Miracle-dz/UniFAR-FTUs huggingface dataset:
Work-in-Progress: Smart data reduction in SLAM methods for embedded systems

Visual-inertial simultaneous localization and mapping methods (SLAM) process and store large amounts of data based on image sequences to estimate accurate and robust real-time trajectories. Real-time performances, memory management and low power consumption are critical for embedded SLAM with restrictive hardware resources. We aim at reducing the amount of injected input data in SLAM algorithms and, thereby, the memory footprint while providing improved real-time performances. Two decimation approaches are used, constant filtering and adaptive filtering. The first one decimates input images to reduce frame rate (from 20 to 10, 7, 5 and 2 fps). The latter one uses inertial measurements to reduce the frame rate when no significant motion is detected. Applied to SLAM methods, it produces more accurate trajectories than the constant filtering approach, while further reducing the amount of injected data up to 85%. It also impacts the resource utilization by reducing up to an average of 91% the peak of memory consumption.
Optimized Driving Profiles with Deep Reinforcement Learning for Driver Assistance Systems in Light Rail Vehicles

The use of artificial intelligence (AI) enables the optimization of driving profiles so that energy efficiency and punctuality in the rail system can be increased. We show that with our comprehensive modelling approach of Deep Reinforcement Learning (DRL) agents, we can determine driving profiles that reduce energy demand by an average of 11.1 % and schedule deviation by 65 seconds compared to average drivers from regular passenger operations. For the first time, the trained agents are then examined for their transfer learning capabilities. In scenarios with highly divergent driving time reserves, the DRL agents reduce energy demand by an average of 12.5 % and schedule deviation by 61 seconds. We also show that the trained DRL agents are able to increase energy efficiency and punctuality on unknown route sections. Thus, we demonstrate that the system is suitable for use as a driver assistance system and is able to adapt to the variable operational conditions and possible detours in light rail operations.
Efficient POMDP Behavior Planning for Autonomous Driving in Dense Urban Environments using Multi-Step Occupancy Grid Maps

Driving through dense urban environments is difficult for autonomous vehicles because they must reason about the unknown intentions of a large number of road users while also dealing with a variety of uncertain information, such as sensor noise and inaccurate predictions. The partially observable Markov decision process (POMDP) is a systematic method for planning optimal policies in stochastic environments. However, using POMDP in scenarios with a large number of road users necessitates significant computational effort. In this paper, we propose a scalable online POMDP behavior planner for autonomous driving in dense urban environments. We enable intention-aware POMDP planning while considering uncertainties by using Multi-step Occupancy Grid Maps (MOGM) to represent the current and predicted states of surrounding road users, as well as their uncertain intentions. Furthermore, MOGM is applied to create a more computationally efficient POMDP model by condensing the state space and reducing the number of calculations used for collision checks. We show by numerical experiments that our approach is computationally more efficient. We demonstrate that our approach is able to navigate a dense urban scenario involving a large number of road users.
Efficient Speed Planning in the Path-Time Space for Urban Autonomous Driving

In this article, an algorithmic speed planning method for an autonomous vehicle dealing with moving obstacles is presented. The path-time space is used to represent predicted collision zones with the moving obstacles, and algorithms are proposed to build an appropriate speed profile to pass before and after these obstacles while ensuring the respect of safety distances, by leveraging the path-time representation. These algorithms are implemented in MATLAB/Simulink and simulations with SCANeR Studio are conducted to provide realistic results of the proposed method. The results show that the computed speed profiles are suited to the situation and obtained with acceptable processing time.
Enhancing E-Governance In The Ministry Of Electricity In Iraq Using Artificial Intelligence

