Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Q-Learning for Routing Schemes in SDN-Based Data Center Networks

In order to adapt to the rapid development of cloud computing, big data, and other technologies, the combination of data center networks and SDN is proposed to make network management more convenient and flexible. With this advantage, routing strategies have been extensively studied by researchers. However, the strategies in the controller mainly rely on manual design, the optimal solutions are difficult to be obtained in the dynamic network environment. So the strategies based on artificial intelligence (AI) are being considered. This paper proposes a novel routing strategy based on deep Q-learning (DQL) to generate optimal routing paths autonomously for SDN-based data center networks. To satisfy the different demands of mice-flows and elephant-flows in data center networks, deep Q networks are trained for them respectively to achieve low latency and low packet loss rate for mice-flows as well as high throughput and low packet loss rate for elephant-flows. Furthermore, with the consideration of the distribution of traffic and the limitated resources of data center networks and SDN, we choose port rate and flow table utilization to describe the network state. Simulation results show that compared with Equal-Cost Multipath (ECMP) routing and Selective Randomized Load Balancing (SRL)+FlowFit, the proposed routing scheme can reduce both the average delay of mice-flows and average packet loss rate, while increase the average throughput of elephant-flows.
A Method for LSTM-Based Trajectory Modeling and Abnormal Trajectory Detection

Nowadays, massive data has been brought by the rapid development of technology. When finding whether trajectory to be detected is abnormal under the premise of given normal trajectories, we innovatively propose 1) Seq2Seq model based on LSTM prediction network for trajectory modelling (SL-Modelling), and 2) abnormal trajectory detection method with spatio-temporal and semantic information. Firstly, SL-Modelling is used to obtain sequence-type trajectory models of normal trajectory groups directly for subsequent detection with no need to extract a large number of features manually and adapting to different sequence length. Then we introduce the concept of distance and semantic interest sequence that makes full use of spatio-temporal and semantic information of trajectories. Finally, the similarity between models and trajectory to be detected is calculated to detect abnormal trajectory. The experimental results of publicly available flight data set show that trajectory models obtained are descriptive enough to represent normal trajectory groups well, and the accuracy of modelling is higher than the existing advanced methods. Besides, the detection with spatio-temporal and semantic information has been verified that it has stronger detection ability with higher accuracy and takes less time.
Cooperative swarm localization and mapping with inter-agent ranging

Compared to a single robot, a swarm system can conduct a given task in a shorter time, and it is more robust to system failures of each agent. To successfully execute cooperative missions with multiple agents, accurate relative positioning is important. If global positioning (e.g. with a GNSS-based positioning) is available, we can easily compute relative positions. In environments where a global positioning system is unreliable or unavailable, visual odometry can be applied for estimating each agent's egomotion, by exploiting onboard cameras. Using these self-localization results, relative positions between agents can be estimated, once the relative geometry between agents is initialized. However, since visual odometry is a dead-reckoning process, the estimation errors accumulate inherently without bounds. We propose a cooperative localization method using visual odometry and inter-agent range measurements. Using the proposed method, we can reduce the drifts in position estimates with very modest requirements on the communication channel between agents.
Lightweight Semantic Segmentation for Road-Surface Damage Recognition Based on Multiscale Learning

With an aging society, the demand for personal mobility for disabled and aging people is increasing. As of 2017, the number of electric wheelchairs in Korea was 90,000 according to the domestic government statistics and has since increased continuously. However, people with disabilities and seniors are more likely to be involved in accidents while driving because their judgment and coordination are inferior to those of ordinary people. One of the factors that could lead to accidents is the interference in the vehicle-steering control owing to unbalanced road-surface conditions. In this paper, we introduce a lightweight semantic segmentation algorithm that can recognize the area with road-surface damage through images at high speed to prevent occurrence of such accidents. To test the algorithm, an experiment was conducted in which more than 1,500 training data and 150 validation data, including road-surface damage, were newly created. By using the data, we propose a new deep neural network composed of only encoder type, unlike the auto-encoding type consisting of encoder and decoder. To evaluate the performance of the proposed algorithm, we considered four metrics of the accuracy and two metrics of the speed. Unlike the conventional method, this deep neural network method shows improvement in all of the accuracy index, a 85.7% decrease in parameters, and an 6.1% increase in computational speed. The application of such a high-speed algorithm is expected to improve safety in personal transportation.
4-Element MIMO Antenna with CSRR and Mendered Line Loaded for Multiband Applications

