Miracle-dz/UniFAR-FTUs huggingface dataset:
On-the-fly Extrinsic Calibration of Non-Overlapping in-Vehicle Cameras based on Visual SLAM under 90-degree Backing-up Parking

Calibration of relative poses between cameras is a challenging problem, known as extrinsic calibration, for nonoverlapping cameras that do not share the field of view. We propose a method for calibrating non-overlapping in-vehicle cameras placed at front, back, left and right positions by using visual SLAM(vSLAM). Our proposal is to calibrate the cameras during the motion of 90-degree backing-up parking on the fly, without using any dedicated calibration equipment. With this motion, the adjacent cameras are able to have the close field of view at different moments. The relative poses can be computed if the maps computed with vSLAM on each camera are merged by using the common structures. Therefore, we propose an efficient calibration framework with this feature. The proposed method is divided into three steps: map reconstruction with vSLAM on each camera, map merging for all the cameras, and extrinsic calibration. Especially, we propose to separately utilize the frames for vSLAM and the ones for the calibration so that the accuracy of vSLAM can be maximized for the calibration. In the evaluation, the calibration was performed in a practical environment to investigate the performance in comparison with the ground truth acquired by using a calibration equipment.
Evaluation of Deep Reinforcement Learning Algorithms for Autonomous Driving

Once considered futuristic, machine learning is already integrated into our everyday life and will shape many areas of our daily life in the future: This success is mainly due to the progress in machine learning and the increase in computing power. While machine learning is used to solve partial problems in autonomous driving, the support of high-resolution maps severely limits the use of autonomous vehicles in unknown areas. At the same time, the structuring of the overall problem into modular subsystems for perception, self-localization, planning, and control limits the performance of the systems. A particularly promising alternative is end-to-end learning, which optimizes the system as a whole. In this work, we investigate the application of an end-to-end learning method for autonomous driving, employing reinforcement learning. For this purpose, a system is developed which allows the examination of different reinforcement learning approaches in a simulated environment. The system receives simulated images of the front camera as input and provides the control values for steering angle, accelerator, and brake pedal position as direct output. The desired behavior is learned automatically through interaction with the environment. The reward function is currently optimized for following a lane at the highest possible speed. Using specially modeled environments with different levels of detail, multiple deep reinforcement learning approaches are compared. Among other aspects, the extent to which a transferability of trained models to unknown environments is possible is examined. Our investigations show that Soft Actor-Critic is the best choice of the tested algorithms concerning learning speed and the ability to generalize to unseen environments.
Deep Transfer Learning Enable End-to-End Steering Angles Prediction for Self-driving Car

Autonomous driving has developed rapidly over the last few years. Predicting the steering angle for self-driving car according to different road conditions is very important. There are some endeavors for this topic, including lane detection, object detection on roads, 3-D reconstruction etc., but in our work we focus on a vision based model that directly maps raw input images to steering angles using deep networks and this model don't depend on specifying the features to learn. In this paper, we propose an end-to-end steering angle prediction model based on deep transfer learning and it can accurately predicts steering angles based on input image sequences which are from onboard camera. This prediction model combine two deep learning models including the convolution neural network (CNN) and the long short-term memory (LSTM). The CNN model we use is VGG16 which is based on transfer learning techniques, and pre-trained on Imagenet with good performance. This network is used to extract spatial features of the input image sequences. And the LSTM network is used to capture the temporal information of the provided images. The model we proposed fully considers spatial-temporal information, and fit the nonlinear relationship well between the input images and the steering angles. In order to validate the proposed model, the experimental study is conducted using the real-world dataset which is provided by Udacity. Experimental results show that the proposed model in this paper can efficiently predict the steering angles and clone humans' driving behaviors, and our model has a better performance, higher accuracy, and less training time.
Highway Toll Forecasting Model

