Miracle-dz/UniFAR-FTUs huggingface dataset:
Remote Monitoring System of four-wheel Drive Robot based on Web and Cloud Platform

Aiming at the shortcomings of existing four-wheeled robot remote monitoring system, such as poor mobility, complex operation and high consumption, a monitoring system based on web and cloud platform is developed. The system is mainly composed of the perception layer responsible for data acquisition, the transmission layer responsible for data transmission, and the application layer responsible for remote monitoring by users. Among them, the sensor and CPU are used to collect the job information of the robot, and then the collected data is sent to the Cloud Server by MQTT protocol through the MQTT module. The data is rendered from the Cloud Server to the page using TCP/IP protocol and AXJX. The experimental results show that the system has good stability, good real-time performance and ideal monitoring effect. It effectively solves the problems of real-time on-site monitoring and remote monitoring of robot operation information.
A 2.2μm stacked back side illuminated voltage domain global shutter CMOS image sensor

this paper presents a 2. 2µm pixel pitch back side illuminated (BSI) Voltage Domain Global Shutter (VDGS) image sensor with Stacked Pixel Level Connection (SPLC) and full backside Deep Trench Isolation (DTI). With these cutting edge technologies, Full Well Capacity (FWC) more than 12,000 electrons and parasitic light sensitivity (PLS) larger than 100 dB are reached. A 38% Quantum Efficiency (QE) and 60% of Modulation Transfer Function (MTF) at 940nm, half Nyquist frequency (Ny/2) is demonstrated.
Design of an Ultra-wideband UHF Scanning Phased Array with End-fire Beams

A low-profile, vertical polarized, ultra-wideband (UWB), end-fire scanning phased array operating at UHF band is proposed in this paper. The array consists of 16 log-periodic monopole array antennas (LPMAs) and is flush-mounted in a shallow cavity to further reduce the array height. Simulated results show an operating frequency band from 300 to 900 MHz and end-fire radiation patterns with ±45° scanning performance in horizontal planes, demonstrating that the proposed end-fire array is suitable for low-profile, conformal applications with satisfactory scanning performance.
Learning to Program Computers: Systematic Literature Review and Vygotskian Discussion

The article presents a Systematic Literature Review that analyzes a particular bibliographic corpus (LACLO Congress papers 2015-2017 about computer programming learning in Latin American educational institutions) from the Sociocultural Approach of learnings. The results allow us to characterize the papers presented, reflect on the contributions that their reading from the Vygotskian Approach provides and propose theoretically oriented future-work hypotheses. Specifically, the centrality of the research devoted to teaching and not to the learning of programming is noted, as well as the interest placed in the material artifacts rather than in the construction processes of the programming language made by the students. Likewise, the research allows us to identify special characteristics assumed by the process of content pedagogical transformation for its transmission in elementary schools.
GPS aided In Motion Coarse Alignment using Fast Optimal Attitude Matrix (FOAM) and Wavelet Filter

The alignment is an essential part of the inertial navigation system. A novel optimization-based initial alignment for global positioning system (GPS) supported inertial navigation system (SINS) is proposed in this paper. In optimization-based alignment, the required attitude between the body frame and navigation frame is divided into three parts, the two attitude matrix are time-varying and one is constant initial attitude matrix. The contribution of proposed method is twofold. Firstly, a novel approach based on Fast Optimal Attitude Matrix (FOAM) is proposed to estimate the constant initial attitude matrix. FOAM is more robust and computationally efficient than many existing attitude estimation methods. Secondly, the on-line pre-filtering of measurements of inertial sensor named the inertial measurement unit (IMU) is used. The pre-filtering and de-noising of the IMU noise is based on the real-time multi-levels discrete wavelet decomposition filter. Due to these proposed modifications, the proposed FOAM based alignment requires less computation power, and it improve the robustness. As the IMU noise affects the performance of the initial alignment, so the proposed pre-filtering improves the performance of alignment. Simulation results demonstrate the validity of the proposed OBA algorithm.
DoS Attack on Networked Control System: From the Viewpoint on Communication-Control Cost

