Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Disturbance Rejection Control with Linear Quadratic Regulator for non-minimum phase systems

In this research, a new method for stabilizing non - minimum phase (NMP) system using Active Disturbance Rejection Control (ADRC) is proposed. The general ADRC maintains stability by determining the observer poles and the controller poles, but the non-minimum phase system is very delicate as the stable bandwidth is very limited. Most research using ADRC is the minimum phase system. Therefore, the proposed method is to easily obtain controller gain with a linear quadratic regulator (LQR) using an extended system including ADRC. This widens the range in which the system becomes stable, and can determine the responsiveness of the system with only the adjustment parameter. Further, by obtaining the condition for stabilizing, the system can be stabilized easily. After designing the system design method as the proposed method, the condition is shown for the system to stabilize. Finally, the effectiveness of the proposed method is verified through simulation experiments.
On Performance Analysis of IEEE 802.22 PHY for Cognitive Radio based Smart Grid Communications

Smart Grid (SG) communication architecture involves multiple network layers starting from control center to consumer areas, in a top-bottom hierarchy. At each network layer, several SG applications need to be supported with diverse Quality-of-Service (QoS) requirements. Cognitive Radio (CR) which provides efficient spectrum utilization through opportunistic spectrum access is a promising solution to meet these versatile requirements. The IEEE 802.22 Wireless Regional Area Network (WRAN) is aimed at the use of CR by allowing the unused spectrum of television broadcast service for providing broadband access to remote areas. This standard provide a variety of the modes of operation to support various QoS demands. In this paper, we provide the performance comparison of various modes of IEEE 802.22 standard when applied to different SG applications to show its compliance with the SG communication infrastructure. The frame sizes and other parameters are taken as per the SG application requirements. Furthermore, various bounds on channel quality are also provided to support the reliability requirements of different SG applications using this standard.
Nuclear localization of γ-tubulin affects E2F transcriptional activity and S-phase progression

We show that the centrosome‐ and microtubule‐regulating protein γ‐tubulin interacts with E2 promoter binding factors (E2Fs) to modulate E2F transcriptional activity and thereby control cell cycle progression. γ‐Tubulin contains a C‐terminal signal that results in its translocation to the nucleus during late G1 to early S phase. γ‐Tubulin mutants showed that the C terminus interacts with the transcription factor E2F1 and that the E2F1–γ‐tubulin complex is formed during the G1/S transition, when E2F1 is transcriptionally active. Furthermore, E2F transcriptional activity is altered by reduced expression of γ‐tubulin or by complex formation between γ‐tubulin and E2F1, E2F2, or E2F3, but not E2F6. In addition, the γ‐tubulin C terminus encodes a DNA‐binding domain that interacts with E2F‐regulated promoters, resulting in γ‐tubulin‐mediated transient activation of E2Fs. Thus, we report a novel mechanism regulating the activity of E2Fs, which can help explain how these proteins affect cell cycle progression in mammalian cells. —Höög, G. , Zarrizi, R. , von Stedingk, K. , Jonsson, K. , Alvarado‐Kristensson, M. Nuclear localization of γ‐tubulin affects E2F transcriptional activity and S‐phase progression. FASEB J. 25, 3815–3827 (2011). www.fasebj. org
Force Analysis of a Spherical Single-wheeled Robot

The spherical robot (Ballbot) is a single-wheeled robot that uses a spherical ball to move on a flat surface. So, the Ballbot is designed so that is balanced on a spherical wheel, and since the robot has only one contact point with the ground, it is considered as omnidirectional. The robot is stabilized through the ball movements, which works such as an inverted pendulum. The Ballbot is suited to interact with the human due to the appropriate height and dimension. The purpose of this article is to improve the design of a spherical single-wheeled robot by force analysis. The stability of the robot on the ball is one of the most important challenges for this robot. In this article, after discussing introduction on wheeled robots, the types of spherical single-wheeled robots are presented. Specifications and design tips for the spherical single-wheeled robots are considered and the dynamic equations of the robot are presented. Therefore, various parts of the robot are investigated as a mechatronic system including electronics, mechanics and controls. In order to control the robot, the controller has been implemented. After design review, simulation of a spherical single-wheeled spherical robot and its force analysis in MSC ADAMS and Matlab software are presented. By examining the contact forces in the simulator, design details have been investigated. Finally, stabilization of the presented Ballbot is discussed.
Automatic ROI Detection in Lumbar Spine MRI

