Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Limits of Mininet-Based Environments for Network Experimentation

Virtualization and emulation have become worthy approaches to save significant amounts of money related to physical resource acquisition expenditures. In light of this, the development of network emulation platforms has led a revolution in the research and testing of novel services and applications, as they provide a cost-effective, flexible and reproducible environment for experimentation. However, they present some practical issues, in particular, their scalability is one of the limiting factors as it links the emulated networks that can be successfully deployed on a given hardware. We address this matter by testing the consumption and exploitation of physical resources of one popular network emulation platform, Mininet. We follow a methodology based on the isolation of the threads associated to the operating system, the virtual hosts, and the monitoring tasks. In such a manner, this approach can measure the effect of the placement of threads in the available cores, and help optimize bottlenecks that jeopardize the results of network emulations. Additionally, we monitor several key performance indicators for general-purpose Mininet deployments in different network topologies, varying the number of active elements, links and network conditions such as packet loss or delay. Our results show that Mininet presents performance bounds in commodity servers that suffice a wide range of general network tests. It achieves aggregated bandwidths above 10 Gb/s and median roundtrip time values around 1 ms, even in demanding scenarios where more than a thousand hosts, up to 64-hop paths and 64
Energy Efficient Localization in Wireless Sensor Networks Using Computational Intelligence

Wireless Sensor Networks consist of many sensing devices which are distributed inside of a given area. Each sensor node consists of multiple heterogeneous components such as power supply, CPU, memory, and a transceiver. Since the location of sensors is needed in most of the WSNs, Trilateration-based localization (TBL) has been used to locate the sensors in the network. This study formulates the concern on how wireless sensor networks can take advantage of the computational intelligent techniques using both single and multi-objective particle swarm optimization (PSO) with an overall aim of concurrently minimizing the required time for localization, minimizing energy consumed during localization, and maximizing the number of nodes fully localized through the adjustment of wireless sensor transmission ranges while using TBL process. A parameter-study of the applied PSO variants is performed, leading to results that show algorithmic improvements of up to 21% in the evaluated objectives.
A Novel Modified Auto-regressive Moving Average Hysteresis Model

A modified auto-regressive moving average (MARMA) model is proposed in this paper, which can be used to describe the dynamic hysteresis nonlinearity accurately. First, combined with the stability condition of auto-regressive moving average (ARMA) model, the Least Square approximation and the Lagrange Multiplier method (LSLM) are used to improve the traditional ARMA model. And then, according to the collected voltage-displacement data set, the parameters of the MARMA model are identified by LMLS method. Meanwhile, aiming at the difficulty of real-time displacement detection in the process of fast tool servo (FTS), a direct feedforward open-loop control (DFOC) strategy is designed based on the identified model. Finally, in order to verify the effectiveness and superiority of the method, a series of high frequency trajectory tracking and contrast experiments have been carried out successfully with the traditional PI and MARMA models. It shows that the MARMA model is nearly 20 times higher than the traditional PI model in terms of control accuracy and linearity, while the control bandwidth is achieved up to 200Hz.
Analytical Solutions to Characteristic Impedance and Losses of Inverted Microstrip Gap Waveguide Based on Variational Method

This paper initially introduces analytical solutions to newly invented inverted microstrip gap waveguide (IMGW). By applying the classical variational method the characteristic impedance, dielectric loss, and conductor loss of the IMGW are obtained. The calculated characteristic impedance and losses of IMGWs with the present analytical method have been verified by commercial software CST. Furthermore, we have theoretically proved that the total loss of electromagnetic energy in the IMGW is much lower than that of covered microstrip line in millimeter waves (mmWs). Thereby, the IMGW has big advantages over covered microstrip lines for high-gain, high-efficiency array antennas in mmWs.
Implementation of a Finite State Automaton to Recognize and Remove Stop Words in English Text on its Retrieval

Now a day's electronic media or the World Wide Web is the main source of information storage. The data that is stored in the web is structured, semistructured as well as unstructured in nature. When we say unstructured data it means all the text data. Text processing is plays a crucial role for processing structured and unstructured data from the web. Preprocessing is the main in any text processing systems. For an efficient computation of text processing it is necessary to remove the common words or stop words from the document while scanning the document. A large number of stop word removal algorithms has been proposed which are generally based on dictionary containing stop word list. Then pattern matching techniques are applied to find identify and remove the stop word. Earlier approaches are time consuming task. In total, these methods are inefficient and very expensive. Here I am proposing a solution to identify the stop word present in the English language using finite state automata. In comparison of that my algorithm has been tested on 220 documents and achieved 99% of accuracy and it also time efficient.
Inductance Calculation for the Symmetrical Non-Salient Dual Three-Phase PMSM Based on Winding Function Approach

