Miracle-dz/UniFAR-FTUs huggingface dataset:
A Signal Blind Detection Method Based on Wavelet Denoising

In this paper, a blind detection method is proposed based on wavelet denoising for short burst signals under low SNR (Signal-to-Noise Ratio), and a definite method to determine the decision threshold is given. The method first performs wavelet denoising on the received data, and then uses the energy detector based on a short window to detect the existence of the signal. Simulation results show that for 2FSK, MSK, QPSK and 16QAM signals, when SNR is −12dB, the detection probability can be higher than 85% and false alarm probability is basically maintained below 20%.
Two genetic arrangements determining flagellar antigen specificities in two diphasic Escherichia coli strains

Two diphasic Escherichia coli strains, Bi7327-41 and P12b, which spontaneously change their flagellar antigenic characters from H3 to H16, and from H17 to H4, respectively, were investigated. New features of the genetic control of flagellar phase variation in Bi7327-41 and Salmonella were found. Two genes responsible for the alternative flagellar phases H17 and H4 were demonstrated in P12b. They differed from hagA(H16) and hagB(H3), the two flagellin genes of Bi7327-41, in not being alleles of hagB(H3) or sensitive to the hagA(H16)-specific repressor.
Prediction of Egional Temperature Change Trend Based on LSTM Algorithm

In order to better predict the trend of temperature in future regions, a time recurrent neural network algorithm LSTM is proposed to predict regional temperature trends. This paper obtains temperature changes in Alberta, Quebec, and Saskatchewan, Canada. Based on the average temperature timing characteristics of each province, LSTM (long short-term memory) is used to analyze the provinces of Canada. Temperature and time trends of temperature and modelling, predicting temperature changes in future Canadian provinces; The results show that after the above model predicts the temperature change trend for the next three years, the predicted temperature change trend is almost consistent with the existing data, and the prediction accuracy is also relatively high. Therefore, the LSTM algorithm based on this paper can be applied to the prediction of regional temperature trends, and the prediction results and accuracy are very good, which has certain value and significance for real life.
Low-Profile Wideband High-Gain and Low-RCS Circularly Polarized Array Using Checkerboard Polarization Rotators

In this paper, a circularly polarized (CP) array based on checkerboard polarization rotators (PRs) with wideband and low radar cross section (RCS) is presented. The proposed array is formed by $2 \times 2$ CP element antennas fed with a sequential feeding network. On the one hand, the CP array has a wide axial ratio (AR) bandwidth of 22.5% (AR $\leq$ 3 dB), an impedance bandwidth of 37.3% (S11 $\leq$ -10 dB), and a 3-dB gain bandwidth of 18.4% with a maximum of 13 dBic. On the other hand, the checkerboard arrangement of the PRs generates both an in-band and a partial out-band RCS reduction under normal incidence for both x- and y- polarizations.
An Analytical Approach for Enhancing the Automatic Detection and Recognition of Skewed Bangla License Plates

Although there has been a huge body of work on Bangla license plate detection and recognition, the successes of these works have largely been limited to correct detection and recognition of undistorted license plates whose images are taken chiefly from the front or the back of vehicles with slight angular variations. As a result, most Bangla automatic license plate recognition (ALPR) systems in practice struggle when the license plates are skewed on the viewing or the image planes of the license plates. In this paper, we address this issue by proposing an analytical approach that can enhance the ALPR of both normal and skewed license plates and can be incorporated into existing Bangla ALPR systems without modifying their internal structures. Specifically, we demonstrate how existing ALPR systems can be treated as black boxes and analyzed to understand what sort of license plate images they work best on and introduce a novel pipeline that combines deep learning and an algorithmic procedure for transforming images of both normal and skewed license plates into formats that are best suited for the ALPR systems. We note that our proposed method can be easily generalized and applied to non-Bangla license plates as well.
Research on an Improved Ant Colony Algorithm Fusion with Genetic Algorithm for Route Planning

This article focuses on an improved ant colony algorithm for solving the problem of route planning. An ant colony algorithm (ACO) fusion with genetic algorithm (GA) is proposed and verified by simulations. The genetic algorithm is used to calculate the initial pheromone according to the route nodes distribution. The output of GA is considered as prior information fed to ACO algorithm by selecting, crossing and variation evolution operations. Comparing with the ACO and GA routing algorithms individually under the condition of 10,20,40 nodes distribution within the region of interest based on MATLAB simulation, the fusion ACO-GA algorithm achieves the shortest route and fitness evolution results. Experimental tests have indicated that with the interior nodes of the interesting region increasing, the route distance obtained by the improved algorithm is shorter than GA and ACO algorithms both. Moreover, the proposed ant colony algorithm fusion with GA has fewer iterations to obtain the optimal solution, and the calculation time cost is decreased obviously.
Simply Robust Control Strategy Based on Model Predictive Control