Low back pain (LBP) is one of the most common diseases affecting a large number of people. Diagnosis and treatment of LBP require quick, accurate imaging methods. Magnetic resonance imaging (MRI) is effective in distinguishing between vertebra, intervertebral disc and spinal cord, and thus is used frequently in spinal cord injury (SCI) diagnosis. This paper proposes a fully automated approach to detecting region of interest (ROI) using T2-weighted MRI images. Our dataset included the cases of 100 patients who suffered from LBP. In total, 2000 axial and 1200 sagittal ROI were marked in the Lumbar spine. Extracted ROIs were used in the cascade classifier learner. In this method, ROI detection consists of two processes. First the ROIs are specified using the cascade classifier, and then via a process, non-regions of interest (NROIs) are discarded. Histogram of Oriented Gradient (HOG) was used as the feature descriptor in each stage of the Cascade classifier. This method does not require background knowledge of input images and it is reliable regardless of the images size, contrast and clinical abnormally of cases. The quantitative and qualitative evaluation results of the proposed ROI detector were 83% and above 94%, respectively.
Combining Challenge-Based Learning and Design Thinking to Teach Mobile App Development

This paper describes a set of practices, techniques and conceptual tools that were tailored by combining ideas from Challenge-based Learning, Design Thinking and Lean Startup. While the former focuses on intrinsically motivating the students to engage in the learning process by tackling real-world problems aligned with their sense of purpose, the latter emphasizes aspects of the system being built, such as feasibility, viability, and desirability. The framework conception is as an innovative approach for mobile application development in long-term courses (1- or 2-year) involving programming, design, and business. We applied the methodology in the context of a regular undergraduate course including students with a diverse computing and non-computing backgrounds and collected their feedback during the process.
Journal Name Extraction from Japanese Scientific News Articles

In Japanese scientific news articles, although the research results are described clearly, the article's sources tend to be uncited. This makes it difficult for readers to know the details of the research. In this paper, we address the task of extracting journal names from Japanese scientific news articles. We hypothesize that a journal name is likely to occur in a specific context. To support the hypothesis, we construct a character-based method and extract journal names using this method. This method only uses the left and right context features of journal names. The results of the journal name extractions suggest that the distribution hypothesis plays an important role in identifying the journal names.
Hybrid Method for Grid Impedance Estimation Based on Local Measurements

A new method for on-line grid impedance estimation is presented in the paper. It is based on an algorithm that requires no measurements from other buses. What is more, in normal operation it does not need to inject any current to the grid at the point of common coupling as it common in active methods for impedance estimation. The paper includes a mathematical derivation of the key equations, simulation results for a simple test system as well as performance evaluation. Laboratory tests are ongoing.
The bacterial N‐end rule pathway: expect the unexpected

The N‐end rule pathway is a highly conserved process that operates in many different organisms. It relates the metabolic stability of a protein to its N‐terminal amino acid. Consequently, amino acids are described as either ‘stabilizing’ or ‘destabilizing’. Destabilizing residues are organized into three hierarchical levels: primary, secondary, and in eukaryotes – tertiary. Secondary and tertiary destabilizing residues act as signals for the post‐translational modification of the target protein, ultimately resulting in the attachment of a primary destabilizing residue to the N‐terminus of the protein. Regardless of their origin, proteins containing N‐terminal primary destabilizing residues are recognized by a key component of the pathway. In prokaryotes, the recognition component is a specialized adaptor protein, known as ClpS, which delivers target proteins directly to the ClpAP protease for degradation. In contrast, eukaryotes use a family of E3 ligases, known as UBRs, to recognize and ubiquitylate their substrates resulting in their turnover by the 26S proteasome. While the physiological role of the N‐end rule pathway is largely understood in eukaryotes, progress on the bacterial pathway has been slow. However, new interest in this area of research has invigorated several recent advances, unlocking some of the secrets of this unique proteolytic pathway in prokaryotes.
Multilingual Stemming and Term extraction for Uyghur, Kazak and Kirghiz

Stemming and term extraction is an important and difficult step on NLP for low resource languages. Inflectional structure and noisy data aggravate this problem for less popular agglutinative languages. A morphological analyzer with the longer context can utilize resources and provide reliable semantic and syntactic information, and effectively reduce ambiguity on stemming and term detection. There are some previous works on stemming on Uyghur texts based on simple morphology like affix and manually collected rules. But the limited information of smaller context and lower segmentation accuracy cost the reliability. We developed a sentence level multilingual morphological processing tool for Uyghur, Kazak, and Kirghiz languages. This tool can provide sentence level morpheme extraction with 98% accuracy, and further analysis like word embedding and longer context modelling can extract remaining unseen and infrequent stems reliably. Combined with word embedding this tool provides a more reliable way of term extraction from large number of noisy text available from internet.
Automatic Detection of White Blood Cells from Microscopic Images for Malignancy Classification of Acute Lymphoblastic Leukemia

