Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Quartz Clock With Integrated Wireless Energy Harvesting and Sensing Functions

There has been an increasing demand for smart devices and smart furniture for home automation, monitoring, and security applications. In this paper, we present a novel method of integrating the function of wireless energy harvesting from ambient RF signals to a conventional quartz clock for home applications. The most attractive feature is that the clock itself is used as the power receiving device, thus no additional antennas are needed. A simple rectifier is designed to directly match with the clock antenna and rectify the power captured by the clock. As a design example, a clock rectenna using the proposed new idea achieves good energy conversion efficiency (up to 65%) over its operating frequency bands at around 1.4–1.5, 1.9–2.1, and 2.4–2.8 GHz, respectively. Moreover, a wireless environmental sensor is integrated with the clock and powered by using the harvested power from the proposed clock rectenna. This novel design greatly expands the functionality of the quartz clock without affecting its size and appearance. We believe that the proposed energy harvesting quartz clock could be adopted for smart home applications.
A simple SPICE model for Negative Capacitance Field-Effect Transistors and its applications

In this paper, a reasonably accurate and understandable SPICE model for negative capacitance field-effect transistors (NCFETs) based on time-dependent Landau-Khalatnikov equation is proposed. Different from previous circuit compatible models, which solve gate charge self-consistently with the transistor equations, we introduce a charge conserved capacitor in series with the gate to extract the gate charge. Our model predicts dynamic behaviors of NCFETs. The basic electrical characteristics of NCFETs show that internal voltage amplification and sub-60 mV/decade subthreshold swing (SS) can be achieved. A ring oscillator analysis shows smaller propagation delay of NCFETs compared with MOSFETs when the supply voltage (VDD) is below 0.3V and damping constant is 0. 01Ω∙m. Total energy consumption per cycle of the ring oscillator based on NCFETs is smaller than MOSFETs at VDD=0.2V. NCFETs-based SRAM shows smaller write time and access time whose time delay reduces by 39% and 83% when VDD=0.2V and TFE=10nm. Besides NCFETs-based SRAM shows 12% larger hold stability, 84% larger read stability and larger write stability than MOSFETs-based SRAM. We use the NCFETs with hysteresis behavior as a Schmitt trigger which can work at VDD=0.2V. The Schmitt trigger has a tunable hysteresis which is tuned by the thickness of ferroelectric thin film. Our results reveal that the NCFETs have the potential for low-power circuit and functional circuits applications.
Weakly Supervised Learning of Object-Part Attention Model for Fine-Grained Image Classification

Fine-grained classification is challengeable due to the small inter-class variance and large intra-class distance between fine-grained categories. The key to solve this problem is to locate the discriminative part in the image. In this paper we propose a weakly supervised method, which only need image-level label for fine-grained classification. In our model, the convolutional neural network (CNN) can location the discriminative region through attention, and automatically focus on subtler features by zooming the discriminative region and feeding it to the next CNN. A Squeeze and Excitation (SE) module is employed for channel-wise attention, and a spatial constrain loss is utilized to keep the diversity of located part. We conduct experiments on CUB-2011-200, Stanford Dogs, and Stanford Cars datasets to evaluate the performance of our model. The experimental results demonstrate the effectiveness of the proposed method as compared other methods.
Charging Area Determining and Power Enhancement Method for Multiexcitation Unit Configuration of Wirelessly Dynamic Charging EV System

Electric vehicle (EV) dynamical wireless charging is an emerging application area of wireless power transfer technologies. Compared with a classical static charging mode, there are two key challenges in a dynamical charging process. One is to determine charging area and corresponding excitation unit. The other is to provide large energy in a short time interval. Based on multiexcitation unit configuration for EV dynamical charging, this paper proposes a charging area determining method. The dynamical mutual inductance can be calculated based on dc current input and load detection. A charging area can be figured out based on output power requirement. And a switching mode method is given to control the charging process for multiexcitation unit configuration. On this basis, a power and efficiency enhancement method is proposed by utilizing a multiexcitation unit in the charging area. This method is beneficial for improving system power capacity without increasing the voltage and current stress. Finally, experimental results show that this method can realize accurate charging area determining and control. And the output power of dual excitation unit configuration can increase almost four times than that of a traditional single excitation unit configuration.
Engineering Self-Adaptive Software Systems – An Organized Tour

Engineering software that is subject to uncertainties that are difficult to anticipate before deployment is challenging. Self-adaptation extends a software system with an external feedback loop system that monitors the system and adapts its configuration or architecture to ensure that its qualities are met under uncertain operating conditions. In this tutorial, we provide a particular perspective on the evolution of the field of selfadaptation in six waves. These waves put complementary aspects of engineering self-adaptive systems in focus that synergetically have contributed to the current body of knowledge in the field.
Nexus between Industrial Growth, Energy Consumption and Environmental Deterioration: OBOR Challenges and Prospects to China

