminary user study shows that SRCH's intelligent adaption of the IR model(s) to the task at hand not only improves precision and recall for SE tasks but may also improve users' satisfaction.
TASOR: A Temperature-Aware Semi-Partitioned Real-time Scheduler

Modern multicore systems, which execute complex functionalities on densely packed multi-million gate platforms, are often prone to unacceptable surges in core temperatures. Increase in temperature in such systems not only results in high cooling costs but also leads to high leakage power dissipation, along with reduced efficiency and lower life-span for the system. Given, a set of periodic real-time tasks to be executed on a thermally constrained multicore system, proportional fair schedulers form an attractive scheduling alternative. This is because of their flexibility and the ability to deliver efficient resource utilization, which can potentially enable accurate control over the timeliness of all tasks as well as stipulated temperature upper bounds on all processing cores, over the entire schedule length. In this paper, we propose a low-overhead three-level hierarchical temperature-aware semi-partitioned proportional fair scheduler, called Temperature-Aware Semi-partitioned Real-Time Scheduler (TASOR). The first level in TASOR partitions, time into discrete slices based on task deadlines, such that accurate proportional fairness is maintained at all slice boundaries. In the second level, it classifies each task as either hot or cool, based on it's temperature characteristics. At the last level, TASOR performs intra-slice scheduling with the objective of maximizing resource utilization. Our experimental results show that TASOR is able to perform appreciably under various realistic scenarios.
SAMS: An IoT Solution for Attendance Management in Universities

An autonomous and effective platform for students' attendance management is presented in this paper by using most of the advanced technologies of the IoT (Internet of Things), such as mobility, wireless network, fingerprint sensor and cloud computing. The research aims at developing a smart device and a system to support attendance management in Universities. Smart Attendance Management System (SAMS) has been developed and implemented to record daily attendance of students in lecture halls and to provide web services for academic staff to manage and maintain attendance. The result reveals that the SAMS overcomes many of the limitations in the traditional methods of taking attendance and ensures the solutions are more accurate, secure, efficient and automatic.
A Reliability Model of Micro-Engines Subject to Natural Degradation and Dependent Zoned Shocks

Most Micro-Electro-Mechanical Systems (MEMS) experience natural degradation and random shocks simultaneously, and their failures are mainly the results of competing soft and hard failure processes. For some MEMS devices like micro-engines, considering that they have resistance against small shock loads, shocks can be categorized into three shock zones according to their magnitudes: safety zone, damage zone, and fatal zone. The fatal shocks can cause hard failure immediately, and the damage shocks can (i) increase the degradation level and (ii) reduce the hard failure threshold. In this paper, the effect (ii) is described by a dependence between the classifications of damage shocks and fatal shocks: after surviving a damage shock, the probability of a shock in fatal zone increases and in damage zone decreases. Due to the dependence, the Poisson process widely used in previous studies is unsuitable. A dependent zoned shock model is developed, where a Hindrance model, which is a special type of Markov point process, is first introduced to describe the damage shocks, and a Cox process is used to model the fatal shocks. Then, a general reliability model of micro-engines subject to degradation and dependent zoned shocks is developed. When the micro-engine degrades linearly, an analytical reliability model is derived. Finally, a numerical example is conducted to illustrate the effectiveness of the developed model.
Urban Air Quality Prediction Using Regression Analysis

In the last several years, air pollution has risen steadily in urban environments. Cities like Gurugram, Faisalabad, Delhi, Beijing are few of the world's most polluted cities and have seen a dangerous rise in air pollution levels. Forecasting is important because of the human, ecologic and economic toll of pollution, and is a useful investment at individual and community levels. Accurate forecasting will help us plan in advance, decreasing the effects on health and the costs associated. Local weather conditions strongly affect air pollution levels. Generating deterministic models to study air pollutant behavior in environmental science research is often not very accurate because they are complex and need simulation at the molecular interaction level. Here comes machine learning to the rescue with high computing facilities to predict air pollution. This paper investigates how effective some available prediction models are in predicting the Air Quality Index(AQI) values given some input data, based on the pollution and meteorological information in New Delhi, India. We perform regression analysis on the dataset, and our results show which meteorological factors affect the AQI values more and how useful the predictive models are to help in air quality forecasting.
Design and Analysis of Wideband Non-Transparent and Optically Transparent Microwave Metamaterial Absorbers

Design and analysis of ultrathin wideband nontransparent, semi-transparent and optically transparent microwave metamaterial absorbers have been reported in this article. Non-transparent absorber composed of FR4 dielectric substrate has been fabricated and experimentally verified. Transparent and semi-transparent absorbers are developed by utilizing AgHT-8 conducting sheet and copper sheet, respectively. Both the absorbers are analyzed and compared for different substrates, viz. glass and PET. The FWHM bandwidth of about 25.8% has been observed for nontransparent absorber which is ranging from 2.14 GHz to 2.77 GHz. Under normal incidence, simulated −10dB absorption bandwidth of about 17.3% has been attained from 2.223 GHz to 2.645 GHz covering S-band frequencies. Standard printed circuit board technique is utilized for fabrication and the absorber prototype is verified by waveguide measurement method. The measured and simulated results are in agreement. The proposed non-transparent absorber has ultrathin thickness of $\boldsymbol{\lambda}_{0}/40$ and is compact with cell size of $0.21\boldsymbol{\lambda}_{0}$ at the lower absorption frequency of 2.28 GHz.
Music Genre Recognition Using Residual Neural Networks