In this communication, a four port MIMO antenna is proposed for multiband applications. The antenna structure is comprised with stepped-fed radiating elements. The multiple resonances are achieved with ground plane loaded with Complementary Split Ring Resonator (CSRR) and meandered line slots. The proposed antenna has hepta-band resonating behavior. The proposed antenna is resonated at the 3.5GHz, 6.5GHz, 10.9 GHz, 18GHz, 11GHz, 14GHz and 28GHz which are useful for WLAN, WiMAX, 5G Network and Radar System. The antenna is implemented on the roger substrate with overall size of 36×36 mm2. The improved isolation of up to-30 dB among the element is accomplished by orthogonal placement of the radiating element. The design of the antenna is simulated and analyzed using CST-Microwave Studio Tool. The average peak gain of the antenna is 7 dBi over the interested bands. The gain, efficiency, and operating bandwidth and isolation of the proposed antenna are also compared with the existing literature. ECC of the proposed MIMO antenna is less than 0.1 and other satisfactory MIMO performance criteria are to be considered as proposed antenna is good candidate for several wireless applications.
Load Migration in Distributed SDN Controllers

Distributed control solutions were introduced to address controller reliability and scalability issues in Software-Defined Networking (SDN). The dynamic nature of network traffic can lead to load imbalance amongst controller instances. A highly loaded controller instance can be slower in responding to datapath queries, and can slow down the entire control platform as state synchronization, and consensus amongst controller instances are performed cooperatively.In this paper, we present a new and efficient load migration protocol, called ERC, for shifting input load associated with overloaded controller instances towards lightly loaded instances. Our protocol has three distinguishing properties compared to prior works on this area: it is extremely efficient, resilient to failures during migration, and ensures consistency among all controller instances. ERC can be used for a wide range of network applications including load balancing, power saving, and resource optimization. It is also significantly more efficient than existing load migration protocols with 25-55% reduction in migration time, and 10-20% reduction in required migration buffer size.
A Power-Efficient BLE augmented GNSS Approach to Site-Specific Navigation

The demand for site-specific localization is increasing. For example, locating patients in hospitals, monitoring worker safety, and guiding visitors in museums are all desired. Active RFID is one of the most common approaches to delevoping site-specific navigation systems. In comparison to passive RFID technologies, active RFID devices can provide wider coverage, basic bidirectional communication functionalities, and it generally requires fewer access points and lower installation costs. Global Navigation Satellite Systems (GNSS) are widely used to perform outdoor positioning. An advantage of GNSS is that ground infrastructure is not required, only a GNSS receiver, but its performance is degraded by the presence of line-of-sight blocking and multipath. Although BLE (Bluetooth Low Energy) based indoor positioning has been broadly studied, using BLE augmented GNSS for real-time pedestrian positioning is uncommon and ambitious. Low power consumption is the key to creating wearable localization devices. The power consumption of GNSS devices increases when the GNSS reception is poor. For active RFID tags, a BLE based localization system can be used to augment the GNSS when the GNSS reception is degraded, even taking over the positioning task to put GNSS into rest. The wearable device is required to wirelessly send its position to a central control system. There are numerous possibilities for this backhaul communication system, however only low data rate is required, and the solution should be low power. LoRa is a recently developed modulation scheme that provides long-range and low power consumption, albeit at a low datarate. This paper proposes a power-efficient scheme for site-specific navigation in which the best estimate of position is determined through data fusion of GNSS and BLE. An algorithm also provides a seamless transition between measurement sources and a low-power accelerometer enabled adaptive smoothing filter of the trajectory. The careful use of low-power modes for GNSS, inertial sensing and the microcontroller, combined with a LoRa backhaul, provided a power-efficient navigation solution. Measurements within an office environment demonstrate the accuracy and reliability of the proposed site-specific navigation system.
Speed Estimator for Direct Torque and Flux Control of PMSM Drive using MRAC based on Rotor flux

This paper deals with the detailed study of model reference adaptive control (MRAC) based on rotor flux for speed estimation in a direct torque and flux control (DTFC) for permanent magnet synchronous motor (PMSM) drive. In this scheme the speed estimated is given as feedback to DTFC of PMSM drive. From reference model (RM) and adaptive model (AM), the rotor flux linkage components of the PMSM drive are obtained. The error signal of speed, which is obtained from the difference of RM and AM quantities are fed through PI controller to obtain the speed estimated. In order to reduce the error signal, the speed estimated is feedback to AM. Using MATLAB/SIMULINK software the MRAC based on rotor flux is compared with the quantities measured for PMSM drive. And the performance of PMSM drive is studied at zero load, loaded condition, sudden speed changes and sinusoidal speed respectively.
The Utilization of DNN-based Semantic Segmentation for Improving Low-Cost Integrated Stereo Visual Odometry in Challenging Urban Environments

