Miracle-dz/UniFAR-FTUs huggingface dataset:
A generic framework for monitoring pollution plumes in emergencies using UAVs

Monitoring air pollution plumes in emergency situations (industrial accidents, natural disasters, deliberate terrorist releases, etc.) becomes an issue of utmost importance in our society given the dramatic effects that the released pollutants can cause. Considering these situations, the pollution plume is strongly dynamic leading to a fast dispersion of pollutants in the atmosphere. Thus, the need for real-time response is very strong and a solution to get precise mapping of pollution dispersion is required to mitigate risks. However, monitoring and forecasting air quality in real time in such situations remains a highly challenging endeavour. In this paper, we suggest a systemic approach for monitoring dynamic air pollution based on aerial sensing (sensors mounted on UAVs). The proposed framework consists of a cycle with feedback loop which will constantly combine a spatio-temporal forecasting model based on a convolutional long short term memory (ConvLSTM) network with a data assimilation technique to get accurate pollution maps, while adjusting at each time the trajectories of drones following uncertainty forecasts. Our solution was evaluated and validated using a highly dynamic real world data set namely Fusion Field Trial 2007 (FFT07). The proposed strategy, together with the obtained evaluation results, are presented, and carefully analyzed.
ZED-TTE: Zone Embedding and Deep Neural Network based Travel Time Estimation Approach

Travel time estimation is an important dynamic measure in developing mobility on the road navigation services of Intelligent Transportation System (ITS). The key challenge is how to accurately assess the time required for a given path that is extensively varied and affected by a wealthy number of spatial, temporal, and road conditions factors. However, former works have focused on capturing the local trajectory patterns for reducing the model's accuracy. In this paper, we introduce a novel approach called Zone Embedding and Deep Neural Network-based Travel Time Estimation Approach (ZED-TTE). The main originality of the latter is that it summarizes the road network into several meaningful zones for extracting global spatial correlations and temporal dependencies. Thus, it has a better overview of the global picture to efficiently gauge the travel time for the full path, by directly providing a source and a destination without intermediate trajectory points involving some road external conditions. Experiments carried out on two large-scale real-world taxi trips datasets show that the proposed approach sharply outperforms the state-of-the-art models.
A Reduced-Scale Cortical Network with Izhikevich's Neurons on SpiNNaker

Following the initial implementation of a full-scale spiking neural network (SNN) of the cortical microcircuit on NEST, the work was replicated to simulate on SpiNNaker, the Juelich CPU cluster, and the Sussex GPU cluster, in order to compare the performances on the different platforms. All of these researches use the Leaky Integrate and Fire (LIF) model as the basic unit of spiking neurons. In comparison, Izhikevich's spiking neuron models (IZK) can mimic a larger variety of known cortical neuronal dynamics. In spite of this versatility, the IZK neuron is easy to implement and computes fast. In this work, we implement the above-mentioned cortical microcircuit at a reduced-scale and using IZK neurons on SpiNNaker. This is aligned with our ongoing research on a reduced-scale thalamocortical circuit of vision with changing IZK neuron dynamics on SpiNNaker. We validate our SNN with the LIF-based full-scale cortical microcircuit by providing Poisson noise inputs, and measuring objectively the outputs in terms of spike rate, irregularity and synchrony. Our reduced-scale SNN shows similar dynamics to the full-scale SNN and operates within the Asynchronous Irregular regime defined by set bounds on the three quantitative attributes. Next, we test our SNN with inputs from a Dynamic Vision Sensor- (DVS-)based electronic retina (e-retina) that converted a simple periodic environmental input to spike trains. With current parameter settings, the model output identifies the low-frequency, but not the high frequency periodic inputs.
Temporal Pattern Coding in Deep Spiking Neural Networks

Deep Artificial Neural Networks (ANNs) employ a simplified analog neuron model that mimics the rate transfer function of integrate-and-fire neurons. In Spiking Neural Networks (SNNs), the predominant information transmission method is based on rate codes. This code is inefficient from a hardware perspective because the number of transmitted spikes is proportional to the encoded analog value. Alternate codes such as temporal codes that are based on single spikes are difficult to scale up for large networks due to their sensitivity to spike timing noise. Here we present a study of an encoding scheme based on temporal spike patterns. This scheme inherits the efficiency of temporal codes but retains the robustness of rate codes. The pattern code is evaluated on MNIST, CIFAR-10, and ImageNet image classification tasks. We compare the network performance of ANNs, rate-coded SNNs, and temporal-coded SNNs, using the classification error and operation count as performance metrics. We also estimate the power consumption of the digital logic needed for the operations associated with each encoding type, and the impact of the bit precision of the weights and activations. On ImageNet, the temporal pattern code achieves up to $35\times$ reduction in the estimated power consumption compared to the rate-coded SNN, and $42\times$ compared to the ANN. The classification error of the pattern-coded SNN is increased by $< {1\%}$ compared to the ANN, and decreased by 2% compared to the rate-coded SNN.
Control System Design for Electromechanical Brake System Using Novel Clamping Force Model and Estimator