Shared network is significant for networked control system with the increasing number of sensors and actuators. Such sharing is challenging not only because it needs to guarantee the control performance, but induces some security problems. Among the known cyber attacks, Denial of Service (DoS) attack is an easy and effective attack method. We discover two patterns of DoS attack resulting in different consequences. One is that the attacker hides through the way of not changing the probability of successful decoding, but increasing the transmission energy cost; the other is both the control and communication cost are increased because of the changed probability of successful decoding with the injection of the attacker. To analyze the cost of the two attack patterns, we establish a control and communication cost function. Then, we analyze the cost changes of the system with this cost function.
Topology Optimization of Complex Network based on NSGA-II

This paper proposes a complex network topology optimization method based on NSGA-II with degrees and edges number unchanged. The method selects the elite parent networks by applying non-dominated layer sorting and congestion degree calculation through the network crossover and mutation operation to achieve maximum network efficiency and network invulnerability. Simulation analysis shows that the method can significantly improve the invulnerability and network efficiency of the initial networks at the same time. Optimizing the network efficiency makes the network connection show a negative correlation. The network presents a "multi-core decentralized" structure; optimizing the network invulnerability makes the connection show a positive correlation, the network presents a "close core and scattered periphery" structure; the cooperative optimization makes the network present a "tighter core and large periphery" structure, which is proved with good theoretical generality and practical application value.
Implementation of ransomware prediction system based on weighted-KNN and real-time isolation architecture on SDN Networks

In May 2017, hackers used the ransomware WannaCry to launch large-scale attacks on 150 countries, affecting every industry. Therefore, detection and control of the ransomware virus has become an important issue for security experts in recent years. Recently, machine learning, deep learning, and artificial intelligence technologies have become increasingly mature. Many companies (such as Google) have introduced software-defined networking (SDN) to replace the original network architecture, traffic routing, and network configuration control management. Therefore, this paper proposes a ransomware prediction system based on weighted-K-Nearest-Neighbor. This system includes the detection and prediction of ransomware packet traffic and design and the implementation of a dynamic isolation system integrated SDN. The experimental results show that the precision of detecting normal flow and abnormal flow is 99.7 and 97.7, respectively.
Energy Efficiency-centric Channel Selecting in Energy Harvesting Cognitive Radio Sensor Network

We investigate the energy efficiency (EE) channel selection problem in energy harvesting cognitive radio sensor network (EH-CRSN). The current channel allocation studies usually only consider a couple of transmitter-receiver pairs or multiple sensor nodes (SNs) with single sink. We study the network scenario with multiple sinks and multiple SNs. In view of the property that energy resource constraints in energy harvesting network, we seek the EE optimization method mechanism. The main idea of the EE-centric optimization is each SN selects channel according to the local EE to make the overall system EE improved and get the convergence at last. In this paper, we firstly introduce a RF-powered EN-CRSN and the EE formulation. Then, we proposed a game model and analyze its properties. Next, we design a SLA based channel select algorithm. Finally, simulation results show that the proposed framework can effectively improve system EE compared with the throughput-centric optimization scheme.
Insulator segmentation algorithm based on k-means

The precise positioning of insulators in complex environments is an important prerequisite for fault detection of smart grid construction. Current location algorithms for insulator string under checking images are not accurate, this paper proposes an insulator sub-segment segmentation algorithm based on k-means clustering. Firstly, the inspection pictures are transferred from RGB to HSI space, and the H component and S component are respectively clustered and synthesized. Then to binarize the segmentation by OTSU. Finally, the segmentation picture is improved by morphological processing to realize insulator segmentation. Experiments show that the proposed algorithm has obvious improvement compared with other segmentation algorithms.
Design of Wideband Bandpass Filter With Simultaneous Bandwidth and Notch Tuning Based on Dual Cross-Shaped Resonator