Since the complexity of artificial design features in model training, the toll data features cannot be used reasonably and efficiently. Toll prediction of one single station based on toll historical data would ignore the interaction between stations in the highway network. Therefore, this paper constructs a highway toll forecast model, named DBN-MSVR combining deep belief network and multi-task learning for multi-station toll forecasting. This model uses the optimized deep belief network to perform feature learning on toll data, and combines multi-task learning and support vector regression on the top layer of the deep belief network to predict tolls. Experiments show that the DBN-MSVR toll prediction model has higher prediction accuracy than traditional methods.
Detection of Transferrin Receptor CD71 on a Shear Horizontal Surface Acoustic Wave Biosensor

A semi-empirical model was applied to estimate the frequency shift of a shear-horizontal surface-acoustic-wave (SH-SAW) biosensor for detecting a disease-related biomarker antigen transferrin receptor (CD71) in the sample. In the simulation to investigate its sensitivity, a shift of the SH-SAW resonant frequency occurred by applying an incremental surface mass density change on the surface. The semi-empirical model was proposed and developed by using the experimental and numerical results to relate the concentration of the biomarker to the frequency shift. Results indicated that the thickness of the SiO2 guiding layer affects the sensitivity of SH-SAW sensing, and the dependence is non-monotonically. The SH-SAW sensor was used for specific detection of biotin at a varied concentration. With the concentration of the targeted antigen in the range 0.4 ∼4.2 μg/mL, a typical exponential relation was found between the quantitative target and the frequency shift. Measurement results showed that the mass-loading effect of the antibody-antigen has a reliable response with a sensitivity of 0.94 kHz/(μg/mL). Effects of the sample flow rate on the antigen- antibody interaction and thus the frequency shift of the SH-SAW sensor were also evaluated. It is demonstrated that the proposed model provides a useful approach to analyze effectively the frequency shift dependence on the concentration and the flow rates of sensed molecules in a flow-type SH-SAW sensor.
Detection of Unauthorized Photo-Voltaic System Connection for Low Voltage Grids

Vast and unauthorized penetration of the photovoltaic systems in distribution systems imposed inadequate monitoring challenge to system operators. Although the installation of required monitoring infrastructure does seem as a solution, due to the high installation cost, it is not considered a feasible one, at least for the near future. As an alternative approach, a two-stage method composed of an ANN-based mapping function, and a regression model is presented to detect unauthorized PV connections to the system. A gradient boosting machine is implemented to build the regression model and the corresponding confidence interval. The Monte Carlo simulations are utilized to generate synthetic training data-sets for both models. Finally, the performance of the approach is assessed with real data.
Feature Extraction with Bidirectional Encoder Representations from Transformers in Hyperspectral Images

The spectral information alone is not sufficient to achieve successful results in the classification of hyperspectral images. In studies on increasing the success of classification results, spatial information is also exploited. In this study, BERT and ALBERT models, which were recently introduced to the literature in natural language processing, have been used as feature extraction models in spatial information acquisition. These models, which try to understand the context by learning the relationship between tokens in the field of natural language processing, are used to learn the relationship between the pixel itself and its neighbors and rearrange the spectral signature. Then, spectral signatures that have been rearranged by using spatial information are trained in classifiers and models are created. The results of the BERT and ALBERT models are presented comparatively. According to the results obtained, although the size of the model is small, ALBERT produced better results in twelve of twenty tests.
Nitrogen/sulfur co-doped non-noble metal material as an efficient electrocatalyst for the oxygen reduction reaction in alkaline media

