Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Object Reconstruction Using Sequentially Activated Multiple Depth Cameras

In this study, 4 depth cameras (Kinect v1) were placed around the object in order to obtain the 3-dimensional structure of an object. Computer bandwidth and interference problems encountered during the operation of multiple Kinects were solved with additional USB 2.0 controllers and electronic shutters. Calibration was performed on infrared images to merge the acquired images. Alignment problems were solved by the Iterative Closest Point method and an alignment with 3.41% error rate was obtained. Poisson Surface Reconstruction was implemented in Meshlab software for 3D modeling objects with different geometrical characteristics.
Dickson-Charge-Pump-Based Voltage-to-Time Conversion for Time-Based ADCs in 28-nm CMOS

This article demonstrates a digitally friendly time-based analog-to-digital converter (ADC) exploiting Dickson charge-pump (CP) as part of a voltage-to-time conversion (VTC) implemented in 28-nm CMOS. In the proposed technique, the Dickson CP generates a ramp signal that is compared with the input voltage to generate a pulse of commensurable widths, which is then fed to a D-type flip-flop (DFF)-based time-to-digital converter (TDC). The TDC is composed of CMOS DFFs and digital circuits for digital conversion without any use of analog-intensive circuits, e.g., amplifiers or current sources. Thanks to the robustness of Dickson CP characteristics, the digital outputs of the proposed ADC can be corrected through a low-complexity deterministic digital mapping with improvements of 1.5-bit in peak effective number of bits (ENOB) and 9 dB in peak signal-to-noise-and-distortion ratio (SNDR) verified over three measured sample prototypes.
Defect reactions and the controlling mechanism in the sintering of magnesium aluminate spinel

Pressureless sintering studies have been conducted for excess Al2O3, stoichiometric, and excess MgO compositions of MgAl2O4 at 1500-1625°C. Initial powders of various compositions are prepared by solid-state reaction of MgO and Al2O3. A Brouwer defect equilibrium diagram is constructed that assumes intrinsic defects of the Schottky type. The densification rate derived from sintering kinetics is compared with the compositions investigated when the concentration is converted to the activity of the two oxide components in MgAl2O4. The grain-size exponent of p similar/congruent 3 suggests that densification takes place by a lattice-diffusion mechanism in the solid state. Determined activation enthalpies of 489-505 kJmol-1 are close to those obtained from oxygen self-diffusion derived in previous sintering studies. It is, therefore, proposed that oxygen lattice diffusion through vacancies is the rate-controlling mechanism for the sintering of nonstoichiometric MgAl2O4 compositions. The discrepancy between densification-rate ratios in experimental results and oxygen vacancy concentration in the Brouwer diagram is accounted for by the defect associates formed in the nonstoichiometric compositions.
A State-of-Charge Estimation Method based on Bidirectional LSTM Networks for Lithium-ion Batteries

State-of-charge (SOC) estimation is the key to the BMS of lithium-ion batteries. At present, data-driven methods are more popularly used to estimate the SOC. Among them, the deep learning technology especially recurrent neural networks (RNNs) performed very well. In this paper, we proposed a bidirectional long short-term memory (Bi-LSTM) neural network to perform SOC estimation. The bidirectional LSTM layer is employed to catch the forward and backward temporal dependencies of battery sequential states, and shows better performance in SOC estimation of LiBs. With a public dataset of vehicle driving cycles, we separately train our proposed network at three different constant temperatures (0 °C, 10 °C, and 25 °C), and the evaluated MAEs are 0.498%, 0.411 %, 0.738%. Besides, the Bi-LSTM network trained with overall datasets at three temperatures achieves a mean absolute error (MAE) of 0.616% and maximum absolute error (MaxE) of 3.809%. It shows that the proposed method is robust and accurate in SOC estimation.
SLHP: Short-/Long-term Hybrid Prediction for Road Users

We propose a novel method called Short-/Long-term Hybrid Prediction (SLHP) that predicts short-term and long-term trajectories of surrounding objects while estimating their future influences on an autonomous ego-vehicle for both types of trajectories. Recently, long-term prediction methods based on trajectory sample generation and verification with road environments and/or interactions have been proposed; however, they entail high computational costs because they need to generate and verify multiple trajectory samples for multiple objects. Therefore, they are not appropriate in scenes where short-term prediction is required such as for sudden motions by surrounding objects. In contrast, our SLHP consists of a hybrid prediction based on short-term and long-term trajectory predictors. SLHP provides flexible predictions that are appropriate to scenes caused by objects' motions, road environments, interactions, and so on. In this paper, we apply and evaluate our method to cut-in prediction as a typical prediction task by using the public road dataset that includes various cut-in events. Experimental results show that SLHP achieves a correctness rate of F-measure = 0.86 for cut-in prediction. Additionally, we confirmed the effectiveness of our hybrid prediction method that provides prediction as early as 3.57 s and 4.82 s before the cut-in event for short-term and long-term trajectory predictions, respectively.
Classification of Brain Tumors using Convolutional Neural Network from MR Images

