Miracle-dz/UniFAR-FTUs huggingface dataset:
Piezoelectric Pressure Sensors for Hypersonic Flow Measurements

A microelectromechanical-system piezoelectric pressure sensor targeting hypersonic flow applications is presented. The sensing diaphragms have 500-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> diameter and are comprised of a composite stack of Si, SiO<sub>2</sub>, and AlN. This paper presents the fabrication and packaging details of the sensor and then presents test results from a Mach-6 hypersonic-flow facility, in which the prototype sensor data are compared against data from a mature commercial sensor present in the same tests. Salient hypersonic-flow features are observed in signals captured by the prototype. The package is comprised of an on-chip two-element array with 650-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> pitch, and signals from the elements captured during hypersonic-flow tests are highly correlated. [2018-0196]
MARKA SEVGİSİ, ANLIK SATIN ALMA VE POZİTİF AĞIZDAN AĞIZA PAZARLAMA İLİŞKİSİ ÜZERİNE AMPİRİK BİR ÇALIŞMA

Recently, the changes in marketing activities and increasingly difficult competition conditions force companies to find new strategies. Against this situation, companies that want to increase their sales and keep their competitive advantages find the solution in the development of the brand love, which is a strong relationship between the brand and the consumer. Consumers can buy the brands they love without thinking rationally and recommend this brand to their environment. In this context, the main purpose of the study is to examine the relationships between brand love, impulse buying and positive word of mouth marketing on students in the context of sportswear. In line with this basic purpose, a questionnaire was carried out on students of the Faculty of Sport Sciences at Selcuk University. As a result of the study, the relationship between brand love's impulse buying and positive word of mouth marketing was evaluated with the structural equation model. After the evaluation, the brand's love is on impulse buying and positive word of mouth marketing; positive word of mouth marketing had a positive effect on impulse buying.
Total-Ionizing-Dose Irradiation-Induced Dielectric Field Enhancement for High-Voltage SOI LDMOS

Breakdown voltage (BV) shifting behavior induced by total-ionizing-dose (TID) irradiation is investigated for high-voltage SOI LDMOS, in this letter. The BV increase caused by irradiation is present for the first time, and the degradation mechanism is revealed by modeling and simulation. Irradiation-induced electric field modulation exerts an impact on the competition between the lateral BV and vertical BV and results in BV shifting. The dielectric field is enhanced by irradiation-induced positive oxide trapped charges in the BOX layer, which is responsible for the BV increasing after irradiation. A TID radiation hardening approach is proposed so that the lateral BV is required to be properly higher than vertical BV instead of pursuing equality.
Multi-Focus Image Fusion With a Natural Enhancement via a Joint Multi-Level Deeply Supervised Convolutional Neural Network

Common non-focused areas are often present in multi-focus images due to the limitation of the number of focused images. This factor severely degrades the fusion quality of multi-focus images. To address this problem, we propose a novel end-to-end multi-focus image fusion with a natural enhancement method based on deep convolutional neural network (CNN). Several end-to-end CNN architectures that are specifically adapted to this task are first designed and researched. On the basis of the observation that low-level feature extraction can capture low-frequency content, whereas high-level feature extraction effectively captures high-frequency details, we further combine multi-level outputs such that the most visually distinctive features can be extracted, fused, and enhanced. In addition, the multi-level outputs are simultaneously supervised during training to boost the performance of image fusion and enhancement. Extensive experiments show that the proposed method can deliver superior fusion and enhancement performance than the state-of-the-art methods in the presence of multi-focus images with common non-focused areas, anisotropic blur, and misregistration.
Transformerless Z-Source Four-Leg PV Inverter With Leakage Current Reduction

Due to the lack of electrical isolation, the leakage current is one of the most important issues for transformerless photovoltaic (PV) systems. In this paper, a new modulation strategy is proposed to reduce the leakage current for a Z-source four-leg transformerless PV inverter. First, the common-mode loop model is presented. And then the common mode voltage behavior and the effect of factors on the leakage current are discussed. A new modulation strategy is proposed to achieve the step-up function and constant common mode voltage. Therefore, the leakage current can be suppressed effectively. Finally, the proposed strategy is digitally implemented and tested on the TMS320F28335 DSP and XC3S400 FPGA controlled hardware platform. The experimental results verify the effectiveness of the proposed solution.
Quasi-Resonant Control for Harmonic Current Suppression of a Magnetically Suspended Rotor

