Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary Study of Deep Learning based Speech Recognition Technique for Nursing Shift Handover Context

Nursing shift handover involves conversations between nurses during duty handover. Handover contents have been recorded manually, and it is desirable to have an automatic speech recognition solution that transcribes audio speeches into texts. Even though there are excellent commercial Speech recognition solutions available, they are mainly designed for general contexts, which may not be most efﬁcient for special contexts. This pilot study is to investigate speech recognition for the special contexts of nursing handover. To be more precise, we are to transcribe audios of 500 Chinese sentences conversed during nursing handover at the Taipei Hospital. A deep learning based network, Deep Speech 2 [1] working in conjunction with Beam Search Decoder with Language Model [2], is adopted for the intended automatic speech transcription. Special adaptation is considered for the audios is in Chinese: the recognition unit in DS2 is Chinese characters instead of letters as in English; the N-gram language model is also on Chinese characters rather than words as in English. Performance of the trained model has been conducted on two experiments: One is to transcribe audios made by people other than the people made the training audios. The other is to transcribe audios whose contents deviated from the recorded 500 sentences. For the former, the character error rate (CER) is 2.33%, as compared to that of 7.94% by Google Speech API [3]. For the latter, when the testing sentences are deviated from the training ones by 21% in word counts, the CER by the trained system is 13%. The results show promising foundation for more complicate sentences patterns.
Invalidating Analysis Knowledge for Code Virtualization Protection Through Partition Diversity

To protect programs from unauthorized analysis, virtualize the code based on Virtual Machine (VM) technologies is emerging as a feasible method for accomplishing code obfuscation. However, in some State-of-the-art VM-based protection approaches, the set of virtual instructions and bytecode interpreters are fixed across the whole programs. This means an experienced attacker could extract the mapping information between virtual instructions and native code from programs, and use this knowledge to uncover the mapping relationships in similar protecting applications. To address this problem, we present CoDiver (Code Virtualization Protection with Diversity), a novel VM-based code obfuscation system in this paper. The main idea of our approach is to obfuscate the mapping between the opcodes of bytecode instructions and their semantics. To achieve this goal, we first turn every protected code region into multiple parts by partition proceeding, randomize the mapping of opcodes and their semantics of each part. By this way, we could translate the bytecode instruction into different native code in different sections of the obfuscated code. This method could increase the diversity of program behavior significantly. As a result, it will be useless to learn the mapping relationship between bytecode and native code of some other programs, then migrate it into a new program. We build a prototype of CoDiver and tested it on a set of real-world applications. Experimental results show that as compared with two state-of-the-art VM-based code obfuscation approaches, our approach is more effective and could provide stronger protection with comparable runtime overhead and code size.
Electric Vehicle Travel Planning with Lazy Evaluation of Recharging Times

The basic premise of existing route planning algorithms is that complete information required for computing optimum routes is available at the time of the search. With the increasing complexity of transport systems, such an assumption is no longer valid as transport service providers are not willing to disclose full information about certain aspects of their services for business sensitivity reasons. Therefore, new approaches capable of computing optimal routes while minimizing the required amount of information about the services are required. In this paper, we investigate the incomplete information route planning problem in the context of planning routes with charging for electric vehicles. We have formalized the problem as a resource-constrained shortest path problem with time-dependent edge costs; the costs are only partially known and their values can be obtained by querying external data sources. We propose an optimum algorithm solving this problem utilizing two interchanging phases built on a multi-objective A* algorithm. We evaluate the properties of the algorithm on a comprehensive suite of test scenarios based on real-world data and derive insights into the properties of this emerging route planning problem.
A Novel Modulation Method of LLC Resonant Converter with Linear Model and High Efficiency

Although frequency modulation (FM) has been widely used in the LLC resonant converter, the challenges remain in the light-load condition. The nonlinear model makes the control design difficult and the efficiency suffers due to high operating frequency in light-load condition. In this paper, a novel fixed-frequency modulation method is proposed for the LLC resonant converter to address these challenges. The proposed modulation method combines any two of the three chosen operating modes in a control period to regulate the output power. A linear mathematical model can be achieved to ease the control design and high efficiency can be maintained from light to heavy load conditions. Experimental results are presented to verify the performance of the proposed method with a 12V/8A LLC resonant converter prototype, achieving over 20% efficiency improvement for light load condition without any extra or upgraded hardware.
Performance Characteristics of Sensing-based SPS of PC5-based C-V2X Mode 4 in Crash Warning Application under Congestion

