Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent command and control of UAV Based on brain computer and eye tracking combined technology

Brain computer interface (BCI) and eye tracking technology are the mainstream human-computer interaction technology in current research. At present, the application in UAV command and control field is not mature. Relying on BCI or eye tracking technology alone, the success rate is difficult to reach 100%. For the application of UAV intelligent command and control, this paper designs an efficient and intelligent human-computer interaction system based on brain computer interface and eye tracking. The EEG control signal based on SSVEP is used in the brain computer interface. Eye tracking uses pupil central corneal reflex technology. In the UAV command and control software interface, eye tracking technology is used to display the eyeball fixation area in real time, which helps the human eye to accurately gaze at the command options, so as to improve the accuracy of brain computer signal relying on visual stimulation. The success rate of command and control command selection of man-machine interaction system reaches 100% through the multi UAV formation flight experiment.
Smart Contract-enabled LightChain Test Network

LightChain is the first Distributed Hash Table (DHT)-based blockchain with a logarithmic asymptotic operational complexity, and a distributed storage layer, which preserves its integrity under the corrupted majority power of nodes. Running smart contract-based transactions, however, was a missing feature in the original implementation of LightChain. In this demo paper, we present the software architecture of our open-source smart contract-enabled test network for LightChain.
Investigation of Gender Bias in Turkish Word Embeddings

Investigating gender bias in Natural Language Processing has recently gained importance due to the negative consequences of a possible sexist approach. Especially by examining such biases in English word embeddings in various contexts, many studies have been conducted on these issues. In this study, the status of Turkish word embeddings in terms of gender bias was examined and the Turkish language structure was compared with English within the scope of gender biases. As a result of the measurement of gender bias in word embeddings, it was concluded that Turkish contains less gender bias in language structure compared to English.
A Low-Texture Monocular Visual Odometer Based on Point-Line Feature

When the camera is in a low-textured scene, it is difficult for the simultaneous localization and mapping (SLAM) algorithm based on point features to track sufficient effective point features, which leads to low accuracy and weak robustness, or even results in algorithm failure. To tackle this problem, a low-texture monocular visual odometer based on point-line feature is proposed in this work. Firstly, the data association accuracy and robustness are improved by using the complementation of point-feature and line-feature. Based on this, an environmental-feature map with geometric information is constructed. We thoroughly evaluate this approach and the new initialization strategy on the TUM RGB-D benchmark. The results demonstrate that the use of lines does not only improve the original ORB-SLAM solution in poorly textured frames, but also systematically improve the solution in sequence frames combining points and lines without compromising the efficiency.
PFC Cuk Converter Fed PMBLDC Motor for Ceiling Fan

This paper analyses effectiveness of PFC Cuk converter in driving a PMBLDC based ceiling fan for variable speed and load. The system consists of VSI fed PMBLDC through a Cuk converter. A simple closed loop control that combines speed and current modulation is carried out to maintain constant DC link voltage. The MATLAB simulation results validate the modeled system for providing a wide range of speed and DC link voltage control with improved power factor.
A Comparative Study on Different Labelling Schemes and Cross-Corpus Experiments in Speech Emotion Recognition

Performance of the speech emotion recognition systems depends on many factors such as quality of the speech data, environment, cultural differences, language, emotion categorization scheme, etc. In this work, we create a baseline speech emotion recognition model based on convolutional neural networks using the RAVDESS dataset. First, we compare the performance of the model with different labeling schemes. Then, we perform cross-corpus experiments on datasets recorded in different languages. The results show that emotion groups with common arousal or valence categories are often confused and using multiple corpora in training improves the generalization capacity of the model.
The Impact of Leadership and Rewards on Project Management Success, mediated by the cohesion of work teams

In order to study the impact of leadership and the rewards on the success of project management, mediated by the cohesion of work teams, a necessary equation model was estimated using the SmartPLS software, based on the data collected by questionnaire in a sample of 102 workers working in project management teams. The obtained results show that the directive leadership and the support leadership positively influence the cohesion of work teams. In this work, it was also found that the directive leadership has a positive impact on the success of project management for the customer and that the financial rewards have the opposite effect.
Information Theory-based Techniques to Detect DDoS in SDN: A Survey

Software Defined Networking (SDN) is a centralized network management technology, which gives total control as well as security over the entire network from a single point Controller. An SDN controller offers versatile flows control. It normally runs on a server and uses procedures to inform switches where and how to send packets. To interrupt smooth functionalities of the SDN, the controller is the central and target point of attackers. There are various network security solutions are given still, the security of the network is an open research challenge. The SDN is a modern networking architecture that separated the control plane from the data forwarding plane and, as a result, offers improved abstract services and functionalities for detecting and dealing with security threats, where distributed denial of service (DDoS) attacks are one of the most troubling security issues, because it can disrupt the services of any network including the SDN. This paper reviews major known DDoS attacks and their information theory-based detection techniques in the SDN. Also, it can help researchers and professionals working in this field to recognize these threats and to see the latest scenarios.
A New Class of High-Overtone Bulk Acoustic Resonators Using Lithium Niobate on Conductive Silicon Carbide

