Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wideband Pattern Diversity Antenna With a Low Profile Based on Metasurface

In this letter, a low-profile wideband pattern diversity antenna based on metasurface (MTS) is proposed. Initially, based on the theory of characteristic mode (TCM), the two adjacent intrinsic modes, which correspond to the broadside and conical radiation pattern, respectively, are exploited to realize a wideband pattern diversity characteristic under a low profile. Then, by analyzing the modal current distribution on the MTS, the optimal locations, orientations and relative phases of the four L-shaped feeding probes are determined to excite the above two modes. However, the in-band high-order modes (HOMs) introduced by the feeding structure will deteriorate the radiation pattern. Therefore, chamfering corners on the MTS is used to suppress the HOMs for radiation pattern improvement. Finally, by combining TCM and MTS, the modes are manipulated to achieve a pattern diversity antenna with an extremely low profile of 0.06 λ0 (the free-space wavelength at the center frequency) and an isolation of better than 38 dB, which also maintains an operating bandwidth of 20.24%. Based on these excellent performances, the proposed antenna is an attractive solution for pattern diversity applications requiring low profile, especially in unmanned aerial vehicle communications.
Heart Disease Prediction using Machine Learning Techniques

As per the recent study by WHO, heart related diseases are increasing. 17.9 million people die every-year due to this. With growing population, it gets further difficult to diagnose and start treatment at early stage. But due to the recent advancement in technology, Machine Learning techniques have accelerated the health sector by multiple researches. Thus, the objective of this paper is to build a ML model for heart disease prediction based on the related parameters. We have used a benchmark dataset of UCI Heart disease prediction for this research work, which consist of 14 different parameters related to Heart Disease. Machine Learning algorithms such as Random Forest, Support Vector Machine (SVM), Naive Bayes and Decision tree have been used for the development of model. In our research we have also tried to find the correlations between the different attributes available in the dataset with the help of standard Machine Learning methods and then using them efficiently in the prediction of chances of Heart disease. Result shows that compared to other ML techniques, Random Forest gives more accuracy in less time for the prediction. This model can be helpful to the medical practitioners at their clinic as decision support system.
A Survey of Bidirectional DC/DC Converters for Battery Storage Applications in Distributed Generation Systems

In solar based distributed generation systems bidirectional DC/DC converters (BDC) are inevitable for the control of power flow between storage units and the DC bus. Wide range of voltage gain is the key feature needed for a BDC for such applications. This paper reviews different BDC topologies with switched capacitor technology for attaining wide range of voltage gain and a comparative study is conducted to identify the most advantageous topology. A detailed theoretical analysis is performed on these topologies and the identified features are validated with simulation studies.
SmartFuzz: An Automated Smart Fuzzing Approach for Testing SmartThings Apps

As IoT ecosystem has been fast-growing recently, there have been various security concerns of this new computing paradigm. Malicious IoT apps gaining access to IoT devices and capabilities to execute sensitive operations (sinks), e.g., controlling door locks and switches, may cause serious security and safety issues. Unlike traditional mobile/web apps, IoT apps highly interact with a wide variety of physical IoT devices and respond to environmental events, in addition to user inputs. It is therefore important to conduct comprehensive testing of IoT apps to identify possible anomalous behaviours. On the other hand, it is also important to optimize the number of test cases generated, considering that there may be many possible ways in which apps, devices, environmental events, and user inputs interact. Existing works investigating security in IoT apps have been using ad-hoc testing approaches, in which test cases are usually designed to test some particular aspects of apps or devices. In this work, we develop an automated, smart fuzzing approach, called SmartFuzz, for testing Samsung SmartThings IoT apps. More specifically, SmartFuzz combines combinatorial test generation with light-weight program analysis, and aims to improve test coverage of sinks in an efficient, automated manner. We have implemented and evaluated our approach using a publicly available dataset of 60 SmartApps. The results have demonstrated the effectiveness and efficiency of SmartFuzz. In particular, SmartFuzz improved coverage of sinks by 184%, while generating and executing 20% fewer test cases as compared to ad-hoc testing.
Designing a HVDC Insulation System to Endure Electrical and Thermal Stresses Under Operation. Part I: Partial Discharge Magnitude and Repetition Rate During Transients and in DC Steady State

