Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Classification of Spoken Utterance Using Feature Vector Statistics and Machine Learning Algorithms

Identifying isolated digits/words is a crucial aspect of Automatic Speech Recognition. Numerous applications, such as gas reservations, bank transactions, and ticket reservation systems, rely on recognizing individual spoken digits, notably voice-based applications. Due to the acoustic proximity of some numbers and the transient nature of voice signals, the recognition of spoken digits is frequently quite complex. The goal of this research was to determine which aspects of speech and which categorization methods were most important for boosting speech-to-digit recognition. This study was conducted using a free, open-source spoken-digit corpus of English, but the methodologies can be applied to any language. Each feature vector's accuracy evaluation using multiple classifiers is recorded. Additional tests were conducted by integrating all feature vectors and classifiers, and the outcomes were compared. Using the Mel-Frequency Cepstral coefficients (MFCC) feature vector and the Quadratic Discriminant Analysis (QDA) classifier yields the best results with a WER of 3%.
Dynamics of the Learning Process of a Multilayer Neural Network when using the AdaDelta Optimization Method

The AdaDelta learning process optimization method has been tested for a multilayer neural network with two hidden layers with 28 neurons each, when recognizing printed numbers. Testing the learning error of this neural network was carried out using the mapping function and Fourier spectra of the error function. The mapping function describes the process of doubling the number of local minima. It is found that the application of the AdaDelta optimization method leads to a radically different picture of the behavior of the branching diagram than when applying the learning optimization methods Adam, AdamMax, and AMSGrad. Namely, with an increase in the number of iterations, the process is “extinguished” by retraining, by correcting the learning step of each neuron. It is shown that the hyperparameter ρ, which describes the contribution of the gradient square of the error function, significantly influences on the learning process of the neural network That is, increasing the optimization parameter ρ to ($0.9 \div 0.999$) causes a decrease in the region of change of the magnitude of the learning step. The step is selected automatically during training, for each neuron according to the AdaDelta optimization algorithm. It is shown that the optimal value of the optimization parameter ρ is 0.9.
Next POILP: Next Point of Interest Location Prediction Using Machine Learning

It is essential to keep an eye on students as soon as they arrive on the university campus, day or night. As a result, students will frequently use a variety of university facilities, such as the cafeteria and library. Recently, several researchers have worked on this topic using Machine Learning (ML) and Non-Machine Learning (Non-ML) methods. In this study, 16 students served as the subjects, while the historical data came from 1641 entries in the web database of the Private Indian University. Logistic regression (LR) is used to predict the students' subsequent POIL (Point of Interest Location). When using Folds 5 to evaluate the LR model on historical data, considering 75% training data and 25% testing data, 60% accuracy for the target feature was reached. In addition, contrast the Support Vector Machine (SVM) and Random Forest algorithms with the LR model.
Design of W-band eight-channel planar integrated phased array receiver

In this paper, a W-band eight-channel phased array receiver system based on gold wire bonding and printed circuit board (PCB) technology is designed. The system has carried out the planar packaging integrated design and processing test for the W-band single-channel T/R bare chip, the grounded coplanar waveguide (CPWG) - substrate integrated waveguide (SIW) transition structure, and the SIW slot antenna. The test results show that the receiver has the same receiving gain in each channel in the 92GHz-94GHz frequency band, and the maximum gain can reach 14dB. At the same time, the beam simulation and test results are also highly consistent, and the half-power beam width is 26°.
Design of a 0.3-2GHz Hybrid Continuous Mode Ultra-wideband Power Amplifier

This paper presents an ultra-wideband power amplifier with an output power over 100W and an ultra-wide bandwidth from 0.3GHz to 2GHz. The hybrid continuous mode is used to achieve broadband impedance match and harmonic wave control. The amplifier has a 2-way configuration using wideband Wilkinson power divider. Each way has a GaN HEMT with 9.6mm gate-width. The whole circuit package size is 27.4mm*30.8mm. The circuit is designed and simulated with ADS and the test results show that the average Pout exceeds 100W the average PAE is about 47% in the bandwidth.
ECG Authentication Reinforced by Pretrained Convolutional Neural Networks

Deep learning is increasingly being used in biometrics. The use of an electrocardiogram (ECG) for person authentication is not an exception. However, the performance of deep learning networks is entirely dependent on the datasets and training. We propose a combination of pre-trained Convolutional Neural Networks (CNN) such as Alexnet and Googlenet for person identification using ECG as a biometric in this research. Before training the networks, the one-dimensional ECG data are filtered and transformed into a standard-size two-dimensional format. An evaluation of performance is made against the two pretrained networks, Aalexnet and Googlenet, before and after their fusion. The accuracy results show that the suggested fusion approach beats the individual performances of Alexnet and Googlenet for the same dataset, with an average accuracy of 96.6%.
Four-Port Dual-Frequency Dual-Polarized Antenna With Reduced Size and Improved Isolation