Forecasting the electricity demand is essential to op-timize future production. This forecasting can be boosted by uti-lizing machine learning as an enhancement to the e-Governance at the Ministry of Electricity in Iraq. This research aims to investigate the effects of machine learning on e-Governance using electricity consumption and exogenous variables (weather data) from 2020 to 2021, in the city of Mosul in northern Iraq. This study utilizes several neural network and statistical models, including (Stacked and Bi-) LSTM and statistical models (ARIMA and SARIMAX), to forecast future consumption and to increase the effectiveness of Governance in detecting anomalies. The results show that deep learning models have a better result in comparison with statistical models in terms of the root mean square error rate. The best model of LSTM achieves a result of 11.2 RMSE while the statistical models comprised of ARIMA and SARIMAx obtain an RMSE value of 15 and 12.2 respectively.
Fare-evasion detection at ticket gates using posture analysis

Ticket acquisition (either paper or digital form) and control is an integral part of Transportation Systems. Intelligent Transportation Systems (ITS) are no exception to the rule. However, the number of people using trains and subways without paying the associated fee is in augmentation each year. Fare-evasion in train station increases the feeling of insecurity for regular passengers. It also represents a loss of millions of euros every year. Hence there is a need to automatically detect fare-evasion and optimize tickets controls. Given the variety of Automatic Ticket Control Gates (ATCG), fraud characterization requires several features such as location of human body parts and body skeletal pose, interpersonal distance and movement direction. This work proposes a framework to detect fraudsters at ATCG. First it detects and segments the ATCG using Mask-RCNN. This step allows to select the area of interest, to crop it and focus only on the control gates. On this new image, the algorithm studies human postures based on skeletal extraction with OpenPose. Pose classification is used to determine if the posture is fraudulent or not.
Lane Change Trajectory Prediction based on Spatiotemporal Attention Mechanism

The motion intentions and future trajectories of traffic participants have great influences on the decision-making and path planning processes of autonomous vehicles. Lane change behaviors needs to be studied with accurate mathematical representation to realize long-term and reliable intention and trajectory prediction. Traditional studies have applied the specific probability model to perform prediction, but this model is limited by strict assumptions and constraints. With the development of deep learning methods, better prediction results have been realized through the introduction of data-driven concepts. In this study, we focused on the spatiotemporal interaction between the ego and surrounding vehicles by mining hidden trajectory features to effectively predict future lane change intentions and the trajectories of the vehicles surrounding an autonomous vehicle. We constructed spatiotemporal attention mechanism-based long short-term memory (LSTM) networks to perform lane change prediction within the future 5 s using the next generation simulation (NGSIM) dataset. The prediction results were represented in a certain trajectory form and were obtained using the regression fitting method. It was shown that the proposed model can accurately predict lane change behaviors within the future 5 s and provide new ideas for future lane change behavior prediction.
Windows to the Sole: Prototyping Soft Sensors for Wearable Ballistocardiography

Continuous measurement of cardiovascular parameters is important for monitoring cardiovascular health. Ballistocardiography is a noninvasive method of recording cardiovascular events. Here, we present a sensor system prototype for recording of the full-body ballistocardiogram in a wearable. An array of soft bladders in each sole are filled with water and connected to barometric pressure sensors. We demonstrate the use of the prototype to estimate the pulse transit time against continuous blood pressure in a validation experiment (n=14). Participants wore the sensor shoes while standing on a reference weight-scale. Simultaneous recordings were taken of the sole pressure arrays, finger-clip photoplethysmography, and continuous blood pressure via the volume-clamp method. Measurements were taken at rest, during cold-pressor intervention for 60 seconds, and 3 minutes following end of intervention. The waveform of the ballistocardiograms captured by the proposed sensor system corresponded well to the simultaneously collected waveforms from the reference weigh-scale. Pulse-transit time estimated from shoe BCG and PPG show inverse correlation to vasoconstriction-induced blood pressure increase. By demonstrating the use of the system to compute a vascular transit time, we show the potential of ballistocardiographic insoles as a wearable sensor interface for cardiovascular monitoring.
Soft X-ray properties of a high-redshift sample of QSOs observed with ROSAT