Leukemia is a group of cancers that typically begins in the bone marrow that usually affects both the children and the adults and remains a leading cause of fatality to death around the whole world. Acute Lymphoblastic Leukemia (ALL) is the most common type of leukemia. ALL diagnosis is performed by expert hematologists from blood microscopic images manually. However, human inspection is lengthy, tedious, and repetitive and fully depends on the operator’s skill; thus its statistical reliability becomes limited. A computer based system can be used as a supportive tool for the hematologists in order to enhance and accelerate the morphological diagnosis process. The aim of this paper is to design an automated system for the detection and classification of acute lymphoblastic leukemia by analyzing morphological, textural and color features from the blood microscopic images using image processing. The proposed system is designed and now ready for the practical usage in medical having a white blood cell detection accuracy of 93% and a malignancy classification accuracy of 93.6% and the system is tested with 108 blood microscopic images of ALL-IDB. The proposed system is successfully implemented in MATLAB R2016a and it is named as CAD-SFA (Computer Aided Diagnosis-System for ALL).
A 13-Levels Module (K-Type) With Two DC Sources for Multilevel Inverters

This paper presents a new reconfiguration module for asymmetrical multilevel inverters in which the capacitors are used as the dc links to create the levels for staircase waveforms. This configuration of the multilevel converter makes a reduction in dc sources. On the other hand, it is possible to generate 13 levels with lower dc sources. The proposed module of the multilevel inverter generates 13 levels with two unequal dc sources (2VDC and 1VDC). It also involves two chargeable capacitors and 14 semiconductor switches. The capacitors are self-charging without any extra circuit. The lower number of components makes it desirable to be used in wide range of applications. The module is schematized as two back-to-back T-type inverters and some other switches around it. Also, it can be connected as a cascade modular which leads to a modular topology with more voltage levels at higher voltages. The proposed module makes the inherent creation of the negative voltage levels without any additional circuit (such as H-bridge circuit). Nearest level control switching modulation scheme is applied to achieve high-quality sinusoidal output voltage. Simulations are executed in MATLAB/Simulink and a prototype is implemented in the power electronics laboratory in which the simulation and experimental results show a good performance.
Augmentation of Visual Experiences using Deep Learning for Peripheral Vision based Extended Display Application

We propose the system that provides an immersive experience by automatically extending the video content of the TV to the wall. Though it is possible to manually create the appropriate content that works on the existing TV screen, the cost is too expensive to create. The proposed deep neural network uses the center of the video as input and the outside area as output. The trained network automatically generates the extrapolated video according to the input video and a projector projects the resulting content on the living room wall around the TV thereby improving the immersive feeling. The proposed system is expected to pave a new road to the home appliance industry by transforming the living room into the new immersive experience platform.
Variable-Voltage Bus Concept for Aircraft Electrical Power System

This paper deals with the innovative “variable-voltage” bus concept for future more-electric aircraft platforms. Using new functionalities and opportunities offered by innovative sources actively controlled by power electronics, there is an opportunity for a significant increase of their output power (within powertrain capabilities, machine thermal limits, and power converter maximum current) to supply increased load demands under certain conditions. This paper investigates applications of this concept to satisfy power demands for the wing ice-protection system for business jet. The study includes a detailed study into the control challenges of the proposed approach and suggests corresponding theoretical solutions. Furthermore, the controller design aspects are explored with considerations to achieve stable operation. The concept is tested in simulation environment and validated through experimental studies. This paper might be of interest for the researchers and engineers focused on innovative solutions for future aircraft platforms.
Server Load Balancing in Software-Defined Networks

Nowadays, web servers have to respond to the huge amount of requests; therefore, balancing the load between server replicas becomes vital and critical. Conventional load balancers have been dedicated hardware that is susceptible being a single point of failure, vendor-dependent and not flexible. In this work, we propose a flexible and programmable scheduling algorithm that ponders different type of requests, various server processing capacity based upon their CPU, memory usage, I/O rate, and a number of connections. This algorithm uses optimization algorithm, considering not only load of servers but also network congestion. The proposed algorithm includes four main phases. The first two phases have the responsibility of creating sub-topology that causes to increase performance and scalability of the proposed algorithm by reduction of overall solution space. In the third phase, the joint optimal network and server-side load balancing approach are performed, and in the last phase, the post-processing is performed in order to select the server, creates a path, and injects appropriate flow table entries on the OpenFlow switches. The evaluation results show that the dynamic load balancing algorithm significantly decreases response-time whereas increases throughput, in comparison with the existing load balancing algorithms.
Wideband Dipole Array Loaded Substrate-Integrated Horn Array With Improved Sidelobe Performance