Dual three-phase permanent magnet synchronous machines (PMSMs) are widely used in the applications which require high power density and high reliability. Inductance calculation for these dual three-phase windings of this machine type is a key issue for further performance analysis and controller design. This paper attempt to use the winding function approach to establish an analytical model for computing the inductance of the dual three-phase PMSM type with symmetrical winding configuration. With this method, a complete analytical expression of self and mutual inductances between phases has been obtained. Finally, simulation and experimental results have been implemented to verify the accuracy of calculation results.
Research on Wetting Characteristics of Insulator in Ultrasonic Fog

Pollution accumulation and wetting of insulator are the two prerequisites for the pollution flashover. In this paper, the wetting characteristics of artificial polluted insulators in the ultrasonic fog were analyzed. Firstly, a method to analyze the water absorption of the contamination layers on the insulator surface is presented in this paper, and then by measuring the surface conductivity of the test specimens in the salt fog, the saturated water absorption of the contamination layers under different NSDD and different angle of inclination is calculated. It has been found that in the ultrasonic fog chamber, both the NSDD and the inclination angles have influence on the saturated water absorption of the insulators. For the specimens with vertical arrangement, the saturated water absorption is in a range of 1.68-2.7mg/cm2, which is greatly influenced by the NSDD. However, for the test specimen with inclination degree at 45°, the saturated water absorption is 3.04-3.42 mg/cm2and less affected by NSDD.
Flexible and Reliable UAV-Assisted Backhaul Operation in 5G mmWave Cellular Networks

To satisfy the stringent capacity and scalability requirements in the fifth generation (5G) mobile networks, both wireless access and backhaul links are envisioned to exploit millimeter wave (mmWave) spectrum. Here, similar to the design of access links, mmWave backhaul connections must also address many challenges such as multipath propagation and dynamic link blockage, which calls for advanced solutions to improve their reliability. To address these challenges, 3GPP New Radio technology is considering a flexible and reconfigurable backhaul architecture, which includes dynamic link rerouting to alternative paths. In this paper, we investigate the use of aerial relay nodes carried by e.g., unmanned aerial vehicles (UAVs) to allow for such dynamic routing, while mitigating the impact of occlusions on the terrestrial links. This novel concept requires an understanding of mmWave backhaul dynamics that accounts for: 1) realistic 3-D multipath mmWave propagation; 2) dynamic blockage of mmWave backhaul links; and 3) heterogeneous mobility of blockers and UAV-based assisting relays. We contribute the required mathematical framework that captures these phenomena to analyze the mmWave backhaul operation in characteristic urban environments. We also utilize this framework for a new assessment of mmWave backhaul performance by studying its spatial and temporal characteristics. We finally quantify the benefits of utilizing UAV assistance for more reliable mmWave backhaul. The numerical results are confirmed with 3GPP-calibrated simulations, while the framework itself can aid in the design of robust UAV-assisted backhaul infrastructures in future 5G mmWave cellular.
Secure Session Key Sharing Using Symmetric Key Cryptography

Information security is a major concern when the information is transmitted via telecommunication systems (wired or wireless). This security is provided by cryptography, which ensures the secure transmission of data over the internet. Two types of cryptographic techniques are used for the encryption and decryption of data, first being symmetric key and second being asymmetric key. In either of these techniques storage of large number of keys or usage of similar keys may lead to either storage overhead or vulnerabilities. Therefore, session keys were evolved as a solution for problems with storage of distinct secret keys over a long period and dependency between communication sessions. The session keys are generated and shared through key establishment protocols. Here, we propose a key generation and sharing protocol between two entities using symmetric key cryptography. The proposed protocol is secure due to usage of symmetric keys and its security analysis could not detect any potential attacks on it. The protocol can be used in a varied number of applications where simplicity and security are major requirements.
A Review on Hybrid Network on Chip

System on chip revolution challenges the NOC engineers for designing scalable, dynamic, reliable interconnections of network. Due to the extensive growth of SOC techniques, large extent of IP modules are used in System-on-chip (SOCs) and these SOCs finds various applications. The bus based communication is not a suitable solution to meet throughput, required bandwidth and reduced Latency for multicore chips and highly intensive parallel communication systems. Nowadays, Network on chip (NOC) is a definite solution for parallel communication requirements. In this paper we review the various NOCs for wired, wireless and hybrid networks also techniques in NOCs that addresses the challenges of deadlock, adaptability of the network, power management, latency reduction and throughput improvement etc.
Closely Spaced Broadband MIMO Differential Filtering Slotline Antenna With CM Suppression