In this paper, a four-port dual-frequency dual-polarized antenna with reduced size and improved isolation is proposed. Initially, the proposed antenna achieves dual-frequency and dual-polarized performance on a symmetrical patch by exciting four ports in the appropriate positions. Then, the four short-circuited T-shape metal baffles are placed symmetrically to improve the isolation among these four ports. Simultaneously, the isolation of the low-frequency band can be further improved by symmetrically loading two sets of short-circuited strip branches. In addition, a notch is introduced by loading a pair of annular branches at the bottom of the substrate to decrease the mutual interference between high and low frequency working bands. In the end, the antenna that was suggested has been tested through simulation and proven to be effective. According to the results, the proposed antenna operates in the frequency bands of 3.46-4.24 GHz (20.2%) and 4.42-5 GHz (12.3%). And the isolation among the four ports is greater than 14.9 dB. Meanwhile, it maintains a compact size of 0. 424λ0 × 0. 379λ0 × 0. 148λ0 (λ0 is the free-space wavelength at the center frequency of the lower-band).
Assisting Crowdsourced Data Collection by using Data Mining Algorithms

Crowdsourcing has emerged as an alternative solution to utilize the collective knowledge and expertise of citizen science communities, and it is also considered a much cheaper solution. However, data quality becomes one of the challenges. Lack of motivation and unfamiliarity with labeled data will lead to poor quality of crowdsourced data. This research proposes data mining models to enhance user experience while filling questions in a crowdsourced-based application by building a recommendation system model. The system is implemented on Alboom, an application that provides early warning information regarding Harmful Algae Blooms. The model is built based on the Apriori and DBSCAN algorithms to provide recommendations on responses to the same questions. As a result, users do not need to submit a similar response repeatedly. The Silhouette Coefficient was used to evaluate the clustering model. In addition, we also evaluate the DBSCAN algorithm model by generating and predicting new random locations. The model successfully predicts which cluster it belongs to and outputs ”−1” if the new points are outside the radius of every existing cluster. The recommendation system performance evaluation with live users will be our future research.
A Dual-Broadband Circularly Polarized Shared-Aperture Array Antenna for C-/K-Band Application

This paper presents a dual broadband circularly polarized (CP) array antenna with shared aperture for C- and K-band application. First, the CP operation for C-band element is generated by four square ring patches which are fed by an unequal cross slot. Second, a CP magnetoelectric (ME) dipole antenna with one pair of horizontal patches connected is used as the main radiator for K-band. The coupling slot etched on the ground is modified as cross-shaped to enhance the CP bandwidth. Last, a C-band 2×2 array and K-band 4×4 array are interlaced together at a shared aperture. The simulated results show the proposed antenna exhibits a -10-dB impedance bandwidth of 16.5% and 3-dB AR bandwidth of 16.2% at C-band, while has a -10-dB impedance bandwidth of 52.4% and 3-dB AR bandwidth of 33.7% at K-band.
Distributional Effects of Monetary Policy in Emerging Market Economies

We develop a two-sector, heterogeneous-agent model with incomplete financial markets to study the distributional effects and aggregate welfare implications of alternative monetary policy rules in emerging market economies. Relative to inflation targeting, exchange rate management benefits households in the tradable goods sector but in the long run these households are worse off due to higher consumption volatility. A fixed exchange rate reduces the welfare of these households and aggregate welfare when the economy is hit by positive shocks to nontradable goods productivity or foreign interest rates. Fiscal policy can more efficiently achieve similar short-run distributional objectives as exchange rate management.
Damage Detection Algorithm Based on Faster-RCNN

The repair and maintenance of road damage is of great significance for ensuring traffic safety, reducing vehicle maintenance costs, alleviating traffic congestion, promoting economic development, and improving residents’quality of life. Therefore, timely detection of road damage is crucial. Currently, traditional manual recognition is one of the common detection methods; however, it suffers from drawbacks such as low detection efficiency and significant time consumption. With the advancement of deep learning, object detection algorithms are gradually being applied in the field of road damage detection. This article proposes a road damage detection method based on the improved Faster-RCNN algorithm, which achieves automatic defect detection of road damage hazard. Firstly, in the feature extraction stage, a feature pyramid is incorporated to combine high and low-level information of the feature map, enhancing the model’s ability to express features. Secondly, the convolution used for feature extraction is replaced with deformable convolution. Since the shape of defects is irregular and diverse, deformable convolution can better extract features. Finally, the last layer operation of the FPN(Feature Pyramid Network) is replaced with cavity convolution, which has a larger receptive field compared to standard convolution. Additionally, this article utilizes several related precision fine-tuning techniques to train deep learning models. The results show that on the public dataset RDD2022, the proposed road damage detection method based on the improved Faster-RCNN algorithm improves the Mean Average Precision(mAP) value of performance indicators by 0.91 compared to the traditional Faster-RCNN object detection algorithm. The improved Faster-RCNN model in this article accurately detects the location of road damage on RDD datasets, highlighting the potential of this method to expand the application prospects of road damage detection[1].
MUBT: An Effective Deep Learning Method for Nidus Segmentation of Brain Tumor MRI Scans

