Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Analysis of a Microstrip Antenna Array for High Gain Applications at mm wave frequencies

Analysis and Design of 1X4 Rectangular microstrip patch antenna array with corporate microstrip line feeding for high gain applications at mm wave frequencies are explained in this work. Prerequisites to design a 1X4 array antenna are a single patch antenna and 1X2 array antenna design. For that, a single patch antenna with quarter wave impedance matching technique has been designed followed by 1X2 microstrip patch array antenna with corporate line feeding. optimization of both designs for the antenna parameters like return loss and gain are carried out. As extension of 1X2 Array, design has been modified into 1X4 array antenna and observed good return loss of 30dB and Gain of 12. 6dB. While increasing the antenna elements, increased antenna gain around 3dB and reduced HPBW in XZ-plane are observed. All the above three antennas are modelled in Ansoft HFSS 3D EM Software. The return loss and VSWR of Fabricated 1X4 array antenna is tested with network analyzer and compared with the simulated results.
API: An Index for Quantifying a Scholar’s Academic Potential

In the context of big scholarly data, various metrics and indicators have been widely applied to evaluate the impact of scholars from different perspectives, such as publication counts, citations, <inline-formula> <tex-math notation="LaTeX">${h}$ </tex-math></inline-formula>-index, and their variants. However, these indicators have limited capacity in characterizing prospective impacts or achievements of scholars. To solve this problem, we propose the <italic>Academic Potential Index</italic> (<italic>API</italic>) to quantify scholar’s academic potential. Furthermore, an algorithm is devised to calculate the value of <italic>API</italic>. It should be noted that <italic>API</italic> is a dynamic index throughout scholar’s academic career. By applying <italic>API</italic> to rank scholars, we can identify scholars who show their academic potentials during the early academic careers. With extensive experiments conducted based on the Microsoft Academic Graph dataset, it can be found that the proposed index evaluates scholars’ academic potentials effectively and captures the variation tendency of their academic impacts. Besides, we also apply this index to identify rising stars in academia. Experimental results show that the proposed <italic>API</italic> can achieve superior performance in identifying potential scholars compared with three baseline methods.
Secure Smart-Home Application Based on IoTCoAP protocol

Internet of Things (IoT) is an embedded technology that enables physical objects to connect with the Internet to exchange and collect data. In practice, these objects rely on protocols to exchange data between server and client. One of these protocols is the Constrained Application Protocol (CoAP). CoAP enables sensors to send and receive data from the server with appropriate energy efficiency. However, these objects are not secure and are vulnerable to attacks, also they are subjected to restricted energy and limited resources. In this paper, message integrity and message authentication have been added to the CoAP protocol. We use two different algorithms, the Advanced Encryption Standard (AES) 128 bit and the hash function SHA256 algorithm, in order to secure the CoAP protocol and compare the resulting performance. We evaluate our proposed scheme based on a smart-home application using the simulation tool Contiki. Our scheme results show that AES takes less time than the hash function SHA256 to initiate a secure session. Conversely, the hash function algorithm SHA256 consumes less energy than AES.
All-Fiber Bidirectional Mode-Locked Ultrafast Fiber Laser at 2 μm

In this paper, we demonstrated an all-fiber bidirectional ultrafast thulium-doped fiber laser at 2 μm with a single-wall carbon nanotube presented as saturable absorber. We successfully obtained bidirectional mode-locking operations with pulse repetition frequency adjustable from 35 to 122 MHz by shortening the cavity length. Meanwhile, with the reduction of the intracavity dispersion, the number of Kelly sidebands was significantly decreased and the output energy was more concentrated on the soliton pulse. Besides, by manipulating the pump power and polarization controller, the two results of bidirectional mode-locked pulses with the same repetition frequency and differential repetition frequency were observed. We found that the repetition rate difference between the clockwise and counterclockwise pulse trains was adjustable by changing the pump power or controlling the intracavity polarization. When the pulse repetition frequency was 35 MHz and 122 MHz, the adjustment range of repetition rate difference was 764–922 Hz and 540–2000 Hz, respectively. It is believed that this novel laser source can support free-running dual optical comb spectroscopy to detect important air gases like H2O or CO2 in the future.
Unsupervised Seismic Random Noise Attenuation Based on Deep Convolutional Neural Network