A control engineer is required to design a controller that will stabilize a system, and satisfy specific performance under the influence of disturbance, observation noise, unmodeled dynamics. Controllers with strong robustness such as an H-infinity control and a Sliding Mode Control (SMC) theory have been proposed, but these controllers are difficult to apply to industries due to the difficulty of theory and problems such as chattering. On the other hand, a Model Predictive Control (MPC) provides a controller with intuitive and easy design and tuning. The MPC is one of the excellent controllers not only having this advantage but also considering constraints of inputs or state variables. However, the controller based on the MPC has insufficient robustness. We propose a new control theory that is the modified MPC to improve robustness by applying matching conditions. This proposed method not only has strong robustness but also Inherits the characteristics of the MPC with easy design and tuning. The new controller proposed in this study is named an Extended Model Predictive Control (EMPC). The theory of the EMPC is explained in this paper and verified effectiveness by numerical simulation.
Relationships between Riparian Cover and the Community Structure of High Desert Streams

Many studies on cool, forested streams have shown that removal of riparian canopy leads to higher incident radiation, blooms in algal and macroinvertebrate populations, and concomitant increases in salmonid abundance. In warm, high-elevation desert streams, however, an open canopy may not increase salmonid density. Our seven study reaches on 3rd-order tributaries of the John Day River in eastern Oregon included riparian areas ranging from denuded, heavily grazed streambanks to intact conifer forest. Average summer solar inputs to these sites varied from 165 to 2230 megajoules/m2, and stream temperatures were influenced by the density and extent of canopy. Densities of steelhead trout (Oncorhynchus mykiss) and sculpin (Cottus supp.) decreased significantly with greater incident radiation and higher stream temperatures, although many warm-water cyprinids increased in abundance in unshaded sites. Periphyton standing crops (g ash-free dry mass/m2) closely tracked solar inputs and was, in turn, strongly positively correlated with biomasses of total invertebrates and of grazers. Collector, shredder, and predator biomasses, and numerical abundances of all invertebrate groups, did not change with canopy density. The abundances of chironomids and baetids were unrelated to increases in light or algal resources, in contrast to studies in Cascades and Coast Range streams where irruptions of these taxa occurred in canopy openings. In our streams the large-bodied caddisfly Dicosmoecus accounted for the higher total invertebrate biomass observed in exposed sites. These insects composed 55-96% of the total biomass in open reaches but only 0-1.4% in the three most shaded sites. Increases in total invertebrate biomass with increasing light levels or periphyton were not observed when Dicosmoecus were removed from the analysis. Dicosmoecus are consumed infrequently by juvenile trout or other small fish species common in John Day tributaries; consequently, extensive openings in the canopy appear to produce few advantages to upper trophic levels in these streams.
Non-Intrusive Load Disaggregation Using Semi-Supervised Learning Method

With the emerging of smart metering around the world, there is a growing demand to analyse the residential energy usage. In this paper, we propose a Deep Neural Network (DNN)-based approach for non-intrusive load monitoring (NILM), which can achieve effective and efficient estimation of individual appliance usage according to a single main meter reading in a non-intrusive manner. Considering practical situations, two training methods are provided. The first training approach is fully supervised learning, which requires a ground truth of label, indicating the state of the appliance (ON/OFF), to build a prediction model. The second training approach is semi-supervised learning, leading to better performance by F-Measure metric while only requiring some more unlabelled training data. Experimental results on the low-sample rate REDD dataset demonstrate the superior performance of our proposed DNN-based method compared with Hidden Markov Model (HMM)based and Graph Signal Processing (GSP)-based approaches.
Enhancing the Credibility of the Optical Performance Monitor With Adversarial Training

The existing optical performance monitoring (OPM) scheme based on deep neural network has no selection capability of the input data. They always accept and process all, which may result in serious monitoring errors and reduce the credibility of the monitoring system. Because the transmitted data in the future heterogeneous fiber-optic networks are diverse, and it’s likely to exceed the scope of the monitoring system. We propose an unsupervised generative adversarial network (GAN) as the judgement module in the new OPM framework to select the legal data within the scope of the monitoring system. The generator consists of encoder-decoder-encoder (EDE) sub-network, jointly learns the image and latent feature distribution of the legal data. And the training data for the network in the new added judgement module is the same as the OPM analyzer network’s, therefore, no extra data are collected, which is low-cost. In the simulation, four modulation formats under two bit-rates are taken into account to verify the model performance in the judgement module. When 60 Gbps 64QAM signal is selected as illegal data, the max value of the area under the curve (AUC) is 0.942. The judgement time for single image is about 12 ms. Moreover, the influence of the task weights and the latent feature shape on the judgement performance are investigated. The new added judgement module largely increases the credibility and safety of the existing OPM scheme.
Wearable Button Antenna with Circular Polarization

