Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor pedestrian location based on multi information fusion

Aiming at the problem that the positioning error will spread over time when only PDR or INS algorithm is used for positioning calculation in complex indoor environment, this paper proposes a positioning method that integrates UWB, PDR and INS. The positioning method is mainly divided into three steps: Firstly, the inertial device data is used to estimate the moving time of the pedestrian; Then, PDR and INS algorithms are used to estimate the initial position of pedestrians; Finally, the particle swarm optimization algorithm is used to fuse the two preliminary positions with the UWB distance data, and finally estimate the final position of the pedestrian. The experimental results show that this method not only effectively solves the problem of drift of positioning results with time in INS and PDR positioning algorithms, but also avoids the possible missing of positioning points in UWB positioning, and significantly improves the stability and positioning accuracy of the positioning system in complex indoor environments.
Security in the Industrial Internet of Drones

The Industrial Internet of Things (IIoT) has played a key role in enabling an efficient and interconnected industry through real-time communication and processing systems, thereby building on the principles of Industry 4.0. Nowadays, industrial systems are in the process of transitioning towards Industry 5.0, where humans will once again take center stage in decision-making, supported by Artificial Intelligence (AI)-based methods. In this context, drones have emerged as a feasible device for enhancing environmental sensing tasks, reducing operational costs, alleviating communication bottlenecks, and cooperating with humans through the use of Virtual Reality (VR) and Augmented Reality (AR) platforms. Therefore, Internet of Drones (IoD) network paradigm has been adopted in the industry, giving rise to the Industrial Internet of Drones (IIoD). Given these aspects, there have been changes in the privacy and security requirements for this network environment, which demands a thorough analysis of these modifications, including the challenges that arise and possible solutions to overcome them. Consequently, this study analyzes the privacy and security issues related to IIoD. Namely, we highlight the elements of IIoD which require protection, the threats and the countermeasures. We also present how these aspects differ from the general IoD environment. Lastly, we discuss the challenges regarding IIoD security and privacy, leveraging the future directions to address Industry 5.0 aspects.
α-Synuclein expression in response to bacterial ligands and metabolites in gut enteroendocrine cells: an in vitro proof of concept study

Abstract Caudo-rostral migration of pathological forms of α-synuclein from the gut to the brain is proposed as an early feature in Parkinson’s disease pathogenesis, but the underlying mechanisms remain unknown. Intestinal epithelial enteroendocrine cells sense and respond to numerous luminal signals, including bacterial factors, and transmit this information to the brain via the enteric nervous system and vagus nerve. There is evidence that gut bacteria composition and their metabolites change in Parkinson’s disease patients, and these alterations can trigger α-synuclein pathology in animal models of the disorder. Here, we investigated the effect of toll-like receptor and free fatty acid receptor agonists on the intracellular level of α-synuclein and its release using mouse secretin tumour cell line 1 enteroendocrine cells. Secretin tumour cell line 1 enteroendocrine cells were treated for 24 or 48 h with toll-like receptor agonists (toll-like receptor 4 selective lipopolysaccharide; toll-like receptor 2 selective Pam3CysSerLys4) and the free fatty acid receptor 2/3 agonists butyrate, propionate and acetate. The effect of selective receptor antagonists on the agonists’ effects after 24 hours was also investigated. The level of α-synuclein protein was measured in cell lysates and cell culture media by western blot and enzyme-linked immunosorbent assay. The level of α-synuclein and tumour necrosis factor messenger RNA was measured by quantitative reverse transcription real-time polymerase chain reaction. Stimulation of secretin tumour cell line 1 enteroendocrine cells for 24 and 48 hours with toll-like receptor and free fatty acid receptor agonists significantly increased the amount of intracellular α-synuclein and the release of α-synuclein from the cells into the culture medium. Both effects were significantly reduced by antagonists selective for each receptor. Toll-like receptor and free fatty acid receptor agonists also significantly increased tumour necrosis factor transcription, and this was effectively inhibited by corresponding antagonists. Elevated intracellular α-synuclein increases the likelihood of aggregation and conversion to toxic forms. Factors derived from bacteria induce α-synuclein accumulation in secretin tumour cell line 1 enteroendocrine cells. Here, we provide support for a mechanism by which exposure of enteroendocrine cells to specific bacterial factors found in Parkinson’s disease gut dysbiosis might facilitate accumulation of α-synuclein pathology in the gut.
Urban Flow Prediction Based on ConvLSTM and Capturing Periodicity