Random noise attenuation is one of the most essential steps in seismic signal processing. We propose a novel approach to attenuate seismic random noise based on deep convolutional neural network (CNN) in an unsupervised learning manner. First, normalization and patch sampling are required to build training dataset and test dataset from raw noisy data. Instead of using synthetic noise-free data or denoised results via conventional methods as training labels, we adopt only the training set constructed from the raw noisy data as the input and design a robust deep CNN that just relies on the noisy input to learn the hidden features. The cross-entropy is chosen as the error criterion for establishing the cost function, which is minimized by the back-propagation algorithm to obtain the optimized parameters of the network. Then, we can reconstruct all patches of the test dataset via the optimized CNN. After patching processing and inverse normalization, the final denoised result can be obtained from reconstructed patches. Experimental tests on synthetic and real data demonstrate the effectiveness and superiority of the proposed method compared with state-of-the-art denoising methods.
Service-Oriented Hybrid-Database-Assisted Spectrum Trading: A Blueprint for Future Licensed Spectrum Sharing

Licensed spectrum sharing (LSS) is an emerging scheme to improve spectrum efficiency by authorizing certain secondary spectrum licensees (SSLs) to share spectra with the primary spectrum licensees (PSLs). Spectrum trading is an effective way to implement LSS by offering incentives for PSLs to open their spectra. Although many research efforts have been devoted to such an economic-based scheme, most of them mainly focus on the trading part from the economic perspective, and the service provisioning related issues when implementing the trading-based LSS (TLSS) are generally ignored. In this article, we study the spectrum trading system from the service-oriented perspective considering all three aspects on SSLs, spectrum market, and PSLs, to promote the implementation of TLSS for the future. First, we introduce a cognitive capacity harvesting network architecture for SSLs to enable them to use the purchased PSLs' spectra for service provisioning. Then, considering the overhead on spectrum trading and sharing-based spectrum usage, we develop a hybrid-database-assisted model to facilitate TLSS, where information storage and trading process are carried locally, and the trading contextual information is clarified as well. Finally, we propose a service-oriented two-tier trading scheme, where two potential trading methods are discussed, namely, ex-post trading and ex-ante trading, which are especially suitable for trading the spectra with short-term and long-term availability, respectively.
Comparing Physiological Feature Selection Methods for Emotion Recognition

Human-computer interactions result in psychological effects on human behavior. The analysis of the human behavior can be done using physiological data of a user in intense emotional states. A user may have intense emotions, which could make the user more nervous, sad or aggressive. This paper shows how physiological data can be used to analyze a user’s emotional state and summarizes the findings of using different feature selection and classification techniques to learn the user’s emotional states. The general flow of this approach is to record physiological signals from a person, extract features and feed them to a machine learning algorithm. This algorithm will then predict the user’s emotional state. The outcome will be helpful to analyze and understand how to train the models with the given dataset. Results of this study can be utilized for future research and applications for mitigating the effects of the content on user’s emotions.
On-demand design of nanophotonic gratings using artificial neural network

The design of nanophotonic structures relies on solving the Maxwell’s equation and sweeping parameters in order to obtain the desired properties. The inverse problem, the retrieval of the appropriate parameters from the required electromagnetic responses, remains challenging and time consuming. Here we report on-demand design of photonic gratings using a recurrent neural network. By using back propagation neural network model, we show that a nanophotonic grating can be designed and optimized with predesignated responses. We expect this work will advance the applications of deep learning algorithms in the design of nanophotonic devices.
Energy Efficient Reference Tracking Modified Enhanced H-Bridge Multilevel Inverter

