Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of Modular SPM Machines Considering Stator Modularity

This paper presents the global optimization of modular surface-mounted permanent magnet machines considering the effect of stator modularity on machine electromagnetic performances. In order to optimize the electromagnetic performance, the multi-objective genetic algorithm is adopted and 2D finite element method is used. Variables such as stator yoke height, tooth width, flux gap width, split ratio and rotor yoke thickness have been selected for the optimization. The objectives are to achieve maximized average torque, minimized torque ripple, copper loss and total machine mass. Amongst all the variables, the flux gap width is a key parameter which has significant impact on machine electromagnetic performance, but has not been included in the optimization process in the literature. The results in this paper have shown that considering the flux gap width in the optimization will lead to further improvement in machine electromagnetic performance. In addition, for machines with lower slot number than rotor pole number, the optimization process shows that an appropriate flux gap width is needed for optimal electromagnetic performance. However, for machines with higher slot number than pole number, the flux gap width tends to be zero, leading to non-modular machine having the optimal performance.
An Efficient Approach for Automatic detection of COVID-19 using Transfer Learning from Chest X-Ray Images

The coronavirus disease 2019 (covid 19), which was declared a pandemic by the World Health Organization (WHO) in December, causes significant alveolar damage and progressive respiratory failure, resulting in death. The only laboratory technique available, RT–PCR, has an accuracy of about 73 percent. Medical specialists may benefit from early detection using CXR. Using deep convolutional neural network architecture, we propose a Com-puter Aided Diagnosis (CADx) for the diagnosis of coronavirus disease 2019.The chest x-ray dataset is used for testing and training of neural networks. The CXR images are segmented using a U net model, and the segmented image is then used to train a classification model using the Inception v3 model, which distinguishes covid 19 from pneumococcal records and safe records. Training of inception v3 is done with different resolutions of Chest X-rays (CXR) and for further optimization adam optimizer is used. This model produces high computational efficiency with an accuracy of 0.97 per-cent. Based on the promising results obtained the proposed method can be used for effective diagnosis of covid 19 during this pandemic.
F0 Perturbation Due to Articulatory Movements: Filtering, Characterization and Applications

This paper addresses the issue of local disturbances in the fundamental frequency contour of speech, caused by the articulation of voiced/unvoiced consonant phonemes. Depending on the intended use of the F0 contour, these disturbances are usually eliminated by a filtering, smoothing or stylization procedure. These procedures that seek to preserve only the F0 points perceptually relevant, are generally applied roughly at a global level, which may not completely eliminate micro intonation in some cases or distort macro intonation in others. In this work we propose a local filtering algorithm based on a fine level analysis of the microprosodic morphologies. The performance of the algorithm is validated by a perceptual experiment. Assuming the algorithm allows partial/total disturbance elimination, we perform a statistical description of the perturbation morphologies. Statistics were collected from a corpus of 741 sentences designed to study Argentine Spanish prosody. The corpus was recorded by four professional announcers native speakers from Buenos Aires city. The results show that perturbation morphologies are affected by: consonant phoneme identity; global F0 contour shape; and speaker identity. As an application case, we use the proposed filtering algorithm as a pre-processing stage in our automatic prominent syllable detection system, with a statistically significant improvement in its performance.
Research on Energy Saving Mechanism of NB-IoT Based on eDRX

3GPP's standardization of the Narrowband Internet of Things (NB-IoT) paved the way to support the use of low-power wide area (LPWA) in cellular networks. The design goal of NB-IoT is to expand coverage, low-power and low-cost devices, and large-scale connections. As a new wireless access technology, this paper establishes a Markov model with the working state of the terminal device as the state variable for the extended discontinuous acceptance (eDRX) mechanism adopted by NB-IoT, and calculates the corresponding power consumption and Time delay model. Given that the previous calculations of power consumption and delay did not consider the impact of random access. in this article, We propose a Markov chain with random access process. The numerical results show that the backoff time of each access failure of the terminal device during the process of accessing the network has a greater impact on the power consumption and delay.
Sensorless Saliency Extraction using Quadratic-Regression-based Current Derivative Estimation