Nowadays, due to the availability of large-scale traffic data and the advancement of deep learning technology, urban flow prediction based on various deep learning methods has become the focus of academic circles and industry. Different from other spatio-temporal forecasting tasks, urban flow prediction has its own unique characteristics, such as the strong periodicity of flow sequences and the non-strictness of periodic patterns. In this paper, we propose a method called PCST based on ConvLSTM and capturing periodicity, which employs a periodically shifted capture module and another attention-based fusion module to make full use of periodicity. We evaluate our PCST in real-world traffic dataset and the results demonstrate the effectiveness of the proposed methods.
Leader-Follower Synchronization for Networked Euler-Lagrange Systems with Input Constraint: A Learning Approach

In this paper, iterative learning control is designed to achieve the leader-follower synchronization objective for the networked Euler-Lagrange systems with input saturation. In order to compensate for the input constraint, we propose an iterative learning control law. Based on the defined composite energy function, all the signals in the closed-loop system are proven to be bounded in each iteration. Along the iteration axis, global full-state synchronization is realized in the sense that all the agents can track the desired trajectory under a connected directed graph. Simulation results are presented to verify the effectiveness of the proposed control schemes.
Improved Hybrid Attention Learning System for Remaining Useful Life Prediction

Remaining useful life (RUL) prediction can improve the availability and efficiency of equipment or system by providing timely maintenance suggestions. In this paper, an improved hybrid attention learning system for RUL prediction is developed. By introducing adaptive weighting, a hybrid attention mechanism with self attention and external attention is designed. The mechanism enhances the attention to samples with similar performance degradation patterns, and improves the feature extraction ability of the learning system for sensor data with strong temporal correlation. Finally, we conducted a series of simulation experiments on the commercial modular aviation propulsion system simulation (C-MAPSS) dataset to verify the effectiveness and superiority of the proposed method in RUL prediction.
A Superquadrics-Based Steering Angle Obstacle Avoidance Method of DMPs

In this paper, we propose a new method for using Dynamic Movement Primitives (DMPs) to enable robots to avoid volumetric obstacles when learning skills from human demonstrations. We can utilize coupling terms in DMPs to modify the planned trajectory of the end-effector. Our method combines superquadrics and a steering angle scheme to construct new coupling terms for obstacle avoidance, without relying on potential fields. This guarantees the convergence of the dynamic system. Additionally, we modify the original center point steering angle formula to improve the ability to avoid obstacles in a wider range of angles. Our proposed method is applicable to most types of superquadrics, including those with flat boundaries, and we validate it through simulations.
Swarm of Quadcopters Position control in ROS Gazebo Simulator

Swarm of quadcopters have many applications in civil and military purposes such as patrol, surveillance, search & rescue, transportation, operation in hazardous environment, support in agriculture, drone delivery, navigation and mapping etc. In a test range scenario, these can be used as aerial tracking sensor platforms and aerial targets. Simulation analysis is required before actual hardware flying to avoid damage to the platform. In this study, simulation of quadrotor is carried out in a 3D simulator and validated with MATLAB/Simulink model. Cascade controller having inner attitude loop and outer position loop is applied for position control of the quadcopter. A novel position controller based on Fuzzy Padé approximation was proposed and its stability was analyzed. Then controllers were implemented on the Quadrotor model and swarm movement of quadcopters was achieved in ROS Gazebo platform.
An effective machine learning approach for predicting ecosystem CO 2 assimilation across space and time