In this work, we present a wearable button antenna with omni-directional radiation pattern and circular polarization. The topology is based on the magnetic dipole concept. The relative impedance bandwidth is 4% and the relative 3 dB axial ratio bandwidth is over 3%. The omnidirectional pattern can serve on-body communications and the circular polarization can be used to mitigate possible polarization mismatch in body-centric communications.
On the use of sub-harmonic mixer in sliding-IF architecture for mm-Wave/THz transceivers

This paper discusses methods to implement a sub-THz transceiver for 300 GHz band with minimal LO frequency. An existing sub-harmonic mixer, used previously in direct conversion transceiver, is applied in a sliding-IF mixer architecture. Sub-harmonic mixer with sliding IF architecture is envisioned to further reduce the need of higher LO frequency for upconversion. In this aspect, two architectures are studied by spectral analysis and further verified through circuit simulator. The first has a problem with overlapping sidebands, making it unsuitable for non-symmetrical double sideband modulations. On the other hand, the second architecture manages to up-convert signals up to fo, using a single LO frequency of fo/3.
Hovering Control of Submersible Transformer Inspection Robot Based on ASMBC Method

Aiming at the difficulty in locating and identifying the faults of oil-immersed transformers, a new spherical submersible transformer inspection robot is designed. The robot has a spherical structure with a zero turn radius, which ensures its flexible motion. It is a difficult problem for the underactuated spherical robot to realize the hovering control for identifying transformer fault points. To address the problem, the dynamic model and kinematics equations of the robot are derived by the hydrodynamic theory at first. Then, an adaptive sliding mode backstepping control (ASMBC) is used to design the cascade control system. The cascade control system consists of the depth control and the yaw control. The performance of the controller is verified by both simulation and real experiments. The simulation experimental results demonstrate that ASMBC controller is superior to the single neuron PID. The tank trials results show that the movement of the robot is flexible, and the hover control can satisfy the requirement of fault observation tasks.
A Robotic Grinding Motion Planning Methodology for a Novel Automatic Seam Bead Grinding Robot Manipulator

Industrial robotics is a continuously developing area of in-depth robotics research, as industrial robots have demonstrated to possess advantages in the robotic automation solutions in the industrial automation applications. In this paper, a novel automatic seam bead grinding robot manipulator with the integration of machining and measuring is proposed and an experimentation platform is developed for the robotic removal of the seam weld beads at welded pipe ends. Also, a robotic grinding motion planning methodology, which consists of a robotic operation process motion planning approach and a robotic seam bead grinding method, is presented for the appropriate motion planning and applications of the operational processes. Furthermore, the effectiveness of the robotic grinding motion planning methodology and the superiorly operational manipulation performance of the proposed robot manipulator are verified through a robotic grinding experiment for the removal of external seam weld beads at a helical welded pipe end.
Electromagnetic Measurement Techniques for Materials and Device Used in 6G Wireless Communications

AIST are researching and developing the electromagnetic measurement technique for materials and circuits used in the 6th generation wireless telecommunication. One of key factor in measurement technologies are operation frequency range. In this paper, balanced type circular disc resonator (BCDR) has been recognized as an accurate measurement method of complex dielectric constant for low loss dielectric materials expanded frequency range up to 170 GHz. On the other hand, for scattering parameter, S-parameter, measurement of planar devices and circuits, precision on-wafer circuit measurement has been achieved by fully automatic prober system with high accuracy up to 340 GHz. In the presentation, material characterization is demonstrated for synthetic resin, and the original on-wafer measurement technique producing scattering parameter measurement with high reproducibility up to 340 GHz.
Adaptive indirect inverse control for nonlinear systems actuated by smart-material actuator*

In this paper, by incorporating implicit inverse technique into a dynamic surface based adaptive control design framework, we have developed a robust adaptive dynamic surface implicit inverse control for a class of nonlinear systems with unknown Prandtl-Ishilinskii (PI) hysteresis. Our development one hand is to eliminate the problem of “explosion of complexity” inherent in the backstepping method, on the other hand, instead of constructing the hysteresis inverse model to eliminate the hysteresis in the system, we eliminate the hysteresis by finding the optimal value of the PI performance index. And solve the difficulty of solving the hysteresis model by optimizing the method. In addition, the stability analysis shows that the system is semi-globally consistent and ultimately bounded, and the effectiveness of the proposed method is proved by simulation results.
Spectrum Challenges for Beyond 5G: The case of Mexico

