Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Traffic Signal Control Based on Maximum Weighted Traffic Flow

The paper proposes a method for adaptive traffic signal control. The proposed method includes two stages, each of which is responsible for a separate algorithm. At the first step, the predicted “flow” of vehicles through the intersection along the given lane is estimated at the allowed traffic light signal. At the second step, a “weighted” flow estimate is formed, which takes into account the waiting time for vehicles at the intersection. The next phase of the traffic signal is determined by the criterion of maximizing the weighted traffic flow through the intersection. An experimental study of the proposed algorithm was conducted both on synthetic and real-world traffic scenarios, including an isolated intersection, a highway, and a city area. Based on experimental results, we can conclude that the proposed algorithm outperforms baseline classical and reinforcement learning methods for traffic signal control in terms of average waiting time and average travel time.
Wide Stopband Centrally Corrugated Folded Bandpass Filter with Trapezoidal Spurline

The present article describes the design of a compact fourth-order centrally corrugated hairpin line bandpass filter with an improved skirt characteristic and an extended stopband. At first, the compactness of 52% has been achieved for a fourth-order bandpass filter centered at 2.5 GHz and fractional bandwidth of 5% over the conventional hairpin-line filter by cascading the centrally corrugated double folded hairpin line (CCDFHL) cells in a hybrid configuration. Accordingly, a symmetrical passband with attenuation levels of more than 40 dB at the edges and sharp skirt characteristics have been occurred due to the strong capacitive coupling between the corrugated folded arms of each double folded hairpin-line (DFHL) cell. However, the harmonics with the attenuation level of 10 dB have limited the applications of such filter for mixers and frequency synthesizers in WLAN. Subsequently, the coupled arms of two adjacent DFHL cells have been perturbed by a trapezoidal-shaped meander spurline with optimum dimensions to achieve modal phase velocity compensation. As a result, an extended stopband with a rejection level of 35 dB up to $3. 88f_{0}$ and 25 dB up to $6f_{0}$ has been obtained. Besides, an overall size reduction of 59.6% compared to the conventional filter has been achieved accordingly.
A Novel Nighttime Dehazing Model Integrating Retinex Algorithm and Atmospheric Scattering Model

To solve the problem of low contrast and serious loss of detail information after dehazing directly using the atmospheric scattering model. This paper proposes a novel night image defogging model that combines the Retinex and the atmospheric scattering models. This model incorporates the best of both algorithms. Retinex can enhance the contrast of the image and the detailed information of the image. The atmospheric scattering model can clearly describe the imaging principle of the haze map, and restore the haze-free image from the root. In qualitative and quantitative experiments, this paper compares four methods on public datasets. This algorithm’s average image information entropy processed by this algorithm is 6.995, the average peak signal-to-noise ratio is 19.340, and the average structural similarity is 0.766. Compared with the other four algorithms, the comparative test results show that the algorithm in this paper can effectively remove the haze in the nighttime image, restore the detailed information of the image, and promote the progress of the algorithm.
Differential group delay for optical vortices in a 3-turn fiber coil resonator

In this thesis, we have studied transmission of optical vortices in a coil fiber resonator composed of 3 evanescently coupled turns of a multimode fiber. We have used a fully vectorial theory of normal modes of such a resonator. On the basis of this theory we have calculated group delay (GD) and differential group delay (DGD) for optical vortices, which pass through the system. We have shown that GD can reach significant value of 4 ns and at the same time correspond to zero DGD.
Design of an Inverted C-Shape Slot Antenna using Substrate Integrated Waveguide

An inverted C-shape slot antenna with a planar and compact substrate integrated waveguide (SIW) is presented in this paper. The main objective is to evaluate the performance of antenna at 2.5 GHz frequency on the basis of radiation pattern, gain, directivity, return loss, and standing wave ratio (SWR). The detailed design of planar and compact structure of 67.5 mm x 67.5 mm based on SIW is presented and simulated using high frequency structure simulator (HFSS). Simulation results shows at 2.5 GHz antenna gives better performance parameters. For further verification, the antenna is fabricated from a single sheet of FR-4-Epoxy substrate with a permittivity of 4.4 and a thickness of 1.6 mm using photo-lithographic process and tested using vector network analyzer (VNA), Anritsu make MS2024B model. It is observed that in experimental results, at frequency 2.49 GHz, the antenna has a very good return loss of −31.50 dB, SWR of 1.03, and it achieves very broad SWR bandwidth of 501 MHz. The antenna is well suited for ISM band applications.
Modelling of Linear Magnetic Gear

In this paper transient 2D and 3D finite element method models of linear magnetic gear have been developed. Results for magnetic force interaction and force density have been obtained and presented. The transient motion of the plungers of the linear magnetic gear has been investigated. The results from the modelling of the linear magnetic gear are carried out by the Ansys-Maxwell software. Results are showing relatively high force density of the linear magnetic gear design and low magnetic field reluctance at high traveling distance and moving speed.
An Automated System for Bone Invasion Detection Occurring at Stage T4 in Oral Squamous Carcinoma using Machine Learning Algorithms

