Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximated sliding-mode control of parallel-connected grid inverters

An approximated sliding-mode controller for an inverter system consisting of parallel connected voltage source converters is presented. The model-based control design considers the whole inverter system and leads to a controller carrying out the control of the grid currents and circulating currents in separate tasks. Due to using hysteresis controllers for both of them, the scheme benefits from advantages of sliding-mode implementations such as fast response to reference changes and good suppression of disturbances. Thus, even sudden changes in the configuration caused by the loss of communication to one of the converters can be mitigated almost perfectly. Furthermore, it features a variable switching frequency avoiding unnecessary switching actions. Since it leads to an interleaved operation of the converters, it offers a lower harmonic content of the grid currents than a benchmark solution with individual hysteresis controllers for each converter with comparable losses. The results are verified experimentally.
Triploidy induction by caffeine-heat shock treatments in the Japanese pearl oyster Pinctada fucata martensii

A combination of caffeine treatment with heat shock treatment on eggs was examined to induce triploidy in the pearl oyster Pinctada fucta martensii. At 31.5°C, different concentrations from 1mM to 13mM of caffeine were applied to eggs before the second meiosis. The increase of caffeine concentration induced an increasing delay or abnormality of metamorphosis from embryo to D-shaped larvae. Histograms of DNA-content in nuclei of cells from D-shaped larvae showed that the highest percentage (35.8%) of triploid D-shaped larvae was induced by 13mM caffeine.
Private Deep Neural Network Models Publishing for Machine Learning as a Service

Machine learning as a service has emerged recently to relieve tensions between heavy deep learning tasks and increasing application demands. A deep learning service provider could help its clients to benefit from deep learning techniques at an affordable price instead of huge resource consumption. However, the service provider may have serious concerns about model privacy when a deep neural network model is published. Previous model publishing solutions mainly depend on additional artificial noise. By adding elaborated noises to parameters or gradients during the training phase, strong privacy guarantees like differential privacy could be achieved. However, this kind of approach cannot give guarantees on some other aspects, such as the quality of the disturbingly trained model and the convergence of the modified learning algorithm. In this paper, we propose an alternative private deep neural network model publishing solution, which caused no interference in the original training phase. We provide privacy, convergence and quality guarantees for the published model at the same time. Furthermore, our solution can achieve a smaller privacy budget when compared with artificial noise based training solutions proposed in previous works. Specifically, our solution gives an acceptable test accuracy with privacy budget ∊ = 1. Meanwhile, membership inference attack accuracy will be deceased from nearly 90% to around 60% across all classes.
SiSbDp - The Technique to Identify Forgery in Legal Handwritten Documents

Forgery is a fraudulent act if not detected it leads to serious repercussions. Documents that can be the object of forgery include contracts, bank cheques, identification cards, legal certificates, insurance claims, bills, wills, etc. Alterations in such documents by either addition of words or modification of the existing words can change its judicial value. Such alteration is made with the intention of gaining profit or incurring wrongful loss upon others. Analysis of ink represents an important area in forensic science to identify possible falsification and changes in documents. Various techniques are employed to identify fraudulent alteration in handwritten legal documents based on similar color ink analysis is presented in this research paper. This paper proposes a non-destructive approach SiSbDp for the identification of forgery in the legal handwritten documents.
Eye Movement Classification Using CNN

In today’s world eye has significant importance in technological advancements. Eye contact and eye gaze are an important factor for communication. Eye movement has a wide range of applications in various technology. In recent years, eye movement detection is gaining a lot of attention. Our aim is to create an approach that employs deep convolutional neural networks which will run for both eyes in parallel for visual feature extractions. Dataset images for training and validation were obtained from the eye chimera dataset. Testing is done by inputting real-time videos.
Hybrid Receiver Model for Interference Cancellation in Multi Carrier-CDMA

In fading channel mode, multi carrier code division multiple access (MC-CDMA) is facing many challenges like phase distortion, random amplitudes and high error probability. The interferences like multiple access interference (MAD disturbs the capacity and performance of MC-CDMA. To overcome these issues, this paper has proposed the hybrid receiver for MC-CDMA by combining threshold based parallel interference cancellation (TPIC) scheme and minimum mean square error based parallel interference cancellation. The TPIC uses decision statistic magnitude and threshold function to remove the MAI from the received signal. The performance of the Hybrid-PIC scheme is compared with two different conditions. It is observed that the HPIC outperforms the Improved-PIC (IPIC) with 2dB at 1E-3 of BER.
A Smart Image Processing System for Hall Management including Social Distancing - “SoDisCop”