Positioning and Navigation (PN) is one of the most important topics in the world of Autonomous Vehicles (AVs). Being equipped with a suite of sensors and high-performance computers, self-driving cars are designed to perceive its surrounding environment prior to planning and control. Among the observations are the semantics of the objects appearing in the scene. While PN is very challenging for extended GNSS outages, vision sensors can also exhibit failures in the case of highly dynamic scenes and lack of texture between consecutive image frames. To overcome this problem, we propose a stereo visual odometry scheme and advocate the use of a pretrained state-of-the-art Semantic Segmentation (SS) Deep Convolutional Neural Networks (CNN) model to forcefully remove features belonging to objects that most likely behave dynamically in the scene prior to egomotion estimation and integration with inertial sensors. When loosely coupled with inertial sensors, the proposed method was able to outperform the integrated algorithm without SS-based outlier rejection during natural GNSS outages.
Electromagnetic Microparticle Control Systems Modeling Based on the Standard Element Method

The paper describes a simulation of the electromagnetic control system structure of microparticles based on containing dc coils. The computational experiment results for magnetic field calculation created by the simulated system are presented. Comparative estimates of modern software environments accuracy based on the finite element method and the SEM software package based on the standard element method are obtained. The authors continued the development of numerical-analytical methods of standard elements introducing a new standard element for modeling DC coils. The introduction of such standard elements opens a possibility of applying standard element method without combining them with the finite element method. The use of standard elements provides for a good approximation of an outer boundary of the computational domain, the interface of its parts with linear and nonlinear properties, complete filling of the computational domain standard elements. The system of linear algebraic equations can be formed with respect to the Fourier coefficients of the representation of solutions at the boundaries of standard elements for their bonding with adjacent standard elements.
PSO Optimized PID Controller Design for Performance Enhancement of Hybrid Renewable Energy System

The increase in power demand has given rise to invest in renewable energy sources which are much better than conventional sources of power. But the renewable power sources such as wind, PV are variable in nature which affects the power quality of grid. Hence, the power system is required to make balance between power demand and generation. In order to improve the stability the PID controllers are used. But these are fixed gain controllers. So, to overcome this issue, AI techniques must be used. This paper presents the hybrid renewable energy system with wind, PV and Fuel cell connected to the grid. The PID controller is employed with the system whose parameters are tuned with Particle Swarm Optimization technique. This optimization improves the stability of the system. The modeling and simulations are done in MATLAB and results are observed for better performance under variable loading conditions.
xUAVs: Towards Efficient Approximate Computing for UAVs—Low Power Approximate Adders With Single LUT Delay for FPGA-Based Aerial Imaging Optimization

High Definition (HD) image processing and real-time analytics over live video feeds have always been the key requirements for Intelligence, Surveillance and Reconnaissance (ISR) applications. With the evolution of optics and image enhancement techniques, computational loads of HD ISR systems are also rising exponentially. On the contrary, the slow-down of Moore’s Law has recently posed challenging bounds over the level of achievable miniaturization for emerging processing and storage units. Field Programmable Gate Arrays (FPGAs) offer a popular choice of implementing ISR algorithms over resource-constrained platforms, such as Unmanned Aerial Vehicles (UAVs), due to favorable features of reconfigurability and rapid prototyping. A promising solution to bridge the gap between resource-constrained host platforms and computation-intensive FPGA applications is the paradigm of Approximate Computing. It compromises on the accuracy of processed results to offer significant performance gains for error-tolerant applications, such as video and image processing. In this paper, we present a novel approximate adder design methodology, for FPGA-based systems with improved SWaP performance, besides preserving the accuracy requirements within acceptable thresholds. The design methodology proposed in this paper focuses on the FPGA-specific Look-Up Table (LUT) architecture to introduce approximations while splitting the carry chain into LUT-based sub-adders, with flexible overlap to tune the adder’s accuracy and achieve the overall latency of a single LUT. The paper presents several variants of the proposed design and offers application-oriented flexibility to adjust for optimal SWaP vs accuracy trade-off. We have further devised a comprehensive assessment approach to verify functional viability of the proposed atomic arithmetic blocks at system level, through their implementation into dense computational imaging applications, such as 2-dimensional Discrete Cosine Transform (DCT), airborne self-localization and moving object tracking algorithms, in comparison with other state-of-the-art adders. Our most accurate design performs at least 9.9% better in power consumption when compared with existing approximate adders, which proves that the proposed methodology holds promising potential to improve SWaP-index for computation-intensive UAV applications.
Features of Precedents Space of Artificial Neural Networks for the Solar PV Station Control