In the magnetically suspended rotor (MSR) system of the flywheel, harmonic current produced mainly by mass imbalance and sensor runout would cause undesirable harmonic vibration. Resonant control (RSC) is one of the remarkable performance alternatives for rejecting a set of selected frequency components of the periodic disturbance in a control system. Based on the intrinsic relationship between RSC and repetitive control, which is deduced and clarified, this paper proposes a novel quasi-resonant control (QRSC) scheme for a baseline prestable MSR system to suppress the harmonic current. By introducing a damping factor, QRSC achieves a good balance between stability and dynamic performance. With hybrid phase compensation, it provides system absolute stability and expands the stability margin of the MSR system. Further, as the domination of harmonic current, low-order frequency components are suppressed by parallel QRSCs (PQRSC). A stability criterion with rigorous proof for the plug-in PQRSC-controlled MSR system is addressed as well. Experimental results obtained with a magnetically suspended flywheel prototype corroborate the theoretical approach.
Intelligent Conversational Agent for Online Sales

A conversational agent is one of emerging technologies that will take place in several of life domains, including e-commerce. The common features of any conversational agent in any domain is its ability to have a dialog with a human user and 24/7 available. Thus, including a conversational agent in an online business will give benefits not only to online retailers, but also e-customers. However, a conversational agent for online sales may have a different feature than a conversational agent in other domain such as health care. The main task of a conversational agent in an online sales domain is to engage and persuade e-commerce customers to buy a product. In a real life, persuasion is considered as an abstract process, which only can be achieved through a series of dialogs. In a virtual world, a conversational agent for online sales is considered intelligent if it is able to demonstrate this feature. The aim of this paper is to propose a method to demonstrate persuasion feature through recommendation and negotiation dialogs to meet e-customers' needs. A prototype of the proposed system has been implemented and preliminary results indicate the proposed method is successful.
A Cavity-Backed Shorted Annular Patch (SAP) Array for Mid-Range V-Band Backhauling Applications

This paper presents a new improved gain array configuration well suited for mid-range V-band backhauling applications. The proposed solution is based on a cavity-backed shorted annular patch (SAP) antenna loaded with a parasitic patch element for gain maximization. This solution is proved by designing and measuring a $4\times 4$ array which presents a reflection coefficient bandwidth covering the frequencies from 58.7 to 63.25 GHz (7.6% at 60 GHz). The experimentally validated array gain is equal to 18 dBi corresponding to 37.9% of aperture efficiency. The antenna and array configurations presented in this paper are optimized to minimize the manufacturing cost and complexity. This result is achieved through a novel beamforming network (BFN) configuration, implemented using a dual-layer substrate-integrated waveguide (SIW) technology, and partly integrated on the array aperture. The achieved results demonstrate how the performance of the cavity backed SAP array is comparable to the ones achieved with more complex and expensive solutions.
Energy Storage in Distribution System Planning and Operation: Current Status and Outstanding Challenges

It is no exaggeration to state that power systems presently undergo a paradigm shift. Driven by an urgent desire to mitigate the effects of global warming and a foreseen end to the world’s fossil fuel resources, an increasing tendency toward renewable energies is fostered by the international community. On the other hand, distributed generation and electric vehicle adoption are altering load nature and profile. Therefore, energy storage (ES) becomes a necessity for its ability to bridge the gap between the dynamically changing supply and demand in addition to other ancillary services it can provide. Meanwhile, advances in smart grid technologies enable escalating the incorporation of new technologies with more efficient control schemes and energy management algorithms. This paper presents an overview on the employment of ES technologies in planning and operation of distribution systems through the literature survey. The problem of sizing and siting ES units in distribution systems is first introduced. The state of the art of the technology is summarized, and some outstanding issues to be addressed through future research are highlighted.
Overlit: New Storage Driver for Localization and Specialization

The container technology, which is an operating system level virtualization technology, has been widely used in server computing environments such as cloud systems and data center systems, to overcome the limitations of conventional hypervisor-based virtualization technologies. Docker, which is developed primarily for Linux, aims to support high performance and convenient management of the server systems, while sacrificing some level of isolation. It supports very powerful storage and volume drivers to split a single image of each application into several sub-images for the repeatability and deduplication, but it still has some problems, such as global failures and bundled performance, caused by sharing a single host filesystem among all the layers. We develop a new container storage driver for localization and specialization, where the former is for eliminating deep dependencies among the layers and the latter is for enabling each layer to use its own filesystem. Our experimental results show that our storage driver improves the performance of read-only layer by up to 15% and that of read-write layer by up to 5 times over the other drivers.
Single and Power-Combined Linear E-Band Power Amplifiers in 0.12- $\mu$ m SiGe With 19-dBm Average Power 1-GBaud 64-QAM Modulated Waveforms