This paper proposes a comprehensive system meeting the multiple objectives of energy saving, crowd control and social distancing in public places such as auditorium, shopping malls, theatres. The system proposed in this paper detects both total and sector wise head count of the people in the hall. The total head count is used for crowd control and the system triggers the relevant authority when it exceeds the capacity of the hall. This paper also proposes the smart management of electrical and electronic devices such as lights and fans based on the sector wise head count. The proposed method monitors the social distancing termed “SoDisCop” and is intentionally kept devoid of complexity targeting low cost implementation. Thenovel feature of this work also meets a critical need during the current covid-19 pandemic.
909.5 Tbit/s Dense SDM and WDM Transmission Based on a Single Source Optical Frequency Comb and Kramers-Kronig Detection

Space-division multiplexing (SDM) has become a promising technology for optical communications to seek a sustainable increment in data capacity to keep up with the ever-increasing bandwidth demand. SDM could provide massive parallelization in addition to existing WDM technology for a high capacity without compromising the signal-to-noise ratio requirement of the transmitter. However, the WDM-SDM combination requires arrays of laser sources with associated active wavelength controls. This is not favorable in terms of energy consumption and cost. Single source optical frequency comb (SS-OFC) is an appealing candidate to provide coherent WDM sources, replacing the laser arrays and surpassing the performance by providing the possibility of joint digital signal processing across multiple channels. In this paper, we have generated an SS-OFC based on highly nonlinear fibers (HNLF) for short-reach applications such as optical interconnects, taking advantage of the high pump-to-comb efficiency. The 50-GHz spaced SS-OFC across the whole C band provides 99 comb lines as WDM channel sources. Combining adaptive-rate modulation, a 7.9-km 37-core fiber, and a polarization diversity Kramers-Kronig receiver, we have successfully transmitted 3663 channels (37 SDM × 99 WDM) with reliably error-free performance, achieving 909.5 Tbit/s net rate with an aggregated spectral efficiency of 184.42 bit/s/Hz.
CNN Based Detection of Healthy and Unhealthy Wheat Crop

Abstract The diagnosis of leaf diseases depict a wide range of information about the crop status. Diagnosis plays a very crucial ro le on the amount of resources available the farmt It gets prioritize because it effects the nation’s gross domestic product (GDP) directly. It is beneficial for the analysis of crop in early stage to Verify the efficient crop yield. Automatic disease identification is one of the interesting and challenging problems in computer vision due to its potential applications. In this paper, a novel method has been proposed for disease identification. The proposed method suggests a feature extraction solution for the identification of healthy and unhealthy wheat plant. A deep convolutional neural network (DCNN) and transfer learning approach is used to train the model. Different CNN models like VGG16, VGG19, AlexNet, ResNet-34, Resnet-101, ResNet-50 and ResNet-18 are used to train our model for obtain ing better results. Accuracy achieved while training the model is up to 98%. Results have showed that the technique used in this paper is beneficial to farmers so that they can identify the spoiled area of a crop and utilizes the resources to increase their productivity.
Hydrogen Source and Diffusion Path for Poly-Si Channel Passivation in Xtacking 3D NAND Flash Memory

Poly-Si channels need well passivated by using hydrogen passivation process in 3D NAND flash memories for better poly-Si quality with low trap density. It is believed that Xtacking 3D NAND flash memory has the advantage of flexible arranging the passivation process. In this article, two different passivation locations were compared in Xtacking structure to achieve better trap passivation. An optimized passivation process conducted at wafer backside with passivation SiN as hydrogen diffusion source was found to have an improved trap passivation result. This benefit was attributed to the sufficient H diffusion paths in Xtacking. These results also indicated the advantage of trap passivation for future higher stacked Xtacking 3D NAND flash memory.
A study on the classification of left - and righthanded eeg signals based on motor imagination

Aiming at the commonly used motion imaging eeg signals in BCI system, this experiment discusses the principle of extraction of left and right hand motion imaging features by cospatial mode and frequency band energy analysis respectively, and compares the two methods. Using PhysioNet's publicly available eeg data set classification and recognition, the results show that the highest classification accuracy is 77.8% when CSP feature is combined with support vector machine. The highest classification accuracy of frequency band energy feature combined with support vector machine is 85.5%. Finally, the method of multi-feature fusion is proposed. The experimental results show that the classification accuracy of multi-feature fusion is higher than that of single feature, which is kept at about 90%. It can better represent the electroencephalogram signal of motion imagination.
Current Control of a Grid-Connected Single-Phase Voltage-Source Inverter with LCL Filter

The control in power electronic systems play an important role in the connection of renewable energy sources to the electrical grid. This paper presents the design of a current control for a grid-connected single-phase voltage source inverter (VSI). The VSI is connected to the grid through an LCL filter in order to attenuate the switching harmonics in the output signal. The proposed control provides an active damping at the resonance frequency and tracks the current reference with a fast dynamic response. The control scheme is based on a resonant regulator implemented in a structure with two nested controllers. The proposed control ensures a direct pole-placement of the closed-loop transfer function without the need for state observers and only measuring the grid current. Simulation results assess the validity of the control method.
Stray Voltage Capture for Robust and Ultra-Fast Short Circuit Detection in Power Electronics with Half-Bridge Structure: the Limitation and Implementation

