Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration of Concentric Tube Continuum Robots: Automatic Alignment of Precurved Elastic Tubes

Joint level calibration is an integral part of robotics as it directly influences the achievable accuracy. As opposed to serial robotic arms, continuum robots are not composed of any rigid links or joints, but of elastic materials that undergo bending and torsion. The jointless composition requires dedicated calibration procedures. In this letter, we introduce an automatic method for aligning precurved elastic tubes for joint level calibration of concentric tube continuum robots. The robot tip is equipped with a sensor in order to track its position during calibration such that subsequent data processing can extract the rotational zero position automatically. While we present a general framework independent of the utilized sensor technology, we evaluate our approach using three different sensing methodologies, i.e. magnetic, inductive, and electromagnetic. Furthermore, we advise on properties for appropriate sensors. Our experimental results show, that the rotational home position can be found reproducibly with a minimal dispersion of 0.011$^\circ$.
Usage of Millimeter-wave and Sub-6GHz Band with Full-duplex Relays in Heterogeneous Networks: An Information-theoretic Analysis

In this paper, we investigate the usage of millimeter- wave (mmWave) band and sub-6GHz band with respect to total transmission time as a performance metric. For mmWave communication, multi-hop relaying is considered, and it is able to operate as half-duplex of full-duplex mode. By consider simple system model, but not disregard its inherent characteristics of each band, we analytically investigate the trade-off between each band, and derive the conditions for each best-performing regime. In the end, we observe the trade-off with respect to various system parameters and our analysis is well matched with the simulation results.
Port Controlled Hamilton With Dissipation-Based Speed Control of IPMSM Drive

This paper presents a port controlled Hamilton with dissipation (PCHD) based control technique for high precision speed regulation of interior permanent magnet synchronous motor (IPMSM) drive. The PCHD controller is designed by assigning proper interconnection and damping matrix for multivariate, nonlinear, and time-varying IPMSM system to prove its passivity and ensure its global stability. A torque observer is also designed based on pole placement method to monitor the external load. The stability analysis of the proposed PCHD and uniformity between the proposed controller and proportional-integral (PI) controller are analyzed subsequently. The performance of the proposed controller is investigated in both simulation and experimental environment using MATLAB-Simulink and dSPACE digital signal processor board DS1104, respectively, at different operating conditions for a laboratory 5 hp motor. It is found that the proposed PCHD controller shows better performance than that of the conventional benchmark tuned PI controller in terms of speed tracking capability with load variations.
Uplink Performance of Ultra Dense Networks with Power Control

In this paper, we study Ultra Dense Networks(UDN) in which the density of BSs is distributed with a density of up to 100 BS/km2. This paper utilizes the stretched path loss model which recently has been introduced as an appropriate model for short communications. We introduces a new approach to control the transmit power of the user in which the user’s transmit power depends on the distance between the user and its associated Base Station (BS), signal power attenuation. The user performance metric in terms of average coverage probability is mathematically derived. The analytical results indicates that in the case of utilizing power control, increasing the transmit power and the density of BSs can produce negative impacts on the average coverage probability of the user.
Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles

One of the major challenges in cybersecurity is the provision of an automated and effective cyber-threats detection technique. In this paper, we present an AI technique for cyber-threats detection, based on artificial neural networks. The proposed technique converts multitude of collected security events to individual event profiles and use a deep learning-based detection method for enhanced cyber-threat detection. For this work, we developed an AI-SIEM system based on a combination of event profiling for data preprocessing and different artificial neural network methods, including FCNN, CNN, and LSTM. The system focuses on discriminating between true positive and false positive alerts, thus helping security analysts to rapidly respond to cyber threats. All experiments in this study are performed by authors using two benchmark datasets (NSLKDD and CICIDS2017) and two datasets collected in the real world. To evaluate the performance comparison with existing methods, we conducted experiments using the five conventional machine-learning methods (SVM, k-NN, RF, NB, and DT). Consequently, the experimental results of this study ensure that our proposed methods are capable of being employed as learning-based models for network intrusion-detection, and show that although it is employed in the real world, the performance outperforms the conventional machine-learning methods.
A Kind of Resilient Latch Structure Base on Cross-coupled Transistors