Accurate predictions of environmental controls on ecosystem photosynthesis are essential for understanding the impacts of climate change and extreme events on the carbon cycle and the provisioning of ecosystem services. Using time-series measurements of ecosystem ﬂuxes paired with measurements of meteorological variables from a network of globally distributed sites and remotely sensed vegetation indices, we train a recurrent deep neural network (Long-Short-Term Memory, LSTM), a simple deep neural network (DNN), and a mechanistic, theory-based photosynthesis model with the aim to predict 5 ecosystem gross primary production (GPP). We test these models’ ability to spatially and temporally generalise across a wide range of environmental conditions. Both neural network models outperform the theory-based model considering leave-site-out cross-validation (LSOCV). The LSTM model performs best and achieves a mean R 2 of 0.78 across sites in the LSOCV and an average R 2 of 0.82 across relatively moist temperate and boreal sites. This suggests that recurrent deep neural networks provide a basis for robust data-driven ecosystem photosynthesis modelling in respective biomes. However, limits to global model 10 upscaling are identiﬁed using cross-validation by vegetation types and by continents. In particular, our model performance is weakest at relatively arid sites where unknown vegetation exposure to water limitation limits model reliability.
Asynchronous Distributed Beamforming Optimization Framework for RIS-Assisted Wireless Communications

Reconfigurable intelligent surface (RIS) is a promising solution to enhance the spectral and energy efficiencies of future wireless networks. In this paper, we aim to maximize the sum rate of the RIS-assisted multiuser system with different availabilities of channel state information (CSI) by jointly optimizing the transmit precoding matrix and the RIS reflection matrix. Considering the large-scale nature of the RIS and the potential large number of served users, the conventional centralized optimization framework suffers from huge computational and communication overheads, and does not scale well with the system size. To tackle this issue, we develop an efficient asynchronous alternating direction method of multipliers (AS-ADMM) framework to maximize the sum rate under both perfect and imperfect CSI. Specifically, we firstly reformulate the original optimization problem under perfect CSI into a tractable consensus problem and then apply the proposed AS-ADMM framework to find a locally optimal solution, in which both the central server (C-server) and distributed servers (D-servers) update their variables with semi-closed-form solutions. Whereas for tackling the worst-case sum rate maximization, we firstly convert it into an equivalent max-min-max counterpart and find its semidefinite programming (SDP) based conservative approximation using the well-known sign-definiteness lemma. To drive a low-complexity solution, we develop an alternating optimization (AO) procedure that alternates between the two layers of the equivalent max-min-max problem with semi-closed-form solutions. The global optimality under a simplified scenario and the convergence behavior of the AS-ADMM algorithm are also discussed. Numerical results demonstrate the good scalability and superior performance of our proposed AS-ADMM framework over the existing benchmark schemes.
AGRICULTURE, LIVESTOCK and FISHERIES

The experiment was conducted at the agriculture field laboratory of Noakhali Science and Technology University during the period from October 2021 to January 2022 to observe the effect of various doses of cowdung and nitrogen on the performance of mustard. The experiment comprised of two factors viz: cowdung and nitrogen. One organic fertilizer like different level of cowdung-control 0 t ha -1 (C 0 ), 15 t ha -1 (C 1 ), 30 t ha -1 (C 2 ), 45 t ha -1 (C 3 ) and another inorganic fertilizer nitrogen - control 0 kg ha -1 (N 0 ), 100 kg ha - 1 (N 1 ), 200 kg ha -1 (N 2 ), 300 Kg ha -1 (N 3 ). The experiment was laid out in Randomized Complete Block Design (RBCD) with three replications. Application
Saturated PD Control of Compliant Robot Arms Using Position Measurements Only