A Multilevel Inverter that uses less hardware, which is energy efficient and has reduced switching and conduction losses is proposed. This topology is developed from a symmetrical structure enhanced multilevel inverter (MLI). It consists of a seventeen level symmetrical structure enhanced H- Bridge, a DC source in series with the load through a switch and another switch to bypass the DC input source. By way of the addition of a DC input source, the number of levels of the proposed inverter has been increased to thirty-three; hence, the total harmonic distortion (THD) is reduced. Only four switches are in its active conducting state in each mode of operation due to which the power losses are reduced compared to a CHB and Developed H-Bridge MLI. The proposed single-phase multilevel inverter is simulated using MATLAB/ Simulink, and the results have been confirmed experimentally with a prototype.
Why Online Services Should Pay You for Your Data? The Arguments for a Human-Centric Data Economy

Data, and the economy around it, are said to be driving the fourth industrial revolution. Interestingly, the people—whose data are what moves the new economy—have a rather passive role in it as they are left outside the direct value flow that transforms raw data into huge monetary benefits. This is a consequence of a de facto understanding (or, one may say, misunderstanding) between people and companies that the former receive unpaid access to online services in exchange for the unpaid access to their personal data. This article argues in favor of an alternative human-centric data economy in which people will be paid whenever their data will be used by revenue-generating products and services. We discuss the benefits of such an economy, the main challenges for realizing it, and its feasibility in the view of existing technologies and business practices.
Design of a Compact Wilkinson Power Divider using Four Asymmetric DGS for Harmonic Suppression

In this paper, a compact configuration of a Wilkinson power divider (WPD) has been proposed operating at 2.45 GHz using asymmetric defected ground structure (DGS). WPD divides the power equally with better isolation between output ports. DGS has been incorporated at the input and output port to works as a bandstop filters and reduces the additional space required during the integration. In this paper, we combine both property of power divider and DGS to achieve equal power division for 2.45GHz with suppression of higher order harmonics (i.e. 5 GHz and 7.5 GHz). Simulation results show that insertion loss decreases from -3dB to -10.10dB at 5GHz and -25 dB at 7.5GHz with the equal phase in both the output ports. Isolation between twoports 2 and 3 is -18.6 dB. The total volume of the proposed WPD is 0.00013 $\lambda_{0}^{3}$, which is marginally less than the previously reported work. The measured results are in fair agreement with simulated results.
Influence of Electrode Placement Error and Contact Impedance Error to Scalp Voltage in Electrical Impedance Tomography Application

Electrical Impedance Tomography (EIT) is an imaging technique which relies on the accuracy of boundary voltage measurement. In application to the head of EIT, the scalp voltage is very small and sensitive to noise and error. Electrode placement error is an error which is difficult to avoid in practice. Furthermore, the exact value of electrode contact impedance is difficult to know. In this study, the influence of electrode placement error and inaccurate electrode contact impedance were investigated by simulation. The result shows that the scalp voltage error due to the placement error was between 0.003 mV and 4.670 mV. Comparing to the scalp voltage response obtained from brain stimulation, which has been reported less than $90 \mu \text{V}$, the most of the error values were much larger. The voltage errors due to inaccurate contact impedance were less than $1.2 \mu \text{V}$ which is very small and ignorable. This indicates the importance of the attachment procedure of electrodes that needs to be very careful.
Design and Simulation of an UWB Vivaldi Antenna for Cancer Detection and Treatment