In order to study systematically the soft X-ray emission of active galactic nuclei (AGNs) at medium to high redshifts, we have analysed ROSAT PSPC and HRI data of QSOs at 0.26 ≤ z ≤ 3.43 selected from the second deepest ROSAT PSPC survey carried out in 1991-1993 by McHardy et al. Our sample of 22 type 1 QSOs is nearly complete above a flux limit of 1.4 × 10 - 1 4 erg cm - 2 s - 1 in the 0.1-2 keV band. Of these, nine QSOs show long-term (∼2 yr) X-ray variability by a factor of 1.5-3.5. Significant excess absorption above the Galactic column is seen in three QSOs. The soft X-ray photon index of the QSOs ranges from 1.4 to 3.7. Three QSOs have steep soft X-ray spectra (Γ X > 3.0), one of which is a narrow-line QSO -a high-luminosity version of narrow-line Seyfert 1 galaxies. The average photon index ((rx)) is 2.40 ′ 0.09 (with a dispersion of 0.57) in the 0.1-2 keV band. The average QSO spectra in four redshift bins flatten from an average photon index of ∼ 2.53 at 0.25 ≤ z ≤ 1 to ∼ 2 at 2 ≤ z ≤ 3.4. The flattening of the average photon index can be understood in terms of the redshift effect of the mean intrinsic QSO spectrum consisting of two components - a soft X-ray excess and a power-law component. We have also studied optical spectra of 12 of the 22 QSOs.
Truth Inference in Crowdsourcing Under Adversarial Attacks

Crowdsourcing is an information system that provides a cost-effective way of solving computationally challenging problems. However, it is potentially vulnerable to adversarial attacks as the service provider cannot manage workers' behavior. Malicious workers provide unreliable answers to manipulate the system. These attacks affect the truth inference process and thus leads to wrong answers for a targeted set of tasks. Eventually, this reduces the accuracy of aggregated results. Existing works have proposed various types of attacks in crowdsourcing systems and indicate that truth inference is the most affected one. So, we propose methods for defending these attacks for improving the truth inference process. We empirically evaluate the proposed truth inference method on a real and synthetic dataset. The performance of the proposed method is verified, and the results show that it is robust to adversarial attacks with comparable accuracy.
Combining Model-Based Controllers and Generative Adversarial Imitation Learning for Traffic Simulation

An accurate model of human drivers is essential to validate the performance of autonomous vehicles in multiagent and interactive scenarios. Previous works on human driver modeling either use model-based controllers that are not adaptive and need laborious parameter-tuning or learn an end-to-end black box model that has few safety guarantees. We propose a two-stage hybrid driver model, where a high-level neural network generates driver traits that are used as the parameters of the low-level model-based controllers for simulated drivers. We train our model using generative adversarial imitation learning with reward augmentation and parameter sharing from real-world vehicle trajectory data. By combining data-driven and model-based approaches, our method simulates traffic agents with expressive, safe, and human-like behaviors. We demonstrate that our method outperforms state-of-the-art baselines in terms of imitation performance and safety in a multi-agent highway driving scenario.
Design and Synthesis of Ferrite Strip-Line Circulator Based on Enhanced Closed Form Solution and Power Handling Analysis