The introduction of One Belt One Road Initiative (OBOR) projects intensively triggered the industrial revolt in China which can greatly degrade its environment by carbon emission in the impending time. This study empirically assesses the long run and short run ties of such industrial revolution with CO2 emission and energy demand from 1971 to 2016. The study reliable its outcomes by applying diagnostics unit root, serial correlation, autocorrelation and normality tests. By applying ARDL bound test, the study reveals the positive and significant long-run linkages between industrial growth, services sector of the economy and energy consumption with CO2 emission, however economic development negatively impacted the emissions in the China. Granger causality reveals the unidirectional causation of energy consumption, industrial progression, and economic growth to CO2 emission correspondingly. It is intimated that the Chinese rulers need to address industrial waste to avoid environmental degradation, harmful pollutant stuff caused by CO2 emission. This step will guarantee Chinese sustainable growth in the long run and will produce a very effective outcome from OBOR project. Furthermore, the policy implication available for China as well as OBOR nations, to cater the environmental issues by making strict policies for industries and focus on renewable energy sources that help to lack the CO2 emission.
An Android Malware Detection Technique Based on Optimized Permissions and API

As the number of smartphone users increases in terms of billions every year, and users now store personal and sensitive information on their mobile device which gives a huge platform for a hacker to steal users sensitive information. We have proposed a method to detect android malware using permissions and API. We have generated two types of feature vector named as common and combined feature vector. We obtained 97.25% accuracy for common and 96.56% accuracy for combined features using logistic regression. Further, to reduce training and testing time of classification we have optimized the feature to 131 by removing low variance features with which we have achieved 95.87% accuracy.
Accelerating the Inference Phase in Ternary Convolutional Neural Networks Using Configurable Processors

The need to automate tasks has led to state-of-the-art algorithms such as Convolutional Neural Networks (CNNs), which can learn representations from images. CNNs are computationally expensive. Hence, they are mostly implemented on Graphical Processing Units (GPU), which consume high power, a large area and have high cost. Due to these factors, it is difficult to implement CNNs in embedded systems where there are severe space and power constraints. Application Specific Instruction-Set Processors (ASIPs) provide an alternative to GPUs and are low power consuming devices. However, floating-point operations and storage are expensive and do not lend themselves well to an ASIP implementation. A more efficient approach to implement CNNs on ASIPs involves expressing the data and parameters with binary, ternary or fixed-point representations. In this paper, we discuss our implementation of the inference phase of ternary CNNs on an Xtensa LX6 ASIP and measure its performance for the MNIST dataset. The complex computations are parallelized with custom instructions developed using the TIE programming language. Compared to a sequential implementation, our results indicate a speed up of 4.7× with an added area of only 20%.
What Clinics are Expecting From Data Scientists? A Review on Data Oriented Studies Through Qualitative and Quantitative Approaches

Ensuring healthy lives and promoting well-being for all, at all ages, is one main objective for sustainable development proposed by the United Nations. The concept of connected health (CH) has been proposed to achieve that goal by connecting all the stakeholders through enabling Telehealth technologies. This paper has first presented an overview of the whole picture of CH along with the data collection process in CH. In the whole picture of CH, translational medicine (TM), as a rapidly growing discipline in biomedical research, aims to expedite the discovery of new diagnostic tools and treatments by using a multi-disciplinary and highly collaborative approach. It has been introduced to bridge the technique gap between the clinics and data scientists, particularly targeting on health related data analysis and evidence medicine. What clinicians are expecting and what researchers can offer will/should all be defined and clarified through TM. To further facilitate the communication between the clinicians and the researchers, electronic health records (EHRs) are often applied in place. This paper first reviews the evolution history of EHR and its current status and standards. Then a detailed and comprehensive discussion on data analysis techniques applied in TM through both quantitative and qualitative approaches is elaborated. We reveal that future work in TM should put an emphasis on data oriented qualitative analysis, using advanced techniques from the artificial intelligence domain to predict health risk, such as heart attacks and early stages of cancers. Multidisciplinary research in the Internet of Medical Things across health science, data science, and engineering will be the main challenge in TM.
LayerOS: Scheduling Apps between the Local System and the Cloud to Extend Scalability of Wearable Devices