An UWB Vivaldi antenna is proposed for cancer detection and treatment. The antenna operates at the resonant frequency of 4.76 GHz within the bandwidth of 4.63 GHz (3.13 GHz-7.76 GHz) in free space. In addition, the antenna possesses a high radiation efficiency of 81.34% and a gain of 5.81 dB in free space condition. The overall dimension of the proposed antenna is 110.46 x 96.77 x 1.67 mm3. The antenna is simulated with 1.6 mm thick FR-4 dielectric substrate since it is cost-effective and easily available. Furthermore, a six-layer biological tissue model comprising of skin, fat, outer cortical bone, cancellous bone, inner cortical bone, and muscle with a tumor of 10 mm radius is modeled to assess antenna performance for cancer detection and treatment. In simulation, a specific gap of 2.5 mm is maintained between the proposed antenna and the proposed phantom model in order to avoid skin burn and other side-effects. Cancerous cells in tumor are detected and killed because of higher SAR value of the cancerous tissue than the normal healthy tissue.
Deep Learning Based Image Classification and Abnormalities Analysis of MRI Brain Images

Intensive researches are being carried out to study the abnormalities present in the brain structures and to detect the type of tumors based on the statistical and textural features extracted from the medical images. In MRI imaging, the images may be clear but the clinicians have to quantify the size and location of the tumors for further treatment planning. The image processing methodologies and the deep learning methods aid the different stages of treatment such as pre and postsurgical procedures. The images captured by Magnetic Resonance Imaging systems are processed by the different software based algorithms in order to segregate the malicious tumor regions from the non-tumor regions. The proposed method includes three phases: feature extraction, image classification and segmentation of tumors. The important features of the images are extracted with Wavelet transform, a multi-resolution technique. The Convolutional Neural Network, which is a very popular deep learning method is used in image classification stage which is helpful in disease or lesion detection, image classification and then the segmentation methods are used to segregate the infected regions from the rest. The proposed method provides better accuracy in classification and segmentation stages.
Data Exchange and Preprocessing Method for a Distributed Quarry Machinery Control System

Remote machinery control enables the operator to track the location of technical facilities and the residual POLs; it also helps avoid human error and enables automatic job reporting. This paper dwells upon using Galileo controllers to automate monitoring the state of quarry machinery. ManageBand LLC develops such system on the basis of client-webserver architecture. To use the method, autonomous devices are equipped with local computers of different types. ManageBand uses Galileosky controllers for computing. The primary objective hereunder was to organize data exchange in an information system, to develop and implement a data exchange model for the control system. The model is based on services that cooperate to receive data from Galileo controllers for preprocessing and storage. The research team has tested multiple versions of the model using different data transmission and storage protocols. As a result, the recommended option is tu use a data buffer. The team has also analyzed JSON and ProtoBuf protocols. The finalized method considerably reduces the amount of data to be stored while also speeding up their preprocessing. The model has been implemented in an existing quarry equipment control system.
Application of n-Fold Rotational Symmetries to Eddy Currents Integral Model in the Time Domain

Group Theory is a powerful “tool” which has been extensively exploited to reduce the computational cost in the numerical analysis of several physical problems, including computational electromagnetics. This article proposes the extension of the application of group theory to an integral formulation of the eddy current problems in the presence of n-fold rotational symmetries. The proposed approach is applied to the eddy currents computation in toroidal fusion devices, where rotational symmetries play a key role.
Gated SPAD Arrays for Single-Photon Time-Resolved Imaging and Spectroscopy

In this paper, we present the architecture and the experimental characterization of an improved version of a previously developed 32 × 32 Single Photon Avalanche Diodes (SPADs) and Time to Digital Converters (TDCs) array, and two new arrays (with 8 × 8 and 128 × 1 pixels) with the additional capability of actively gating the detectors with sub-nanosecond rise time. The arrays include high performance SPADs (0.04 cps/μm2, 50% peak PDE) and provide down to 410 ps Full-Width at Half-Maximum (FWHM) single shot precision and excellent linearity. We developed a camera to exploit these imagers in time-resolved, single-photon applications.
ESD Protection Design for Open-Drain Power Amplifier in CMOS Technology