This letter presents a new class of High-overtone Bulk Acoustic Resonators (HBARs) with only top electrodes but excellent performance using lithium niobate (LN) thin films on conductive silicon carbide (SiC). The 4-inch single-crystalline lithium niobate thin film on conductive SiC (LNCSiC) substrate was prepared by ion-cutting process. Since the conductive SiC serves as both the resonant cavity and the floating electrode, the HBARs based on LNCSiC with only top electrodes were designed and fabricated. The devices show a wide frequency response span exceeding 2.0 GHz, a frequency spacing of 10 MHz, an excellent <italic>f</italic><inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula><italic>Q</italic><inline-formula> <tex-math notation="LaTeX">$_{\textit {LAKIN}}$ </tex-math></inline-formula> product of <inline-formula> <tex-math notation="LaTeX">${9.6}\times {10} ^{{13}}$ </tex-math></inline-formula> Hz (<italic>f</italic><inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula><italic>Bode-Q of</italic> <inline-formula> <tex-math notation="LaTeX">${1.1}\times {10} ^{{14}}$ </tex-math></inline-formula> Hz), and a low temperature coefficient of frequency (TCF) of −18.9 ppm/°C. The frequency response shift of the proposed HBAR can be achieved by just adjusting the in-plane orientation of the top electrodes. Additionally, a multi-band filter was numerically synthesized based on the proposed HBARs, showing a spectral spacing of 10 MHz and a passband span of more than 2.0 GHz. Upon further optimizations, the proposed HBARs with a wide frequency response, a high-<italic>f</italic><inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula><italic>Q</italic>, and a low-TCF will be very promising for radio-frequency applications.
Android Malware Detection using Function Call Graph with Graph Convolutional Networks

As smartphone adoption is happening at a rapid rate, its threat landscape is also widening. Android is a popular smartphone Operating System (OS) which was subject to many malware attacks in recent years, compromising the privacy and security of its users. Although many works are developed to detect Android malware, few use graphs extracted from the Android Package (APK) directly as an input to the deep learning model due to the lack of suitable architectures. Graph Convolutional Networks (GCNs) are becoming a popular architecture in the deep learning community that can directly take a graph as an input. However, their applicability to Android malware detection is less explored. To bridge this gap, this work proposes an Android malware detection model using GCNs based on Function Call Graph (FCG). FCG captures the caller-callee relationships between the methods inside an APK as a directed graph. Every node in FCG is assigned a feature vector that represents its characteristics. To evaluate the performance of the proposed model, a set of experiments is conducted by varying GCN algorithms, node features and the number of GCN layers in the model. A recent Android malware dataset is used to conduct experiments. As GCNs consider the node count of the FCG, the dataset is balanced using a new technique to make node count distributions of benign and malware APKs similar. As a result of these experiments, the maximum accuracy of 92.29% with the F1-score of 0.9223 is obtained, suggesting that the GCNs have the potential to detect Android malware.
Performance comparison of Extreme Learning Machines and other machine learning methods on WBCD data set

Breast cancer is one of the most common forms of cancer among women in our country and the world. Artificial intelligence studies are growing in order to reduce the mortality and early diagnosis needed for appropriate treatment. The Excessive Learning Machines (ELM) method, one of the machine learning approaches, is applied to the Wisconsin Breast Cancer Diagnostic (WBCD) dataset in this study, and the findings are compared to those of other machine learning methods. For this purpose, the same dataset is also classified using Multi-Layer Perceptron (MLP), Sequential Minimum Optimization (SMO), Decision Tree Learning (J48), Naive Bayes (NB), and K-Nearest Neighbor (KNN) methods. According to the results of the study, the ELM approach is more successful than other approaches on the WBCD dataset. It's also worth noting that as the number of neurons in the ELM grows, so does the learning ability of the network. However, after a certain number of neurons have passed, test performance begins to decline sharply. Finally, the ELM's performance is compared to the results of other studies in the literature.
High Gain Non-Isolated Current-fed Half-Bridge Partial Series Resonance Pulsed Based Zero Current Switching Voltage Quadrupler

