Добірка наукової літератури з теми "Smart embedded micro-Network"

Electronic technology plays a vital role in healthcare, not only for sensory equipment but also for communication and recording. As a result, the Internet of Things (IoT) is the most recent communication breakthrough in healthcare. In this work, we present a system that tracks patient health using a Blynk application, a micro-controller as a communication gateway, and sensors. When the output of the detector changes, a buzzer is embedded into the controller to alert the nursing staff. The sensor connects to a micro-controller, which is then interfaced with the liquid-crystal display (LCD) panel and wireless local area network (LAN) to provide notifications. An alert will be sent to the doctor through IoT if the system detects a change in the patient’s pulse rate or blood pressure, and the patient’s heartbeat, blood pressure, and body temperature will be displayed in real-time via Cloud. As a result, an IoT-based patient health monitoring system could save lives by efficiently monitoring patients’ health in real- time.

In this study, the synthesis of smart, polymerically embedded titanium dioxide (TiO2) nanoparticles aimed to exhibit photo-induced anticancer properties under visible light irradiation is investigated. The TiO2 nanoparticles were prepared by utilizing the sol gel method with different dopants, including nitrogen (N-doped), iron (Fe-doped), and nitrogen and iron (Fe,N-doped). The dopants were embedded in an interpenetrating (IP) network microgel synthesized by stimuli responsive poly (N-Isopropylacrylamide-co-polyacrylicacid)–pNipam-co-PAA forming composite particles. All the types of produced particles were characterized by X-ray powder diffraction, micro-Raman, Fourier-transform infrared, X-ray photoelectron, ultra-violet-visible spectroscopy, Field Emission Scanning Electron, Transmission Electron microscopy, and Dynamic Light Scattering techniques. The experimental findings indicate that the doped TiO2 nanoparticles were successfully embedded in the microgel. The N-doped TiO2 nano-powders and composite particles exhibit the best photocatalytic degradation of the pollutant methylene blue under visible light irradiation. Similarly, the highly malignant MDA-MB-231 breast cancer epithelial cells were susceptible to the inhibition of cell proliferation at visible light, especially in the presence of N-doped powders and composites, compared to the non-metastatic MCF-7 cells, which were not affected.

Radar-based human activity recognition (HAR) provides a non-contact method for many scenarios, such as human–computer interaction, smart security, and advanced surveillance with privacy protection. Feeding radar-preprocessed micro-Doppler signals into a deep learning (DL) network is a promising approach for HAR. Conventional DL algorithms can achieve high performance in terms of accuracy, but the complex network structure causes difficulty for their real-time embedded application. In this study, an efficient network with an attention mechanism is proposed. This network decouples the Doppler and temporal features of radar preprocessed signals according to the feature representation of human activity in the time–frequency domain. The Doppler feature representation is obtained in sequence using the one-dimensional convolutional neural network (1D CNN) following the sliding window. Then, HAR is realized by inputting the Doppler features into the attention-mechanism-based long short-term memory (LSTM) as a time sequence. Moreover, the activity features are effectively enhanced using the averaged cancellation method, which improves the clutter suppression effect under the micro-motion conditions. Compared with the traditional moving target indicator (MTI), the recognition accuracy is improved by about 3.7%. Experiments based on two human activity datasets confirm the superiority of our method compared to traditional methods in terms of expressiveness and computational efficiency. Specifically, our method achieves an accuracy close to 96.9% on both datasets and has a more lightweight network structure compared to algorithms with similar recognition accuracy. The method proposed in this article has great potential for real-time embedded applications of HAR.