The classification of brain tumors has great importance in medical applications that benefit from computer-assisted diagnosis. Misdiagnosis of brain tumor types, both prevents the patient's response to treatment effectively and reduce the chance of survival. This study proposes a solution for the classification of brain tumors using MR images. The most common brain tumors, glioma, meningioma and pituitary, are detected using convolutional neural networks. The convolutional network is trained and tested on an accessible Figshare dataset containing 3064 MR images using four different optimizers. AUC, sensitivity, specificity and accuracy are used as performance measure. The proposed method is comparable to the literature and classifies brain tumors with an average accuracy of 96.84% and a maximum accuracy of 97.75%.
Control Barrier Function Based Nonlinear Controller for Automatic Carrier Landing

This paper proposes a novel control barrier function(CBF) based output constraints path following control method, which is applied to automatic carrier landing system. The 6 degrees-of-freedom of aircraft model is converted to an affine model. Based on the transformed affine model, backstepping is used as the main control frame. In order to meet the demand of output constraints, a novel controller based on CBF is designed to constrian path following error. Due to automatic carrier landing system's relative-degree is 4, the exponential control barrier function(ECBF) which constrian path following error is adopted. The stability of the designed controller is verified by Lyapunov analysis. Simulation results show that the error requirement for path following in the final approach can be achieved, so the controller we designed has good path following performance.
Formation Tracking Control with Adaptive Neural Networks Estimation

This paper takes the planar formation tracking control problem of second-order agents with model uncertainties into consideration. A novel adaptive neural network (ANN) estimation method based on neighborhood information is designed to estimate model uncertainties. By using the tools of the back-stepping method and ANN, a robust formation tracking control law is given. The law can guarantee this formation tracking errors uniformly bounded under connected topologies. Numerical simulation results verify the validity of the analysis results.
Robust Tracking of Reference Trajectories for Autonomous Driving in Intelligent Roadside Infrastructure

High quality reference data is crucial for the development of autonomous driving applications. Unfortunately, datasets including fixed, reproducible static environments that contain manifold interactions between traffic participants are not widely available. In this paper we propose a camera based trajectory estimation framework that enables the generation of reference trajectory data in stationary roadside infrastructure. We develop a Simple Online Realtime Tracking (SORT) algorithm that tracks objects in image space utilizing the tracking-by-detection paradigm with a deep neural network detector. By projecting tracks to a ground model, we are able to gather cartesian and georeferenced trajectories for manually driven and autonomous vehicles in the field. We evaluate the framework in stationary roadside infrastructure in the Test Area Autonomous Driving Baden-Württemberg, Germany. A vehicle equipped with inertial measurement unit and differential GPS is used to generate ground truth positions that are compared with our framework.
Manifold-Based Nonlocal Second-Order Regularization for Hyperspectral Image Inpainting

The low-dimensional manifold of image patches has been introduced as regularizer term, and shown effective in hyperspectral image inpainting. However, in this article, we find that using only the low-dimensional property of manifold may not always generate smooth results. In terms of this, we first present a higher order term to the low-dimensional manifold model, namely nonlocal second-order regularization (NSR), which provides better approximation to the real data distribution and manifests both the properties of low dimensionality and smoothness. Moreover, in order to balance the known and unknown sets, we further propose a weighted version of NSR, called WNSR. The generalized minimal residual algorithm is adopted to solve this unsymmetrical model, in which a semi-patch is applied for acceleration of the nearest neighbor search. Finally, we conduct intensive numerical experiments on five well-known datasets to verify the superiority of our method. The inpainting results show that our proposed (W)NSR significantly outperforms the state-of-the-art methods with respect to both visual and numerical quality.
On The Manipulation of Wheel Speed Sensors and Their Impact in Autonomous Vehicles

Modern vehicles and in particular automated or autonomous vehicles utilize data gained from wheel speed sensors for various functionalities like determining current speed or electronic stability control. The wheel speed is derived from a multitude of hall sensors. By generating an electromagnetic field with a fitting frequency it is possible to change the wheel speed sensor's interpretation of wheel speed. The braking system and electronic stability control would then base their actions on incorrect data possibly leading to a dangerous situation. In this research, we investigate practical feasibility and impact of such attacks. For this, an audio amplifier and a Helmholtz coil were used to emit electromagnetic fields with varying frequencies. We then analyse the effect of a single manipulated wheel speed sensor on the electronic stability control used in modern vehicles (using a hardware-in-the-loop setup). We finally describe consequences and possible countermeasure for the upcoming autonomous and highly automated vehicles.
Improvement of RTK-GNSS with Low-Cost Sensors Based on Accurate Vehicle Motion Estimation Using GNSS Doppler

