Miracle-dz/UniFAR-FTUs huggingface dataset:
FastSGG: Efficient Social Graph Generation Using a Degree Distribution Generation Model

With the popularity of social networks, large-scale social graphs are necessary to evaluate the algorithms for various social network analysis tasks, especially in the era of big data. An efficient and configurable social graph generator has become more important than ever before because it is difficult to obtain billion-scale real-world social graphs for various scenarios.In this paper, we present an efficient and widely-applicable social graph generator called FastSGG. FastSGG generates social graphs according to a user-defined configuration depicting the features of the target social graph, which is a flexible way to generate graphs in a variety of applications. The generation method consists of two main steps: the determination of out-degree for a source vertex and the determination of a target vertex to construct an edge. In order to accelerate the graph generation process, a degree distribution generation (D2G) model is proposed. The D2G model is a universal model for generating graphs following different degree distributions as long as the probability density functions or probability mass functions are given. The extensive experimental results demonstrate that FastSGG can generate high-quality social graphs with small world properties, power-law degree distributions, and community structures. Moreover, FastSGG generates graphs at least four times faster than the state-of-the-art graph generators. In addition, the peak memory usage of FastSGG is less than one seventh of that of the state-of-the-art method.
Design of Duty-Ratio and Phase-Shift Control Circuits for MPPT of SPV Source using ZV-ZCS PSFB Converters

This paper presents two separate control circuits for a soft-switching phase-shift full-bridge (PSFB) pulse-width modulated (PWM) dc-dc converter. The PSFB converter is operated with maximum power point tracking (MPPT) controller to extract maximum power from a solar photovoltaic (SPV) source, either by controlling the duty ratio or by varying the phase-shift angle of switching gate pulses. The MPPT controller is used in the feed forward voltage control loop of SPV source. Both the duty ratio and the phase-shift controllers have been tried with conventional PID controller and linear quadratic Gaussian (LQG) controller for performance comparison in MATLAB environment. Finally, experimental studies have been performed to verify the ability of the controllers to extract maximum power from the SPV source using the PSFB converter.
Big Data Analytics System Adoption in SMEs of Manufacturing

In the information and communication technology era, the scales and types of big data are expanding rapidly, playing an increasingly important role in business. Big data technology is undoubtedly a big wave between information technology and manufacturing enterprise. However, some small and medium-sized manufacturing enterprises may not adopt and use big data technology as ICT related companies do. In this paper, we use the TOE model to test the factors (technological context, organizational context and environmental context) affecting BDA system adoption in SMEs of manufacturing.
Power Supply Noise Aware Physical Design with Decoupling Capacitance Allocation in System-on-Chip

Reduction of switching noise is a challenging task in integrated circuit design. The supply noise occurs in the pad locations due to inductive effect, resistive loss occurs due to electrical wiring on the chip and is also an effect of high switching activity of the transistors. This variation in voltage in the various electrical nodes may result in functional failure of the integrated circuit. Use of decoupling capacitors is a commonly used technique to reduce the power supply noise. Estimation and placement of decoupling capacitors is a difficult task in the design process. Thus a lot of effort has to be given in the latter stages to improve the overall performance of the integrated circuit. This article presents computer aided design approach to estimate decoupling capacitor and placement at the physical design stage to reduce the power supply noise. The main focus of this work is on allocation of decoupling capacitors so as to reduce the supply noise and also to keep cross-sectional area at its best after allocation of decoupling capacitor. Simulation results shows the overall design parameters are also satisfactory. This technique can also be used in other system-on-chip design.
VR Photography Embedded Map With Grid based Indoor Positioning

We propose a VR Photography embedded map construction specifically designed for the navigation of indoor places for various surrounding conditions and different terrains. A broad variety of mapping and navigation systems are unusable in indoor places due to the intricate details that are needed for navigating small places. They also fail because of inaccuracy to small distances due to not being able to map these regions. Also, navigation in these places needs location data which can distinguish position to highly smaller distances. We present a system that can construct a map of an indoor place based on its images or videos and a navigation system that can accurately help in any kind of indoor place irrespective of the surrounding. This navigation system is built on a grid based system that has a position estimator model trained with data from each block of the grid and can specifically locate the position of each such block.
Ultrathin Three-Axis FBG Wrist Force Sensor for Collaborative Robots