Magnetic Resonance imaging (MRI) serves as an effective tool for detecting intracranial tumors. Traditionally, when brain tumor scans undergo manual clinical nidus segmentation, the daunting task costs a clinician excessive amount of time and energy, often resulting in poor consistency. This paper exploits and modifies a deep learning model to automate this process. Our novel Modified U-Net for Brain Tumor Segmentation (MUBT) is an effective convolutional neural network that segments nidus areas from brain tumor MRI images. We apply our model to the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 2020, which is well established for this type of benchmarking. Our measures of performance include val-loss, dice-loss, and dice score. From our validation experiments, MUBT achieves a dice score of 93.1%, far surpassing existing models. Moreover, we demonstrate the optimizing techniques and training process with significantly more detailed information than other relevant research, making it possible to further optimize the model upon reproduction.
Triple-Resonance Antenna with Stable Radiation Pattern Based on Characteristic Mode Analysis

A three mode resonance antenna with stable radiation pattern is designed using characteristic mode analysis. The antenna is composed of two layers of substrate, the upper and lower metal surfaces of upper substrate are used to form substrate integrated waveguide cavity-backed slot antenna (SIW-CBSA) which constitutes microstrip patch. And the mode TM01 of patch and modes TE110, TE120 of SIW-CBSA have basically consistent radiation pattern performance based on characteristic mode analysis. The coplanar waveguide is applied to excite modes TE110 and TE120, which connects microstrip feedline with double-layer substrate thus excite mode TM01 of patch simultaneously. Simulation results show that impedance bandwidth better than -10dB is from 8.32 to 8.77GHz, and the radiation pattern is basically consistent in whole bandwidth. Prototype size of the scheme is 1. 13λ0 × 0. 79λ0 × 0. 03λ0 (wavelength in free space).
An Omnidirectional EMAT Employing Spiral Coil and Annular Magnets for Enhanced Generation of A0 Mode Lamb Wave

As a typical noncontact ultrasonic testing method, electromagnetic ultrasonic guided wave testing has received extensive attention in recent years. However, the low energy efficiency and signal-to-noise ratio is especially obvious in omnidirectional electromagnetic acoustic transducers. To solve this problem for noncontact guided wave positioning detction, this article proposes an omnidirectional electromagnetic acoustic transducer (EMAT) for enhanced generation of A0 mode ultrasonic Lamb waves with pure mode in non-ferromagnetic plates. The proposed EMAT consists of a spiral coil and a group of annular magnets. The annular magnets build a special kind of periodic permanent magnets (PPM). The radius difference between the adjacent annular magnets is set to half the wavelength of the desire wave mode to maximize the excitation efficiency. In 2D axisymmetric FE simulations, the distribution of the bias magnetic fields and eddy currents was analyzed, and the mode of the generated guided waves was determined. In experiments, the omnidirectional EMATs were fabricated to verify the performance of generating omnidirectional A0 mode Lamb waves. The results prove the performance of the omnidirectional EMATs for effective generation of pure antisymmetric mode Lamb waves at specific frequency.
Design and Development of Offline Navigation System (ONS) Using the Smart Phone Sensors

An Offline Navigation system to navigate the user without any connectivity is implemented by using smartphone sensors. The implementation of the system is carried out by extracting the relevant data from the smartphone sensors and discovering some meaningful insights from it. The offline navigation system is completely independent of GPS and any network connectivity such as Wi-Fi, Cellular Network, Bluetooth, etc. The system uses the Accelerometer sensor, Gyroscope sensor, and Magnetic sensor which are already present in the smartphone. The sensor fusion concept is used to combine the data from all the sensors and fuse it to give the device's orientation. The accelerometer sensor provides the acceleration of the device in all three directions. Similarly, the gyroscope sensor provides the angular velocity of the device, and the magnetometer sensor provides the magnetic fields. The data from all three sensors are combined to calculate the orientation angle such as pitch, roll, yaw, and azimuth of the device. The filtering algorithm used is known as Kalman Filter. It fuses the data from different sensors which results in less error on the combined data. The angles then use the position estimation algorithm and step detection algorithm, to find the next step and the coordinates of the device to track the user. The algorithm makes use of the previously determined coordinates of the device.
Authoring Cricothyroidotomy Teaching with Augmented and Virtual Reality

