Castor: Causal Temporal Regime Structure Learning

The task of uncovering causal relationships among multivariate time series data stands as an essential and challenging objective that cuts across a broad array of disciplines ranging from climate science to healthcare. Such data entails linear or non-linear relationships, and usually follow multiple a priori unknown regimes. Existing causal discovery methods can infer summary causal graphs from heterogeneous data with known regimes, but they fall short in comprehensively learning both regimes and the corresponding causal graph. In this paper, we introduce CASTOR, a novel framework designed to learn causal relationships in heterogeneous time series data composed of various regimes, each governed by a distinct causal graph. Through the maximization of a score function via the EM algorithm, CASTOR infers the number of regimes and learns linear or non-linear causal relationships in each regime. We demonstrate the robust convergence properties of CASTOR, specifically highlighting its proficiency in accurately identifying unique regimes. Empirical evidence, garnered from exhaustive synthetic experiments and two real-world benchmarks, confirm CASTOR's superior performance in causal discovery compared to baseline methods. By learning a full temporal causal graph for each regime, CASTOR establishes itself as a distinctly interpretable method for causal discovery in heterogeneous time series

A Meta-GNN approach to personalized seizure detection and classification

In this paper, we propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We achieve this by combining two novel paradigms that have recently seen much success in a wide variety of real-world applications: graph neural networks (GNN), and meta-learning. We train a Meta-GNN based classifier that learns a global model from a set of training patients such that this global model can eventually be adapted to a new unseen patient using very limited samples. We apply our approach on the TUSZ-dataset, one of the largest and publicly available benchmark datasets for epilepsy. We show that our method outperforms the baselines by reaching 82.7% on accuracy and 82.08% on F1 score after only 20 iterations on new unseen patients

A New MPPT-Based Extended Grey Wolf Optimizer for Stand-Alone PV System: A Performance Evaluation versus Four Smart MPPT Techniques in Diverse Scenarios

Photovoltaic (PV) systems play a crucial role in clean energy systems. Effective maximum power point tracking (MPPT) techniques are essential to optimize their performance. However, conventional MPPT methods exhibit limitations and challenges in real-world scenarios characterized by rapidly changing environmental factors and various operating conditions. To address these challenges, this paper presents a performance evaluation of a novel extended grey wolf optimizer (EGWO). The EGWO has been meticulously designed in order to improve the efficiency of PV systems by rapidly tracking and maintaining the maximum power point (MPP). In this study, a comparison is made between the EGWO and other prominent MPPT techniques, including the grey wolf optimizer (GWO), equilibrium optimization algorithm (EOA), particle swarm optimization (PSO) and sin cos algorithm (SCA) techniques. To evaluate these MPPT methods, a model of a PV module integrated with a DC/DC boost converter is employed, and simulations are conducted using Simulink-MATLAB software under standard test conditions (STC) and various environmental conditions. In particular, the results demonstrate that the novel EGWO outperforms the GWO, EOA, PSO and SCA techniques and shows fast tracking speed, superior dynamic response, high robustness and minimal power fluctuations across both STC and variable conditions. Thus, a power fluctuation of 0.09 W could be achieved by using the proposed EGWO technique. Finally, according to these results, the proposed approach can offer an improvement in energy consumption. These findings underscore the potential benefits of employing the novel MPPT EGWO to enhance the efficiency and performance of MPPT in PV systems. Further exploration of this intelligent technique could lead to significant advancements in optimizing PV system performance, making it a promising option for real-world applications.The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ II (ELKARTEK KK-2023/00051); to the Diputación Foral de Álava (DFA), through the project CONAVANTER; and to the UPV/EHU, through the project GIU20/063 for supporting this work

Efficient and secure routing protocol based on Blockchain approach for wireless sensor networks

Embedded systems and wireless sensor networks (WSN) are found today in increasingly critical areas of applications. They have become integrated and embedded in nearly all aspects of everyday life, including manufacturing, healthcare, education, critical infrastructure, and entertainment. The number of connected devices continues to grow, and due to the insecure nature of these devices, the amount of risk continues to grow as well. These risks, however, can be mitigated with the creation and adoption of WSN security standards developed to create an environment of safety, security, and confidence in the technology. Designing the security policy for WSNs requires asking some preliminary questions. These questions are particularly important in the case of WSNs because their use is highly decentralized. Blockchain's ability on governing decentralized networks makes it especially suitable for designing a self‐managing system on WSN devices. This article proposes a routing protocol that uses Blockchain technology to offer a shared memory between the network's nodes. The simulation results have shown that this solution could be applicable and could resolve the issues cited above

New insight into the effect of nozzle diameter on the properties of sprayed ZnO thin films

International audienceZinc oxide (ZnO) nanofibers and nanopetals were successfully deposited onto mesoporous silicon (meso-PSi), silicon, and glass substrates using zinc acetate via Spray Pyrolysis method. Electrochemical etching of the P-type (100) silicon wafer was used to prepare the mesoporous silicon layer. The effects of nozzle diameter and substrate type on the morphological, structural, and optical properties were investigated using XRD, SEM, EDX techniques, FT-IR, and UV-Vis spectrometry. Scanning Electron Microscopy (SEM) confirms the meso-PSi morphology with a diameter varying from 20 nm to 45 nm and illustrates the prepared ZnO nanostructures. EDX results show that the ratio of Zn:O is found to be similar to 1:1 for the 3-mm diameter when the oxygen is much higher than the Zn element in the 18-mm diameter. XRD measurements indicated that all films show a hexagonal Wurtzite structure with a variation of crystallographic properties and orientation according to the prepared morphology. The mean value of the crystallite size is 14.27nm for the 3-mm diameter and 19.01 nm for the 18-mm diameter. The variation in the morphological characteristics of the deposited ZnO leads to a variation in the optical properties of the sprayed ZnO thin films. The layers bandgap energy (Eg) was estimated to be 3.28 and 3.26 eV for the ZnO layers prepared by 18-mm and 3-mm nozzle diameters, respectively. This study is also helpful for subsequent studies on the tailoring of morphology and ZnO growth control on PSi substrates

Low Cost Automation System for Smart Houses based on PIC Microcontrollers

International audienceThe high electricity bills of houses have triggered a significant research for solutions to mitigate this issue. The house automation systems offer everything necessary to put an end to these inconveniences as, most often, they incorporate a number of smart devices (microcontrollers and sensors). The presented work outlines the development of a microcontroller-based automation system of a solar smart house using automatic lighting and thermal comfort sensors (temperature, humidity) and safety functions (gas leakage, smoke detection). This system is based on PIC microcontrollers and is applied through the implementation of a complete algorithm. The program is written on Micro C, and in order to realize the control circuit and the simulation, the testing software made use of Proteus software which is freely available from the Internet. The control of temperature and air pollution was implemented using the PIC16F877A, while the light control was applied with the PIC18F4550. The hardware prototype was also provided to experiment on the designed control strategy. The results of this work allowed some conclusions to be reached and confirmed the benefits of this kind of automation systems