Bone invasion is the condition where a cancer tumor invades nearby bones. Detection of bone invasion in oral squamous carcinoma is important for treatment planning and surgery of patients. This bone invasion can be a subtle erosion or deep erosion. It is challenging for radiologists to identify the subtle erosion in ct-scan images of oral cancer patients. This paper presents three machine learning (ML) classifiers: Support Vector Machine (SVM), Random Forest (RF), and XG Boost classifiers for automatic detection of bone invasion in CT images of oral cancer patients. The proposed framework classifies CT images as images without invasion and images with invasion. Due to the unavailability of the public dataset for this work, a dataset of 37 patients is collected from S.M.S. hospital, Jaipur. All the three classifiers SVM, RF, and XG Boost have given outstanding results with 0.94, 0.93, and 0.95 accuracies respectively. For this, both time and spatial domain features are extracted using Grey Level Co-occurrence Matrix (GLCM), Gray Level Difference Method (GLDM), Wavelet Transform (WT), and Fast Fourier Transform (FFT) methods, and extracted features are concatenated and a feature pool is created. Among all XG Boost has outperformed others.
A Cascade Fractional and Fuzzy Controller for Hybrid Power System with FACTS Device

The Load Frequency Control (LFC) of a compound power system is investigated in this research under deregulation conditions. The test system is divided into two areas, each of which includes a thermal system, a biogas plant, Distributed Generation (DG), and an Electric Vehicle (EV). The HVDC line is utilized to improve tie-line power transfer capability. Frequency regulation is accomplished using the HVDC energy that has been stored. A crossbreed fuzzy and three degree freedom controller is considered. Uncontracted load is going to be handled by EV. To improve controller gain, the Quasi Opposition Lion Optimization Algorithm (QOLOA) is applied. Static Series Synchronous Compensator (SSSC) is used to improve transfer capability of AC tie-line.
Performance Investigation Of SPS Controlled Dual Active Bridge Converter For Alleviating Solar Energy Intermittency In DC Microgrid

In this paper the analysis of Dual Active Bridge (DAB) converter working in single phase shift (SPS) control mode is presented. The bidirectional power exchange in the DC-microgrid with high power density is facilitated by the DAB. An output voltage based closed loop controller is presented in the present work. As the transformation ratio is different from unity so the efficiency of the DAB will reduce drastically. The optimum operating zone for DAB operation in DC-microgrid is obtained. The closed loop controller alleviates the load power intermittency and subsequently maintains a fixed load voltage. The proposed controller along-with DAB as a bidirectional in DC microgrid is verified in MATLAB/Simulink environment. The DAB is found to yield excellent results acting as a bidirectional energy exchanger.
New Constrained Quantum Optimization Algorithm for Power Allocation in MIMO

Multiple-input multiple-output (MIMO) has acquired significant interest as the demand for high data rates has increased and, it is regarded as a key technique in 5G wireless technology. Previously, the water filling algorithm (WFA) has been found as an effective method for power allocation channels in (MIMO). However, this solution is computationally intractable owing to the increasing size of channels. To address this problem, this paper applies the constrained quantum optimization algorithm (CQOA) for reducing the overall transmit power and the computational complexity of the MIMO downlink system. An analytical comparison has been performed between the CQOA and water filling algorithm-based binary searching algorithm (WFA-BSA). Numerical results show the efficiency of the CQOA against the WFA-BSA in terms of power and computation.
The Realization of Electronic Cylinder Control of Hydraulic Brake Test Bench Based on CANopen Communication

Aiming at the problems of complex structure, poor commonality and low repeatability of the existing hydraulic brake test bench, the servo electric cylinder control based on CANopen communication is proposed to replace the foot pedal. The object dictionary of CANopen communication sub-protocol DS301 and driver sub-protocol DS402 and the format of CANopen communication message are analyzed, and the transformation relation between each state of servo driver and the setting of PDO mapping message under three modes of position, speed and torque are described in detail. DSPACE cabinet, servo driver and electric cylinder assembly are used to form the test platform, and the servo electric cylinder communication mapping based on CANopen communication and closed-loop control in three modes of position, speed and torque are realized on the upper computer through Matlab/Simulink. The test results show that using CANopen communication to realize the servo electric cylinder control response fast, high repeatability, high precision, simple and easy to operate, by associating the brake pedal opening signal with the vehicle model, users can well achieve a variety of complex working conditions of the bench test.
Semi-Automatic Colorization Pipeline for Anime Characters and its Evaluation in Production