This paper has the purpose to investigate HVDC insulation design considering real operating conditions, when DC steady-state is affected by frequent voltage transients or load variations that may be present during all life. Electrical field distribution in insulation, and in insulation defects, may change significantly from DC steady-state when voltage and load vary with time, which can cause partial discharge activity often not been properly accounted for at the design stage. The Part I of this paper is dedicated to prove, through experiments, models and simulations, that electrical and thermal transients may incept partial discharges in defective insulations during cable energization, voltage polarity inversion at a constant nominal load, as well as during load variations at a constant nominal voltage. This can cause accelerated aging and premature breakdown even if the insulation system is designed properly to withstand DC electrothermal stress, without partial discharges in steady state, for all life, as it will be shown in Part II. Focus is on cables, but the approach described here is general for any DC insulation system.
Performance of NOMA-Enabled Cognitive Satellite-Terrestrial Networks With Non-Ideal System Limitations

Satellite-terrestrial networks (STNs) have received significant attention from research and industry due to their capability of providing a stable connection to rural and distant areas, where the allocation of terrestrial infrastructures is uneconomical or difficult. Moreover, the STNs are considered as a promising enabler of fifth-generation communication networks. However, expected massive connectivity in future communication networks will face issues associated with spectrum scarcity. In this regard, the integration of cognitive radio and non-orthogonal multiple access (NOMA) techniques into STNs is considered as a promising remedy. Thereafter, in this article, we investigate NOMA-assisted cognitive STN under practical system conditions, such as transceiver hardware impairments, channel state information mismatch, imperfect successive interference cancellation, and interference noises. Generalized coverage probability formulas for NOMA users in both primary and secondary networks are derived considering the impact of interference temperature constraint and its correctness is verified through Monte Carlo simulation. Furthermore, to achieve performance fairness among the users, power allocation factors based on coverage fairness for primary and secondary NOMA users are provided. Moreover, the numerical results demonstrate superior performance compared to the ones obtained from an orthogonal multiple access scheme and examine the imperfection’s impact on the system performance in terms of coverage and throughput.
A Linear Algebraic Approach to Strongly Secure Ramp Secret Sharing for General Access Structures

Secret sharing is a cryptographic technique to share a secret among participants in such a way that only authorized subsets are able to recover the secret. Ramp secret sharing schemes can achieve better information ratio than perfect schemes while some partial information on a secret which is composed of several sub-secrets leaks out. The notion of strong security has been introduced to control the amount of information on every subset of the sub-secrets unauthorized sets can obtain. In this paper, we reduce the construction of strongly secure ramp secret sharing for general access structures to a linear algebraic problem. As a result, we show that previous results on strongly secure network coding imply two constructions of a linear transformation which makes a given linear ramp scheme strongly secure. They are explicit or provide a deterministic algorithm while the previous method which works for any linear ramp scheme is probabilistic.
Estimation of Driver Demand Torque using Parametric and Nonparametric Data-driven Model

The complexity of vehicle dynamics is increasing due to growing demands for the inclusion of a large number of functionalities in vehicle model. New generation electronic control units (ECUs) regulate different components of powertrain to produce optimum power and torque necessary to meet the complex functional requirements. But the lookup table or map used in different ECUs e.g., engine ECU, gearbox ECU to create these functionalities are not capable enough to capture the dynamic behavior of system. Thus, effective control of vehicle by ECUs requires a model that is able to accurately predict the dynamic behavior of the system over its complete operating range. The present work proposes both parametric and nonparametric data-driven models that can replace lookup tables or maps used for the estimation of driver torque request. The driver input module estimates the driver demand torque or driver torque request. The inputs to the driver input module are engine speed and accelerator pedal. A data-driven parametric polynomial regression model and nonparametric Volterra model are developed to describe the dynamic behavior of multivariable nonlinear driver input module. The parameters of both the models are estimated using least square optimization algorithm. The input-output data taken from real vehicle dataset is used for both identification and validation of the model. The validation experiments show good fit of the predicted output with actual output. The accuracy obtained from the Volterra and polynomial regression models are 98.27% and 98.6% respectively.
Anonymous Dynamic Spectrum Access and Sharing Mechanisms for the CBRS Band