In this letter, a novel millimeter-wave substrate-integrated H-plane horn array is presented by combining a loaded dipole array and the gap substrate-integrated waveguide consisting of eight etched slits. The operating mechanism and design process of the array are investigated. A wide impedance bandwidth of 33%, a gain up to 14.6 dBi, and symmetrical radiation patterns with cross polarizations of less than –23 dB are verified experimentally by a 1 × 4 fabricated prototype. An improved sidelobe level of almost less than –10 dB is also achieved over the wideband even though the element spacing of the array is larger than two operating wavelengths. The proposed method is valuable for the realization of wideband high-gain planar horn arrays with large element spacing, which is an attractive candidate for millimeter-wave applications.
A Sequential Bayesian Approach for Remaining Useful Life Prediction of Dependent Competing Failure Processes

A sequential Bayesian approach is presented for remaining useful life (RUL) prediction of dependent competing failure processes (DCFP). The DCFP considered comprises of soft failure processes due to degradation and hard failure processes due to random shocks, where dependency arises due to the abrupt changes to the degradation processes brought by the random shocks. In practice, random shock processes are often unobservable, which makes it difficult to accurately estimate the shock intensities and predict the RUL. In the proposed method, the problem is solved recursively in a two-stage framework: in the first stage, parameters related to the degradation processes are updated using particle filtering, based on the degradation data observed through condition monitoring; in the second stage, the intensities of the random shock processes are updated using the Metropolis–Hastings algorithm, considering the dependency between the degradation and shock processes, and the fact that no hard failure has occurred. The updated parameters are, then, used to predict the RUL of the system. Two numerical examples are considered for demonstration purposes and a real dataset from milling machines is used for application purposes. Results show that the proposed method can be used to accurately predict the RUL in DCFP conditions.
Leaf-Less-Tree feature for semantic labeling applications on Google Earth Engine

Semantic labeling methods classify the earth surface into predefined classes. Very High Resolution images are popular in recent researches. Although they contains huge amount of data, obtaining required information is challenging. Semantic labeling methods utilize spectral and spatial features to define feature of classes. Tree class is typically considered in semantic labeling methods. Spectral information is insufficient in leaf less trees for feature extraction as there is no leaf to utilize vegetation indices. On the other side, tree branches are visible that could be used for spatial feature. A new spatial feature named Leaf-Less-Tree (LLT) is developed and presented in this work to improve leaf less tree detection in semantic labeling applications. Evaluation indicates accuracy improvement in test images.
A Novel Multi-Level Constellation Compression Modulation for GFDM-PON

In this paper, for the first time, a novel multi-level constellation compression modulation (MCCM) is proposed and applied for generalized frequency division multiplexing passive optical network (GFDM-PON). The principle of the proposed MCCM is to transfer the constellation points of the outer circle to the ones of the inner circle with different probabilities, thereby significantly reducing the average transmitted power. Also, by dynamically adjusting the compression factor, MCCM can also achieve different types of constellations to accommodate various scenarios of optical fiber transmission. In order to verify the feasibility of the proposed coded modulation technology and its transmission performance, a GFDM-PON system is set up based on intensity-modulation and direct-detection. The experimental results indicate not only that one-level constellation compression modulation (CCM) is superior to the conventional modulation technology, but also two-level CCM can significantly reduce the bit error ratio (BER) of the system. When the transmission performance is unfavorable, the system BER can be further reduced by the enhanced CCM. Therefore, the system based on the proposed MCCM technology can meet different access scenarios with robust adaptability and scalability.
Design of Circularly-Polarized Cavity-Backed Slot Array Antenna Using Higher-Order Mode Excitation Based on Gap Waveguide Technology

In this paper, higher-order mode excitation is used to achieve a high gain circularly polarized slot array cavity backed antenna. In the cavity, which is made by Gap waveguide pins, TE440 mode is simply excited by a coaxial probe. Also, a polarizing layer is designed which converts the linearly polarized radiation from the slots to circular polarization. The antenna exhibits a simulated gain of 17.8 dB and radiation efficiency of about 97% with good return loss. The simulated |S11| < -10 dB 10dB is in the range of 27.1-28.4 GHz and the axial ratio bandwidth is about 9.5%. Furthermore, the antenna can be considered as an appropriate candidate for use in Future fifth generation (5G) wireless systems.
Kinship verification using BSIF and LBP

In recent years, the kinship verification has got more intention due to its potential applications like images annotation, organizing the photo albums and recognizing similarities among humans. In this paper, we propose a new approach based on the Local Binary Pattern (LBP) and Binarized Statistical Image features (BSIF) descriptors and the PML (Pyramid Multi-Level) representation for the kinship verification from the static images. In particular, we investigate the best parameters of each method (LBP, BSIF and PML) for the kinship verification, then conclude the suitable combination. The approach consists three main stages which are: (1) Face preprocessing,(2) features extraction, and (3) classification and decision (kin or non kin). The proposed approach is tested and analyzed on four public databases (Cornell KinFace, UB Kin database, KinFace-I, and KinFace-II). The obtained results of our approach have shown better performance compared to the state-of-the-art approaches