This paper proposes a novel non-isolated current-fed voltage quadrupler using series LC resonant circuit for soft-switching in high voltage gain applications. The proposed topology employs a half-bridge current-fed converter at the source and a voltage-quadrupler at the load. The proposed converter enables soft-commutation of the semiconductor devices at turn-off owing to the partial resonance pulse produced during the overlap conduction time, which causes natural zero current commutation before the gating signal is turned-off. It eliminates the traditional turn-off voltage spike and avoids the traditional requirement of the snubber across the devices in current-fed circuits. In addition, partial resonance reduces the peak and circulating currents and so the conduction loss in the devices is minimized. Variable frequency modulation with fixed duty cycle is employed to achieve the zero-current switching (ZCS) operation over wide range of load and input variations with load regulation and high efficiency. Simulation results are reported using PSIM11.1. 64 for validating the proposed converter operation, analysis and design. A rated load efficiency of 97.2% is obtained showing 3% improvement compared to hard-switching converter.
Elastic Expansion Model of Container based on Combination Prediction

Container management platform needs to allocate resources reasonably for containers running different services. At present, the container mainly allocates resources through the responsive elastic scaling scheme, and the speed of resource adjustment is difficult to guarantee, while the predictive elastic scaling scheme has not reached the actual production requirements in terms of speed and accuracy. In this paper, after studying the existing responsive elastic expansion scheme and predictive elastic expansion scheme, a container elastic expansion scheme based on combined prediction model is proposed. Combining the advantages of ARIMA model and SVM model in short-term time series prediction, a combined prediction algorithm model based on ARIMA and SVM is designed. Compared with the existing prediction algorithm model, the accuracy of the combined prediction algorithm is improved by about 9 percentage points, and the accuracy can reach 92.03%, which provides a strong support for the realization of the elastic expansion of the predictive container.
A High Throughput QC-LDPC Decoder Architecture for Near-Earth Satellite Communication

This paper presents a high throughput decoder architecture for the (8176,7154) quasi-cyclic (QC) low density parity check (LDPC) code (C2) recommended by the Consultative Committee for Space Data Systems (CCSDS) for near-earth applications. The architecture avoids memory conflict through the use of multiple shift register based memory circuits and a pipe stage forwarding mechanism, thus allowing for heavy pipelining of the core processing unit. The decoder is implemented on the Xilinx XCVU9P FPGA platform and achieves a throughput of 2.65 Gbps at 10 iterations at a clock frequency of 253 MHz.
The Role of Cryptography in Cryptocurrency

In today’s world of digital media, people are mainly sharing all of their resources via digital platforms. Today, in almost all regions across the globe, the physical exchange of money is becoming less in practice. People are more flexible with buying and paying for their necessities via digital platforms rather than exchanging physical money. Cryptocurrency, the new global money for the internet age, is also a medium of exchange similar to other currencies. Still, here, the sole purpose of the exchange is via digital means. How secure is your cryptocurrency? How anonymous are users of cryptocurrencies? Since the currency is digital, it is more prone to attacks, data theft, and money. Here comes the role of cryptography. Cryptography is an essential mechanism for securing information in computer systems. Without cryptography, cryptocurrency is just a central hub for attackers and scammers. Cryptocurrency requires cryptography for mainly two purposes; to secure the transactions and to verify these transfers. This paper discusses the types of cryptographic techniques used in cryptocurrencies, studies their characteristics, and explores the working of these techniques.
Reliability Analysis of Fast Electric Vehicle Charging Systems

To improve electric vehicle (EV) uptake, fast charging systems must be widely deployed. However, fast EV charging mission profiles expose power electronic components to extremely high-power stresses within short periods of time. Consequently, power electronic components in fast EV charging systems are expected to degrade/wear-out at a faster rate, requiring frequent replacement within the lifespan of the charging system. It is, therefore, important to both design and build fast EV charging systems with a known level of reliability. This paper proposes a model to investigate the reliability of fast EV charging systems. Using the model, the reliability of a typical fast EV charging system is analyzed, and results are presented to show how the lifetime and reliability of semiconductor switches used in fast EV charging systems can be predicted, even under widely varying mission profiles.
Measurement Analysis of Co-occurrence Degree of Web Objects

As a technique to reduce the web response time, HTTP/2 and HTTP/3 have been standardized. However, parallel download in these techniques is possible only for web objects which are provided from the identical object server, so many web objects need to be downloaded from a small number of object servers to improve the effect of HTTP/2 and HTTP/3. To deliver web objects, CDNs delivering objects from cache servers located near requesting users have been widely used. Hence, if we define the co-occurrence degree as the number of webpages in which an object set is included, it is desirable to replace cached objects so that object sets with high co-occurrence degree are remained in the cache. However, the effectiveness of replacing objects based on the co-occurrence degree strongly depends on how the co-occurrence phenomenon of objects appears in the actual webpages. Therefore, in this paper, to clarify the potential of cache replacement based on the co-occurrence degree, we investigate how much the co-occurrence phenomena of objects are observed in actual webpages by measuring the most popular 7,604 webpages. We confirm that the distribution of co-occurrence degree of objects obeys a power law, and the co-occurrence degrees of about 0.01% two-objects sets and 0.001% three-object sets exceed 500. Hence, we confirm the potential of cache replacement based on the co-occurrence degree of objects.
Design and technology for uniform aluminum nitride piezoelectric micromachined ultrasonic transducers with radial array