This study proposes a method for estimating the positions of vehicles in urban environments with high accuracy. We employ satellite positioning by GNSS for position estimation. Real-time kinematic-global navigation satellite systems (RTK-GNSS) with high precision in satellite positioning can estimate positions with centimeter-scale accuracy. However, in urban areas, the position estimation performance deteriorates owing to multipath errors. Therefore, we propose a method to improve the positioning results by increasing the robustness against multipath using vehicle trajectory. The vehicle trajectory estimates the travel route using the attitude angle and speed. Attitude angles are heading, pitching and slip angle. Trajectories can be generated with 0.5m error performance per 100m. In the proposed method, the trajectory is used as a constraint to solve the multipath of RTK-GNSS. In the evaluation test, the ratio of high-accuracy position estimation improved by up to approximately 30% compared to the conventional method. It is assumed that this method can enhance the development of self-driving cars, AGV control and SLAM technology by eliminating errors and calculating reliability.
A Novel Modified U-shaped 3-D Capsule Network (MUDCap3) for Stroke Lesion Segmentation from Brain MRI

Stroke is the death of brain cells due to lack of blood supply to the cells caused by a blood clot or a blood vessel rupture. It is a dominant cause of disability and death globally. Rehabilitation of post-stroke patients require an efficient treatment plan. Segmentation of stroke lesions from brain images is the first step towards treatment planning. Generally, stroke lesions are manually segmented from brain Magnetic Resonance (MR) images by neuroradiologists. Manual segmentation requires expertise, which is costly, time-consuming, error-prone and laborintensive. Automated stroke lesion segmentation overcomes drawbacks in manual segmentation. U-nets have given state-of-theart results even in small scale medical image datasets. Medical images like MRI are usually noisy even after pre-processing. As a result, the pooling layer used in U-nets, which reduces the number of parameters, tends to loose important information. Capsule networks has been proposed to overcome the drawbacks of pooling layer for classification tasks. Using this information as a motivation, a novel modified U-shaped 3D capsule network (MUDCap3) has been proposed. The 3D nature of the network is to learn temporal information from 3DMRI. The proposed MUDCap3 model has achieved a dice score of 0.67 on the ATLAS dataset. The obtained results outperformed over several state-of-the-art models in the literature.
Body Sensor Network for Remote Monitoring of Patient Cardiac Status

The paper presents a wearable medical electronic system - body sensor network (BSN), intended for real-time monitoring of the physiological parameters of patients. BSN is a compact platform for sensory registration of cardio rhythm, body temperature, etc. The sensors are miniature devices for detection of physiological parameters. The transmitted information is stored in real time in a wearable “hub” communicating wirelessly with computerized equipment. The information obtained can be used for individual monitoring of health status, disease warnings and feedback.
RPL rank attack detection using Deep Learning

Internet of Things (IoT) is a network of interconnected smart devices. It provides a set of services in different domains to improve the quality of human daily life. However, protecting information systems and transmitted data from attacks is critical in IoT especially for devices running over Low Power and Lossy Networks (LLNs) and using RPL routing protocol. In recent times, the enormous network traffic generated in seconds is difficult to analyze with the traditional rule-based approaches. Therefore, Intrusion detection systems (IDS) are seen as the most important tool to ensure this role. The proposed work focus on 1) Creating a misbehaving of RPL protocol by implementing a rank attack in the network and 2) proposing an IDS based on the multi-Layer Perceptron (MLP) neural network with the aim to verify and classify normal and abnormal network traffic. The experiment achieved a high percentage of training dataset accuracy F1 scores and Recall up to (94.57%), (98%) and (100%), respectively.
Influence of Lubrication Technology on Internal Combustion Engine Performance: An Overview

Modern engines depend directly on the effectiveness of lubricating systems in terms of reliability and efficiency. An engine lubrication system does have the functions of reducing the friction between the bearing surfaces of moving parts and of heated emission and cleaning up the engine parts with the elimination of carbon and other foreign matter in order to be effectively operational. The lubrication system for these functions is the forced lubrication system in nearly all modern internal combustion engines. Although lubrication systems for internal combustion engines are subject to many variations, their components and operational methods are virtually identical. The lubrication industry strives to create environmentally friendly lubrication options for internal combustion engines, given the continuous environmental legislation. To tackle this challenge, lubricants that comply with environmental standards while maintaining high lubrication performance are required. This paper reviewed the performance of various types of engine oils under different conditions, showing that a great many available references demonstrate that lubricant technologies have a strong potential to develop modern engines. The operational parameters, such as compression ratio, injection pressure and time effects, were reviewed to perform a better comparison to the vegetable oils. In conclusion, current researches on engine oil efficiency still have some problems and challenges to take into account in terms of mechanisms to improve heat transfer, the practical use of engine oil for vehicles and further progress.
Detecting Medical Rumors on Twitter Using Machine Learning