Embedded systems in Agriculture play a vital role in unifying the work involved and improve conservations. Designing a smart as well as a cost efficient and more user-friendly system will be idealistic challenge. The following system that has been proposed is designed with those ideal constraints in mind. It consists of a Raspberry pi3 as a gateway that links the sensor networks with the cloud. To improve security an MQTT protocol is used for cloud connectivity. The communication between the sensor networks is managed by NRF24L01. The Sensor network is a separate entity that can used like a plug and play device and is built by a micro controller with a LCD display and an interfaced GPS. Multicasting is also possible between sensor networks and the gateway. The processed data from the sensor networks is sent through NRF24L01 to the gateway. The gateway further processes and encapsulates the data and through MQTT the data gets stored on the cloud. This cloud data can be accessed through computer or mobile device

To set right the usage of water for crops of agriculture an automated irrigation system has been implemented. A moisture soil sensor; and a temperature measure sensor which is called as network of the distributed wireless is used at base of the plant. Along with these, we implemented a gateway unit. which gathers information and regulate it and by activating the triggers actuators, it can send and receive the transmits data to and from the web application. I proposed the algorithm which having the temperature and soil moisture threshold values that embedded in a gate way based on micro controller. It implemented panels of the photovoltaic; and having a duplex communication link; and works with the interface i.e. cellular-Internet which offers that data inspection & irrigation timing. All this can be programmed by using a web page. Implemented automated Crop water saving system tested for 136 days in sage crop field. It can be saved 90% water compared to others. The main 3 advantages of this automated system make it place successfully in any place for 18 months. As it is energy self-rule, cost less, so it can be efficiently useful in limited water geographical lands.

An investigation was conducted to develop an effective automated tool to deploy micro-fabricated stretchable networks of distributed sensors onto the surface of large structures at macroscale to create “smart” structures with embedded distributed sensor networks. Integrating a large network of distributed sensors with structures has been a major challenge in the design of so-called smart structures or devices for cyber-physical applications where a large amount of usage data from structures or devices can be generated for artificial intelligence applications. Indeed, many “island-and-serpentine”-type distributed sensor networks, while promising, remain difficult to deploy. This study aims to enable such networks to be deployed in a safe, automated, and efficient way. To this end, a scissor-hinge controlled system was proposed as the basis for a deployment mechanism for such stretchable sensor networks (SSNs). A model based on a kinematic scissor-hinge mechanism was developed to simulate and design the proposed system to automatically stretch a micro-scaled square network with uniformly distributed sensor nodes. A prototype of an automatic scissor-hinge stretchable tool was constructed during the study with an array of four scissor-hinge mechanisms, each belt-driven by a single stepper motor. Two micro-fabricated SSNs from a 100 mm wafer were fabricated at the Stanford Nanofabrication Facility for this deployment study. The networks were designed to be able to cover an area 100 times their manufacturing size (from a 100 mm diameter wafer to a 1 m2 active area) once stretched. It was demonstrated that the proposed deployment tool could place sensor nodes in prescribed locations efficiently within a drastically shorter time than in current labor-intensive manual deployment methods.

The technology for human activity recognition has diverse applications within the Internet of Things spectrum, including medical sensing, security measures, smart home systems, and more. Predominantly, human activity recognition methods have relied on contact sensors, and some research uses inertial sensors embedded in smartphones or other devices, which present several limitations. Additionally, most research has concentrated on recognizing discrete activities, even though activities in real-life scenarios tend to be continuous. In this paper, we introduce a method to classify continuous human activities, such as walking, running, squatting, standing, and jumping. Our approach hinges on the micro-Doppler (MD) features derived from continuous-wave radar signals. We first process the radar echo signals generated from human activities to produce MD spectrograms. Subsequently, a bidirectional gate recurrent unit (Bi-GRU) network is employed to train and test these extracted features. Preliminary results highlight the efficacy of our approach, with an average recognition accuracy exceeding 90%.

Tactile sensing is paramount for robots operating in human-centered environments to help in understanding interaction with objects. To enable robots to have sophisticated tactile sensing capability, researchers have developed different kinds of electronic skins for robotic hands and arms in order to realize the ‘sense of touch’. Recently, Stanford Structures and Composites Laboratory developed a robotic electronic skin based on a network of multi-modal micro-sensors. This skin was able to identify temperature profiles and detect arm strikes through embedded sensors. However, sensing for the static pressure load is yet to be investigated. In this work, an electromechanical impedance-based method is proposed to investigate the response of piezoelectric sensors under static normal pressure loads. The smart skin sample was firstly fabricated by embedding a piezoelectric sensor into the soft silicone. Then, a series of static pressure tests to the skin were conducted. Test results showed that the first peak of the real part impedance signal was sensitive to static pressure load, and by using the proposed diagnostic method, this test setup could detect a resolution of 0.5 N force. Numerical simulation methods were then performed to validate the experimental results. The results of the numerical simulation prove the validity of the experiments, as well as the robustness of the proposed method in detecting static pressure loads using the smart skin.