The Federal Communications Commission (FCC) has released the 3.5 GHz (3550-3700 MHz) band, termed Citizens Broadband Radio Service (CBRS), for shared broadband use between incumbent federal and secondary users through dynamic and opportunistic spectrum access. FCC requires that this band be operated and managed through the use of spectrum access systems (<inline-formula> <tex-math notation="LaTeX">$\mathit {SAS}$ </tex-math></inline-formula>s), which are to be deployed specifically for this purpose. The challenge is that <inline-formula> <tex-math notation="LaTeX">$\mathit {SAS}$ </tex-math></inline-formula> requires that secondary users provide some of their private operational data, such as their physical location, identity and spectrum usage, in order for them to acquire spectrum availability information. In this paper, we propose a privacy-preserving <inline-formula> <tex-math notation="LaTeX">$\mathit {SAS}$ </tex-math></inline-formula> framework, <inline-formula> <tex-math notation="LaTeX">$\mathit {TrustSAS}$ </tex-math></inline-formula>, that synergizes state-of-the-art cryptographic mechanisms with blockchain technology to enable anonymous access to <inline-formula> <tex-math notation="LaTeX">$\mathit {SAS}$ </tex-math></inline-formula> by protecting users’ privacy while still complying with FCC’s regulatory design requirements and rules. We evaluate the performance of <inline-formula> <tex-math notation="LaTeX">$\mathit {TrustSAS}$ </tex-math></inline-formula> through theoretic analysis, computer simulation and testbed experimentation, and show that it can offer high security guarantees, making it suitable for <inline-formula> <tex-math notation="LaTeX">$\mathit {SAS}$ </tex-math></inline-formula> environments without needing to compromise private information of its secondary users.
Dispersion des vitesses des ondes acoustiques dans l'aluminium

Intensity measurements of X-ray diffuse scattering by single crystals of aluminium at ordinary temperatures give information on the mode of propagation of transverse and longitudinal elastic waves in the crystal. Using monochromatic radiation each intensity (measured by an ionization chamber) can be expressed in terms of an average total scattering power (including Compton scattering) which has to be corrected (a) by a divergence factor corresponding to the resolving power of the spectrometer; (b) for the Compton effect associated with an atom-forming part of a crystal; (c) for second and third order scattering due to simultaneous scattering on two or three acoustic waves; and (d) for the true quantized energy distribution among the acoustic waves. From these results the phase velocities of longitudinal and transverse elastic waves in aluminium are obtained as functions of the elastic wave vector S = 1/Λ. For very small values of S (long acoustic waves) the velocities found correspond to those quoted in the literature for static measurements. Otherwise a remarkable curve of dispersion, v= f (S), is established for the elastic waves, which for transverse waves makes f (S) very nearly a sine function such as would be expected if only the twelve nearest neighbours about each atom interacted with it. For longitudinal waves this is definitely not the case.
Low Cost Planar Millimeter Wave Antenna Array for 5G Mobile Applications

A compact design of efficient and low profile millimeter wave antenna array is proposed for 5G mobile applications. Bandwidth is enhanced by implementing rectangular slot on patch and by using partial ground plane. 16 element linear array is designed in H plane to improve the gain. Proposed antenna array resonates from 12.69 GHz to 35.20 GHz with reflection coefficient less than -10 dB and impedance bandwidth of 94.02 % at center frequency of 23.94 GHz. Maximum gain of 16.3 dB is achieved at 28 GHz and very small variation in the gain over whole operating frequency range is observed. Main beam can be steered from -45 degree to +45 degree with maximum scanning loss of 3.6 dB. Side lobe levels are below -10 dB over whole scanning range. The proposed design has sharp narrow radiation pattern which is desired for 5G mobile stations. Thickness of antenna is only 0.254 mm so it can be easily integrated with printed circuit board of mobile phone. The radiation characteristics suggest that proposed antenna is a potential candidate for millimeter wave applications and 5G mobile communications.
Bridging the Broadband Gap and Power Up To 5G