In digital circuits, memories, like SRAMs, latches and flip-flops, are extremely susceptible to various radiation effects. This paper proposes a double-node upset (DNU) resilient latch called "Octahedron", which is completely composed of cross-coupled transistors. Advantages with respect to area, power delay product and resilient ability are revealed when our design is compared with previously reported latches. Finally, a formula for coarse estimation of DNU resilient latch failure probability is proposed. Octahedron shows lowest failure probability when the probability of single node upset (SNU) is small.
Delay-Aware Task Congestion Control and Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) is considered as a promising paradigm to extend the computation capability of smart mobile devices (SMDs) and reduce the task execution delay. In this paper, we formulate a stochastic optimization problem, which maximizes the system utility and ensures the queues stability subject to the power, subcarrier and computation resources constraints by the joint congestion control and resource allocation. Leveraging on the Lyapunov optimization technique, four subproblems are decomposed. Because the system utility maximization subproblem, congestion control subproblem and SMDs computation resource allocation subproblem are all single variable problems, we can obtain the solutions directly. The joint power and subcarrier allocation subproblem can be efficiently solved by utilizing alternating and time-sharing methods. By solving the four separate subproblems at each slot, we proposed a delay-aware task congestion control and resource allocation (DTCCRA) algorithm. Theoretical analysis shows that the system utility of proposed DTCCRA algorithm increases by 18.91% and 26.14% respectively compared with traditional average power allocation algorithm and average subcarrier allocation algorithm.
A Study on Signal Complexity Measurement for Epileptic Seizure Detection

Epilepsy is a sudden brain malfunction that represented by an exaggerated, high, and hypersynchronous neuron activity inside the brain. Electroencephalogram (EEG) is a tool that can be used to measure the electrical changes of the brain by using electrodes placed on the scalp. An ictal pattern in the EEG signal can be used to identify seizure condition. Neurologist search for the ictal state by determining slow, spike, and sharp wave in the EEG recording manually. However, the manual method is very vulnerable to human errors. That is why many research has been conducted to develop an automatic seizure detection system. Since the EEG signal is a nonlinear, nonstationary, and chaotic, it is considered as a complex signal. This paper discusses a brief study on signal complexity measurement in the EEG signal in the term of epileptic seizure detection. Complexity measurement based on statistic, entropy, and chaos analysis is explored in six processing domain.
Anti-arthritic activity of Tribulus terrestris studied in Freund's Adjuvant induced arthritic rats

In the present study, methanolic extract of Tribulus terrestris fruit was assessed for anti-arthritic activity in rat. the anti-arthritic activity of Tribulus terrestris (200mg/kg and 300 mg/kg p.o) was evaluated using Frund’s complete adjuvant (FcA) induced arthritis in rats. the herbal extracts at dose 200mg/kg and 300 mg/kg p.o was administered for 21 days after the injection of FCA in the rats paws. A significant (P ≤ 0.01) inhibition of paw edema volume was observed from day 5 th to 21 st in the treated groups. the biochemical parameters like erythrocyte sedimentation rate (ESr), alkaline phosphatase (ALP), acid phospatase (AcP) and total WBc count was observed which are the major markers of arthritis. A significant increase in the level of all the markers were found in the arthritic rats where as in case of prednisolan and Tribulus terrestris (200mg/kg and 300 mg/kg) treated groups a marked decrease in the level was observed. A better anti-arthritic activity was obtained at 300 mg/kg dose of Tribulus terrestris. these results indicate that the methanolic extact of Tribulus terrestris has anti-arthritic activity.
A Performance Enhancement Technique for a Joint FMCW RadCom System