In this article, Y-junction strip-line circulators are designed using an enhanced closed form solution consisting of unified key equations and a flowchart design, which serves as a direct procedure for designing a ferrite circulator. This design methodology is combined with a 3D EM simulation that allows for the calculation of the saturation magnetization, uniform biasing, and ferrite dimensions needed for a circulator to operate in both modes. In addition, the closed form can be used to calculate the Y-junction parameters and allow the matching network to be calculated and the uniform circulator biasing design ended. Our analysis starts with the ferrite calculation to ensure that it is uniformly biased magnetically, and this is followed by strip-line design, Y-junction matching, and biasing circuit design. Using full-wave electromagnetic (EM), HFSS and magnetostatic (MS), Maxwell3D simulations, several key design parameters such as the saturation magnetization, coupling angle, and various external permanent magnetic biases are investigated. To validate the proposed concept, two strip-line circulators are designed and fabricated for operation in wide-bandwidth and narrow-bandwidth applications. Good agreement is found between the simulated and experimental responses for the S-parameters of the circulators. We also present a semi-analytical analysis of the concept of breakdown, which enables calculation of the peak power and total thermal rise in the strip-line structure. The average power handling capability of a strip-line circulator is studied for the first time. The proposed closed form expressions can also be implemented in various ferrite circulators using different guiding technologies for millimeter-wave applications.
A Model Predictive Trajectory Planning Framework for Autonomous Ground Vehicles on Structured and Unstructured Roads

Trajectory planning is one of the fundamental and core components of autonomous ground vehicles (AGVs). A sequence of movement states should be planned satisfying both kinematics constraints and dynamics constraints. This makes the problem computationally demanding as the planning space dimensionally increases. Moreover, there are massive structured and unstructured roads in urban scenarios. However, it is difficult to address the structured and unstructured trajectory planning problem simultaneously. In this paper, a model predictive trajectory planning framework is proposed to address the above challenges. Firstly, a nonlinear vehicle system model considering the kinematics and dynamics constraints is established. Secondly, a generator based on the deep neural network is introduced to output intermediate trajectory points towards the goal state. Then, a discriminator is trained utilizing the expert data to select the best solution for the extension. Finally, the model predictive controller (MPC) is leveraged to further optimize the trajectory to satisfy the kinematics and dynamics constraints. Simulation results demonstrate that the proposed algorithm can implement feasible, reasonable and human-like trajectory planning on both structured and unstructured roads. Moreover, the trajectory generated by the proposed planner satisfies the kinematics and dynamics constraints of vehicles.
Application Layer Specification & Modeling for a Modular Wireless Sensor Network (MWSN) in Avionics domain

The paper provides specification and modeling of the application protocol for a Modular Wireless Sensor Network (MWSN) in aircraft. We propose a generic protocol able to identify the data produced by the devices belonging to the distributed embedded systems in a modern aircraft and fit them into application packets. The protocol acquires the application requirements to guarantee and verifies that they are maintained when data are transmitted over a wireless communication network, as the one envisioned by Wireless Avionics Intra-Communications (WAIC) initiative.
A Theoretical Classification of Organizational Success Factors for Workplace Learning Analytics

When training is conducted in the workplace and data are stored based on training interactions, the data often remain unexploited. These data need to be analyzed, visualized and interpreted by various stakeholders to make better business decisions, which can be achieved by Workplace Learning Analytics (WLA). However, it is not clear what factors influence the success of WLA projects. There is a noticeable gap in WLA research, specifically for success factors of WLA. This paper reports on a Systematic Literature Review that was conducted to identify key organizational success factors that can be used to plan and measure the success of WLA. The findings show that stakeholder commitment and strategy alignment; data, information and knowledge quality; and skills are three of the most highly cited organizational success factors for WLA. The contribution of the paper is the set of success factors classified according to the constructs of the DeLone & McLean Information Systems (IS) Success Model. This classification assisted with understanding the factors and their relationship with the success of WLA, which is one type of IS. The comprehensive set of nine organizational success factors can be used by companies in the workplace to plan and/or measure the success of WLA.
Integrated control of traffic signal and automated vehicles for mixed traffic: Platoon-based bi-level optimization approach