As the commercial wireless communication technology evolves from 4G to 5G, it’ll boost the demand for data rate and spectrum efficiency. Doherty power amplifiers (DPAs), one of the foremost widely used power amplifiers (PAs) within the cellular communication systems, had received extensive attention in modern research thanks to its higher efficiency across an out-sized power range and featured simplicity within the circuit implementation. Several researchers proved that it could operate in mm-wave frequency. Unfortunately, it’s some intrinsic bandwidth (BW) limitations. BW extension of DPA plays a significant step in modern 5G PA designs. This paper briefly reviews PA evolution techniques, envelope tracking (ET) PAs, DPAs, reasons for DPA BW limitations, BW extension techniques followed by major challenges headed by BW extended DPAs shortly.
Wireless Capsule Endoscopy Bleeding Images Classification Using CNN Based Model

Wireless capsule endoscopy (WCE) is an efficient tool to investigate gastrointestinal tract disorders and perform painless imaging of the intestine. Despite that, several concerns make its wide applicability and adaptation challenging like efficacy, tolerance, safety, and performance. Besides, automatic analysis of the WCE provided dataset is of great importance for detecting abnormalities. Imaging of the patient’s digestive tract through WCE produces a large dataset that requires a substantial amount of time and a special skill set from a medical practitioner for analysis. Several computer-aided and vision-based solutions have been proposed to resolve these issues, yet, they do not provide the desired level of accuracy and further improvements are still needed. The current study aims to devise a system that can perform the task of automatic analysis of WCE images to identify abnormalities and assist practitioners for robust diagnosis. This study adopts a deep neural network approach and proposes a model name BIR (bleedy image recognizer) that combines the MobileNet with a custom-built convolutional neural network (CNN) model to classify WCE bleedy images. BIR uses the MobileNet model for initial-level computation for its lower computation power requirement and subsequently the output is fed to the CNN for further processing. A dataset of 1650 WCE images is used to train and test the model using the measures of accuracy, precision, recall, F1 score, and Cohen’s kappa to evaluate the performance of the BIR. Results indicate the promising outcomes with achieved accuracy, precision, recall, F1 score, and Cohen’s kappa of 0.993, 1.000, 0.994, 0.997, and 0.995 respectively. The accuracy of the BIR model is 0.978 with the Google collected WCE image dataset which is better than the state-of-art approaches.
Refined Global Word Embeddings Based on Sentiment Concept for Sentiment Analysis

Sentiment Analysis is an important research direction of natural language processing, and it is widely used in politics, news and other fields. Word embeddings play a significant role in sentiment analysis. The existing sentiment embeddings methods directly embed the sentiment lexicons into traditional word representation. This sentiment representation methods can only differentiate the sentiment information of different words, not the same word in different contexts, so it cannot provide accurate sentiment information for word in different contexts. This paper proposes sentiment concept to solve the problem. First, we found the optimal sentiment concept of words in Microsoft Concept Graph according to the context of words. Then we obtained the sentiment information of words under optimal sentiment concept from the multi-semantics sentiment intensity lexicon which we constructed in this paper to achieve accurate embedding of sentiment information and provide more accurate semantics and sentiment representation for words. Finally, we combined two refined word embeddings methods to achieve a more comprehensive word representation. Compared with traditional and sentiment embeddings methods on six representative datasets, the validity of the word embeddings method based on sentiment concept proposed in this paper is verified.
32.2 A 14nm Analog Sampling Fractional-N PLL with a Digital-to-Time Converter Range-Reduction Technique Achieving 80fs Integrated Jitter and 93fs at Near-Integer Channels