This work demonstrates the feasibility of nitrogen/sulfur co-doped non-noble metal materials (Fe–N/C–TsOH) as platinum-free catalysts for the oxygen reduction reaction (ORR) in alkaline media. Electrochemical techniques such as cyclic voltammetry (CV), rotating disk electrodes (RDEs) and rotating ring-disk electrodes (RRDEs) are employed with the Koutecky–Levich theory to investigate the ORR kinetic constants and the reaction mechanism. It is found that the catalysts doped with TsOH (p-toluenesulfonic acid) show significantly improved ORR activity relative to a TsOH-free catalyst. The overall electron transfer numbers for the catalyzed ORR are determined to be 3.899 and 3.098, respectively, for the catalysts with and without TsOH-doping. Catalysts heat treated at 600 °C exhibit relatively higher activity. In addition, the catalyst doped with TsOH (Fe–N/C–TsOH-600) not only exhibits exceptional stability in 0.1 mol L−1 KOH solution but also has higher methanol tolerance compared to commercial Pt/C catalyst in 0.1 mol L−1 KOH. To some extent, increasing the Fe–N/C–TsOH-600 loading on the electrode favors a faster reduction of H2O2 to intermediate to H2O. X-ray photoelectron spectroscopy analysis indicates that pyrrolic N groups are the most active sites, and that sulfur species are structurally bound to carbon in the forms of C–S(n)–C and oxidized –SO(n)– bonds, an additional beneficial factor for the ORR.
Gaussian Process Model Predictive Control Based Cooperative Motion Planning Structure for UAGVS

Gaussian process (GP) models based model predictive control (MPC) structure for cooperative motion planning of Unmanned Aerial and Ground Vehicle System (UAGVS) is proposed in this article. The GP models are firstly trained to describe the dynamics of UAVs and UGVs with their uncertainties. Stochastic optimization problems are designed for motion planning and controlling based on the GP models' probabilistic predictions. And for the necessary interactions among agents, the predicted motion information is also communicated with each other, so that they can obtain others' future planned behaviors. Simulation results show that the proposed method exhibit promising effects of motion planning for UAVGS.
A Novel Hybrid-HHOPSO Algorithm Based Optimal Compensators of Four-Layer Cascaded Control for a New Structurally Modified AC Microgrid

This paper proposes a new modified architecture for AC microgrid consisting of multiple grid-supporting master units (MUs) and multiple grid-feeding slave units (SUs). In this study, a coordinated four-layer hierarchal control (HC) approach is applied to the proposed structure for allowing the MUs, SUs and loads to be easily integrated as a microgrid and operated in both grid-integrated and standalone operation mode. The proposed structure of the AC microgrid enhances the system redundancy to prevent the single point of failure of MU and has more stability, efficiency, flexibility and reliability than the conventional structures. Furthermore, optimal design guidelines, based on a new hybrid Harries hawks and particle swarm optimization algorithm (H-HHOPSO) with the cooperation of different types of proposed multi-objective functions, are presented to fulfill the study objectives. The optimization constraints/objectives are employed for optimal parameters selection of HC controllers to improve the power quality, enhance dynamic and steady-state performance and guarantee a seamless transition between operation modes. To accomplish this work, the newly modified structure is modeled, constructed in MATLAB/SIMULINK and tested under the variations of generations and loads. This structure is also examined when the fault occurs at any one of the MUs and during the connecting and disconnecting of utility grid. This testing is to verify its flexibility and reliability, and confirm the effectiveness and robustness of the proposed optimal controllers. Additionally, the experimental work is carried out using the hardware-in-the-loop real-time emulation to prove the optimal controllers’ feasibility. Finally, the experimental and simulation results are compared.
Video Frame Prediction via Deep Learning

This paper provides new results over our previous work presented in ICIP 2019 on the performance of learned frame prediction architectures and associated training methods. More speciﬁcally, we show that using an end-to-end residual connection in the fully convolutional neural network (FCNN) provides improved performance. In order to provide comparative results, we trained a residual FCNN, a convolutional RNN (CRNN), and a convolutional long-short term memory (CLSTM) network for next frame prediction using the mean square loss. We performed both stateless and stateful training for recurrent networks. Experimental results show that the residual FCNN architecture performs the best in terms of peak signal to noise ratio (PSNR) at the expense of higher training and test (inference) computational complexity. The CRNN can be stably and efﬁciently trained using the stateful truncated backpropagation through time procedure, and requires an order of magnitude less inference runtime to achieve an acceptable performance in near real-time.
Reference Aware Model Predictive Control for Autonomous Vehicles11This work was partially supported by the Wallenberg Artificial Intelligence, Autonomous Systems, and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