This paper proposes a robust and ultra-fast short circuit detection method based on the voltage dip on the half-bridge due to the presence of stray inductance. Results show that the short circuit is detected in less than 200ns, which is a promising solution against the Single-Event Burnout failure type occurrence.
A Real-time Simulation Framework for Complex and Large-scale Optical Transport Networks based on the SDN Paradigm

Thanks to the recent advancements in the Software-Defined Networking (SDN) and Network Function Virtualization research domains, telecom operators are encouraged to upgrade their optical transport networks towards programmable, energy-efficient, service-oriented, and interoperable architectures. The availability of a large set of open-source building blocks, supported by different standardization bodies makes the selection and the integration of such technologies a very complex task. In this context, the INTENTO project has the objective to create an innovative simulation framework by selecting the best technologies and use it to test applications, services, and advanced optimization algorithms in a real environment. In the initial phase, the project designed a large-scale, distributed, and hierarchical Transport SDN architecture, where optical switches and networking functionalities are monitored and dynamically configured through a two-level structure of SDN controllers. On top of that, Virtual Network Functions are optimally deployed and managed by a centralized orchestrator, based on network condition, user requests, and application requirements. Based on this architecture, the project team started to develop a complex simulation environment that harmoniously integrates within the OpenStack cloud: optical node simulators composed by simulation agent and a suitable hardware emulation layer; proprietary SDN network controller designed to enable the innovative optical nodes characteristics; Open Network Operating System as the second level controller, enabling the integration of third-party or standardized models (multivendor environment), based on standardized interfaces and communication protocols. After having described the main components and functionalities already implemented into the simulation framework, the paper concludes by highlighting future research and development activities.
Artificial neural networks and complexity: an overview

is completely determined and can be calculated by means of well-established mathematical techniques. Conversely, non-linear differential equations do not have a general solution mechanism and in most cases do not admit analytical solutions. Anyway, several mathematical and geometrical techniques were developed to define the long-term evolution of this kind of equations and to outline the global behaviour of the differential dynamical system. Autonomous differential systems have steady states if there exists some combination of x variables
Application of a Deep Learning Technique to the Development of a Fast Accident Scenario Identifier

To obtain more accurate results of probabilistic safety assessment (PSA), it is necessary to reflect more complete dynamics of nuclear power plants. In analyzing these more realistic PSA models, numerous thermal-hydraulic code runs should be performed that typically take from a few minutes to several hours. This paper proposes a fast running model using deep learning techniques to obtain plausible accident scenarios while reducing the resources required to conduct PSA. The developed model is built from a conditional autoencoder, and an analysis of its performance is carried out under both trained and untrained ranges. Taking about one second per scenario, the developed model shows about 0.4% and 1.6% error in the trained and untrained ranges, respectively. As a feasibility study, the aggressive cooldown operation under a small break loss-of-coolant accident in the APR1400 plant was considered. The proposed method can reduce uncertainty in PSA and contribute a key technique to dynamic PSA.
Vision-Based Smart Parking Detection System Using Object Tracking

Parking is essential to all vehicle owners. The inconvenience caused to disabled people due to the lack of disabled parking bays is a real problem, especially in urban areas. This paper proposes a solution consisting of three parts: first, it provides disabled drivers with real-time disabled parking information so they can plan their trips ahead; second, it provides parking operators with an insight into the trends of disabled parking demand so they may provide sufficient disabled parking bays at all times; finally, it prevents non-disabled drivers from parking in the disabled parking bays. The real-time disabled parking information is provided by a Raspberry Pi-based visual tracking system. It tracks vehicles in the disabled parking bays and sends the information to a server. Then, an android application delivers this information to the disabled drivers in real-time. Additionally, a dashboard keeps track of the disabled parking information and presents the usage data history in neat and easy to read graphs so that parking operator can conveniently review the usage trends. To prevent usage abuse, an IoT-enabled barricade is deployed at the center of each disabled parking bay. The barricades can only be controlled by registered disabled drivers. The proposed system provides a resolution to the problem and is an improvement upon existing solutions.
UAV Collision Avoidance with Varying Trigger Time