Collaborative robots equipped with multicomponent force-sensing systems at the human–robot–environment interaction interface provides competitive advantages, such as collaboration, flexibility, and reliability. Most wrist force sensors use the cross-beam structures with electrical strain gauge measurement technologies. This article proposes an approach to develop a new ultrathin three-axis fiber Bragg grating (FBG) force sensor. First, an appropriate elastomer with embedded floating beams in ultrathin dimensions to measure three-axis force is designed using a finite-element method (FEM). A temperature-compensated extreme learning machine (TC-ELM) network is proposed to achieve the characteristics of low coupling and temperature compensation (TC). Calibration and experiment on the UR5 robot are conducted, and results illustrate that the highest sensitivity of the sensor can reach 34.07 pm/N, the repetition error is less than 5%, and the maximum coupling error is less than 0.21%. The maximum temperature drift after compensation is less than 20 pm. The proposed sensor has identical output as a commercial sensor and performs well in dynamic loading and unloading operations. Finally, comparison results from commercial and academic alternatives demonstrate the interest of the proposed approach to develop high-performance and integrated force-sensing solutions.
Real-time Driver Drowsiness Detection based on Eye Movement and Yawning using Facial Landmark

The drowsiness of the drivers might increase road accidents. Nowadays, computer vision and image processing technology can solve the problem and decrease the number of accidents by detecting the driver's drowsiness and gives an alert to beware of sleep that leads to an accident. This study has developed a real-time driver drowsiness detection based on eye movement and yawning using facial landmarks and dlib. This system helps to avoid accidents caused by drowsiness by detecting eye movements and yawning of the driver. The advantages of this system are low cost and minimized the requires the resource. The behavioral analysis method monitor results from the driver's facial landmark while driving without the need to place sensors in the driver's body.
Research on Incoherent Frequency Discrimination Algorithm of FM Telemetry Signal Based on GPU

To improve the flexibility of FM telemetry system and the universality of the hardware platform, an incoherent demodulation method of FM telemetry signal based on GPU is designed. Based on CUDA parallel operation architecture, the efficient orthogonal down-conversion module, parallel filter module, and incoherent frequency discrimination module are developed. The experimental results show that the demodulation method based on GPU is correct, efficient and reliable, and can realize the real-time demodulation of FM telemetry signal.
Research on Wind Power Prediction Based on Time Series

Due to the uncontrollable characteristics of wind, such as randomness and volatility, the uncertainty of wind power production will be caused to some extent. In order to minimize the influence of wind farm on the stability and safety of the power grid, the prediction of wind power becomes particularly important. In this paper, considering many factors affecting power generation, the ARIMA models based on unary and multivariate time series are used for modeling and analysis of multiple input sequences and output sequences respectively. After comprehensive comparison and analysis, the optimal prediction method is obtained, which provides a new idea for wind power prediction.
DS2: Handling Data Skew Using Data Stealings over High-Speed Networks

Distributed in-memory computing systems have dramatic performance improvement over traditional disk-based systems, which makes them widely used in large-scale data processing applications. Unfortunately, uneven and unpredictable data distributions caused by data skew have a significant impact on the performance. In Spark, when data skew happens, some tasks will process much more data than other tasks and become the performance bottleneck. The traditional approaches to handling data skew are based on sampling and repartitioning, which incur additional overhead. In this paper, we divide data skew in distributed data processing systems into intra-node and inter-node skew. Based on data stealing, we proposed DS2 to handle both intra-node and inter-node data skew. It aims to improve the performance under data skew, without involving additional overhead. DS2 first balances the skewed data distribution in the local and then handles the inter-node skew by RDMA during execution. It achieves up to 2.96× speedup on the aggregation operator and 2.81× speedup on the join operator.
Soft-Mesh: A Robust Routing Architecture for Hybrid SDN and Wireless Mesh Networks

Wireless Mesh Networks (WMNs) are considered self-organizing, self-healing, and self-configuring networks. Despite these exciting features, WMNs face several routing challenges including scalability, reliability and link failures, mobility, flexibility, and other network management issues. To address these challenges, WMNs need to make programmable to allow modifications of standard techniques to be configured and implemented through software programs that can be resolved by integrating Software Defined Networking (SDN) architecture. SDN, being a cutting-edge technology promises the facilitation of network management as well as routing issues of wireless mesh networks. However, the evolution of the legacy IP-based network model in its entirety leads to technical, operational, and economic problems that can be mitigated by full interoperability between SDN and existing IP devices. This study introduces a Robust Routing Architecture for Hybrid Software-Defined and Wireless Mesh Networks (Soft-Mesh), by systematic and gradual transitioning of WMNs to SDNs in an efficient manner. The main objective of this paper is to suggest improvements to the architecture of the SDN node that allow the implementation of various network functions such as routing, load balancing, network control, and traffic engineering for the hybrid SDN and IP networks. Mininet-WiFi Simulator is used to perform various experiments to evaluate the performance of proposed architecture by creating a hybrid network topology with a varying number of nodes that is 50, 100, 150, 200, and 250 including SDN hybrid and legacy nodes with varying proportion of SDN hybrid and legacy nodes. Results are taken for the average UDP throughput, end-to-end delay, packet drop ratio, and routing overhead while comparing with traditional routing protocols including Optimized Link State Routing (OLSR) and Better Approach to Mobile Adhoc Networking (BATMAN) and with existing hybrid SDN/IP routing architectures including Hakiri and wmSDN. The analysis of simulation results shows that the proposed architecture Soft-Mesh outperforms in terms of the aforementioned performance metrics than the traditional and exiting hybrid routing protocols. Soft-Mesh gives 50% to 70% improved results concerning the incremental proportion of SDN hybrid nodes.
Review of Recent Research on Data-Based Process Monitoring

Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.
Vibration Characteristics of Piezoelectric Timoshenko Nanobeam in Viscoelastic Medium*

The mechanical behavior of piezoelectric components has a marked impact on the performance of nano electromechanical systems (NEMS). Consequently, it is of great importance to investigate the mechanical behavior of piezoelectric nanomaterials. This work takes the nanobeam placed in a visco-Pasternak medium as the research object. The vibration governing equations and the corresponding boundary conditions of the system are obtained based on the nonlocal Timoshenko theory and the Hamiltonian principle, considering the effect of the piezoelectric effect and the flexoelectric effect. The solutions of the governing equations are obtained by utilizing the transfer function method (TFM). On this basis, the analysis of the effects, including the nonlocal effect, the flexoelectric effect, and the viscoelastic medium, on the nanobeam's vibration characteristics is carried out by analyzing the numerical result. Theoretical guidance for the design and analysis of the piezoelectric nano components can be offered through this paper.
RF Performance of Devices Processed in Low-Temperature Sequential Integration

RF performance and intertier coupling of CMOS processed in 3-D sequential integration are investigated. pMOS transistor fabricated with a 500 °C thermal budget features good RF figures of merit with <inline-formula> <tex-math notation="LaTeX">${f}_{t} =105$ </tex-math></inline-formula> GHz and <inline-formula> <tex-math notation="LaTeX">${f}_{\text {max}} =175$ </tex-math></inline-formula> GHz for a gate length of 45 nm and <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DD}} = -1$ </tex-math></inline-formula> V. Moreover, we demonstrate that the low-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> SiCO oxide spacer and low polysilicon gate resistance obtained with the low temperature process contribute to <inline-formula> <tex-math notation="LaTeX">${f}_{\text {max}}$ </tex-math></inline-formula> results that are better than high temperature process (above 1000°). Finally, we illustrate the crosstalk effects and the influence of the low-tier transistor gate voltage <inline-formula> <tex-math notation="LaTeX">${V}_{\text {G}}$ </tex-math></inline-formula> on the top-tier threshold voltage <inline-formula> <tex-math notation="LaTeX">${V}_{T}$ </tex-math></inline-formula>. We also show that a polysilicon ground shield integrated under the top-tier transistor substantially attenuates the intertier RF field coupling effects.
Quality Evaluation of Fundus Images using Transfer Learning

Fundus images with poor quality may contain insufficient information to represent characteristics of eye diseases, such as diabetic retinopathy (DR), glaucoma, and age-related macular degeneration (AMD). This negatively affects the performance of automated systems. In this paper, we present a method, which is based on the transfer learning technique with Inception-v3 model, to evaluate the quality of fundus images and eliminate fundus images that are classified as having poor quality. The original Inception-v3 model was modified by adding more layers at the classification layer of the model to adapt to quality fundus image evaluation. The experiment performed acceptable results of quality evaluation for fundus images into two categories: good and poor-quality images. Therefore, the proposed method can assess the quality of the image by identifying the contributing factors such as over dark and over bright.
Semantic Search Pipeline: From Query Expansion to Concept Forging

When searching a database for a topic (e.g. Covid-19), there may not exist a precise match, especially if the topic is novel. Furthermore, the topic may surface in the data under different guises (‘Covid-19,’ ‘coronavirus,’ ‘pandemic’, etc.). The results of a keyword search are limited by the querier’s imagination and familiarity with the data. Such searches have high precision, but low recall. In order to increase the recall of searches, we present the Semantic Search Pipeline, a novel approach to document retrieval that uses distributional semantic models and locality sensitive hashing to expand queries and efficiently identify other relevant documents that may not contain the obvious query terms. We evaluate the pipeline using a dataset curated from financial customer service call centers, resulting in an increase in recall of 32% over a simple keyword baseline, with a negligible drop in precision. Furthermore, we present the notion of concept forging, a process of tracing a topic or concept through time and through its various surface realizations. Applied to Covid-19, the search pipeline retrieves a set of documents that allow us to uncover the short- and long-term effects of Covid-19 on the lives of the people and businesses impacted by it. Although Covid-19 is a timely test case, our search pipeline is general in nature and can be easily applied to any range of topics.
Pallet Detection Based on Halcon for Warehouse Robots