Twitter is a platform that is used extensively to share medical-related information. However, it is considered a challenge to distinguish between rumors and trust-worthy medical tweets. The purpose of this paper is to develop an automated solution that detects medical rumors on Twitter using machine learning. The system streams real-time Twitter data related to the health field using a list of medical keywords, which is automatically updated by using the Wikipedia API. Assertions are a type of speech-acts that can be characterized as true or false, they are detected through a classifier that has been built for this project. The assertions provided by the classifier are then ranked by their credibility; verified sources are ranked higher than non-verified sources. Non-verified sources_on the other hand, are compared to trusted tweets by context and sentiment, if the tweets are dissimilar then they go through a machine learning classifier that analyzes user-based, content-based and network-based features with an accuracy of 90%. The ranked tweets are then used to monitor the credibility of health-related tweets in real-time. The system has shown to be operational and the algorithms, web-application, and database have shown successful integration with each other to provide an effective user-friendly interface.
A Data-Driven Weighted LP Method for Formant Estimation

In this paper we propose an algorithm for formant estimation using a data-driven approach. The vocal tract is typically modeled as an all-pole filter, whose spectral peaks are taken as the formant estimates. Estimating the filter parameters by minimizing the $\ell_{2}$-norm of the residual signal leads to pitch-locking tendency. This is avoided if the $\ell_{1}$-norm criterion is used, but the solution is computationally burdensome. Minimizing the weighted $\ell_{2}$-norm provides a good compromise between the pitch-locking tendency and computation, but requires the knowledge of the Glottal Closure Instants (GCIs). In our method knowledge of the GCI is not required; instead, we adjust the weighting sequence parameters in a data-driven manner. Our results on both synthetic as well as natural voiced-speech show that our method is superior to LPC; when compared with SWLP, our method gives estimates with lower variance.
Deep Learning based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images

The segmentation of cells is necessary for biologists in the morphological statistics for quantitative and qualitative analysis in Phase-contrast Microscopy (PCM) images. In this paper, we address the cell segmentation problem in PCM images. Deep Neural Networks (DNNs) commonly is initialized with weights from a network pre-trained on a large annotated data set like ImageNet have superior performance than those trained from scratch on a small dataset. Here, we demonstrate how encoder-decoder type architectures such as U-Net and Feature Pyramid Network (FPN) can be improved by an alternative encoder which pre-trained on the ImageNet dataset. In particular, our experimental results confirm that the image descriptors from ResNet-18 are highly effective in accurate prediction of the cell boundary and have higher Intersection over Union (IoU) in comparison to the classical U-Net and require fewer training epochs.
Wind Turbine Gearbox Failure Detection Based on SCADA Data: A Deep Learning-Based Approach

Gearbox failure is one of top-ranked factors leading to the unavailability of wind turbines (WTs). Existing data-driven studies of gearbox failure detection (GFD) focus on improving detection accuracies while reducing false alarms has not received sufficient discussions. In this article, we propose a deep joint variational autoencoder (JVAE)-based monitoring method using wind farm supervisory control and data acquisition (SCADA) data to more effectively detect WT gearbox failures. The JVAE-based monitoring method includes two parts. First, a novel JVAE that takes a chunk of multivariate time series derived from collected SCADA data as inputs is developed. The JVAE utilizes two types of predefined parameters, behavior parameters (BPs) and conditional parameters (CPs), to produce reconstruction errors (REs) of the BP, which reflects the gearbox abnormality. Next, a statistical process control chart is developed to monitor REs and raise alarms. To validate advantages of the proposed method in GFD, five methods, the joint latent variational autoencoder (JLVAE)-, the variational autoencoder (VAE)-, full-dimensional VAE (FDVAE)-, recurrent autoencoder (RAE)-, and one-class support vector machine (OCSVM)-based monitoring methods, are considered as benchmarks. SCADA data with field reports of gearbox failure events collected from four commercial wind farms are utilized to demonstrate the effectiveness of the JVAE-based monitoring method on GFD and its stronger ability to resist false alarms.
Feedforward Approach to Sequential Morphological Analysis in the Tagalog Language

Morphological analysis is a preprocessing task used to normalize and get the grammatical information of a word which may also be referred to as lemmatization. Several NLP tasks use this preprocessing technique to improve their implementations in different languages, however languages like Tagalog, are considered to be morphosyntactically rich for having a diverse number of morphological phenomena. This paper presents a Tagalog morphological analyzer implemented using a Feed Forward Neural Networks model (FFNN). We transformed our data to its character-level representation and used the model to identify its capability of learning the language’s inflections. We compared our MA to previous rule-based Tagalog MA works and saw an improved accuracy of 0.93.