Stable field oriented control of induction machines in the proximity of zero electrical frequency relies on the extraction of machine saliencies for rotor flux/positon acquisition. To obtain such saliency information, voltage step excitation methods can be used. They excite the machine with a voltage step caused by the inverter and calculate the resulting phase current derivative, which contains several terms including the superposition of saliency components. Multiple strategies have been used to calculate the current derivative, such as FFT, neural networks, or linear regression. However, they do not take into account the influence of a curvature of the current response. This paper proposes using a least-square quadratic regression to calculate machine saliency information. In this sense, the linear response of the phase current can be accurately isolated from the inherent curvature. It will be proved by experimental measurements that the different saliencies' components are observed in both the second order and also first order term of the quadratic regression function. A performance comparison between linear regression and quadratic regression will be shown in terms of saliency acquisition.
A Novel Modelling Technique for Early Recognition and Classification of Alzheimer’s disease

A new algorithm is proposed in this paper using combined point detection based feature extraction methods like SURF, FAST, BRISK, Harris and Min Eigen for early prediction of various stages of Alzheimer's disease An analysis of the proposed method is done by combining it with Random Forest and Tree Bagger classifiers and the performance parameters are evaluated. From the experimental results, the classification accuracy obtained when random forest classifier is used is 98.42% and the classification accuracy obtained when Tree Bagger classifier is used is 98.17%. The random forest classifier provides more accuracy rate with high sensitivity and specificity when compared to Tree Bagger classifier. The new method is flexible to both classification and regression problems. Also it works well with both categorical and continuous values. From the experimental results, the proposed algorithm is found to be superior to the methods which uses single feature extraction and feature selection methods which are developed for the prediction and classification of Alzheimer’s disease.
AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction for High-Mobility Scenarios

The integration of functions into future communication systems that predict crucial Quality of Service (QoS) parameters is expected to enable many new or enhanced use cases, for example, in vehicular networks and Industry 4.0. Especially with high user mobility, QoS prediction is required in an End-to-End (E2E) fashion to guarantee uninterrupted connectivity and provisioning of real-time applications. In this paper, we present a concise list of mobility use cases, both from automotive and industrial production domains, that benefit from Artificial Intelligence-based QoS prediction. These applications are investigated in the publicly-funded research project AI4Mobile by a representative consortium of industry and academia. Based on a literature review, we identify the main challenges in realizing predictive QoS at high mobility, and we propose research directions to enable the envisioned E2E solutions.
A Task Assignment Scheme for Parked-Vehicle Assisted Edge Computing in IoV

Vehicular edge computing (VEC) has been envisioned as an important application of edge computing in vehicular networks. Parked vehicles with embedded computation resources could be exploited as a supplement for VEC. They cooperate with edge severs to process offloading tasks at the vehicular network edge, leading to a new paradigm called parked-vehicle assisted edge computing (PVEC) in the Internet of Vehicles (IoV). However, recent researchers mostly focus on how to optimize the total cost of requesting vehicle (RV), and rarely pay attention to the optimization of the utility of PVs that provide services, including the reward from RV and the overhead of executing task. In this paper, we study a task assignment problem with computing delay constraints for PVEC in IoV. Specially, extra performance loss caused by offloading subtasks to PVs is taken into the cost function of RV. The optimal task assignment problem is formulated and solved with the Stackelberg game framework and a ternary search-based algorithm to minimize the cost of RV and maximize the utility of PVs. Finally, extensive numerical results are provided to demonstrate that our scheme is more efficient in deducing the total cost of RV and increasing the reward for PVs than other two existing schemes.
Modeling Perception Errors of Automated Vehicles

The assessment of automated driving relies increasingly on scenario-based virtual tests to achieve sufficient test coverage. Scenarios are generally based on ground truth information. Therefore, it is necessary to reproduce the view of the environment of the automated vehicle as it is seen by the autonomous driving function in the simulation. Typically, this view is erroneous compared to the ground truth due to sensor errors. This paper presents a novel approach to cluster, identify and finally to reproduce sensor errors by maneuver-dependent statistical models for the detection of other traffic objects. Errors are classified by their static and dynamic influences and incorporated into individual error models. These are evaluated in a final step based on real driving data.
FB-TCP: A 5G mmWave Friendly TCP for Urban Deployments