As control input saturation may severely affect control performance, we should pay more attention to control input saturation for compliant robots owing to the limited output range of the compliant actuators compared to rigid-link robots. This paper proposes a saturated PD (proportional-derivative) regulation control algorithm for robot arms driven by series elastic actuators (SEAs) with control input saturation using position measurements only. The designed controller, composed of a hyperbolic tangent function-based saturated PD control term and a gravity compensation term guarantees the control constraint satisfaction by using the hyperbolic tangent function and the boundedness of the gravity compensation term. The saturated PD control term guarantees the control stability and the static gravity compensation ensures the stabilization towards the desired position. According to the Krasovskii-LaSalle theorem, we prove asymptotical control stability. We validate the saturated PD control by taking simulations on a compliant manipulator and experiments on a one-joint SEA-driven robot arm.
Statistics of SAR Interferograms with Application to Moving Target Detection

Abstract : This report examines the statistics of the phase and magnitude of SAR interferograms towards the deployment of along-track interferometry (ATI) for slow ground moving target indication (GMTI). Great importance is attached to the practical applicability of the derived theoretical results, particularly with regard to the experimental MTI-mode of Radarsat2. Therefore, the results are evaluated with experimental airborne SAR data acquired during flight trials conducted in Petawawa in 1999. While the known probability density function (pdf) of the interferogram's phase (derived under the assumption of Gaussian backscatter) is shown to agree almost perfectly for a wide variety of backscatter conditions, the corresponding magnitude's pdf tends to deviate strongly in most cases. Motivated by this discrepancy, a novel distribution is derived for the interferogram's magnitude. This pdf, called the polynomial or p-distribution, matches the real data much more accurately, particularly for extremely heterogeneous composite terrain. Based on these statistics, a completely automatic detection scheme with constant false alarm rates for slow moving targets is proposed. All involved parameters required to determine the detection thresholds are estimated from the sample data. It is demonstrated on the basis of a real SAR scene that this detector is capable of detecting slow moving vehicles within severe ground clutter. Finally, practical aspects of the implementation and numerical stability are addressed, since many of the functions involved are comprised of indefinite power series which have to be handled cautiously.
Akkermansia muciniphila regulates food allergy in a diet-dependent manner

Alterations in the gut microbiome, including diet-driven changes, are linked to the rising prevalence of food allergy, yet little is known about how speci�c gut bacteria incite breakdown of oral tolerance. Here, we show that depriving specic-pathogen-free mice of dietary �ber leads to an increase of the mucolytic bacterium Akkermansia muciniphila, which is associated with a surge in the colonic type 2 immune cells and IgE-coated commensals, and microbiota-mediated gut mucosal barrier dysfunction. These changes manifest into exacerbated sensitization to food allergens, ovalbumin and peanut. To demonstrate the causal role of A. muciniphila, we employed a tractable synthetic human gut microbiota in gnotobiotic mice. The presence of A. muciniphila within the microbiota, combined with �ber deprivation, resulted in stronger anti-commensal IgE coating and type 2 immune responses, which worsened symptoms of food allergy. Our study supports a mechanistic link between diet and a mucolytic gut microbe in regulating food allergy.
Cross-Teager Energy Cepstral Coefficients For Dysarthric Severity-Level Classification

Dysarthria is a degenerative motor speech impairment, generally resulting into neurological damage in human body. This impairment causes the speech to be unintelligible to the humans, depending on the patient’s severity-level. Classification of dysarthric severity-level aids as a diagnostic tool to assess advancement of the patient’s condition, which also aids in dysarthric Automatic Speech Recognition (ASR), as the traditional ASR systems performs poorly on dysarthric speech. This study investigates the effect of Cross-Teager Energy Cep-stral Coefficients (CTECC) on standard and statically meaningful UA-Speech corpus, which captures the energy-based signal from microphone array using the deep learning architecture, such as Convolutional Neural Network (CNN) with classification accuracy of 95 . 76% . The key objective of this thesis is to select optimal microphone (channel) with minimum amount of energy, which captures the maximum linguistic information of dysarthric speech. Additionally, the performance of CTECC feature is compared with Short-Time Fourier Trans-form (STFT)-based features, which gave classification accuracy of 91 . 76% on CNN classifier. Further, the Jaccard index, Matthew’s Correlation Coefficient (MCC), F 1 -score, and Hamming loss are used to examine feature discrimination power. Finally, we analyze the latency period for the proposed CTECC feature set for practical deployment of the classification system.
Objective PDF-Shaping Based Stochastic Optimization With Probabilistic Constraints and Its Application