In this paper, a wideband bandpass filter (BPF) with simultaneous bandwidth and notch tuning is proposed, by using a dual cross-shaped resonator (DCSR) with parallel-coupled three-line (PCTL) feeding. It is found that three intrinsic transmission zeros can be generated by the proposed DCSR, while two of the zeros are used for the implementation of passband edge zeros and the other one is for a notch. Three varactors are used to implement the bandwidth and notch tuning independently. These innovative integrated functions of both tunable bandwidth and notch in a single architecture are proposed for the first time. On the other hand, the PCTL can provide sufficiently strong coupling in the desired passband. The proposed BPF can achieve the bandwidth tuning with a fixed notch inside/outside the passband, and the bandwidth can also be kept unchanged when tuning a notch within the passband. The prototyped BPF centered at 6 GHz reports that the absolute bandwidth (ABW) can be varied from 2.5 to 5.8 GHz, while the fractional bandwidth (FBW) is tuned from 41.7% to 96.7% together with notch frequency tuning of 5.0 to 8.2 GHz. The experimental measurements are in good agreement with the theoretical analysis.
Multi-robot Formation Control Based on Parameter optimization Algorithm

This paper proposes a multi-robot formation control algorithm. The controller is designed by the difference between the distance and the angle of the two robots. It is proved that the algorithm can guarantee the stability of the formation. Furthermore, the obtained formation control algorithm is tested on the software platform and Pioneer3-DX platform. According to the multi-robot formation control algorithm, a control method of adaptive parameters is proposed. The existing parameters are replaced by fitting equations, which improves the efficiency of parameter adjustment. Simulation and experimental results verify the feasibility and effectiveness of the proposed method.
Natural Gas Load Forecasting using Fuzzy Sigmoid Kernel Support Vector Machines with Genetic Algorithms

Natural gas load forecasting is very crucial for effective accurate function of pipeline networks. To ensure a balanced network operation and provide confident gas supply with minimum cost, precise forecasting is necessary. In this study, the Support Vector Machines (SVM) is employed to propose a new load forecasting method. To improve the prediction capability, facilitate the hardware implementation, and provide a straightforward interpretability, the sigmoid kernel in SVM is combined with the fuzzy logic approach. Moreover, the Genetic Algorithms (GA) approach is employed to calculate the optimal values of the SVM parameters. A data set containing two years load values is applied to the SVM based prediction model to predict the corresponding values for the next 31days. According to the short-term load predicted values for the city natural gas, the proposed SVM with fuzzy sigmoid kernel gives superior prediction capability compared with the neural network. Moreover, the average CPU execution time is considerably decreased in favor of hardware implementation for load forecasting.
The design of controller in permanent magnet linear synchronous motor

In this paper, the q-d axis currents in the vector control of permanent magnet synchronous linear motor are coupled with each other, which affects the control performance of the motor. By establishing a model of permanent magnet synchronous linear motor and analyzing the coupling reasons, a feedforward decoupling method is proposed to realize the current control of motor. On this basis, an anti-saturation integration algorithm is proposed to solve the saturation integral problem in motor speed-loop control. The system simulation results show that the decoupling current control performance is significantly improved, and the speed response performance is good.
IoT-based Solar Hydroponics Farming

Agriculture is the largest employment sector in India. It contributes a large amount to the Gross Domestic Product (GDP) of our country. Traditionally, agriculture practices in India are highly dependent on external factors such as the weather. Slow manual agricultural techniques and the lack of awareness about efficient farming techniques results in high production cost. Hydroponics is a subset of agricultural practices to grow plants without using soil as the medium. Instead, water replaces it as the growth medium for transferring essential nutrients such as Nitrogen (N), Phosphorous (P) and Potassium (K) to the plant-roots. The plants have their roots submerged in water. Water mixed with essential nutrients circulates around the system with the help of a water pump and provided to the roots of plants. Hence, approximately 10% water is required as compared to conventional farming methods. Our prototype incorporates Hydroponics techniques with a modern embedded system which includes various sensors and the ability to connect to the internet. This increases the growth rate of plants and provides an opportunity to the user to operate the system with the press of a button from anywhere in the world.
Resource Utilization Analysis of Early Scheduling in Parallel State Machine Replication