Improving the efficiency of a colorization process in anime productions is necessary to enhance the quality of animes. In this paper, we introduce a semi-automatic anime character colorization pipeline based on few-shot patch-based learning which is specific to the anime style. Our pipeline requires only a small number of line-drawings and their colorized images, which is intermediate products in their current workflow as training data. The advantage of our method is that it is possible to complete the training process of a sequence-specific colorization model on the fly. To evaluate the effectiveness of our pipeline, we conduct a questionnaire survey for colorization artists after several trials of our colorization pipeline in an actual anime production. As a result, our pipeline has proven to be effective in improving the colorization process efficiency.
Metamaterial based triple-band Circular Patch antenna for 5G Sub 6 GHz Applications

A triple-band compact circular patch antenna with hexagonal complementary split ring resonators (CSRR) is proposed for sub-6 GHz 5G applications. A circular patch antenna is created on the top plane of the proposed triband antenna. The ground plane is etched with hexagonal CSRRs, multiple band generation and size reduction (at the lower bands). The circular patch antenna's initial resonance is likewise shifted from 5.25 GHz to 6.52 GHz and 4.88 GHz due to the CSRR. WiMAX, 5G sub-6 GHz, and WLAN operating frequencies can all be covered by the proposed triple antenna band of 3.35 GHz (3.27-3.83 GHz), 4.46 GHz (4.20-4.69 GHz), and 5.63 GHz (5.54-5.99 GHz) respectively. This can be performed by meticulously calculating the CSRR's size and location on the ground plane. The design is $32\times 36\ \text{mm}^{2}$ in size and is made on a low-cost FR-4 substrate.
Music Recommendation System based on Emotion Detection using Image Processing and Deep Networks

There are several techniques in which a user can decide the music they want to listen to from a variety of collections. Playing music based on the mood of the user is an application of deep learning which is introduced to the listeners. This can be done by figuring out the user's facial expression which depicts their mood. Expressions talk a lot more about humans than words do. Due to increased numbers of songs being produced every day, it is becoming difficult for the users to figure out the song they would want to listen to. Our Recommendation System aims to solve this problem by providing users a list of songs directly into their personalised playlist based on the mood. The aim is to build a music recommendation system using image processing which is based on neural networks. The novelty lies in the fact that we plan to leverage a set of model architecture which has been developed by the scientist at Meta. The Multitask Cascaded Convolution Neural Network(MTCNN) and FaceNet Architecture have been used for face detection and recognition through the embedding generated. We then run a Convolution Neural Network Model to predict the emotion. Finally a classifier is used to recommend music based on their emotion from a spotify dataset which consists of almost 6 hundred thousand songs. The user can set the number of songs he/she wants to be recommended. We will also add the recommended songs back to user playlist.
Visual-based Confusion Detection using a Cooperative Spatio-Temporal Deep Neural Networks

Confusion is one of the most frequently observed emotions in daily life and can greatly affect the effectiveness and efficiency of communication. Especially in education, detecting learners' confusion and resolving it timely is crucial for effective teaching. Most research on facial expression recognition has only focused on detecting six basic emotions that do not include confusion. Even though the problem of detecting confusion has recently received more attention from researchers, analysis of both the spatial and temporal information with sufficient data is still short. In this study, we present a spatial-temporal network for detecting confusion in video images that was trained on the BAUM-1 database that collected by Zhalehpour et al. in 2017, which is, as far as we know, the largest public video dataset of labelled confusion images. The model includes the ResNet-18 Convolutional Neural Network for learning spatial information from facial images, and a Long-Short Term Memory (LSTM) recurrent neural network (RNN) for learning temporal information. By cascading two deep learning structures, our method reaches a more accurate result (73%) on the BAUM-1a database than the baseline LSTM network (67%). We also tested with our CUPIC-Confusion video dataset, which was collected from recording 15 participants’ faces while they were watching a confusing video in an uncontrolled environment. The proposed model predicted one instance from 30 consecutive facial images within 0.04 sec. with 66% accuracy, while the baseline model took 0.02 sec. with 47%. Our method can be applied to any task that gains benefit from the automatic detection of confusion, such as supporting teachers or speakers to recognize confusion in their audience without manually observing them, improving human-machine interaction tasks, and supporting those who have difficulties with confusion perception.
Dynamic penetration test based on YOLOv5

Dynamic penetration test is a common geological exploration test method used to distinguish soil types and determine the bearing capacity of foundation soil. In the test, it is necessary for the recorder to manually record the number of hammer strokes that the probe rod is hit into the specified soil depth. However, miscalculation caused by possible omissions, overcalculations and calibration line judgment errors in the recording process will greatly affect the accuracy of the test. This paper proposes a cone dynamic penetration test based on YOLOv5(You Only Look Once). YOLOv5 object detection algorithm is used to identify the position of penetration hammer in the dynamic penetration test. The falling distance of this hammer is determined by the position difference of penetration hammer after two adjacent hammer strikes. Thus, YOLOv5 can be used to replace the role of recorder in current dynamic penetration test. Through the experiment, it is concluded that in the same dynamic penetration test, the difference between the algorithm in this paper and the data recorded manually is small, which has certain practical significance. It can be used to simplify the dynamic penetration test process and improve the exploration efficiency. The data obtained by the algorithm in this paper can be used as a reference for the design of the actual building foundation.
Design of a Barque-Shaped Two-Port MIMO Antenna with Enhanced Isolation Characteristics for WLAN Applications