The optimal operation of complicated industrial processes is mainly reflected in the optimization of objective functions, such as product quality and energy consumption. The conventional use of the mean-value of objective function is difficult to describe all the information of the optimized objective when the system is subjected to random uncertainties. In this article, the probability density function (PDF) of the objective function to be optimized is introduced to describe and evaluate the impact of the randomness and uncertainty to the optimization of objective function. A stochastic optimization method based on the objective PDF-shaping with probabilistic constraints is then proposed. For this purpose, firstly the kernel density estimation method is used to estimate the PDF of the objective function. Then, the idea of stochastic distribution control is applied to the objective function PDF-shaping with the probabilistic constraints-based stochastic optimization for industrial processes. The output PDF control and minimum entropy with mean constraint-based optimization methods are proposed. Finally, the optimal decision variables are obtained by optimizing the reconstructed objective function. Both numerical simulation and industrial experiment on a blast furnace ironmaking process have demonstrated the effectiveness and superiority of the proposed method.
The Emergence of Cooperation in Multi-Agent Pursuit-Evasion Game

Pursuit and evasion conflicts are a kind of challenging problems that have important applications in many fields such as aerospace and robotics. In a pursuit-evasion game, players of the same side should cooperate in order to win the game. Existing value based methods such as VDN and QMIX cannot handle environments with continuous action space, thus cannot take kinematic constraints into consideration. Meanwhile, actor-critic methods such as MADDPG are faced with the so-called lazy agent problem, which severely damages cooperation. Moreover, the effect of all above methods is limited when reward is sparse, which makes the learning of cooperation more difficult. In this paper, a curriculum reinforcement learning with reward shaping method is proposed. By designing curriculum of different difficulty and reward function satisfying specific conditions, the proposed method helps training and boosts the emergence of cooperation among agents. Experimental results show that agents trained with the proposed method can learn more effective cooperation and show more efficiency in completing the task.
Solving Magnetohydrodynamics Equations Base on Improved PINNs

Physics Informed Neural Networks (PINNs) have gained significant popularity for solving partial differential equations (PDEs) due to their powerful modeling capabilities. However, their application to magnetohydrodynamics (MHD) equations remains limited, mainly due to the challenges posed by the shocks generated in MHD solutions. In this paper, we propose an enhanced PINNs algorithm that incorporates skip connections through cascading, enabling the transfer of positional information from low to high layers, thereby ensuring better adherence to initial and boundary constraints. Additionally, we introduce a dynamic weight method that effectively balances different components within the loss function, leading to improved model performance. Experimental results on the Dai-Woodward shock tube problem demonstrate that the improved PINNs approach effectively simulates the example and achieves accurate results.
A Rear and Side Object Detection System for Autonomous Driving Bus

To guarantee the safety of autonomous buses when changing lanes, this paper improves and implements a reliable and cost-effective object detection system that suits low-speed automatic driving buses. In this system, the ECX-1400-PEG embedded industrial control computer served as the primary computing unit. The detecting system and control unit communicates via the Redis database. Our System's detection part uses YOLOv5-Rep, an improved network whose kernel size is as large as $31\times 31$. Experimental results show that compared with YOLOv5s, the number of YOLOv5-Rep's parameters was reduced to 6.67M, and the $\text{F}_{1}$ score increased to 0.9236. The system can meet the detection requirements of the rears and sides of low-speed autonomous driving buses when changing lanes.