This letter proposes closely spaced broadband multiple-input-multiple-output (MIMO) differential filtering slotline antennas. A good impedance matching performance can be obtained by well distributing the resonant frequencies of slotline radiator and dual-mode resonator. Under common-mode operation, the differential antenna does not work because the vertical electrical field is hardly introduced on the slotline radiator. Furthermore, four filtering antenna elements are distributed centrosymmetrically along the center of the substrate. The adjacent two filtering antenna elements are placed perpendicularly with a spacing of 0. 05λA. Compared with the single filtering antenna, a filtering performance of the MIMO antenna has no significant change. For demonstration, a MIMO differential filtering antenna is simulated and measured, which has a fractional bandwidth of 25% at 5.2 GHz. The simulated results agree well with measured ones. A low mutual coupling of less than –16 dB and low envelope correlation coefficient of better than 0.01 are obtained.
Predicting Complainers on Social Media: A Machine Learning Approach

Recent reports showed that more than 5.5M customer service-related tweets are generated per month, and customers who complain on social media spend 20% more on products and services than other customers. Despite its popularity and importance, research on social media-based customer service is still in its infancy. In this work, we aim to predict customer complaining behavior on social media platform Twitter. We propose a novel machine learning model called knowledge regularized matrix factorization which seamlessly integrates two well-known antecedents of customer complaint behavior from prior literature namely, customer's personality traits and firm's characteristics as well several other domain-independent cues such as sentiment lexicon to into a principled framework to regulate the model factorization process to enhance the model performance and result interpretability. We propose a linear-time learning algorithm with convergence guarantee to learn the parameters of our model. Our comprehensive evaluations show that our model significantly outperforms other competitive methods by an average of 22% gain in predicting which customer will engage with a target firm on Twitter and whether the customer is a complainer. Our factorization results further shed lights on customer complaining behavior on social media by showing the divergent effect of personality traits of complainer and non-complainers on Twitter as well as the correlation between complainer's personality traits and firm's industry.
Kinematic Calibration of a 2-DoF Over-Constrained Parallel Mechanism Using Real Inverse Kinematics

The kinematic accuracy improvement is a key challenge in the development of over-constrained parallel mechanisms (PMs). Taking a two degree-of-freedom (DoF) over-constrained PM applied in assembly line as an example, this paper addresses the kinematic calibration problem of over-constrained PM to improve accuracy and promote its practical application. Instead of establishing conventional error mapping model, a nonlinear error model is built by inserting geometric errors of parts to the real inverse position analysis. On this basis, a set of nonlinear identification equations are formulated. Unlike other methods that identify the geometric errors by an identification Jacobian matrix and pay extra attention to the robustness of the matrix, these nonlinear identification equations are directly solved by optimization technique. Herein, the hybrid genetic algorithm is adopted in the optimization due to its high robustness, efficiency, and accuracy. Finally, error compensation is implemented by modifying the motor outputs in the controller. Simulations and experiments are then carried out to verify the calibration method, which show that the orientation accuracy of the 2-DoF over-constrained PM improves by 93.96% and 90.38%, respectively. Comparative studies to the conventional regularization method and four other optimization algorithms are also investigated. The results further confirm the high accuracy of the proposed kinematic calibration method for over-constrained PMs.
Depth from Focus Enhancement Through Joint Filtering

In this paper, we propose to apply weighted least squares (WLS) for depth from focus enhancement. The proposed method attempts to validate those depth estimates that comply with the geometric information of the object. By taking the all-in-focus image as the guidance image in joint filtering, we transfer the structural details of the object contained in this image to the depth map. This structural information enforces the regularization of depth map in a spatially varying manner. Thus, by employing joint filtering through WLS regularization, we recover better depth maps. The experimental results for synthetic and real image sequences show the effectiveness of the proposed scheme.
Passivation of Crystalline Silicon Wafers by Ultrathin Oxide Layers: Comparison of Wet-chemical, Plasma and Thermal Oxidation Techniques

Ultrathin oxide layers on crystalline silicon (c-Si) were prepared by different wet-chemical, plasma- and thermal oxidation techniques. The resulting electronic and chemical SiO2/Si interface properties were analyzed, compared and evaluated by combined x-ray photoemission (XPS) and surface photovoltage (SPV) measurements. Depending on the oxidation technique, chemically abrupt SiO2/Si interfaces (suboxide amounts $< 5$ %) with low densities of interface states ($< 1\times 10^{\mathbf{12}} \mathbf{cm} ^{\mathbf{-2}}eV-^{\mathbf{1}}$) were obtained on c-Si either at low temperatures as well as at short oxidation times and in wet-chemical environment, resulting in each case in excellent interface passivation. It is shown, that chemically abrupt SiO2/Si interfaces are able to generate low interface defect states densities.
Building Sequential Inference Models for End-to-End Response Selection