5G era opens a new horizon toward communication with new features and capabilities. The new mobile generation comes up with a multi-gigabit per second data rate along with its huge available bandwidths provided by millimeter-wave frequency bands. Dispensing a fully connected world throughout low latency is the supreme aim for 5G networks. However, attaining high-speed, reliable communication is challenging in the new generation, especially in scenarios with numerous obstacles such as the urban one. As the frequency rises, the signal’s penetration power declines, which can mislead TCP in adjusting the sending rate because TCP cannot distinguish that a packet drop in a network is due to congestion or other shortcomings of the network such as blockage or random packet drops. This paper proposes a new TCP based on Fuzzy logic, which strives to prevent performance reduction in urban deployments. The Fuzzy rules are implemented in the congestion avoidance phase of the new protocol to adjust the sending rate intelligently and prevent blockage impacts. The ultimate aim of the protocol is to control the sending rate based on the current situation of the network so it can attain the highest possible performance. Moreover, it tries to reach its goal through low latency and keep the average sending rate as small as possible to restrain the buffer exhaustion. The extensive conducted simulations showed that the newly proposed protocol could attain higher performance compared to BBR, HighSpeed, Cubic, and NewReno in terms of throughput, RTT, and sending rate adjustment in the urban scenario.
Improved Predictive Torque control of PMSM Considering Inductance Parameter Mismatch

As for the tradition predictive torque control (T-PTC) algorithm in PMSM drive system, the time-varying electromagnetic parameters in the actual system, especially the inductance mismatch, cause a serious degradation in the control quality. To suppress the disturbance to the control system caused by inductance mismatch effectively, this paper proposes an improved robust predictive torque control (IR-PTC) algorithm without using the inductance parameter of the motor. In the proposed algorithm, the previous sampling information is selected to estimate the actual parameter, and the prediction model is established based on the time-derivatives (slopes) of torque and flux, which to improve the steady-state control performance under inductance mismatch. The feasibility and effectiveness of the proposed algorithm are verified by simulation and experimental results.
A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its pre-eminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.
A Flexible Implantable Polyimide Catheter Device for Targeted Treatment of Cardiovascular Diseases by Aggregating Magnetic Nanoparticles

This article presents a flexible implantable device fabricated on a polyimide (PI) catheter for targeted treatment of cardiovascular diseases. The copper (Cu) microcoil was fabricated on a PI catheter surface using the rotated UV lithography system and surrounding electroplating technology. The simulation on the magnetic fields generated from the Cu microcoil with 0.2 A current and the driving force analysis on magnetic nanoparticles (MNPs) were conducted. The results demonstrated the precise control of MNPs motions within the magnetic fields generated from the Cu microcoil fabricated on the PI surface. The maximum temperature that increased from the magnetized Cu microcoil was 2 °C at room temperature, suggesting no induced hyperthermia. Moreover, the electrical performance of Cu microcoil was excellently stable in a variety of environments, such as wet, dry, and mechanical fatigue conditions. In vivo tests displayed great biocompatibility of the fabricated device within the carotid artery of a rat model. The directional assembly of MNPs can be achieved by a Cu microcoil fabricated on the surface of a PI catheter. The fabricated device shows notable advantages in promoting the targeted therapeutic strategy of cardiovascular diseases.
A Q-learning based Resource Allocation Algorithm for D2D-Unlicensed communications

The spectrum resources licensed to the mobile operators become increasingly scarce because of the explosive growth of the mobile traffic. Device-to-Device (D2D) communication is thus proposed to be deployed in unlicensed frequency bands, i.e. D2D-Unlicensed (D2D-U). The fixed duty cycle method is generally adopted in the coexistence scenario of D2D and WiFi, which may lead to unfair unlicensed spectrum usage since it cannot adapt the data traffic change. Therefore, a Q-learning (QL) based resource allocation algorithm for D2D-U is proposed in this paper. In the algorithm, the considered cellular base station acts as the agent. The actions of agent are defined as the different combinations of the transmission power and the duty cycle of D2D-U users, and the states of agent are defined as the different combinations of the total throughput, fairness and signal-to-noise ratio (SNR) of cellular users. Based on the proposed QL framework, the agent can always learn the optimal power allocation and duty cycle by interacting with the environment, which can maximize the total throughput and fairness while ensuring the satisfactory SNR of cellular users. The simulation results show that the proposed algorithm can obtain the largest throughput and the best fairness while ensuring the satisfactory SNR of LTE-U users among all traditional algorithms.
Heart Rate and CGM Feature Representation Diabetes Detection From Heart Rate: Learning Joint Features of Heart Rate and Continuous Glucose Monitors Yields Better Representations