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment.

— Scientific researches advancements achieving miniaturization of Micro-electromechanical Systems, has enable smarts autonomous embedded devices know as sensor nodes, that are developed on numerous platforms using the proprietary of hardware and software with capability to communicate wirelessly. Today more than ever these sensors are continuously spreading to civilian usage side of things and in many others applications , including military, security, medical, environments, and animal among others, to sense specific occurrence of desired event and may carried very important data on physical device such as the mica-mote. Hence a plethora of security protocols arises in order to mitigate the risks of malicious attacks such as eavesdrop communications, or data alteration, using cryptographic techniques such as Elliptic curve for data privacy, accuracy, integrity, efficiency, and reducing the energy consumption by mote and processors. We focus on securing data by all means. Hence the in-network processing technique is used to reduce considerably the energy consumption, considering that sensors deployments in inaccessible and resource-constrained environments. After the drawback, we investigate the secured data aggregation and give security requirements that are mandatory to a Trusted Data Aggregation with Low Energy model to satisfy. The security and energy performances are analysed comparing with others methods. Index Terms—TDALE, Aggregation, Elliptic curve, RSA, Security, Energy

La mise en place de micro-réseaux électriques décentralisés à sources d’énergies renouvelables (EnR) est actuellement un challenge pour l’alimentation d’infrastructures de télécommunication. La production d’électricité à partir d’installations photovoltaïque (PV) et/ou micro-éolienne est plus adaptée du point vu de la disponibilité des ressources renouvelables et leur déploiement à proximité des antennes télécoms. Cependant, l’intermittence et la variabilité des sources renouvelables, en particulier pour le PV, posent un problème d’équilibre entre la production et la consommation qui doit être assuré à tout moment. De plus, la question du dimensionnement prenant en compte cette intermittence est un véritable compromis entre la taille, le coût d’investissement, la marge de sécurité et de sureté de fonctionnement. Il est donc indispensable de développer des outils de prévision et de prédiction de la production électrique PV. L’objectif de ces travaux de thèse est d’établir un modèle pragmatique de prédiction de la ressource PV capable de caractériser la variabilité et l’intermittence de l’irradiation solaire à partir des données recueillies sur sites et sur une longue période. En s’appuyant sur des données de mesures expérimentales issues d’installations PV, nous montrons qu’une distribution adaptée de Weibull permet une prédiction fiable, caractérisant la variabilité de l’irradiation globale de sites à climat semi-continental (sites du territoire français). En particulier, ces travaux étudient et recherchent parmi les méthodes de détermination des paramètres de Weibull, celles qui sont les plus adaptées pour le domaine du PV pour prévoir la production annuelle d'énergie et la prédire dans le temps. L'originalité de la méthode proposée est l’obtention d’une production d'énergie PV annuelle fiable et d’une modélisation de la distribution pouvant contribuer à la génération de séries chronologiques synthétiques de données d'irradiation globale. Le modèle de Weibull proposé est adapté à la prédiction du gisement solaire et s’appuie sur une formulation mathématique permettant des résultats fiables pouvant être intégrée dans un outil d’environnement et d’acquisition temps réel de données de mesures. Pour valider l’approche développée de prévision pour une prédiction de sources d’alimentation PV pour des sites télécoms, nous proposons un démonstrateur d’acquisition temps réel à large couverture basé sur l'instrumentation d’une antenne télécom de la société TDF SAS par des capteurs IoT (Internet of Things) déployés en réseau. Le déploiement de capteurs IoT au protocole LoRaWAN (Long Range Wide Area Network) sur une tour de télécommunication traditionnelle, dans les conditions réelles de fonctionnement d’interférences des fréquences, a permis de démontrer que le réseau de capteurs est adapté pour développer des services de mesures, de prévisions d’énergies disponibles, de suivis et de supervisions d’installations PV servant à alimenter des antennes télécoms. Les capteurs IoT communiquent sans interférer aux installations préexistantes des tours tout en conservant la qualité des données issues des capteurs. Ce réseau LoRaWAN démontre la pertinence de déployer des stations météorologiques sur des sites existants d’antennes télécoms permettant en temps réel des exécutions du modèle de prédiction PV développé. Ces travaux dégagent des perspectives d’applications et de développement d’EnR à grande échelle sur un large territoire en considérant le climat dominant. Notamment, l’implémentation du modèle de prédiction à court terme au sein d’un environnement interconnecté à un réseau de capteurs météorologiques et communiquant en temps réel permettra une gestion intelligente d’installations EnR déployées dans un territoire
The implementation of decentralized electrical micro-grids base on renewable energies is a challenge for the power supplies of telecommunication infrastructures. These power sources supply from off-grid photovoltaic and micro-wind systems are more suitable for remote or isolated sites like telecom antennas considering the local installation feasibilities and renewable resource availabilities. However, the intermittence and the variability of renewable sources require to ensure the balance between the production and the consumption of energies. In addition, the system sizing requires a trade-off between size, investment cost, safety and reliability of the power supply due to the intermittency. This thesis aims to develop a model characterizing the global solar irradiation variability to predict PV productions from data recorded on sites and over a long period. We validated the calibration methods of the Weibull function allowing a reliable prediction of the global irradiation in a semi-continental climate (mainland French sites). More precisely, we experimentally determine the Weibull parameters for obtaining a reliable global irradiation forecast prediction by considering the sun incident radiation during time periods. This forecast allows to estimate the yearly energy generation and its prediction during the time evolution from a PV plant in considered sites. The originality of the proposed approach is to obtain reliable predictive yearly PV energy distributions which also can be used to generate synthetic data times series of the global irradiation. The proposed model is based a parametrized mathematical formula providing reliable prediction results and which can be integrated to a real-time data acquisition system. To validate the proposed approach for the prediction of the PV power source supplying of telecommunication infrastructures, we developed an IoT sensors network for real-time acquisition to instrument a telecom antenna allowing a wide area network coverage. We demonstrate that deployed networked IoT sensors based on LoRaWAN protocol on a traditional telecommunication tower operating under real conditions communicate reliably without affecting the tower’s functions while keeping the data quality of the sensors. The proposed LoRaWAN network is used for the data acquisition of the weather parameters performing the proposed forecast of the real-time PV production. The software implementation of the proposed prediction model interfaced to weather sensors allows a real-time and intelligent management of deployed renewable energy systems. Future work is also discussed to develop renewable energies on a wide territory with a semi-continental climate