The emerging trend of autonomously driving vehicles brings an increasing need for communication. In a setting comprising connected vehicles, information about the environment and vehicles themselves will be shared with other vehicles. Existing communication standards may not be able to meet this growing demand on communication bandwidth. Radar embedded communication can help overcome this bottleneck at the expense of degraded radar performance. In this work we introduce and demonstrate a novel radar signal processing technique to compensate for the self-interference due to the communication content on a chirp modulated radar signal. Recovery of the radar performance is demonstrated by simulations for the continuous phase modulation on the chirp modulation.
Fingerspelled Alphabet Sign Recognition in Upper-Body Videos

Fingerspelling is a crucial part of sign-based communication, however its recognition remains a challenging and mostly overlooked computer vision problem. To address it, this paper presents a system that recognizes the 24 static fingerspelled alphabet signs of the American Sign Language. The system consists of two algorithmic stages, comprising an efficient preprocessing phase that generates candidate hand-region proposals, followed by their deep-learning based classification. Specifically, the first stage exploits own earlier work on hand detection and segmentation in videos that also contain the signer’s face, allowing face detection to drive skin-tone based hand segmentation, with motion further utilized to localize hands, extending it with a peak detection module that yields proposal regions likely to contain the signs of interest. These regions are then classified by a variant of a convolutional neural network that extends traditional convolutions to quadratic operations on the inputs, being, to our knowledge, the first application of such architecture to this task. Both system stages are evaluated on three well-known fingerspelling corpora, significantly outperforming a number of alternative approaches under both multi-signer and signer-independent experimental frameworks.
Simultaneous Control of Two Points for Snake Robot and Its Application to Transportation

This letter presents a simultaneous trajectory tracking control method for two points of a snake robot. The kinematic model considering two regulated points is determined as a switched system that is switched by lifting a few wheels. The system becomes a kinematically redundant system by the lifting of the wheels; however, it has to prevent two types of singular configurations; one is the traditional one, and the other is caused by regulating two points simultaneously. Using this redundancy and selecting the lifted wheels, we design a trajectory tracking controller for two points by preventing the two types of singular configurations. The effectiveness of the proposed control method was demonstrated by tracking experiments as well as applications, namely, the transportation of an object by caging manipulation and the steering of a handcart.
Spatial and Time Dispersions Compensation With Double-Equalization for Optical Camera Communications

In a mobile phone-based optical camera communication (OCC) system, the spatial and time dispersions, due to inter-pixel crosstalk and exposure time overlapping, deteriorate the transmission performance considerably. In this letter, a double-equalization method is studied and experimentally demonstrated in a rolling-shutter based OCC system, showing a record-high transmission data rate for a single white light-emitting diode (LED) system. Moreover, we further propose an improved technique to reduce the overhead of the signal extraction process while achieving similar performance. The effect of the normalization factor in the gamma correction processing is also investigated.
Interference Cancellation in Overlay Cognitive Radio Based on OFDM Using Nonlinear Kalman-Filter for Channel Estimation

Cognitive radio (CR) has been proposed as a technology to improve the spectrum efficiency. We consider an overlay CR consisting of a primary macro-cell and cognitive small cells of cooperative secondary base stations (SBS). We suggest studying a CR where an orthogonal frequency division multiplexing (OFDM) is used for both the primary users (PU) and the secondary users (SU). In order to cancel the interferences, zero forcing beamforming (ZFBF) is considered. However, applying ZFBF depends on the channels between the SBS and the PU. A channel estimation is hence necessary. For this purpose, we propose to approximate the channel by an autoregressive process (AR) and to consider the channel estimation issue by using a training sequence. In that case, the AR parameters and the channel can be jointly estimated from the received noisy signal by using a recursive approach. Nevertheless, the corresponding state space representation of the system is non-linear. Then, we propose to carry out a complementary study by compare non-linear Kalman filter based approaches.
Surface Discharge Characteristics of Different Solid–Liquid Insulation Materials in Power Transformers