This paper examines the effect that the selection of the different collision avoidance trigger time has on UAV safety and avoidance efficiency and finds the optimal trigger time for different cases. The trigger time indicates when a collision avoidance maneuver begins. An earlier trigger time is safer for the UAV to avoid all the obstacles but causes the UAV to deviate away from its intended path, while a delayed avoidance trigger action reduces overall path deviation but entails a higher risk of collision. Thus, the balance between safety and avoidance efficiency is important. Simulations for different mission scenarios show that by selecting specific avoidance trigger times, missed waypoints which are a result of the avoidance maneuver, could be reduced by over 40%. In addition, avoidance time and space required by the avoidance maneuver are also reduced, as compared to always starting the avoidance maneuver when the obstacle is first detected. Hence, selecting the avoidance trigger time can improve the performance of the UAV’s avoidance maneuver, especially for real-time applications.
Enhanced Power System Damping Estimation via Optimal Probing Signal Design

For real-time power system dynamic monitoring, it is important to provide accurate estimations of the network's critical electro-mechanical modes, which are time-varying frequency and damping values. This paper employs a framework for designing a multisine probing signal that, when applied in the control inputs of one of the power electronics-based grid actuators, is able to provide a damping estimation with user specified variance. The employed framework is demonstrated through simulations in a nonlinear simulator using models of varying complexity.
Finedge: A Dynamic Cost-Efficient Edge Resource Management Platform for NFV Network

With the evolution of network function virtualization (NFV) and edge computing, software-based network functions (NFs) can be deployed on closer-to-end-user edge servers to support a broad range of new services with high bandwidth and low latency. However, due to the resource limitation, strict QoS requirements and real-time flow fluctuations in edge network, existing cloud-based resource management strategy in NFV platforms is inefficient to be applied to the edge. Thus, we propose Finedge, $a$ dynamic, fine-grained and cost-efficient edge resource management platform for NFV network. First, we conduct empirical experiments to find out the effect of NFs' resource allocation and their flow-level characteristics on performance. Then, by jointly considering these factors and QoS requirements (e.g., latency and packet loss rate), Finedge can automatically assign the most suitable CPU core and tune the most cost-efficient CPU quota to each NF. Finedge is also implemented with some key strategies including real-time flow monitoring, elastic resource scaling up and down, and also flexible NF migration among cores. Through extensive evaluations, we validate that Finedge can efficiently handle heterogeneous flows with the lowest CPU quota and the highest SLA satisfaction rate as compared with the default OS scheduler and other state-of-the-art resource management schemes.
Geographic ranges of plant species in relation to dispersal morphology, growth form and diaspore weight

We investigated the relationship of geographic range to growth form, dispersal morphology and diaspore weight within three comparative databases of 219-325 native species from arid central Australia, semi-arid western NSW and maritime Sydney. Geographic range was measured as the number of regions occupied. In the arid and semi-arid floras, but not in the maritime flora, species adapted for dispersal by vertebrates (mainly birds) tended to occupy more regions than species adapted for dispersal by other means. The same pattern occurred within four major families (Chenopodiaceae, Myoporaceae, Mimosaceae and Solanaceae), and thus was not a product of differences between families. We suggest that lineages with adaptations for long-range dispersal may have undergone a lesser incidence of geographic speciation, within the arid and semi-arid flora.
Decode-and-Compare: An Efficient Verification Scheme for Coded Edge Computing

Edge computing is a promising technology that can fulfill the requirements of latency-critical and computation-intensive applications. To further enhance the performance, coded edge computing has emerged because it can optimally utilize edge devices to speed up the computation. In this paper, we tackle a major security issue in coded edge computing: how to verify the correctness of results and identify attackers. Specifically, we propose an efficient verification scheme, namely Decode-and-Compare (DC), by leveraging both the coding redundancy of edge devices and the properties of linear coding itself. To design the DC scheme, we conduct a solid theoretical analysis to show the required coding redundancy, the expected number of decoding operations, and the tradeoff between them. To evaluate the performance of DC, we conduct extensive simulation experiments and the results confirm that the DC scheme can outperform existing solutions, such as homomorphic encryption and computing locally at the user device.
Evaluation of Experimental Studies Published in the Jundishapur Journal of Chronic Disease Care: A Brief Report

Background: Thequalityof reportingtheinterventionalstudiesisrelatedtotheirusefulness. Thereportingguidelinesareeﬀective tools in this ﬁeld. Objectives: Thecurrentstudyaimedatassessingthequalityof reportedexperimentalstudiespublishedintheJundishapurJournal of Chronic Disease Care. Methods: In the current descriptive, cross sectional study, 66 randomized (RCT) and nonrandomized clinical trials were evaluated by CONSORT and TREND checklists. These articles were published from July 2012 to July 2018. Results: The study identiﬁed 43 RCTs and 23 nonrandomized trials. The percentage of adherence to both checklists was more than 50% since 2014 but no article met all criteria of the CONSORT and TREND statements. Conclusions: The quality of reporting improved during the time, but it was not at the optimum level. It seems that placing a link fortheseguidelinesishelpful,althoughthechecklistlinkhasalreadybeenplacedonthehomepageof JJCDCsince2014. Therefore, persuading the authors and reviewer to beneﬁt from them can be helpful.