In recent years, there has been an increasing interest in satisfying the spectrum demand for communications beyond 5G (B5G) at a global level. However, in such a large and diverse country as Mexico, the implementation of B5G wireless networks towards 6G will require new spectrum regulation policies that also take into account the particular aspects of the country, such as population density, geographic diversity, as well as social and economic issues. This paper discusses the asymmetries of the urban and rural scenarios of mobile communications and examines their relationship to the current status of the spectrum for 5G mobile communications in Mexico. Then, it explores the spectrum-related challenges that must be overcome to attain ambitious goals beyond 5G technologies. Thus showing the necessity of novel spectrum allocation mechanisms that consider the myriad of scenarios and their different demands in similar countries, in order to satisfy the requirements of B5G wireless communications and networks.
An End to End Method for Taxi Receipt Automatic Recognition Based on Neural Network

Usually, a lot of manpower was consumed by companies, schools and other institutions on gathering and managing the data of taxi receipts. Due to the bad painting and the irregular arrangement of text, the automatic taxi receipt character recognition task have not been solved yet. This paper proposes an end-to-end taxi receipt automatic recognition method based on two different types of neural network without traditional image processing technology and ordinary optical character recognition(OCR) technology. The Single Shot Multi-Box Detector(SSD) neural network is used to determine the data region, and a CNN (convolution neural network) -GRU (gated recurrent unit) combined neural network is designed for character recognition. The two neural network enhance the method's robustness. Our data set contains 1800 images of taxi receipts, and the cross validation result shows that the accuracy reaches 94.36%.
Point Pair Feature based 6D pose estimation for robotic grasping

6D pose estimation is a crucial research topic for flexible and autonomous systems. With the development of 3D sensors, methods using range data or point clouds show great potentials. This paper proposes an effective approach to estimate the target object's 6D pose based on point pair features. Several improvements including cluster-based downsampling, neighbor search using K-D tree, multi-frame point clouds fusion and pose verification were made to optimize the performance of the approach. Based on the object's pose, we propose a strategy to grasp the object. We tested our approach in real environment and get 97.5% success rate of pose estimation and 95.8% success rate of grasping objects.
Gait Recognition Based on Feedback Weight Capsule Network

Gait is a change in the posture of a person while walking, and has been widely used in human recognition in recent years. For multi-views of different walking conditions with a small number of markers, inorder to capture robust gait features we propose a gait recognition model based on feedback weighted capsule network. We use a pair of gait energy image(GEI) as input to the network, update the input image with pixel-level feedback weights matrix, extract the gait characteristics of the input image with a convolutional network, and output the similarity of the image pairs with the improved capsule network. We experimented with our model on the CASIA-B dataset and the OU-ISIR dataset. The experimental results show that our proposed model performs better than other existing models.
Identification of Heart Disease With Iridology Using Backpropagation Neural Network

Heart is an organ which function is to pump blood throughout the whole body. Because of it’s never-ending work, heart is prone to have problems. Which is why a simpler and more efficient method to identify and recognize heart complication is needed. By using iridology, heart complication can be recognized through the iris. This research was done to create a system which can recognize a problem that is happening on the heart by observing the iris. The system will feature extraction by using two methods, Principal Component Analysis (PCA) and Gray Level Co-occurrence Matrix (GLCM), these are done to identify the effects of feature extraction method against the success rate and classification using Backpropagation Neural Network. The result showed that the success rate of using PCA produced 90% and using GLCM was 77.5%.
Configuring RTK-GPS Architecture for System Redundancy in Multi-Drone Operations

The researches on Real Time Kinematic GPS (RTK-GPS) have been actively conducted to accurately estimate the location of drones, and there were many use cases applied to drones. However, such systems using RTK-GPS do not provide the protection against network instability, failure of the components that make up the RTK-GPS service. In other words, availability is not provided to ensure the stability of the RTK-GPS. In this paper, we propose an architecture enabling redundancy to improve the availability of RTK-GPS system by switching or parallelizing the GPS correction data sources. We also introduce how to propagate the correction messages through the drone network. After evaluating the system on empirical testbed, we validated that the correction messages propagated without delay, the message source was switched or multiplexed among several message sources through the proposed system when the network was unstable, and the switching time was short enough not to affect our propagation system. Consequently, we have confirmed availability through the proposed architecture of RTK-GPS redundancy.
The Engaged University: Providing a Platform for Research That Transforms Society

Despite a growing recognition that the solutions to current environmental problems will be developed through collaborations between scientists and stakeholders, substantial challenges stifle such cooperation and slow the transfer of knowledge. Challenges occur at several levels, including individual, disciplinary, and institutional. All of these have implications for scholars working at academic and research institutions. Fortunately, creative ideas and tested models exist that provide opportunities for conversation and serious consideration about how such institutions can facilitate the dialogue between scientists and society.