Pallet detection is the key step of cargo handling for warehouse robots. In order to improve the recognition rate of pallet detection due to the influence of complex background, a pallet detection method based on point cloud is proposed. In this method, time-of-flight (ToF) camera is used to collect the point cloud. ResNet50 neural network model which is provided by Halcon software is used for deep learning, and deep learning is used to extract the region of interest of pallet contour. The extracted region of interest is processed to obtain the regions of the pallet pockets, and the minimum rectangles surrounding each of the pocket regions are solved to obtain the position coordinates of the pocket centers. The experimental results show that the precision of pallet detection can reach 94.5%. This method has high recognition rate in complex background, and has reference value for the design of pallet detection system of warehouse robots.
Defected Ground Planar Antenna For GSM 1800/1900, LTE 46/47 and Wi-Fi 5 Wireless Applications

This work presents dual band planar antenna with defected ground structure (DGS) operating in Global System for Mobile (GSM) 1800/1900, Long Term Evolution (LTE) 46/47 and Wireless Fidelity (Wi-Fi) 5 frequency bands for wireless applications. The proposed antenna possesses back-to-back printing structure engraved on FR-4 substrate. The top printing structure carries two radiators namely Pi ($\pi$) and Omega ($\omega$) which when deployed are interlocked and generates two resonances at 1.7 GHz and 5.2 GHz, while the back printing structure consist of inverted-T shaped as a DGS. The simulated analysis sights that the proposed planar antenna provides fractional impedance bandwidth of 15.89 % (1.68-1.97GHz) and 24.65 % (4.80-6.15GHz) centered at resonances 1.7 and 5.2 GHz, respectively. The achieved bandwidths support the proposed antenna to function smoothly in GSM 1800/1900, LTE 46/47 and Wi-Fi 5 frequency bands with gain and radiation efficiency more than 3dBi and 85%, respectively throughout the functioning bands. Therefore, because of multiband, miniaturized footprint and covering of essential GSM/LTE/Wi-Fi bands with good performances, the proposed antenna is a considerable contender for wireless applications.
Land Cover Classification from Time Series Satellite Images

The earth surface is continuously observed by satellites which are continuously orbiting around earth. This results in the generation of large satellite image data sets. This data is of increasing spatial resolution and temporal density. Land cover classification is an important application of satellite imagery. The results obtained from land cover classification is used to observe land use and changes such as resource planning, to detect deforestation, desertification and water scarcity. Land cover classification also helps in identifying the changes in the land cover. Most satellites along with high-end sensors provides pixel wise data. Due to high resolution images, the number of pixels which are generated are in millions even for the study of a small area. Therefore there is a need to balance runtime and accuracy for this task. In this paper, we did a comparative study of the models comparing Multi CNN model with traditional models to classify land cover from multi-spectral temporal Landsat-8 satellite data.
Direction-of-Arrival Estimation With Virtual Antenna Array: Observability Analysis, Local Oscillator Frequency Offset Compensation, and Experimental Results

Estimating the direction-of-arrival (DoA) of radio frequency sources is essential in many wireless applications. Traditional DoA estimation based on bearing measurements requires multiantenna arrays, which is not suitable for portable electronics for its large form factor. In this article, we improved a novelty single-antenna-based DoA estimation technique, called virtual multiantenna arrays. While the device is moving and continuously receiving signals, the DoA can be estimated by measuring the intercepted signals at several positions along the receiver’s trajectory. Previous virtual array technology left two unsolved problems. Some receiver movements are not able to estimate the DoA, without a theoretical basis to screen feasible trajectories; the virtual array requires precise, relative coordinates of the receiver, which is challenging in reality. This article investigates the feasibility of the virtual array and improves its robustness by addressing these two unsolved problems. A theoretical foundation for feasible receiver trajectory determination is provided; the receiver has to move with accelerations to make the DoA observable. Also, we prove the nature that the DoA could be estimated by measuring the accelerations only, where precise receiver position is not mandatory. These results indicate that the virtual array is feasible with simple receiver movements and low-cost commercial equipment, thus significantly reducing system complexity and cost. Simulations are conducted to validate our theoretical predictions. The proof of concept implemented on a software-defined radio testbed also proves the validity and suitability of the improved DoA estimation technology in applications with form-factor constraints.
Anti-inflammatory, analgesic and anti-lipid peroxidative properties of Wattakaka volubilis (Linn.f.) Stapf.

The ethanolic extract of Wattakaka volubilis (Linn.f.) Stapf. was screened for its anti-inflammatory and analgesic effects in animals. The extract showed a significant inhibition of carrageenan-induced rat paw edema and acetic acid-induced writhing in mice compared to the standard anti-inflammatory and analgesic drug, indomethacin. The extract also showed potent in vitro inhibition of FeCl 2 -ascorbic acid-stimulated mice liver lipid peroxidation.