In this paper, we propose LayerOS, a proactive framework that can be used to schedule apps in order to improve the scalability of wearable devices. LayerOS generates scheduling strategies which wille be deployed to devices ahead of time based on the analysis of user behavior, device capability, etc. According to these policies, LayerOS can offload apps to the cloud or load apps from the cloud dynamically. Hence, users can execute any app provided by the cloud directly, and there is no need to modify installed apps and interrupt users' normal operations. In addition, LayerOS uses app state migration to ensure consistency of view and state of the application on different mobile devices. Experiments based on a prototype system have shown that, at the same level of limited space, LayerOS enables users to run virtually unlimited applications directly and increases the running fluency by 25.6% from the perspective of FPS. At the same time, LayerOS can effectively reduce the waiting time when launching one application. More specifically, the average waiting time can be reduced by up to 13.0% in the experiments.
FMCW Radar Post Processing Method for Small Displacement Detection

A conventional Frequency Modulated Continuous Wave (FMCW) radar needs a large bandwidth in detecting a small displacement with millimeter scale. Phase data processing usually was implemented to avoid the needs of large bandwidth in radar system. Many implementations of phase detection in FMCW, generally utilized the IQ demodulation that placed at radio frequency (RF) circuit which a significant change is needed in RF circuit part. This paper proposes a method of advance post processing method in FMCW radar for small displacement detection. In the proposed method, IQ demodulation is performed at computation domain to minimize the change in RF hardware meanwhile the beat frequency detection using Fast Fourier Transform (FFT) still elaborate in the proposed post processing to detect the initial position of the target. Theoretical and simulation analysis has performed to investigate the ability of proposed method and the results show that the proposed method is able to detect a small displacement in millimeter scale without increasing the FMCW bandwidth and modification in RF hardware.
Forgery Detection in Digital Images through Lighting Environment Inconsistencies

In image splicing forgery, parts from two or more images are used to create a new composite image. Among the different approaches to expose splicing forgery, lighting environment-based forensics methods are more robust to different post-processing operations. In these methods, the 3D lighting environments are estimated from different parts of the image under investigation. They are later compared with each other to check the authenticity of the image. This paper proposes a novel 3D lighting environment-based image forensics method which can detect splicing forgeries in images containing human faces. The proposed method estimates the lighting environments from facial regions present in the image using shape, illumination, and reflectance from shading or the SIRFS method. SIRFS performs an optimization procedure to get the most likely shape, reflectance and illumination that construct a given image by imposing priors on shape, reflectance and illumination. Once the lighting environments are estimated from all the faces present in the image, they are compared with each other. In case of an authentic image under uniform illumination, the lighting environments estimated from different faces will be similar while there will be at least one pair of faces with different lighting environments in the case of a spliced image. Experimental results on two different datasets show that the proposed method can discriminate different lighting environments better than the state-of-the-art 3D lighting environment-based forensics methods and hence can expose forgeries better.
Using Long Short Term Memory (LSTM) For Radar Cross Section Prediction

Radar Cross Section (RCS) is a measurement of scattering performance of an object. RCS plays an important role in the design of stealth weapon system. However, the method of calculating RCS of every angle of azimuth and elevation is complex, and the efficiency is low. Evidently, it is of great significance for predicting the unknown RCS given a set of RCS data of an object. Towards this aim, we propose a novel approach using multilayers Long Short Term Memory (LSTM) networks based on unsupervised learning — the mechanism is similar to that of autoencoder. The encoder LSTM maps the input RCS into a fixed length representation. The decoder LSTM decodes the representation to predict RCS. For the purpose of this work, we create an 3D object and collect its RCS for a dataset with the help of electromagnetic simulation software, FEKO. The proposed networks have been applied on the test dataset and yield satisfactory results.
Rotation and Precession Periods Estimation of Unstable Satellite via Variational Mode Decomposition

Attitude motion periods of the unstable satellites are important parameters for space target surveillance. Traditional period estimation methods can only be used for single period. Aiming at rotation and precession of unstable satellite, a double-period estimation method based on variational mode decomposition (VMD) and mutual information is proposed. First, the radar cross section (RCS) of unstable satellite is processed by VMD to obtain intrinsic mode functions (IMFs) and corresponding center-frequencies. Then, through calculating and comparing the mutual information of IMFs and RCS sequence, the rotation period and precession period of satellite are obtained. The experimental results indicate that both rotation period and precession period can be estimated correctly. Compared with spectrum analysis, autocorrelation function and empirical mode decomposition (EMD), the phenomenon of frequency multiplication and mode mixing can be restrained effectively, and the accuracy of period estimation is improved.
A Simple ML Detection for Coded Generalized Frequency Division Multiplexing in MIMO Channels