The surface discharge along the interface of solid–liquid insulation is one of the main causes of the deterioration of the life of a power transformer. This experimental study investigates the effects of different solid and liquid insulation materials on the surface partial discharge (PD) characteristics. The liquid insulation materials utilized are mineral oil and synthetic ester, whereas the solid insulation materials are cellulose board and Nomex board. The surface PD characteristics in various combinations of solid and liquid insulation materials are investigated using PD inception voltages (PDIV), apparent charges (pC), and phase-resolved PD (PRPD) patterns, and the comparative analysis is presented. Moreover, topography and composition of samples during surface discharges phenomena have been discussed using high-resolution images and chemical composition obtained using the scanning electron microscope (SEM) and energy dispersive spectroscopy (EDAX). The findings of this article will assist in understanding the behavior of surface PDs under various conditions.
Knowledge-Based Question and Answering System for Turkish

A knowledge-based QA system for Turkish is presented in this paper. The developed QA system has two phases: 1° Learning question and constitute two databases named question and answer database. 2° Uses the question and answer databases as knowledge-base for QA operation. The first phase of the project consists of pre-processing (tokenizing of sentences, parsing a word into syllable, phonological testing (consonant softening, vowel reduction, haplology, head and tail letters, morphological testing, mumpsimus, foreign words), morphological parsing, finding of roles of words, interpretation of question and answering. All these parts developed according to Turkish grammar. In the first phase the QA system can understand question and answers. Later on, constitutes question and answer parallel corpus in other word data bases. The second phase consist of the same pre-processing then searches for the same and similar question in question database if finds the answer gets from answer database. In order to fine similar question similarity vectors approaches were used.
Therapy of allergic bronchial asthma with omalizumab – an anti-IgE monoclonal antibody

The immunoglobulin E (IgE) antibody plays a central role in the allergic immune responses, such as those which characterise allergic bronchial asthma. The ability to reduce circulating IgE by using anti-IgE antibodies represents a novel therapeutic approach for IgE-mediated allergic diseases. Omalizumab (rhuMAb-E25/Xolair™; Genentech, USA/Tanox, Inc., USA/Novartis Pharma AG, Switzerland) is a non-anaphylactogen humanised murine monoclonal antibody which binds to circulating IgE. Therapeutic use of an anti-IgE antibody in the treatment of asthma was first suggested in preliminary studies in which omalizumab demonstrated efficacy in attenuating both the early- and late-phase bronchial responses to inhaled allergens in atopic subjects. Treatment with omalizumab has demonstrated both a significant beneficial effect on a number of measures, and a favourable safety profile. It reduces the frequency of asthma exacerbations and the need for inhaled corticosteroids (ICSs), and improves asthma symptoms, lung function and quality of life. The anti-IgE approach to asthma treatment has a number of further potential advantages, such as the treatment of other concomitant atopic diseases (allergic conjunctivitis and rhinitis, atopic dermatitis and food allergy), regardless of the type of allergic sensitisation (seasonal or perennial). On the basis of several studies, omalizumab is likely to provide a new strategy for treating atopic asthma.
Analysis and Comparison of Disassemblers for OpCode Based Malware Analysis

In this work, disassembler effect has been investigated on the detection performance of opcode-based malware detection methods. Opcode analysis is widely used to detect malware programs. Our experimental results presented which disassembler is more suitable with the analysis method for the best performance. Therefore, researchers will be guided with the results obtained in this work for their static opcode based PE file analysis.
The Concept of Monitoring the Reliability of Information in the Professional Social Network Using a Convolutional Neural Network

The paper considers the problem of assessing the reliability of information in the social network. The paper deals with the existing approaches to the evaluation of the reliability of information, and suggests the approach based on the use of object classification methods. The paper describes the algorithm for creating and using the system for control of the reliability of information and suggests possible options for implementing this system.
Validating Industrial Requirements with a Contract-Based Approach