A local oscillator (LO) for 5G new radio requires sub-100fs rms jitter to support 64-OAM and $2\times2$ MIMO under non-ideal channel conditions [1]. Although fractional-N phase-locked loops (PLLs) employing digital-to-time converters (DTCs) and sampling phase detectors (SPDs) have demonstrated integrated jitter below 100fs [1, 2], such a DTC remains the most challenging block in this PLL architecture. It needs to achieve a fine resolution (e.g., 0.3ps) for low quantization noise (ON) and a large delay range to cancel the quantization error (OE) due to the delta-sigma modulator (DSM) used for fractional-N synthesis (e.g., 400ps for a5GHz VCO and mash1-1 DSM). The DTC also contributes to the in-band phase noise (PN) and its nonlinearity increases the fractional spur and noise folding. Reducing the DTC range alleviates the stringent design tradeoffs among PN, linearity, and design complexity [3]. This work presents a 6GHz fractional-N sampling PLL with a DTC range-reduction technique. With the modified multi-modulus divider (MMD) and the DSM that controls it, the accumulated OE at the MMD output is halved for any given order of the DSM. The same technique can also be applied to a charge-pump PLL or a digital PLL to ease the design of the phase detector. This PLL achieves 80fsrms jitter and -72dBc fractional spur at l4.2mW power consumption. lt also supports a low-power mode with 91.5fsrms jitter and consumes 8.2mW, offering power and jitter tradeoff.
A 400-to-1000nm 24μ W Monolithic PPG Sensor with 0.3A/W Spectral Responsivity for Miniature Wearables

Incorporating different wavelength (400 to 1000nm) LEDs, photoplethysmography (PPG) sensors allow wearable devices to monitor various health parameters such as heart rate (HR), oxygen saturation (SpO2), and blood pressure (BP). Nowadays, PPG sensing technology at the wrist is well established. To cope with the large degree of motion turbulence presented at the wrist, PPG sensors use Green (Gr) LEDs together with multiple photodiodes (PD), and they are driven by wide-dynamic-range (DR) current-sensing front-ends [1]. It is attractive to use a near-infra-red (nIR) PPG sensor in a True Wireless Stereo (TWS), as the ear provides the best site to measure heart rhythm (more blood flow, constant distance from the heart, and less motion than at the finger or wrist). However, TWS requires a PPG sensor that is more stringent on size and power consumption (shown in Fig. 28.2.1). A promising solution [2, 3] is integrating an array of PDs with an ADC to dramatically reduce power while also providing monolithic integration. However, the limited DR (<80 dB) and the poor spectral responsivity remain challenging. This work advances [1] by demonstrating a CMOS monolithic PPG sensor, and improves spectral responsivity more than $4 \times (0.3\mathrm{A} /\mathrm{W}$ across 400 to 1000nm) compared to [2]. The sensor is fabricated by back-side illumination (BSI) CMOS technology providing 90dB DR (18dB improvement from [3]) while consuming only $24 \mu \mathrm{W}$ power and 5.5mm 2 silicon area.
Epoxy Networks Containing Large Mass Fractions of a Monofunctional Polyhedral Oligomeric Silsesquioxane (POSS)

A polyhedral oligomeric silsesquioxane (POSS) containing one epoxy group and seven isobutyl groups per molecule was incorporated into an epoxy network following a two-stage process. In the first stage, POSS was reacted with an aromatic diamine, employing a 1:1 molar ratio of both reactants. The distribution of species at the end of reaction, determined by size exclusion chromatography (SEC), was close to the ideal one. In a second step, this precursor was reacted with the stoichiometric amount of an aromatic diepoxide to generate an organic−inorganic hybrid material containing 51.8 wt % POSS. A primary liquid−liquid phase separation process occurred at the time of adding the diepoxide to the POSS−diamine precursor. This led to a macrophase separation into epoxy-rich and POSS-rich regions, possibly derived from the incompatibility of the isobutyl groups attached to the POSS with the aromatic epoxy−amine network. A secondary phase separation occurred in the epoxy-rich phase in the course of polymerization, .
Suppression of Even-Order Distortions in Directly Modulated APL for the Mobile Fronthaul System

A method based on the push-pull manner is proposed in the directly-modulated analog photonic link (APL) to eliminate IMD2. Nine 200 MHz IF signals are experimentally achieved the reduction (> 1 %) of EVM over 1.5km SSMF. © 2020 The Author(s)