In the partial shading conditions, when photovoltaic modules in different parts of the vast area of the solar station are under different insolation, many local maxima appear on the energy characteristic. Only one among them provides the maximum power generated by the power station. Standard methods for search and maintaining the maximum power point, designed to work under uniform insolation conditions, lose the ability to detect the maximum power point with partial shading and bring the photovoltaic station mode to a local peak point with significantly less power generation. Since the configuration of insolation can change relatively quickly, special algorithms are used to control the efficiency of the photovoltaic station, providing a quick determination of the vicinity of the global maximum power point. The paper presents a new algorithm implementing a high-speed output of a photovoltaic station mode in the vicinity of a point with maximum power generation with subsequent pass of control to standard methods of maintaining a working point. The basis of the algorithm is a neural network using four nonlinear classifiers, tuned with the use of support vector method. The space of precedents of the training sample of the neural network has a dimension equal to 3, although the measurement vector includes power values at 4 points of the energy characteristic. To make the algorithm universal, it is proposed to present the characteristics of the photovoltaic modules of the power station in the form of normalized dependencies. The process of tuning the neural network is illustrated by images of dividing surfaces in the use-case space of the training sample. A tuned neural network transfers the photovoltaic station mode to maximum energy production mode in a maximum of four stages of voltage change at the output of the photovoltaic modules.
Microsatellites: A tool for evolutionary genetic studies of western Palearctic water frogs

Genotypic variation at six of 67 microsatellite loci we developed from Rana ridibunda, containing di- or trinucleotide simplesequence repeats, confirms the value of microsatellites as an evolutionary genetic tool for studying western Palearctic water frogs, a model system characterized by clonal reproduction in natural hybrid lineages. R. ridibunda, Rana lessonae. and their hemiclonal hybrid Rana esculenta, which transmits only its ridibunda genome to gametes, are highly variable in central Poland and northern Switzerland: with one exception (a locus that is part of a coding region), each locus was polymorphic in each taxon when scorable, and the number of alleles was high (in Swiss lessonae-esculenta populations, 1-6 per locus, total 13, for ridibunda genomes; 1-9 per locus, total 23, for lessonae genomes; 3 shared by both genomes). Estimated repeat numbers ranged from 3 to 14 for trinucleotide- and from 6 to 27 for dinucleotide-repeats. Nonamplifying (null) alleles, problematic because they underestimate genotypic variability if undetected, occur in ridibunda genomes of Swiss R. esculenta at one locus: apparent lessonae null alleles at another locus were subsequently resolved for several segregating alleles evidenced by bands of much lower intensity. Two loci had multiple ridibunda alleles in Swiss R. esculenta and thus discriminate between hemiclones. with a resolution superior to that of allozymes. Adding these two loci to our protein electrophoretic data (7 loci) more than doubled the number of hemiclones detected, showed that several allozyme-defined hemiclones are composites of multiple microsatellite hemiclones, and revealed clonal diversity in populations that are uniclonal by allozyme data. The extent to which microsatellite-defined hemiclones result from primary hybridizations or from subsequent mutations in ridibunda genomes of R. esculenta lineages remains to be determined. A sample of newly metamorphosed R. ridibunda from hybrid × hybrid matings collected at a Swiss population in the fall had a significant heterozygote excess at the two loci that varied in ridibunda genomes; this shows that most, probably all, of them originated from inter- rather than intrahemiclonal matings, in accord with the idea that the usual hybrid × hybrid inviability in lessonae-esculenta systems is caused by deleterious recessive alleles on the clonally inherited ridibunda genomes. Three of four microsatellite loci tested amplified in most species of western Palearctic water frogs.
Adaptive Control System for DC Electric Drive under Uncertainty

In this paper the current problem of regulating the position of electric drive executive links is considered. The electric drive is built on the basis of a DC motor and is considered in non-deterministic conditions of random external influences. The lifting mechanism electric drive is selected in this article for example. The achievement of the required parameters for quality regulation is ensured by the use of an adaptive automatic control system, which includes a decision block and a non-linear PD controller. The multi-channel control system structure is presented, the individual blocks of the structural scheme are described. The measure algorithms of various parameters of the drive measuring system, which includes current and voltage sensors, a position sensor, motor winding temperature sensor, etc. are proposed. The mathematical models of the device are developed for testing the proposed technique, which implements adaptive control, The results of numerical experiments are obtained, a comparative analysis of the ordinary PD controller and adaptive control system is conducted, which showed the efficiency and effectiveness of the proposed algorithms.