Most approaches to scaling the throughput of state machine replication resort to concurrent execution of non-conflicting requests. While these approaches enhance throughput, conflict detection and handling introduce overhead. Early scheduling is a promising technique that trades concurrency for expeditious scheduling decisions. In this technique, requests are grouped in classes and a fixed subset of threads is assigned to each class, respecting request dependencies. Early scheduling has been shown to provide significant performance improvements in state machine replication. This paper evaluates the impact of the restrictions imposed by the early scheduling technique. In particular, it shows that threads may be idle while pending independent requests are available to be executed, leading to poor processor utilization. We characterize resource underutilization for workloads with different rates of conflicting requests, number of threads, and number of request classes. The paper opens up new opportunities to further enhance the early scheduling technique.
Approach for MIMO Wireless Channel Modelling and System Characterization for an Indoor Environment

The MIMO communication channel plays an important role in determining system performance. In its determination, the channel coding and signal processing must be taken into account. Many approaches are known for modeling MIMO channels. This paper is focus on modelling that use the spatial-temporal properties of the MIMO channel for an indoor environment as an example. This report also takes into account the electromagnetic propagation of the signals and parameters of the antennas used to characterize the MIMO channel.
Insights on Bloom Forming Jellyfish (Class: Scyphozoa) in the Gulf of Mexico: Environmental Tolerance Ranges and Limits Suggest Differences in Habitat Preference and Resistance to Climate Change Among Congeners

Jellyfish are important components of marine food webs and form problematic blooms that negatively impact human enterprise. Jellyfish of the genus Aurelia (Class Scyphozoa) are common bloom-formers in the Gulf of Mexico (GoM). Aurelia have a multi-modal life cycle where the perennial polyp produces seasonal medusae. Abiotic tolerance ranges and limits strongly influence the distribution of marine species but are unknown for most jellyfish species. Tolerance limits for survival are crucial to understanding present polyp distribution and how distribution may change in climate change scenarios. We sampled and barcoded two Aurelia species from the GoM, namely Aurelia sp. 9 and a possible new species found offshore (Aurelia sp. new). Planulae obtained from one medusa of Aurelia sp. new, and five medusae of Aurelia sp. 9 were used to establish laboratory cultures. Polyps of Aurelia coerulea, a species native to Japan but introduced in North America, Australia, and Europe, were obtained from a local aquarium, barcoded, and used to establish laboratory cultures. Using controlled laboratory experiments, we determined the temperature and salinity limits for polyp survival of the two GoM species and A. coerulea. We find that A. sp. 9 and A. coerulea were tolerant of a broad range of temperatures and salinities, but differed in tolerance limits, suggesting potential differences in habitat and resistance to climate change. A. sp. 9 was most tolerant of high temperatures and low salinities, such as those found in the estuaries of the GoM. Summer high temperatures in the coastal GoM exceed the upper thermal tolerance limit of A. sp. new and A. coerulea, suggesting that A. sp. new is an offshore species and that the coastal GoM waters may not be a suitable environment for A. coerulea. Based on the upper thermal limits identified in this study, the 4°C ocean temperature increase projected for the GoM by the next century may negatively impact Aurelia sp. 9 and Aurelia sp. new populations and is expected to deter A. coerulea from invading the GoM. This is the first account of Aurelia sp. new and the first report of temperature and salinity ranges and tolerance limits for Aurelia species.
Neuroprotective Effects of Qingnao Dripping Pills Against Cerebral Ischemia via Inhibiting NLRP3 Inflammasome Signaling Pathway: In Vivo and In Vitro