The electromechanical brake (EMB) is a key component of the brake-by-wire (BBW) system generating the clamping force between the brake pad and disk through an electric motor and mechanical transmission. Control of the clamping force has significant impact on the brake performance. In this paper, an EMB clamping force control system is developed that addresses several major challenges in the practical implementation. Firstly, a nonlinear EMB model including the DC motor, planetary gear set, ball screw, and clamping force model is built. In particular, a novel clamping force model is introduced based on a linear transform of two polynomial functions. For the control system design, a clamping force estimation algorithm is proposed that requires only the existing measurements of motor torque, angular speed, and position. The contact point of the brake pad and the disk is also estimated using an internal model controller and a Rauch–Tung–Striebel smoother. The tracking controller is based on the disturbance observer structure, with a PI feedback controller and a zero-phase error tracking feedforward controller. Furthermore, a unified architecture is proposed such that a smooth transition between gap closing and clamping force tracking is realized. Finally, the performance of the entire control system under various conditions is evaluated based on simulation.
Comparative Analysis and Energy-Efficient Write Scheme of Ferroelectric FET-Based Memory Cells

The ferroelectric field-effect transistor (FeFET) is one of the most promising candidates for emerging nonvolatile memory devices owing to its low write energy and high <inline-formula> <tex-math notation="LaTeX">$I_{\mathrm {ON}}/I_{\mathrm {OFF}}$ </tex-math></inline-formula> ratio. For FeFET applications as nonvolatile memory devices, 1FeFET, 1T-1FeFET, 2T-1FeFET, and 3T-1FeFET cells have been proposed. The 1FeFET cell exhibits the highest density but suffers from write disturbance. Although the 1T-1FeFET and 2T-1FeFET cells resolve the write disturbance, they use a write scheme with a negative write voltage (<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {W}}$ </tex-math></inline-formula>), which requires voltage swings of many control signals, leading to a significantly high write energy consumption. The 3T-1FeFET cell uses a write scheme without a negative <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {W}}$ </tex-math></inline-formula>; however, it exhibits the largest area overhead. Although the 1T-1FeFET cell resolves the write disturbance with a small area overhead; however, it exhibits high write energy consumption because of the use of a negative <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {W}}$ </tex-math></inline-formula>. In this paper, to significantly reduce the write energy consumption, we propose a less control signal swing (LCSS) write scheme without using a negative <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {W}}$ </tex-math></inline-formula>. Simulation results indicate that the worst, average, and best cases of the proposed LCSS write scheme can achieve 35%, 66%, and 96% lower write energy consumption, respectively, than the write scheme with a negative <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {W}}$ </tex-math></inline-formula> in the 1T-1FeFET cell. We also identify the available sensing schemes for each FeFET cell in the read operation according to the FeFET threshold voltage distribution.
Novel Robust Augmentation Approach Based on Sensing Features for Data Classification

Learning networks have become extremely powerful tools for data classification. One drawback in many of these applications, however, is that they require a large dataset for efficient performance. Medical imaging, for example, cannot make large enough databases available due to strong data privacy concerns and high data collection costs. Fortunately, data augmentation can provide a solution to improve the classification performance of these systems. This paper proposes a novel method for data augmentation based on the compressive sensing technique. Our approach uses sensing matrices to manipulate data features into different spaces in a compressed form, which are then combined and used as extensions of the original limited dataset. This new feature representation is allowing for a more robust learning network that thereby enables improved data classification. When applying our approach to binary-class and multi-class datasets, results showed that our approach can significantly improve the classifier performance and enhance classification results for both datasets.
CAROS-Q: Climbing Aerial RObot System Adopting Rotor Offset With a Quasi-Decoupling Controller

Unmanned Aerial Vehicles (UAVs) have continually proven their effectiveness in various fields. However, UAVs have not yet matured enough to be used for vertical surface maintenance tasks, such as building inspection or cleaning. To mitigate this issue, this letter proposes a novel design for a coaxial hexarotor (Y6) with a tilting mechanism that can morph midair while in a hover, changing the flight stage from a horizontal to a vertical orientation, and vice versa, thus allowing wall-perching and wall-climbing maneuvers. Furthermore, a rotor offset design has been adopted, in which the tilting rotors are offset from the tilting axle by a certain distance. In this configuration, the required distance between the wall and the platform is reduced, improving stability and increasing the opportunity to enhance power efficiency. However, owing to the rotor offset, the servo motor slows down and the tilting angle is one-way constrained. These conditions can cause a critical issue with the conventional control method. To tackle the problem, a novel control method called quasi-decoupling control is proposed. Our proposed method attains promising performance, overcoming the limitations of a servo motor's slow dynamics and angular constraint.
Power Fluctuation Suppression Method for EV Dynamic Wireless Charging System Based on Integrated Magnetic Coupler