This paper presents the performance characteristics (notably, the adverse effects on communication quality) of Sensing-based Semi-Persistent Scheduling (Sensing-based SPS) of PC5-based Cellular-V2X mode 4 in crash warning application under congested environments. Sensing-based SPS is a new Cellular-V2X standard. This scheduling protocol has two interference estimation mechanisms to select a transmission slot considered as low interference from a history of slot usages. As the mode 4's channel becomes severely congested, frequent collision errors become to cause missing slot information and, the estimated interference becomes to differ from the actual one. Hence, the two mechanisms become to select a slot with higher interference. Our simulation results revealed that Sensing-based SPS was of 9% lower communication quality than that of a method with no estimation mechanisms in a congested situation. Our behavior analysis and computer simulations pointed out new disadvantages of Sensing-based SPS and presented a necessity to improve the algorithm of Sensing-based SPS.
Traffic density estimation method from small satellite imagery: Towards frequent remote sensing of car traffic

Traffic flow monitoring is essential to take effective measures on traffic jams. Wide-ranging monitoring is still a challenging task, because the conventional observation methods by roadside detectors only observe the location where the costly sensors are installed. In this study, we propose a wide-ranging traffic flow monitoring method utilizing remote sensing by small satellites, which is a new approach of remote sensing. Compared with the conventional remote sensing satellites, small satellites have a significant advantage: they can conduct frequent observation (e.g., few hours) by using dozens of satellites simultaneously. On the other hand, a severe disadvantage is that their sensor is inferior due to the size limitation, and thus existing vehicle detection methods are not applicable to small satellite imagery. In order to solve this issue, we develop a convolutional neural network-based image recognition algorithm that estimate traffic density. The proposed method is applied to satellite imagery with 93 cm resolution, which corresponding to small satellite imagery. The accuracy of the proposed method was RMSE 11 veh/km, sufficient to recognize traffic situations.
Enhancing WiFi RSS fingerprint positioning accuracy: lobe-forming in radiation pattern enabled by an air-gap

We propose a physical arrangement consisting of a 2.4 GHz WiFi radio, and high attenuation surfaces with an air-gap in-between, to delimit a fingerprinting region. We show by simulations, and field measurements, that such arrangement allows to form a characteristic lobe in the radiation pattern after the gap, which is characterized by an abrupt change in the Received Signal Strengths (RSSs) along the direction parallel to the surfaces. From measured RSS samples, we construct, to aid our analysis, an equation that approximates the radiation pattern as a continuous RSS radio map, using symbolic regression. Finally, we observe the positioning performance of this arrangement, performing RSS fingerprint pattern matching with a Neural Network. Results are compared to the positioning accuracy that would be obtained by using an omnidirectional radiation pattern antenna. We conclude that, the abrupt change in RSSs obtained with this arrangement, translates into a better positioning accuracy, close and along a direction parallel to the surfaces.
Evaluation of Riemannian ASR on cEEGrid data: an artifact correction method for BCIs

Artifact Subspace Reconstruction (ASR) is an online artifact correction method for multichannel data recordings. We recently proposed an adaptation of the ASR method using Riemannian geometry (rASR) and found that rASR outperforms ASR in terms of artifact correction capabilities and speed. In our previous investigation we focused on event-related potentials and eye-blink artifacts, in this analysis we extend our recent work by evaluating the performance of rASR in preserving a well-studied oscillation (alpha) while correcting muscle artifacts captured in the EEG data recorded with sparse electrode coverage. We use EEG data recorded with behind-the-ear electrodes (cEEGrid) and find that rASR preserves the power of the alpha frequency band while correcting the muscle artifacts in the EEG data successfully.
A Multisensor Data Fusion Approach for Simultaneous Localization and Mapping*

Simultaneous localization and mapping (SLAM) has been an emerging research topic in the fields of robotics, autonomous driving, and unmanned aerial vehicles over the past thirty years. State of the art SLAM research is often inaccessible for undergraduate student researchers due to expensive hardware and difficult software setup. We present a cost-friendly vehicle research platform and a robust implementation of SLAM. Our SLAM algorithm fuses visual stereo image and 2D light detection and ranging (Lidar) data and uses loop closure for accurate odometry estimation. Our algorithm is benchmarked against other popular SLAM algorithms using the publicly available KITTI dataset and shown to be very accurate. For educational purposes, we publicly share the models and code presented in this work*.
A Novel Control Strategy to Share Power among Dispatchable Battery Sources and Achieve SOC Balancing without a Droop Control in a DC MicroGrid