Ischemic stroke patients suffer from relatively limited treatment options. Studies have shown that in cerebral ischemia, NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome is a key mediator in mediating inflammatory responses and results in activation of apoptosis signaling pathways. Here we assessed the in vivo and in vitro effects of Qingnao Dripping Pills (QNDP), a traditional Chinese prescription, on inflammatory responses and apoptosis. Our results showed that QNDP could significantly decrease cerebral ischemia injury, improve neurological function and inhibit apoptosis in rats impaired by middle cerebral artery occlusion (MCAO). Further, we found that QNDP inhibited NLRP3 inflammasome expression both in MCAO rats and in SH-SY5Y cells under OGD. Moreover, the levels of inflammatory cytokines including interleukin-1β (IL-1β) and IL-18, which mediated by NLRP3 inflammasome and increased in MCAO rats, could be reduced by QNDP, suggesting that QNDP could protect the neurons against inflammation through a mechanism mediated by NLRP3 inflammasome. Nuclear factor-kappa B (NF-κB) was also involved in the anti-inflammatory effect of QNDP. In conclusion, QNDP had neuroprotective effects against cerebral ischemia via inhibiting NLRP3 inflammasome signaling pathway, and was a potential candidate for the future treatment of ischemic stroke.
A Method for Predicting Hemolytic Potency of Chemically Modified Peptides From Its Structure

In the present study, a systematic effort has been made to predict the hemolytic potency of chemically modified peptides. All models have been trained, tested, and evaluated on a dataset that contains 583 modified hemolytic peptides and a balanced number of non-hemolytic peptides. Machine learning techniques have been used to build the classification models using an immense range of peptide features that include 2D, 3D descriptors, fingerprints, atom, and diatom compositions. Random Forest based model developed using fingerprints as an input feature achieved maximum accuracy of 78.33% with AUC of 0.86 on the main dataset and accuracy of 78.29% with AUC of 0.85 on the validation dataset. Models developed in this study have been incorporated in a web server “HemoPImod” to facilitate the scientific community (http://webs.iiitd.edu.in/raghava/hemopimod/).
Sedimentation Yields Long-Term Stable Protein Samples as Shown by Solid-State NMR

Today, the sedimentation of proteins into a magic-angle spinning (MAS) rotor gives access to fast and reliable sample preparation for solid-state Nuclear Magnetic Resonance (NMR), and this has allowed for the investigation of a variety of non-crystalline protein samples. High protein concentrations on the order of 400 mg/mL can be achieved, meaning that around 50–60% of the NMR rotor content is protein; the rest is a buffer solution, which includes counter ions to compensate for the charge of the protein. We have demonstrated herein the long-term stability of four sedimented proteins and complexes thereof with nucleotides, comprising a bacterial DnaB helicase, an ABC transporter, an archaeal primase, and an RNA polymerase subunit. Solid-state NMR spectra recorded directly after sample filling and up to 5 years later indicated no spectral differences and no loss in signal intensity, allowing us to conclude that protein sediments in the rotor can be stable over many years. We have illustrated, using an example of an ABC transporter, that not only the structure is maintained, but that the protein is still functional after long-term storage in the sedimented state.
Genome-Wide Association Study of Salinity Tolerance During Germination in Barley (Hordeum vulgare L.)

Barley seeds need to be able to germinate and establish seedlings in saline soils in Mediterranean-type climates. Despite being a major cereal crop, barley has few reported quantitative trait loci (QTL) and candidate genes underlying salt tolerance at the germination stage. Breeding programs targeting salinity tolerance at germination require an understanding of genetic loci and alleles in the current germplasm. In this study, we investigated seed-germination-related traits under control and salt stress conditions in 350 diverse barley accessions. A genome-wide association study, using ~24,000 genetic markers, was undertaken to detect marker-trait associations (MTA) and the underlying candidate genes for salinity tolerance during germination. We detected 19 loci containing 52 significant salt-tolerance-associated markers across all chromosomes, and 4 genes belonging to 4 family functions underlying the predicted MTAs. Our results provide new genetic resources and information to improve salt tolerance at germination in future barley varieties via genomic and marker-assisted selection and to open up avenues for further functional characterization of the identified candidate genes.