In the electric vehicle dynamic wireless charging (EVDWC) system, when the electric vehicle (EV) drives from one track to the next, it will produce output power fluctuation. This article proposes a multicoupling LCC-compensated method for EVDWC system based on an integrated magnetic coupler to suppress the power fluctuation. The magnetic coupler includes three couplings. The first coupling is between the main coils. The second coupling is between the adjacent compensation inductor coils integrated into main coils. The third coupling is between the primary compensation inductor coils and the secondary compensation inductor coil. The main coils are unipolar and the compensation inductor coils are in a double D structure. The parameter matching rules and optimized design process considering the additional cross-couplings are given. The main advantage of the proposed method is to realize compensation inductance using coupling coils, thereby adding two cross-couplings, and to suppress output power fluctuation based on system normal operation without adding new components. A prototype is designed and implemented to validate the proposed magnetic coupler and parameter design rules. Experimental results show that the output power fluctuation is within ±4% during dynamic charging at a power level of 12 kW, and the maximum efficiency reaches 92.3%.
Incremental 3D Maps Server based on Feature Matching for Multi-robot Systems

This paper describes an incremental 3D maps server based on the point feature matching, which is dedicated to multi-robot systems. The idea behind the presented map server is that it maintains the 3D map by subsequent merging of received partial maps. The solution can be applied to distributed systems where each robot have its own map server instance and maintains a separate map instance. On the other side, in centralized robots systems, it can be one map server instance which integrates partial maps from robots, creates the common map and propagates it to the robots. The process of adding incoming maps consists of few steps. The first one is called a global alignment and is based on the feature extraction, description and matching with the assumption that the maps have an overlapping area. Then, the solution from the first step is used as the initial solution in the next step and it is corrected with a local alignment method. The last step is the data merging to obtain one, globally consistent model in the octree based representation. The presented solution was verified in the experiment with a group of wheeled robots - Turtlebots.
Soft Body Belt-Type Touch Sensor With Impact Resistance: A Study of Dynamic Behavior

We report the development of a novel soft body touch sensor capable of measuring contact force along its length. The sensor can be embedded in soft objects and on curved surfaces for smart furniture, interactive electronics, and general robotics applications. The sensor uses pneumatic transduction principle and achieves both high sensitivity and impact loading tolerance. The development is motivated by the needs of consumer electronics industries for smart furniture and interactive toys. The current work fills a gap of existing soft-bodied touch sensors. The main sensor body is completely polymeric and contains no conductive or semiconducting material elements. It exhibits high pass frequency response to reject common mode contact forces or shape variations. The central question addressed by this paper is dynamic behavior of this sensor under normal and impact touch force. In this study we developed a theoretical model for the sensor output and validated with experimental measurements. We modeled sensor behavior under two operational regimes of different unloading speed. Depending on the rate of change, the sensor could be in force limited relaxation mode or elastic relaxation (free relaxation) mode. Measurements have been performed to capture time constants of occupant behavior in a smart cushion application.
Variable Measurement Interval for Channel-Adaptive Random Access

In this paper, variable measurement interval is proposed for channel-adaptive random access with discontinuous channel measurement. After each channel measurement, the next measurement interval is adaptively determined based on previous channel measurement. Results show that receiver power consumption for channel measurement can be reduced with variable measurement interval under the same average transmission delay constraint compared with fixed measurement interval.
A comparison of Gain Scheduling PID and $\mu$-Synthesis Robust Level Control for a Conical Tank System

Level Control for tanks systems are a great challenge due to the non-linear behaviour in the process. In order to cover that task, this paper proposes a Gain Scheduling PID and a $\mu-$Synthesis Robust controllers comparison. A modeling of a conical tank level system is described for its linearization, and PSO algorithm is implemented to PID controllers. The final comparison details the step response with disturbance over three selected liquid heights and the performance of each controller described as ITAE,IAE and ISE values. Robust controller presents better results, however, its control signals make it difficult to implement in a real valve. Alternatively, Gain Scheduling PID controller presents more realistic results, but its implementations requires design a PID controller for each operating point.
Inverse Modeling of ADI for Prediction of Process Parameters Using PSO-MLP Technique