The on-chip electrostatic discharge (ESD) protection device for radio-frequency (RF) power amplifier (PA) with open-drain structure is studied in this work. The conventional ESD protection device of stacked diodes and the proposed ESD protection device of stacked diodes with embedded silicon-controlled rectifiers (SCRs) are compared in silicon and applied to the 2.4GHz PAs. The proposed ESD protection device has the advantages including higher ESD-current-handling ability, lower ESD-clamping voltage, and sufficiently low parasitic capacitance. Besides, the proposed ESD protection device does not degrade the PA performances. Therefore, the proposed ESD protection device of stacked diodes with embedded SCRs is more suitable for CMOS PAs.
Stroke Thrombus Segmentation on SWAN with Multi-Directional U-Nets

The thrombus causing a stroke can be seen on the susceptibility weighted angiography (SWAN) magnetic resonance imaging (MRI) sequence. But it is very small and hard to detect by humans. Up to date the thrombus is identified by trained human experts. But as stroke needs quick treatment, an automatic detection of the thrombus would be useful to speed up the diagnosis of acute stroke. We propose a method for automatic thrombus detection from SWAN using three separate U-Nets which work on the axial, coronal and sagittal planes.
Blockchain Guided Trustworthy Interactions for Distributed Disaster Management

Given the devastating outcomes of the recent hurricanes, earthquakes, and floods across the globe, governments all over the world started to pay serious attention to their disaster management protocols. The current reliance on centralized, infrastructure-dependent solutions is a serious threat to the whole management process. The entire process is mainly about processing help requests based on real-time information about the disaster, and efficiently responding to such requests by dispensing a set of limited resources available at the management’s disposal. Relying on independent anonymous help givers can expand the resource availability and facilitate help provisioning. Further, Crowdsourcing can be a valuable source of information that can better guide help provisioning process. However, establishing dynamic trust relationship between help requesters, information providers, and help givers is the key enabler to such enhancement. Help requests should be probably authenticated not to waste the valuable resources, help provisioning should be rewarded to motivate help givers, and Data sharing should be verified not to act on false information. In this paper, we present a decentralized Blockchain based trust management framework to facilitate cooperative anonymous help provisioning at disaster time considering infrastructure-supported scenario.
Use Case Extraction through Knowledge Acquisition

Most challenges in requirements analysis and use case extraction are related to the correct comprehension of clients’ core processes and activities, as well as their needs. This information is usually available in documents, such as the business vision, written in natural language. This kind of language may lead to interpretation bias and information loss, thus causing project delays and escalation of costs. In order to overcome natural language interpretation challenges in Requirements Engineering, we propose the use of Verbka, a knowledge acquisition process based on verbal semantics, as a complement to traditional requirements analysis. This process extracts the causal relationships among the actors mentioned in the business vision document. We used this process as a use case pre-modeling tool, aiming to minimize subjectivity in the identification of actors and use cases. We tested Verbka using a business vision from a classical textbook. The results show that this process is able to obtain a list containing all requirements defined by the client, all actors involved in the business vision, and how they interact with each other. This process is systematic and provides a textual and visual representation of user requirements and use cases consistently. Its application reduced interpretation bias, thus allowing a more detailed and structured requirements analysis.
P2CADL Privacy Preserving in Cloud using Autoencoder based Deep Learning Classifier for Smart City Applications

Nowadays, smart cities enhance the life style of urban people in various disciplines, viz., smart transports, smart environment, smart parking, smart health care, etc. As the cities become smarter, privacy is the major issue and the private sensitive data needs protection. To address this issue, this paper proposes an autoencoder based deep learning classifier by preserving the privacy in cloud for smart city applications. The private data is protected using homomorphic encryption and deep learning is performed in cloud servers. Experiments are conducted on three datasets, viz., Road Traffic Data, Pollution Data, and Parking Data provided by CityPulse Smart City Dataset. It is seen that the proposed approach is efficient compared to conventional classifiers as the non-disclosure of private data in cloud computing. The proposed approach is scalable and it is suitable for big data based smart city applications.