Cricothyroidotomy is a medical procedure whereby a tube is placed through an incision in the midline of the cricothyroid membrane. The procedure allows airway communication with the external environment for oxygenation and ventilation. Cricothyroidotomy became a life-saving procedure during the COVID-19 pandemic as the number of intubations increased to ensure breathing. Mastering cricothyroidotomy requires extensive practice, which sparked the need for rethinking training to meet its high demand. Cricothyroidotomy training relies on high-end mechatronics simulators, the use of swine tracheas, and, most recently, the use of 3D printing and computer-based simulation. While 3D printing has become widely available, there are still barriers to accessing the technology, so immersive technologies such as augmented and virtual reality have been adopted as complementary tools to help develop cognitive and motor skills. This paper presents work in progress into the development and preliminary usability, cognitive load, and presence assessments of an Augmented Reality (AR), Virtual Reality (VR), and Desktop cricothyroidotomy scenario with physicians. The VR scenario led the usability and presence scores, followed by the AR and Desktop, which ranked last. Regarding the cognitive load, the AR scenario had the lowest, followed by the VR and the Desktop scenario, which had the highest.
A Broadband Filter Phase Shifter Based on The Load Line

In this paper, a broadband filter phase shifter based on the load line is proposed and simulated. The problem that the filter phase shifter cannot achieve broadband and low phase shift error at the same time is solved. The filter phase shifter adopts a dual-channel structure, including the main path and the reference path, which are composed of loaded line resonators. The phase shift value is the phase difference between the main path and the reference path. In this paper, the resonant characteristics of the load line resonator are studied by odd-even mode analysis and transfer matrix analysis. By adjusting the position of the resonant point, the main path and the reference path form a band-pass network with similar performance. By adjusting the impedance ratio between the load line and the main transmission line, the phase-shift value will fluctuate, and its linear transformation law will be changed to realize the broadband phase-shift function. The simulation results show that this structure can achieve the filtering bandwidth of 1.5GHz to 2.5GHz, the return loss is better than 16dB, and the phase shift of 90 ° ± 2 ° can be achieved.
A Ship Trajectory Analysis and Prediction System

The analysis and prediction of ship trajectory based on shipping big data is a research hotspot in the shipping industry, which helps the port and shipping industry to explore and discover new business opportunities, create new industrial value, and enhance the competitiveness of shipping enterprises. In this paper, a ship trajectory analysis and prediction system is proposed, and the key technologies including standard trajectory association technology, historical pattern mining technology, anomaly detection technology, trajectory analysis and prediction technology, and target entity relationship prediction technology based on deep learning, are analyzed.
Research on Substation Diagram Automatic Generation Based on Artificial Intelligence Algorithms

With the development of smart grids, intelligent image recognition technology has been applied to the automatic generation of power grid station wiring diagrams. The paper proposes a set of automatic mapping methods for power system station wiring diagrams based on artificial intelligence algorithms to improve the accuracy of station diagram recognition. Firstly, this algorithm introduces YOLOv5 to detect electrical elements and solve the problem of identifying electrical elements in the power grid station wiring diagram; Secondly, the algorithm in the paper introduces PP-OCR to detect electrical text, improving the accuracy of electrical text recognition in power grid station diagrams; Finally, based on the obtained bus information, identify the connection relationship to improve the accuracy of wiring identification. Experimental results have shown that the proposed method achieves good performance, with an accuracy rate of 95% for electrical element recognition and 93% for text recognition, meeting the requirements for automatic generation of factory station diagrams.
A Q-Enhanced Notch Filter Using Active Capacitance for UWB Interferer-Rejected LNA

This letter proposes a Q-enhanced double-inductor notch filter for the rejection of the IEEE 802.11a interference in the UWB system. The active capacitance is introduced into the double-inductor notch filter to improve the Q factor. Due to the active capacitance's negative resistance effect, the notch filter's passive losses are compensated and therefore a narrow trap bandwidth is achieved. To verify the feasibility of the proposed notch filter, it is integrated into a 2.5-15 GHz low-noise amplifier (LNA) and designed using the 0.15 μm-GaAs pseudomorphic high electron mobility transistor (pHEMT) process. The LNA is biased at -0.6/3 V and the filter employs a 3 V self-bias structure to simplify the DC supply circuit. The simulated results show that the interferer rejected LNA achieves a maximum gain of 23.4 dB, a minimum noise figure of 1.51 dB, and a maximum attenuation of 45 dB at 5.85 GHz. The LNA has a power consumption of 230 mW with a size of 1.93 × 0.72 mm2 including testing pads.