In a DC microgrid, several batteries with their converters are controlled to share the load power among themselves. Battery power management strategies are incorporated to charge and discharge the batteries. The life of the storage system can be maintained properly by avoiding overcharging, limiting current and deep discharging of batteries. Along with the battery management to improve reliability and power sharing, the SOC balancing of all the batteries also needs to be achieved. A novel control strategy is proposed in this paper to share the power among the batteries according to their power ratings and SOC. In the proposed scheme, a common time reference is generated according to the discharging or charging power demand. The proposed strategy does not require sensing of the output current of the converters. This reduces the overall complexity and cost of the system as compared to the droop control system. Low speed communication is adequate to convey the reference time for the controllers of the other batteries. The batteries are charged or discharged at the same time due to the SOC balancing. The simulation and hardware results validate the working of the proposed control scheme.
Stability Boundary Acquisition of Weak Grid-Tied Single-Stage Inverter

In several large-scale PV stations, it is reported that as solar radiation increases, whenever the output power reaches to a critical value, the grid-tied inverters will lose the stability. To explain this phenomenon, this paper builds the small-signal impedance model of the single-stage inverter, which considers the effect of DC-link voltage loop and phase-locked loop. Meanwhile, the impedance model is verified through frequency sweep. Based on the model, the output impedance of the inverter and the stability of the weak grid-tied system are analyzed. For the first time, the steady state operating point of the inverter is discussed in the model. It is found that high proportional gain of the DC-link voltage loop, large active current and capacitive reactive current would reduce system stability margin. The stability boundary for a specific prototype is further acquired through numerical method. Finally, experimental tests verify the theoretical analyses.
On Computing with Diagonally Structured Matrices

We present a storage scheme for storing matrices by diagonals and algorithms for performing matrix-matrix and matrix-vector multiplication by diagonals. Matrix elements are accessed with stride-1 and involve no indirect referencing. Access to the transposed matrix requires no additional effort. The proposed storage scheme handles dense matrices and matrices with special structure e.g., banded, triangular, symmetric in a uniform manner. Test results from preliminary numerical experiments with an OpenMP implementation of our method are encouraging.
Smart Longitudinal Velocity Control of Autonomous Vehicles in Interactions With Distracted Human-Driven Vehicles

With the development of commercialized autonomous vehicles (AVs), the interaction between AVs and human-driven vehicles has become increasingly important. Nevertheless, on the one hand, complex driver behaviors like distraction are hard to detect by AVs, which may lead to traffic accidents because of the late alert to the following vehicles. On the other hand, advanced techniques such as the real-time image or video processing and vehicle-to-vehicle (V2V) communications make it possible to let AVs receive monitoring signals from nearby vehicles, predict the latent risks, and make smart control to avoid the vehicles driven by distracted drivers. Hence, in this paper, we envisage a collaborative framework integrating human driver distraction monitoring, V2V communications, and AV velocity control. Then, we design the smart velocity control of AVs by taking into consideration the distraction behaviors of the drivers in the human-driven vehicles, and by formulating it as a feasible optimization problem based on model predictive control (MPC) strategies. Furthermore, we analyze the safety benefits that the collaborative framework could help improve on the condition of preserving traffic performance. Finally, we implement the contrast tests of real-time evaluation on driver distraction monitoring based on convolutional neural networks (CNNs) and perform simulations of smart velocity control strategies of the AV at avoiding the distracted driver and reducing rear-end collisions. Through the analysis and the simulations, we show our framework could increase the safety regions, reduce the rear-end collisions, and thus increase the safety of the whole transportation networks.
Chronic Kidney Disease Stage Classification Using Renal Artery Doppler-Derived Parameters

In renal medicine, Estimated Glomerular Filtration Rate (eGFR) based method is a standard for the diagnosis of chronic kidney disease. However, this method is invasive, uncomfortable, costly, and could be dangerous because it requires to draw blood from the artery vessels. Researchers have developed several non-invasive Doppler-derived measures based chronic kidney disease (CKD) stage diagnosing or prognosing approaches; however, there is no adequate automatic renal artery Doppler-derived CKD stage classification method in the literature. Thus, we propose a non-invasive, safer, faster, and low cost, SVM-based CKD stage classification method from a sonogram of the renal artery blood flow. The proposed method extracts kurtosis and curvature parameters of the probability distribution that generated from renal artery blood flow waveform. Kurtosis and curvatures are employed to measure the tailedness and curvedness of the probability distribution. We collected a total of 528 sonograms from 110 (49 males) CKD patients during 2010-2013. The experimental results revealed a statistically significant correlation between the parameters and CKD progress stages. Post-voting results revealed the best f1score of 0.956 for Positive (stages 1-5) CKD stages.
MMSE-based Optimized Transfer Strategy for Transfer Prediction of Parking Data