This paper presents a set of wideband, fully integrated E-band power amplifiers (PAs) designed in 0.12-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> silicon-germanium (SiGe) BiCMOS and working in the range of 60–75 GHz. The single PA is based on common-emitter (CE) driver stages followed by a CE output stage. A three-stage and four-stage versions of the single-PA as well as four- and eight-way combined variants of the four-stage PA are measured and compared. The single, four-way, and eight-way combined PAs achieve saturated output powers of 16, 19.5, and 24 dBm with peak power-added efficiencies (PAEs) of 18%, 11%, and 12%, respectively. Modulated waveforms are passed through each amplifier and data rates as high as 32 Gb/s are demonstrated for all amplifiers when driven in the linear mode. Measurements of how error-vector magnitude (EVM) and PAE degrade versus output power are presented and it is demonstrated that the eight-way combined PA can deliver 17–20 dBm of average power in a 64-quadratic-amplitude modulation, 1-Gbaud waveform (6 Gb/s), with an EVM of −32 to −24 dB and a PAE of 3%–5% without predistortion. To the author’s knowledge, this is the first paper reporting detailed measurements of PA EVM and PAE versus output power at these frequencies.
Multi-Winding Configuration Optimization of Multi-Output Planar Transformers in GaN Active Forward Converters for Satellite Applications

It is a serious challenge to find the optimal winding configuration that realizes minimum leakage inductance of multi-output multi-winding planar transformers due to the complex coupling relationship. The proposed idea is to build the mathematical model of leakage magnetic field energy and screen out all possible winding configurations to solve the minimum value with MATLAB programming. Then, only limited potential winding configurations need to be three-dimensionally simulated in Maxwell. By analyzing the inductance matrix, the leakage inductance of multi-winding planar transformers is solved. A full gallium nitride (GaN) active clamp forward converter with self-driven synchronous rectifiers (SRs) is presented with complete mode analysis. It is noted that the proposed active clamp technology uses the auxiliary winding with the nonfloating GaN switch compared to the conventional high-side clamping circuit, which is important for the satellite applications. The GaN drive chips for high reliable gate voltage are combined with self-driving. Considering the leakage inductance in each winding and junction capacitance of the GaN high electron mobility transistors, the oscillation frequency and amplitude over the switches are modeled quantitatively, which is important to minimize powertrain loop at MHz. The explanation of the root cause of the voltage spike in the converter is also presented. A prototype with 1 MHz, 100 V input, 5 V/6 A and ±12 V/0.83 A outputs was built to verify the proposed techniques.
A Framework for Implementing Join Operation between Multiple MOLAPs

In this modern era, a data warehouse is keenly interested in storing data in the form of OLAP(Online Analytical Processing). OLAP is widely used nowadays as it extends special aid to a user, regarding viewing a set of data from various views. To facilitate the analysis further, a multidimensional database is required which is ably supported by MOLAP, a special kind of OLAP. We can extract data from a single MOLAP in different possible ways. There have been many kinds of research to make this analytical process more efficient. But the research area, of extracting data from multiple MOLAPs, has been explored hardly. However, we have proposed a new model for faster querying in multiple MOLAPs using snowflake schema. This model uses array index reference, for creating a MOLAP by joining two-dimensional databases. Thus we can derive summarized MOLAP from fundamental MOLAP. Then we have used summarized MOLAP for creating a fact table, which preserves the summarized data. Our model provides a satisfactory result after comparing it with the MySQL database result.
Intuitive Adaptive Orientation Control for Enhanced Human–Robot Interaction

Robotic devices can be leveraged to raise the abilities of humans to perform demanding and complex tasks with less effort. Although the first priority of such human–robot interaction (HRI) is safety, robotic devices must also be intuitive and efficient in order to be adopted by a broad range of users. One challenge in the control of such assistive robots is the management of the end-effector orientation, that is not always intuitive for the human operator, especially for neophytes. This paper presents a novel orientation control algorithm designed for robotic arms in the context of HRI. This paper aims at making the control of the robot's orientation easier and more intuitive for the user, both in the fields of rehabilitation (in particular individuals living with upper limb disabilities) and industrial robotics. The performance and intuitiveness of the proposed orientation control algorithm is assessed and improved through two experiments with a JACO assistive robot with 25 able-bodied subjects, an online survey with 117 respondents via the Amazon Mechanical Turk and through two experiments with a UR5 industrial robot with 12 able-bodied subjects.
Speech Enhancement Using Convolutional Denoising Autoencoder