This paper presents a path following controller for autonomous vehicles, making use of the linear time-varying model predictive control (LTV-MPC) framework. The controller takes into consideration control input rates and accelerations, not only to account for limitations in the steering dynamics, but also to provide a safe and comfortable ride while minimizing wear and tear of the vehicle components. Furthermore, it introduces a method to handle model references generated by motion planning algorithms that can consider different vehicle models from the controller. The proposed controller is verified by simulations and through experiments in a Scania construction truck, and is shown to have better performance than the state-of-the-art smooth and accurate MPC.
EFFECT OF THERMOPHORESIS ON TRANSIENT FREE CONVECTION BOUNDARY LAYER FLOW OF A WALTERS-B FLUID

The present paper considers the free convective, unsteady, laminar convective heat and mass transfer in a viscoelastic fluid along an impulsively started vertical plate in presence of thermophoresis. The Walters-B viscoelastic liquid model is employed to simulate medical creams and other rheological liquids encountered in biotechnology and chemical engineering. This rheological model introduces supplementary terms into the momentum conservation equation. The dimensionless unsteady, coupled, and non-linear partial differential conservation equations for the boundary layer regime are solved by an efficient, accurate and unconditionally stable finite difference scheme of the Crank-Nicolson type. The velocity, temperature, and concentration fields have been studied for the effect of thermophoresis, Prandtl number, viscoelasticity parameter, Schmidt number, and buoyancy ratio parameter. The local skin-friction, Nusselt number and Sherwood number are also presented and analyzed graphically. It is observed that, when the viscoelasticity parameter increases, the velocity increases close to the plate surface. An increase in Schmidt number is observed to significantly decrease both velocity and concentration.
Development of an integrated innovation management methodology based on Moroccan Agro-food firms

This article aims at answering the question: How to innovate? Thus, we adopted the process mapping method to develop an integrated innovation management method. A synthesis of the existing innovation process models was drawn up while respecting the process mapping standards. An integrated innovation process mapping model was developed. This model was validated in two agro-food companies in Morocco. This model could be enhanced by future studies in order to integrate the processes installed by the company to affect the business environment.
Comparison for Acceptance of Mobile Payment Technology with Two Methods

This study was conducted to compare the UTAUT2 model and SEM technique against the UTAUT2 model and DEMATEL technique for mobile payment technology acceptance per their respective advantages and disadvantages. There are indeed notable differences between the two methods as revealed by the model constructed to analyze the influence of various factors. The SEM method can also be used for large-scale surveys of mobile payment users.
Security Analysis of Secure Multi-Party Computation using Virtual Parties for Computation on Encrypted Data along with Performance

There are many computations and surveys which involve confidential data from many parties or organizations. As the concerned data is property of the organization or the party, preservation and security of this data is of prime importance for such type of computations. Although the computation requires data from all the parties, but none of the associated parties would want to reveal their data to the other parties. The Virtual Party Protocol is a highly scalable protocol to perform computation on encrypted data using forced encryption and virtual cryptography. The data is encrypted in a manner that it does not affect the result of the computation. It uses modifier tokens which are distributed among virtual parties, and finally used in the computation. The computation function uses the acquired data and modifier tokens to compute right result from the encrypted data. Thus without revealing the data, right result can be computed and privacy of the parties is maintained. In this paper we have shown the security analysis of VPP protocol. We have given a probabilistic security analysis of hacking the protocol. We have obtained the results using MATLAB and shown how zero hacking security can be achieved.
Digital Passport for Unmanned Vehicles