Genre is an abstract, yet a characteristic feature of music. Existing works for automatic genre classification compute a set of features from the audio and design a classifier on top of it. Such models, in general, compute these features over a relatively long duration of the audio. In this paper, a residual neural network based model is proposed for genre classification which is trained on short clips of just 3 seconds duration. Also, traditional genre classification algorithms will assign a single genre to an audio clip. However, it is well established that different genres have overlapping characteristics. Considering this ambiguous nature of the genre, the model proposed in this work can assign three genre labels to a music clip, with each genre associated with some probability. The proposed model has an error rate of 18%, 9%, and 5.5% while predicting into top-1, top-2 and top-3 genres for a music clip respectively. We demonstrate in this work that the predictions made by the classifier align with the broader understood meaning of genre in a realistic setting.
Multiple eye disease detection using Deep Neural Network

India has a blind population of approximately 15 million, and the sad reality is that 75% of these cases are curable. The doctor-patient ratio in India is 1:10,000. Studies have found that Diabetic Retinopathy(DR) and Glaucoma are the leading causes of blindness in India. Diabetic retinopathy is caused mainly due to enduring diabetes in a person and is found to be the principal cause of blindness among the working-age population in developed and developing nations. Glaucoma causes damages to the optic nerve, thereby leads to blindness. Early stages of both diseases are asymptomatic, making its detection difficult, and if left untreated, this can cause irreversible damages to vision. The proposed deep neural network model helps to detect the presence of diabetic retinopathy and glaucoma at its early stages. It can alert the patients to consult an ophthalmologist on a screening standpoint. The developed model is less complicated and resulted in an accuracy of 80%.
AIFood: A Large Scale Food Images Dataset for Ingredient Recognition

In this paper, we introduce a large-scale food images dataset namely AIFood, which is constructed to aim ingredient recognition in food image research. AIFood dataset includes 24 categories and totally 372,095 food images around the world. We collect food images from eight existing food image datasets and a food website. The food images are relabeled using 24 categories. We preliminarily label each image using existing food information, e.g. dish name or ingredient information. Next, we manually check food images to find out undiscovered ingredients and relabel them. Every image can be labeled more than one category. In addition, food images may have color cast or uneven contrast problems, which may disturb performance of image recognition system. So, we applied preprocessing method which contains automatic white balancing and contrast limited adaptive histogram equalization methods to improve visual quality of food images. We set constraints which are defined by luminance and chrominance of image to determine if the image is to be preprocessed.
Ensuring the Security and Performances in Tactile Internet using Physical Unclonable Functions

We consider The tactile internet as the evolution of technologies from GSM to Internet of things (IoT); it surrounds human-to-human and machine to machine interaction. It enables real time applications with raft of business, societal and industrial cases. In particular, people will not be needed to be physically near to the systems they operate, but they could control them remotely. However, like any new complex system and new technology, there are many problems that must be resolved, such as: latency, reliability, security and capacity. In this paper, we will present all these challenges and we will focus on security aspects. We propose Physical unclonable functions (PUF) as a solution to secure this new Next Generation Network (NGN) and technology.
Role Model Features in Educational Serious Games

The problem of group interaction in game tasks is considered. Due to the fact that modern learning tools based on gamification are often distributed, user-oriented and practically lacking in security subsystems, the logical debugging of group interaction models is information security. Based on information security training as an example, the main features of the interaction model role formation are chosen, such models' peculiarities for various types of game tasks are specified. The issues of assigning and minimizing privileges, phishing and prohibited variants of the interaction of roles counteraction, changing the role and privileges during the game are considered. The limitation of game information flows to prohibit or control the transmitted confidential information is considered separately. The experimental model shows the limitations and features approach, which solves group interaction problems using the role model. The results obtained allow improving the group interaction security by filtering and limiting information flows. Solutions implementation described in the article is possible when developing game cases, quests or other educational game tasks.
k-Wave toolbox for studying elastic property in Photoacoustic imaging

We report a simulation study - adopting K- wave toolbox which is presently used for optical absorption characterization - that shows the contribution of the elastic property from the photoacoustic initial pressure rise. In this simulation, we consider two different circular targets having similar radius but different contrast in physical property (optical, mechanical) and hence the initial pressure rise is used in different cases due to: 1) two different elastic coefficients 94 kPa and 202 kPa and 2) two different absorption coefficients 1 cm−1 and 2 cm−1. Simulation results show that the initial pressure rise due to elastic contrast is very low compared to that of optical contrast, although elastic coefficient varies strongly (∼ 50 times) against optical absorption coefficient (∼ 2 times) in diseased state (say, cancer). This study demonstrates not only practicality of elastic property imaging using photoacoustic initial pressure rise but also horizontal extension of practical applicability of MATLAB based k-Wave toolbox for simulation study for characterization of elastic property.
Accelerating Clinical Trial Documentation Through Human Aided Medical Writing Automation System

Medical Writing Automation is needed for speeding up the process of new drug development and reducing the costs involved in creating and updating required documents. This paper presents a system that focuses on automating many critical Medical Writing Documents. Proposed System will not be a replacement of Medical Writers, but will help medical writers in speeding up the process of preparation and update of many critical clinical trial and scientific writing related documents with ease, faster and with requisite quality that comply with regulations. Proposed system will minimally involve domain expert for validation and update of output documents.