This paper presents an end-to-end response selection model for Track 1 of the 7th Dialogue System Technology Challenges (DSTC7). This task focuses on selecting the correct next utterance from a set of candidates given a partial conversation. We propose an end-to-end neural network based on enhanced sequential inference model (ESIM) for this task. Our proposed model differs from the original ESIM model in the following four aspects. First, a new word representation method which combines the general pre-trained word embeddings with those estimated on the task-specific training set is adopted in order to address the challenge of out-of-vocabulary (OOV) words. Second, an attentive hierarchical recurrent encoder (AHRE) is designed which is capable to encode sentences hierarchically and generate more descriptive representations by aggregation. Third, a new pooling method which combines multi-dimensional pooling and last-state pooling is used instead of the simple combination of max pooling and average pooling in the original ESIM. Last, a modification layer is added before the softmax layer to emphasize the importance of the last utterance in the context for response selection. In the released evaluation results of DSTC7, our proposed method ranked second on the Ubuntu dataset and third on the Advising dataset in subtask 1 of Track 1.
Spatial-Temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition

Abnormal driving behaviour is one of the leading cause of terrible traffic accidents endangering human life. Therefore, study on driving behaviour surveillance has become essential to traffic security and public management. In this paper, we conduct this promising research and employ a two stream CNN framework for video-based driving behaviour recognition, in which spatial stream CNN captures appearance information from still frames, whilst temporal stream CNN captures motion information with pre-computed optical flow displacement between a few adjacent video frames. We investigate different spatial-temporal fusion strategies to combine the intra frame static clues and inter frame dynamic clues for final behaviour recognition. So as to validate the effectiveness of the designed spatial-temporal deep learning based model, we create a simulated driving behaviour dataset, containing 1237 videos with 6 different driving behavior for recognition. Experiment result shows that our proposed method obtains noticeable performance improvements compared to the existing methods.
Transferable HMM Trained Matrices for Accelerating Statistical Segmentation Time

Segmentation problem has been solved in the last decade, many techniques have been implemented to discover this issue. Many problems arise during the segmentation process including the acceptable error rate, low quality assessment, and time complexity. A variety of acceleration techniques have been applied to speed up the segmentation time and achieve a segmented result in real time. GPU and parallel processing using hardware have been employed efficiently here, but still limited in 3D images segmentation. Hidden Markov Model (HMM) is one of the best statistical segmentation techniques that played a significant rule recently. The problem associated with HMM is the time complexity due to the training steps. This issue has been resolved using different accelerator but still not efficient with 3D volumes. In this research, we propose a methodology for transferring the trained matrices of HMM from image to another skipping the training time for the rest of the 3D volume. One HMM train is generated and generalized to the whole volume. An accurate segmentation results have been achieved in less processing time. And fixed class belongings for the pixels have been achieved without any class membership variations. Which will increase the possibility of segmenting medical images using HMMs on GPUs instead of CPUs.
Achievable Rate of Indoor THz Communication Systems with Finite-Bit ADCs

In this paper, the achievable rate of the downlink terahertz (THz) communication systems equipped with finite-bit analog-to-digital converters (ADCs) at user sides is investigated. Considering the array of subarray antenna structure, the hybrid precoding with the maximum ratio transmission (MRT) and zero-forcing (ZF) precoding are applied at the baseband. The closed-form expression of the lower bound of the achievable rate is derived when the number of subarray antennas goes to infinity. It is shown that the coarse quantization distortion cannot be ignored in large antenna subarray regime. On the other hand, the rate loss caused by the phase uncertainties can be negligible in large subarray antenna regime. Overall, the impact of quantized receivers on the performance of indoor THz communication systems is characterized.
Novel Multilevel Inverter Design with Reduced Device Count

In this paper, a new topology for the multilevel inverter is proposed. Here, in this proposed topology we are using two well-known topologies as cascaded H-bridge multilevel inverter topology and combining it with multi-string multi-level inverter topology, and proposing a new multilevel inverter topology which is on the base reduced device count. The concept of Reduce device count is to reduce the size of hardware as well as cost and complexity of hardware. Here, with the help of this topologies, there is in the output we are able to make the thirty-one level and that too with the help of only ten switching devices and four dc sources. The operating details of strategy and system design of this proposed multilevel inverter system is described and verified by simulation results.
Unsupervised Salient Object Detection by Aggregating Multi-Level Cues

In this paper, we present a novel method to detect salient object based on multi-level cues. First, a proposal processing scheme is developed by various object-level saliency cues to generate an initial saliency map. For the sake of more accurate object boundaries, a two-stage optimization mechanism is then proposed upon superpixel-level. Finally, the superpixel-level saliency map is further improved to construct the final saliency map by applying superpixel-to-pixel mapping. Extensive experimental results demonstrate that the proposed algorithm performs favorably against the state-of-art saliency detection methods in terms of different evaluation metrics on several benchmark datasets.