—In this work, a system for issuing digital passports for unmanned mobile robots was developed. The system uses Ethereum Blockchain and peer-to-peer technologies to send messages. As a result of registration of an unmanned robot (for example, an unmanned aerial vehicle or an autonomous marine vehicle), a person receives a copy of the passport and a unique and immutable identiﬁer protected by cryptography methods. The system is completely decentralized, so it is not affected by the disadvantages of a centralized system such as a single point of failure.
Classification of Omani’s Dates Varieties Using Artificial Intelligence Techniques

—Date fruits are considered as one of the most popular fruits in the Middle East. Oman is one of the countries that have many varieties of dates and the most well-known are Khalas, Fardh and Khunaizi. Nowadays, the process of classifying different varieties of dates in date’s industries is done manually by human workers. The manual process affects the quality of dates, which however is subjective, time consuming, laborious and expensive. The objective of this paper is to classify automatically six popular date varieties, namely, Khalas, Khunaizi, Fardh, Qash, Naghal, and Maan from their images based on color, shape, size, and texture features. Three different artificial intelligence techniques have been used for automatic classification and qualitative comparison; (i) Artificial Neural Network (ANN), (ii) Support Vector Machine (SVM), and (iii) K-Nearest Neighbor (KNN). The Dates’ varieties were obtained from AL-Dhahira Governorate. In total, 600 date samples (100 dates/class) were selected. These samples were imaged individually, one date per image. Nineteen features were extracted from each image and used in classification models. Experimental results show that the ANN algorithm outperforms the SVM and KNN based algorithms in all criteria used. The achieved results of ANN using 15 features and seven tan-sigmoid neurons in the hidden layer were 99.2% in classification accuracy, 99.12% in average recall and 99.25% in average precision.
STRUCTURE FROM MOTION PHOTOGRAMMETRY ENHANCES PALEONTOLOGICAL RESOURCE DOCUMENTATION, RESEARCH, PRESERVATION AND EDUCATION EFFORTS FOR NATIONAL PARK SERVICE AREAS

Structure-from-motion (SfM) photogrammetry is an increasingly common component of paleontological research and fossil resources management. The three-dimensional (3D) data and the derived products allow for novel and useful avenues to engage and problem-solve with park resource managers and stakeholders. The National Park Service (NPS) is developing a robust SfM program to support park units with resource documentation and monitoring efforts, training for staff, and building capacity for 3D data processing. We report on three case studies as examples where SfM techniques have been applied: 1) monitoring of paleontological localities; 2) documenting in situ fossil discoveries; and 3) digitization of fossil specimens in museum collections. The capacity within the NPS for photogrammetry to support paleontological research is also enhancing collaborative efforts resulting in new fossil discoveries in NPS areas. The case study for Curecanti National Recreation Area (CURE) is the design and testing of monitoring methods for paleontological resources subject to accelerated erosion due to reservoir management. The fossil locations at CURE are inundated by reservoir pool level changes, with loss of paleontological resources to erosion, burial and permanent submersion as the beach is reworked by wave energy due to fluctuating water level elevations. At Glen Canyon National Recreation Area a recently discovered track block with numerous vertebrate trace fossils was documented. The fossil tracks on the block appear on a bedding surface of a large vertically oriented fallen slab of Wingate Sandstone. Photogrammetry has enabled detailed 3D mapping and surface topology analysis of the slab, revealing in more detail the abundant theropod tracks and trackways. The digital nature of photogrammetric data also opens new avenues for engaging with scientists and the public. These data can be grouped into digital collections for showcasing a park’s paleontological resources and are easily adapted for producing 3D replicas. Use of such models allows outreach to current and new park audiences and others who benefit from interaction with tactile elements. Rapid prototyping technology (e.g., 3D printing) employs newer materials and comes with lower costs when compared to traditional fossil replication methods. New applications for 3D data and SfM photogrammetry methods will continue to expand within paleontological research. The NPS continues to take positive strides to be at the forefront of developing SfM methods for paleontology.