Generalized frequency division multiplexing (GFDM) is a promising multicarrier scheme, which has shown to be a good candidate to meet the rising demands of 5G networks. The inherent self-interference of GFDM prohibits the direct utilization of maximum likelihood (ML) detection of space-time coding (STC) in multi-input multi-output (MIMO) channels. Recently, a modified version of GFDM, called coded GFDM (CGFDM), has been proposed, which solves the problem of interference mitigation. In this paper, a simple near-optimal ML detection of Alamouti STC is proposed for MIMO CGFDM. Simulation results show that without sacrificing bandwidth efficiency, the proposed scheme outperforms the current MIMO GFDM detection techniques in terms of BER and computational complexity.
Deep learning for online harassment detection in tweets

In recent years, the strong rise in the use of social network platforms such as Twitter has resulted in millions of users sharing their thoughts and opinions about different aspects and events on the micro-blogging platforms. However, the unfiltered exchange of the content and the missing protection of private information can lead to sending or broadcasting bully and harassment messages in different social media platforms. This kind of messages may have serious negative impact on a person such as causing low self-esteem and depression. Therefore, automatic detection of such messages is more than necessary. This paper casts the problem as a classification task and investigates the effectiveness of deep learning to detect online harassment in Large Human-Labeled corpus specially designed for harassment research purpose. To this end, models are considered namely Long short-term memory(LSTM) ,Bidirectional Long Short-Term Memory (BLSTM),Convolutional neural Network (CNN), and compared with other classification models. Obtained results are very encouraging.
Embedding Power Line Communication in Photovoltaic Optimizer by Modulating Data in Power Control Loop

In a photovoltaic system, a dc–dc power optimizer (DCPO) is an option to maximize the output power. At the same time, data links among DCPOs are often required for system monitoring and controlling. This paper proposes a novel power line communication method for the DCPOs, in which the data of a DCPO are modulated into the control loop of a power converter, and then transmitted through the series-connected dc power line to other DCPOs. In the process of communication, differential phase-shift keying modulation and discrete Fourier transformation demodulation are employed. To analyze the quality of communication, the communication model of the system is built, based on a small-signal model. Furthermore, the noises of the system, including switching, maximum power point tracking, and additive white Gaussian noise, are discussed and measured to evaluate the signal-to-noise ratio. Lastly, an experimental system including six DCPOs is established and tested, which verifies the feasibility and effectiveness of the proposed method.
Evolution From Single to Hybrid Nanogenerator: A Contemporary Review on Multimode Energy Harvesting for Self-Powered Electronics

Energy harvesting devices have strong potential to not only meet growing global energy demand but also support a wide range of self-powered electronics applications. Solar cells, electrochemical cells, piezoelectric/triboelectric/pyroelectric nanogenerators, and magnetoelectric energy harvesters are enabling technologies for converting solar, chemical, mechanical, thermal, and magnetic energy to electricity. Merging these harvesters to form hybrid energy cells can help optimize operation of self-powered systems, providing multimode energy harvesting capability that can leverage several energy sources either simultaneously or individually. Energy produced from these hybrid energy cells even can be stored in Li-ion batteries to power various personal electronics, sensors, and next generation technology for the Internet of Things. Ultimately, hybridization provides another degree of freedom in terms of more effective energy utility. This review presents the evolution of the hybrid energy cell concept and development, explores the fabrication approaches taken, and provides insights on the limitations of existing devices, steps toward performance optimization, and the enormous potential for these technologies to benefit myriad applications. Hybrid energy cells show higher output performance by providing better charging characteristics than individual energy harvester unit.
A Distributed Strategy for Volt/VAR Control in Distribution Networks: A Smart Buildings Approach

The advent of distributed energy resources (DERs) at the distribution and consumer level imposes imperative operation challenges for distribution utilities. Novel strategies have been proposed to use the thermal inertia of buildings to provide ancillary services to the grid to tackle the problems posed by increasing penetration of renewable energy sources. Synthetic inertia and control of energy storage systems (ESS) have been proposed to improve transient stability with increased penetration of renewable energy sources. The aforementioned strategies deal with frequency control and only consider modulation of active power of the loads or ESS and don’t consider the voltage control problem. Hitherto, the effect of the power modulation of loads’ reactive power consumption on the grid’s voltage profile has not been studied to the best of our knowledge. In this paper, a multi-agent distributed control (MADC) strategy for Volt/VAR control in distribution networks to provide reactive power support from smart buildings to improve power quality at distribution level is presented. The robustness of this strategy is validated on a modified IEEE 13 bus system. The results show that, for short periods, this can be a valuable strategy to tackle the nominal voltage range violations on electrical distribution networks (EDN).