Connected and autonomous vehicle (CAV) technologies are widely recognized to help improve the operational efficiency of urban road traffic systems and thus mitigate traffic congestion. In this study, we propose a bi-level integrated control method to optimize traffic signals and automated vehicle trajectories simultaneously for mixed traffic with human-driven vehicles (HDVs) and CAVs. Instead of considering individual vehicles in the optimization process, the vehicles are divided into platoons, which are used as the basic units for optimal control, such that the computational cost can be reduced. A mixed integer linear programming (MILP) model is developed to optimize the signal timing scheme and arrival time of the platoon at the stop line with the objective of minimizing the total delay. Based on the optimized arrival time, the trajectory of the first CAV in the moving platoon is optimized to reduce the start-stop phenomenon at the intersection. Numerical experiments are performed and the results demonstrate that the proposed method outperforms the vehicle-actuated control method significantly in terms of the delay and fuel consumption reduction under different traffic conditions. Furthermore, the performance improvement of the proposed model compared to that of the vehicle-actuated control method increases substantially with the increase of the CAV penetration rate.
Generating Emergency Evacuation Route Directions Based on Crowd Simulations with Reinforcement Learning

In an emergency, it is vital to evacuate individuals from the dangerous environments. Emergency evacuation plan-ning ensures that the evacuation is safe and optimal in terms of evacuation time for all of the people in evacuation. To this end, the computer-enabled evacuation simulation systems are used to generate optimal routes for the evacuees. In this paper, a dynamic emergency evacuation route generator has been proposed based on indoor plans of the building and the locations of the evacuees. To generate the optimal routes in real-time, a reinforcement learning algorithm (proximal policy optimization) is presented. Comparative performance results show that the proposed model is successful for evacuating the individuals from the building in different scenarios.
Active Learning Based Audio Tampering Detection

Audio authentication is the primary task in an audio forensics scenario in which audio tampering detection is one of the objectives. In this paper, we offer a fresh approach to audio tampering detection using supervised learning and active learning methods. The present techniques are based on supervised learning, and they require a massive amount of labeled data for classification. There is very little availability of standard data. The paper provides a comparative study of supervised and active learning approaches. The work uses unlabeled dataset for classification which is the primary focus in any active learning method. The proposed work uses less than 1-sec audio files for copy and move tampering. Result gives 92.78% accuracy for supervised learning using stft whereas for active learning it gives 87.38%. Active learning reduces the cost of annotation as we do not have to label all the data.
Connecting CMB anisotropy and cold dark matter phenomenology via reheating

Understanding the properties of dark matter has been proved to be one of the most challenging problems of particle phenomenology. In this paper, we tried to understand the phenomenology of dark matter in light of very well understood properties of Cosmic Microwave Background (CMB) anisotropy. To connect these two, inflation and its subsequent evolution known as reheating phase play the important role. Following previous analysis, we first established one to one correspondence between the CMB power spectrum and the reheating temperature assuming perturbative reheating scenario. Further by incorporating a possible dark matter candidate through radiation annihilation process during reheating and the current value of dark matter abundance, we constrain the dark matter parameter space through the inflationary power spectrum for different inflationary models.
Two- and three-dimensional wide-field weak lensing mass maps from the Hyper Suprime-Cam Subaru Strategic Program S16A data

We present wide-field (167 deg$^2$) weak lensing mass maps from the Hyper Supreme-Cam Subaru Strategic Program (HSC-SSP). We compare these weak lensing based dark matter maps with maps of the distribution of the stellar mass associated with luminous red galaxies. We find a strong correlation between these two maps with a correlation coefficient of $\rho=0.54\pm0.03$ (for a smoothing size of $8'$). This correlation is detected even with a smaller smoothing scale of $2'$ ($\rho=0.34\pm 0.01$). This detection is made uniquely possible because of the high source density of the HSC-SSP weak lensing survey ($\bar{n}\sim 25$ arcmin$^{-2}$). We also present a variety of tests to demonstrate that our maps are not significantly affected by systematic effects. By using the photometric redshift information associated with source galaxies, we reconstruct a three-dimensional mass map. This three-dimensional mass map is also found to correlate with the three-dimensional galaxy mass map. Cross-correlation tests presented in this paper demonstrate that the HSC-SSP weak lensing mass maps are ready for further science analyses.