To predict the spaces available of parking lots in a short term is a key technique in development of parking guidance systems. Time-series prediction is usually used to resolve this kind of problems, and work well after being trained with sufficient data. But as for city-wide parking guidance system (CPGS), we cannot acquire the data from every parking lots at the beginning, which lead to failure to train the specialized prediction model for them all individually. So, we introduce transfer learning to achieve the expected effect. There are so many papers to discuss how to use transfer learning in many specified tasks, as far as we know. So just applying it to another case is not innovative very much and we don’t want to be satisfied with that as well. In fact, how to transfer more effectively is novel topic and challenge to researchers. In this paper, we try to explain the mathematic meanings behind transfer learning for the first time. Then we build a LSTM-based network for transfer prediction, meanwhile propose an optimized transfer strategy, based on MMSE, to help build the target neural network more effectively and accurately. The following experiments show the strategy is very effective, although simple, it finds the most optimized transfer solution actively and directly. It is the first time we can build and evaluate the transfer learning solution at the design stage, rather than waiting passively till the moment when end results come out.
A personalized Adaptive Cruise Control driving style characterization based on a learning approach

Advanced Assistance Driver Systems (ADAS) adaptation with respect to driver driving style is a research field of major interest, given the additional benefits that could be obtained in terms of comfort and safety perceived by the user. In this work, a personalized Adaptive Cruise Control (ACC) oriented driving style features extraction method is proposed, meant to be used to choose an ACC tuning which better fits the driver on road behaviour. The method exploits an Artificial Neural Network driver model, capable of capturing the driver behaviour in a car following scenario, trained and validated over real data. From a closed-loop model analysis in a simulation environment driving style features are then extracted, looking at the system response to variations of the preceding vehicle speed. Finally, the effectiveness of the extracted features for a non-trivial characterization of the driver behaviour is assessed, comparing the results obtained considering three different drivers.
Triangular and Trapezoidal Modulation for Dual Active Bridge DC-DC Converters with Fast Switching Semiconductors

This paper presents improved equations to operate a dual active bridge converter with the combined triangular and trapezoidal modulation. They increase the accuracy of the predicted switching times by incorporating parasitic effects such as equivalent resistance, dead time, and limited dv/dt for the semiconductors. The derived equations are verified with a 20kW prototype. Measurements done with the prototype show a significant improvement over the idealized, lossless equations. This allows for predictive, open loop (sensor less) control of the DAB. The measurements show an accurate and fast step response of the DAB converter.
A Comparative Study of Estimating Road Surface Condition Using Support Vector Machine and Deep Neural Networ

In this paper, we present a comparative study of two machine learning methods to estimate the road surface condition without directly estimating tire-road friction coefficient. It is well known that using either a vehicle model-based approach or an end-to-end artificial intelligent method is not satisfactory to estimate the tire-road friction coefficient due to sensor noise, parameter uncertainty, and disturbances. To cope with this problem, three feature vectors obtained based on the vehicle dynamics are utilized for support vector machine (SVM) and deep neural network (DNN) with a time-window approach. The effectiveness of the proposed method is verified using experimental data obtained with a test vehicle on proving grounds. From the experimental study, we observed that the road surface condition estimation using DNN is superior to that using SVM.
A Particle Swarm Optimization Approach with Time-Varying Acceleration Coefficient for Hierarchical Clustering of Energy Efficiency Architecture in Wireless Sensor Networks

In this paper, we propose the hierarchical clustering of energy efficiency with particle swarm optimization (HCEE-PSO) algorithm for three-layer cluster architecture, divided into cluster and sub-cluster phases. We propose the total routing cost (TRC) to balance the energy of the sensor node, the sub-cluster head and the cluster head. In the sub-cluster head selection phase, we use the PSO algorithm to search for the suitable sub-cluster head. The cost function of the PSO algorithm ensures that the distance between the sub-cluster head and cluster head is not too close and that the amount of sub-cluster head energy is close to the cluster head energy. The entire wireless sensor networks (WSNs) is divided into three phases of transmission to save battery energy consumption of the cluster members and disperse the energy consumption of the cluster head. Simulation results show that our proposed HCEE-PSO algorithm reduces the energy consumption of WSNs and so extends their lifetime more than do LEACH architecture, the EECS algorithm or the MOECS algorithm.