In this work, a compact two element MIMO consisting of a Barque-shaped antenna structure (BSA) is proposed for WLAN applications. The two element MIMO resonates at a 5.2GHz frequency. This structure was designed by using a pentagon patch with a V-shaped asymmetrical slot etched from the patch and an L-shaped DGS (Defective Ground Structure) for higher isolation improvement. The overall proposed antenna occupies a size is $44\times 32\times 1.6\mathrm{m}3$. The proposed structure uses a Rogers RT5880, which has a dielectric constant of $\mathcal{E}\mathrm{r}$ is 2.2. In this design, a U-shaped feed line is used for impedance matching. Initially, a two-element MIMO without DGS was developed, but the required isolation values were not achieved. To enhance isolation, a sequence of opposing L-shaped slot DGS structures is employed in series. The two radiating elements are separated by 1.8mm. It is taken into consideration that there is very short spacing. The antenna performance analysis parameters such as S-parameters, radiation patterns, and surface current distributions, and the MIMO antenna performance parameters are also studied, such as DG (Diversity Gain) and ECC (Envelope correlation Coefficient). The gain is achieved at the desired frequency and is 5.26dB. Without DGS, the S11 measures −14.15dB and the S21 measures −16.56dB. With L-shaped slot DGS, the S11 measures 18.38dB and the S21 measures 32.18dB. As a result of the observations, the isolation has been enhanced in doubles (−32.18dB). The proposed MIMO antenna has a simple structure and is well suited for WLAN applications. The proposed two element MIMO antenna S-parameter was measured using both the MITS eleven lab and the Rohde & Schwarz ZVL vector network analyzer, and then the prototype MIMO with DGS antenna was fabricated
Decision Calibration Network for Semantic Labeling of High-Resolution Remote Sensing Images

Semantic labeling of high-resolution remote sensing images is a challenging task, requiring the models to effectively distinguish different classes of ground objects while learning advanced feature representations. First of all, we propose a dual-decoder semantic labeling neural network based on the atrous spatial pyramid pooling module and attention mechanism to achieve the high-precision classification of different ground objects. The main idea is to enhance the high-level feature representation by using the complementary relationship that may exist between different decoders. Furthermore, based on this network structure, a decision calibration auxiliary loss is proposed to improve the models’s ability to classify examples of highly ambiguous output by different decoders. Finally, we conduct experimental verification on the ISPRS Vaihingen and Potsdam datasets, and the results show that the auxiliary loss can effectively improve the classification accuracy of the model.
Sliding Mode Current Control of a Single-Phase Three-Level Active Rectifier

This paper presents the sliding mode current control (SMCC) applied to control the power grid current of a single-phase three-level active rectifier (STAR). When contrasted with typical topologies of power factor correction, the STAR converter works with three voltage levels, giving a set of benefits. By pondering the possible wide range of applications of the STAR converter, the proposed control based on the SMCC suggests a robust current tracking. With the intention of managing the power grid current, throughout every sampling period, the SMCC specifies the reference for the modulation and the state of all switching devices, intending to diminish the current error. A whole clarification involving the SMCC functioning is displayed along the paper, which is founded fitting the whole operation of the STAR converter. The taken computational results, contemplating crucial working conditions of power ranges and rapid variations, justify the correct application of the SMCC when employed to control the power grid current of the STAR converter. An assessment of the SMCC is also given in contrast with a different current control, letting to verify the exact application and advantages of the SMCC.
IoT and LPWAN Networks: Increasing Efficiency by Communication Planning

The Internet of Things is an area of technical, social and economic dimension. IoT mainly describes scenarios in which the Internet connection and computing capabilities are extended to everyday objects not necessarily considered com-puters. Those devices have limited computational power and are often battery-powered or energy harvesting. Collisions negatively affect transmission reliability and cause data corruption. In LPWAN networks, collisions are related to the simultaneous transmissions of multiple devices. A promising SCPP protocol enables communication scheduling at the gateway. The drawback of the SCPP protocol is that the gateway represents a single point of failure. In this paper a NS-SCPP protocol is proposed, which aims at eliminating the single point of failure by communication planning on the network server. Because the network server is able to perform rescheduling, the end node does not need to re-initiate time slot reservation, which results in more efficient bandwidth utilization and lower power consumption.