Austempered ductile iron (ADI) has recently gained popularity over forged and cast steels for manufacturing of components used in heavy machinery, such as, automobiles, railways and farm equipment due to its high strength to weight ratio, casting capabilities and other attractive properties. The current industry practice for producing ADI is through the use of ADI standards. These standards specify grades of ADI with strength and hardness specified with a range for each grade. This grading system leads to precious alloying elements being wasted and also leads to increase in cost. In this study, a multilayer perceptron (MLP)-based forward modeling scheme is proposed to simulate the ADI process and accurately predict Vickers hardness number (VHN) of ADI based on the austempering process parameters and alloying element proportions. Then, using the trained MLP-based forward model and the particle swarm optimization (PSO) algorithm, a novel PSO-MLP-based inverse model has been proposed, which can predict the austempering process parameters and the alloying element proportions for a given VHN. With extensive simulation results, we have shown that MLP-based forward model can predict VHN of ADI with a mean absolute percent error of 0.21, which is much lower than a recently reported paper and the inverse model can accurately predict the ADI process parameters for a given VHN. This model can reduce wastage and cost by producing ADI with a specific VHN rather than within a range.
Wind Speed Forecasting Using the Stationary Wavelet Transform and Quaternion Adaptive-Gradient Methods

Accurate wind speed forecasting is a fundamental requirement for advanced and economically viable large-scale wind power integration. The hybridization of the quaternion-valued neural networks and stationary wavelet transform has not been proposed before. In this paper, we propose a novel wind-speed forecasting model that combines the stationary wavelet transform with quaternion-valued neural networks. The proposed model represents wavelet subbands in quaternion vectors, which avoid separating the naturally correlated subbands. The model consists of three main steps. First, the wind speed signal is decomposed using the stationary wavelet transform into sublevels. Second, a quaternion-valued neural network is used to forecast wind speed components in the stationary wavelet domain. Finally, the inverse stationary wavelet transform is applied to estimate the predicted wind speed. In addition, a softplus quaternion variant of the RMSProp learning algorithm is developed and used to improve the performance and convergence speed of the proposed model. The proposed model is tested on wind speed data collected from different sites in China and the United States, and the results demonstrate that it consistently outperforms similar models. In the meteorological terminal aviation routine (METAR) dataset experiment, the proposed wind speed forecasting model reduces the mean absolute error, and root mean squared error of predicted wind speed values by 26.5% and 33%, respectively, in comparison to several existing approaches.
Analysis and Implementation of 3D Magnetic Field Shaping via a 2D Planar Transmitting Coil Array

Wireless power transfer (WPT) systems operating at several MHz are known to be advantageous for realizing high spatial freedom of power transfer and compact and lightweight designs. The combined use of multiple transmitting (Tx) coils for magnetic field shaping is expected to further increase the spatial freedom. This article studies an extendable planar Tx-coil array architecture. This architecture provides new conveniences for the MHz WPT systems to efficiently charge devices in 3D space. Time-domain and phasor-domain modelings are carried out in turn to analyze the magnetic field shaping effect under the current phase shift modulation of the Tx coils. An overlap design of the Tx-coil layout is also developed to minimize the cross coupling between the coils, which reduces the interference between Tx coils. Finally, the above concepts are experimentally implemented. The results clearly demonstrate the new advantages of the planar Tx-coil array-based magnetic field shaping, in terms of spatial freedom of the power transfer, efficiency, and output power capability, when charging receivers with six-degrees-of-freedom positions and orientations in 3D space. For instance, a perpendicular receiver in the center of the Tx-coil array can be charged with 82% dc–dc efficiency and 45 W, which is difficult for a conventional single Tx-coil solution.
A Survey on Classical and Deep Learning based Intermittent Time Series Forecasting Methods

Demand forecasting is a fundamental aspect of inventory and supply chain management. Due to the sporadic nature of the demand, demand forecasting involves dealing with intermittent time series in domains such as retail, manufacturing. Conventional forecasting methods do not work well for intermittent time series due to inherent sparsity in such series. Researchers have proposed multiple methods to deal with intermittent time series such as Croston and its variants. Our work aims to provide an insight into the various forecasting methods traditionally known to work well for forecasting intermittent series. We have also explored deep learning methods that have been proposed in recent literature. These methods are thoroughly reviewed and explained in this survey paper. Additionally, experiments are done on two publicly available datasets to compare the performance of the traditional methods with deep learning models. Furthermore, a hybrid model made of independent classification and regression trees has been implemented and studied as well. We provide a comprehensive evaluation that aims at selecting the appropriate method, given the dataset, context, and objectives that have to be met by the forecasting practitioner/researcher.