This paper reports on design, technology and mechanical characterization in air of a piezoelectric aluminum nitride (AlN) based micromachined ultrasonic transducer (PMUT) with six radial symmetric arrays. The PMUT consists of 4018 single membranes with a resonance frequency of 3.4 MHz, a quasi-static deflection of 272 pm/V and a resonant deflection of up to 71.3 nm/V. The small standard deviation of the resonance frequency over the chip is 47 kHz.
Optimization of PV Array-to-Inverter Power Ratio in Grid-Connected Systems to Maximize System Profit

Since PV arrays do not generate nominal power most of the time due to climate conditions, determining the optimal array-to-inverter power ratio (AIPR) is a significant factor in extracting the maximum energy with the highest efficiency to connect to the grid. Previous studies have tried to minimize investment costs, but this does not maximize profit as the time-of-use (TOU) electricity price is not considered. In the present paper, a method is proposed for the optimization of AIPR to maximize the profit during the entire lifetime of the PV system, considering TOU electricity pricing, climate conditions, and optimal array installation conditions. The proposed method results, implemented for a 10 kW PV system considers climate data, and electricity pricing for the city of London in Ontario, Canada, show that the optimal AIPR for this system in the city of London is 2, which results in $8446 more profit than an AIPR of 1 for the entire PV system lifetime. In addition, a comparison of the results of the proposed method with those obtained by minimizing the levelized cost of energy (LCOE) index shows that using the proposed method results in $4130 more profit.
Microwaves Are Everywhere: RFID-Do You Know Where Your Pet Is?

This is the third article in our continuing series of general interest tutorial papers on the applications of microwaves in areas of science and technology that might not be appreciated by every engineer. The tagging of people, animals, and all manner of things for individual identification purposes has been an obsession of humanity since the earliest days of community existence and personal property. Doing so permanently, discretely, and without the possibility of removal or alteration was the realm of science fiction and spy novels until very recently. In this article we examine, from a microwave perspective, the techniques, devices, development and basic operation of the now ubiquitous RFID industry which, in my humble opinion, and like so many technological and science innovations, takes on the mantra of the Roman deity, Janus: transitioning from a past seeking change for the better, to a future which promises both benefits and dangers in the embrace of new technology. The author hopes you will enjoy this month's “Microwaves are Everywhere. ”
Agricultural Production and Environmental Pollution: Relationship between Agricultural Methane Gas Emissions, Agricultural Production and Exports of ICT Services in Latin America

The objective of this article is to estimate the environmental Kuznets Curve for a group of 17 countries belonging to Latin America, in the period 1970-2008, by specifying a panel data model, to show whether there really is a Curve Kuznets environmental in these countries. The main results show that the relationship between Agricultural Emissions of Methane Gas, Agricultural Production and Exports of ICT Services, can describe an inverted U-shaped curve, indicating that Agricultural Production and Exports of ICT Services play an important role in increasing the impacts of growth on environmental degradation.
Multi-node Calibration Motion Control System of Three-Wheel Omnidirectional Mobile Robot

The omnidirectional mobile robot has attracted lots of attention due to its flexibility compared to traditional wheeled robots. However, there remains the problems of motion control accuracy caused by the start-up slip and error accumulation. In this work, we designed a three-wheel omnidirectional mobile robot and proposed a method of accurate moving control by suppressing the start-up slip and calibrating the deviation of the location through multi-node reference. The angle and distance errors were systemically analyzed and calibrated. Firstly, the method of proportional integral derivative control was optimized. Secondly, the problem of start-up slip was solved by dynamic gradient acceleration start-up method. Furthermore, a calibration system was designed and realized based on the ultrasonic and photoelectric sensors to reduce motion control error. Eventually, we achieved a three-wheel omnidirectional mobile robot with less 0.60% trajectory error, offering an economical and precise solution for robot motion control.
Planning a HVDC South American Pacific Electric Interconnection Based on MTDC

The continuous development of DC technology and its increasing implementation in projects, mainly in Europe, North America, and Asia, have created the need for methodologies to plan the DC interconnections between countries. However, the planning process should not be developed in a local context but rather, in the long run, thus enabling the inclusion of regional electrical systems. This paper presents two main methodologies. The first one aims to design local Multi-Terminal DC grids (MTDC) at the national level, using the graph theory. The second methodology presented is the integration of local MTDC grids that can later be extended to regional, continental, or subcontinental grids. The methodology is applied in the planning of regional electrical integration of the Pacific basin countries.