Continuous Glucose Monitors (CGMs) provide tremendous value for diabetes detection and management. However, their high cost and regulatory complications have prevented the widespread use of CGMs. On the other hand, Heart Rate (HR) monitors are in wide use and growing in popularity. In this work, we investigate the connection between HR monitor and CGM devices to find a cheaper alternative for measuring glucose dysregulation. We recruited 550 volunteers that included healthy, type 2 diabetic, pre-diabetic, and gestational diabetic cohorts to wear CGM and HR monitors for 10 days. Although the physiological mechanisms underlying glucose regulation and heart rate share many commonalities, we find that commonly used features in time series analysis yield poor correlations between CGM and HR signals. However, by learning a joint representation between CGM and HR using Canonical Correlation Analysis (CCA), we can learn CGM and HR features in CCA space respectively that have a statistically significant correlation. Finding HR features that maximize the CCA objective with CGM enables us to learn about a subject’s glucose regulatory system using an HR monitor alone and not require cumbersome CGMs. Here we consider the detection of diabetes with heart rate monitors as a particular application. We find that CCA representations of heart rate can serve as a proxy for using CGMs in diabetes classification.
Precise Delay Calibration of Wideband Digital Array Based on FPGA

The wideband digital array radar has a considerable advantage for its high-resolution. However, the inconsistency of the time delay in each channel leads to degradation of the beamforming performance. The traditional solutions have several disadvantages, such as precision deficiency and hardware cost. In this paper, we proposed a precise delay calibration method for wideband digital arrays. The time-frequency characteristic of LFM signal is exploited to equivalently achieve the delay calibration by frequency compensation. Parameterized iterative CZT is used to realize the high-precision frequency measurement by Cordic. Then the corresponding frequency compensation can be embedded into the existing digital down-conversion module without adding extra hardware. Finally, simulation and measured results are performed to confirm the superiority of the proposal.
Research on Real-Time 2-D DOA Estimation Algorithm for Uniform Circular Array

The correlation interferometer algorithm is a commonly used algorithm in the estimation of the direction of arrival (DOA) of a uniform circular array (UCA). However, the real-time performance of the conventional algorithm is poor because of its high computational complexity. In this paper, an improved correlation interferometer algorithm is proposed by reconstructing cost function and narrowing search set. The reconstructed cost function has a steep peak near the actual value of DOA, and the value in other points is 0. A novel search set pre-estimation method is used to narrow search set. By calculating the sine function value of the phase difference of each side baseline, a narrower pre-estimation set can be obtained according to the sign of each sine function value. In the pre-estimation set, the new cost function is used for searching, which can greatly reduce the number of searching times. Experiments show that the proposed algorithm and the conventional algorithm have approximately the same estimation accuracy under different signal-to-noise ratios (SNR), and the processing time of the proposed algorithm is only 12.64% of the conventional algorithm.
Cooperative Caching Scheme for Machine-to-Machine Information-Centric IoT Networks

Information-centric networks (ICNs), a foreseen future Internet architecture, are focusing on the content itself rather than its hosting location. With this, the existing Internet protocol (IP)-based host-centric communication model can be transformed into a content-centric communication model with the support of in-network caching, name-based routing, and location-independent content domain names. Recently, ICN is proposed to be a potential Internet architecture for Internet-of-Things (IoT) networks due to minimal retrieval delays and reduced load on the data producer. Content retrieval from IoT nodes plays a prominent role in enhancing the performance of the ICN-IoT networks. State-of-the-art cache retrieval mainly focuses on the noncooperative caching mechanism, which is not efficient for constrained IoT networks. As an improvement, this article proposes a cooperative caching scheme for information-centric-IoT networks to optimize the cache hit with the support of a caching network topology model, a content popularity model, and an exogenous request access model. Simulations results reveal that the proposed cooperative caching scheme improves the cache hit rate, resource utilization, and reduce the network delay in IoT networks.