This paper presents our contract-based design technique for formalizing requirements during the design phase of a complicated and safety-critical automotive component. In our approach, contracts are created using property specification patterns to eliminate ambiguous unstructured natural language requirements, which could lead to misinterpretations or mismatched interfaces in the integration phases of the design process. These patterns are then automatically transformed into Signal Temporal Logic (STL) formulas. The STL formulas are verified on a modeled system of the component, utilizing the Matlab® toolbox Breach. This approach validates the industrial requirements described in the contracts, and can help achieve the requirement-based testing demanded by automotive safety standard ISO 26262.
Inkstų vėžio epidemiologija ir rizikos veiksniai: literatūros apžvalga ir epidemiologinių rizikos veiksnių koreliacija Lietuvos gyventojų populiacijoje

Background Renal cell carcinoma (RCC) is one of the most dangerous urological cancers. Several risk factors for RCC have been identified, including smoking, obesity, and hypertension. Other risk factors for RCC could be dietary practices (alcohol consumption, food), physical activity, occupational exposures, drugs, other diseases, reproductive and hormonal factors, and others. Results Our literature analysis revealed a high smoking incidence in the past and the present decrease in smoking by 50%. If smoking is a strong causal factor of RCC, in the nearest future, in 10–20 years, we will have a decrease in RCC incidence. In Lithuania, over the last ten years, the prevalence of overweight and obesity among men has increased, and in women it has decreased. The significant reduction in obesity, therefore, would dramatically decrease the incidence of those cancers and lower the mortality rate of RCC in Lithuania. The high prevalence of hypertension and the still low awareness among hypertensive men and women could also be a cause of high incidence of RCC in Lithuania. The Lithuanian population eats many fatty and high- -protein foods. Meat is seen almost in everyday meal, and another unfavorable factor is the low consumption of vegetables, which could be a preventive meal. This problem is seen even in school-age children. Conclusion In the analysis of risk factors and their contribution to the high incidence of RCC in the Lithuanian population, we have found that the main risk factor of RCC is highly frequent in population and could be the main reason of high RCC incidence.
New Viewpoint About Cardiac Diseases: A Cardiac Disease Can Be a Rheumatic Disease

Many rheumatic diseases can be presented with cardiac manifestations such as pericarditis, myocarditis, arrhythmias and etc. In addition, there are valvular and vessels involvements in rheumatic diseases. These manifestations can be the first demonstrations of rheumatic diseases. According to previous studies many rheumatologic diseases demonstrate themselves with cardiac manifestations. Many rheumatologic diseases are treated with wrong treatment because they have cardiac manifestations and the physicians treat these kinds of manifestations as cardiac diseases instead of rheumatologic diseases. The cardiologists must pay attention carefully to these cardiac manifestations that can be considered by mistake as a cardiac disease instead of rheumatologic disease. Nowadays, many rheumatic diseases are treated with wrong treatment because they are considered by mistake as cardiac diseases. Also many surgeries are done by mistake and wrong treatments are received by patients because of wrong diagnosis. Early diagnosis of rheumatologic diseases with cardiac manifestations is very important to choose best treatment and also it can prevent from wrong treatment and surgery. Cardiovascular diseases morbidity and mortality rate is higher in patients with rheumatic disorders than patients with non-rheumatic disorders. This point is important and importance that must be paid attention certainly. Immunosuppressive treatment for rheumatic diseases with cardiac involvements can prevent from developing cardiovascular involvement especially ischemic heart disease.Also it can reduce morbidity and mortality of these patients. The present editorial focuses on importance of right diagnosis of patients with cardiac manifestations for choosing best treatment for them. It is very important for cardiologists and other physicians to consider rheumatic conditions in patients with cardiovascular disorders.