Speech signals are complex in nature with respect to other forms of communication media such as text or image. Different forms of noises (e.g., additive noise, channel noise, babble noise) interfere with the speech signals and drastically hamper the quality of the speech in the noisy speech signals. Enhancement of speech signals is a daunting task considering multiple forms of noises while denoising speech signals. Certain analog noise eliminator models have been studied over the years for this purpose. Researchers have also delved into some deep learning techniques to enhance speech signals. In this study, a speech enhancement system is investigated using Convolutional Denoising Autoencoder (CDAE). It takes advantages from the 2D structured inputs of the features extracted from speech signals and also considers the local temporal relationship among the features. In the proposed system, CDAE is trained considering features from noisy speech signal as input and clean speech features as desired output. The proposed CDAE based method has been tested on a benchmark dataset, called Speech Command Dataset, and attained 80% similarity between denoised speech and actual clean speech. The proposed system achieved perceptual evaluation of speech quality (PESQ) value of 2.43 which outperformed other related existing methods.
Watersheds for Semi-Supervised Classification

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered – How do the boundaries of the watershed operator behave? Which loss function does the watershed operator optimize? How does watershed operator relate with existing ideas from machine learning. In this letter, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, morphMedian, resulting in the maximum margin principle. It is then shown that watersheds form a particular class of morphMedian classifiers. Using the ensemble technique, watersheds are also extended to ensemble watersheds. These techniques are compared with relevant methods from the literature and it is shown that watersheds perform better than support vector machines on some datasets, and ensemble watersheds usually outperform random forest classifiers.
Ground Control Station for an Unmanned Aerial Vehicle Integrated in IoT

The paper presents the Murros Unmanned Aerial System (UAS) and its integration in IoT. Murros UAS is a multisensorial robotic system used for aerial monitoring of wireless sensors network in precision agriculture. This document details the software and hardware architecture of Murros UAS Ground Control Station and common situations when we are operating in an over-the-internet configuration. Experimental results are obtained from real missions.
Performance Enhancement of the ERC using Cloud

Cloud Computing and IoT (Internet of Thing) have gained significant attention in recent years. These two technologies can be utilized for collection, storage, processing and access of the data. Due to the advent of these technologies, analysis of data from controller, prediction and fault diagnosis of hardware have now become easier. ERC (Electronic Refrigeration Controller) is used in controlling temperature in Glass Door Merchants (GDM) of commercial refrigerators. Presently ERC users have been facing challenges as the data can't be published and stored. To overcome this problem, this paper describes a scheme for connecting ERC to the cloud using gateway and storing data in cloud for processing. The proposed cloud-based ERC provides improvement in flexibility, security, collaboration and globalization of ERC. This also facilitates global access of ERC data, control of refrigerator and minimization of the access time as the data is accessible anywhere any time. The proposed scheme is implemented in AWS cloud platform for storing the refrigerator data and controlling the ERC of the commercial refrigerator to clients. From implementation results, it is found that the cloud provides efficient storage and access and facilitates control of ERC.
Big Data for E-Governance

The notion of governance is innate to any society and the use of big-data allows governance as a competitive advantage to attract resources, with the emergence of the social web and exponential data generated accordingly that can be analyzed to give the human the path of governance and maintain an intelligent global state of the megacity. Data from multiple heterogeneous sources of governments are usually combined. Government agencies are improving the services and applications they offer to citizens and businesses through Big Data, which is a global opportunity to integrate a large number of public administration databases to improve fraud detection and tax control activities. The impact of Big Data on application- facilitated governance is illustrated by the demonstration of its use in research and practice. Therefore, this paper exposes an approach based on interoperability, while the integration of large data is a major concern that needs to be addressed at different levels.
A Novel Weakly-Supervised Approach for RGB-D-Based Nuclear Waste Object Detection

This paper addresses the problem of RGBD-based detection and categorization of waste objects for nuclear decommissioning. To enable autonomous robotic manipulation for nuclear decommissioning, nuclear waste objects must be detected and categorized. However, as a novel industrial application, large amounts of annotated waste object data are currently unavailable. To overcome this problem, we propose a weakly supervised learning approach which is able to learn a deep convolutional neural network from unlabeled RGBD videos while requiring very few annotations. The proposed method also has the potential to be applied to other household or industrial applications. We evaluate our approach on the Washington RGB-D object recognition benchmark, achieving the state-of-the-art performance among semi-supervised methods. More importantly, we introduce a novel dataset, i.e., Birmingham nuclear waste simulants dataset, and evaluate our proposed approach on this novel industrial object recognition challenge. We further propose a complete real-time pipeline for RGBD-based detection and categorization of nuclear waste simulants. Our weakly supervised approach has demonstrated to be highly effective in solving a novel RGB-D object detection and recognition application with limited human annotations.