Internet of Things (IoT) is not a futuristic intuition, it is present everywhere. It is with devices, Sensors, Clouds, Big data, and data with business. It is the combination of traditional embedded systems combined with small wireless micro sensors, control systems with automation, and others that makes a huge infrastructure. The integration of wireless communication, micro electro mechanical devices, and Internet has led to the development of new things in the Internet. It is a network of network objects that can be accessed through the Internet and every object can be identified by unique identifier. By replacing IPV4, IPV6 plays a key role and provides a huge increase of address spaces for the development of things in the Internet. The objective of IoT application is to make the things smart without the human intervention. With the increasing number of smart nodes and amount of data that generated by each node is expected to create new concerns about data privacy, data scalability, data security, data manageability and many more issues that have been discussed in this chapter.

Internet of Things (IoT) is not a futuristic intuition, it is present everywhere. It is with devices, Sensors, Clouds, Big data, and data with business. It is the combination of traditional embedded systems combined with small wireless micro sensors, control systems with automation, and others that makes a huge infrastructure. The integration of wireless communication, micro electro mechanical devices, and Internet has led to the development of new things in the Internet. It is a network of network objects that can be accessed through the Internet and every object can be identified by unique identifier. By replacing IPV4, IPV6 plays a key role and provides a huge increase of address spaces for the development of things in the Internet. The objective of IoT application is to make the things smart without the human intervention. With the increasing number of smart nodes and amount of data that generated by each node is expected to create new concerns about data privacy, data scalability, data security, data manageability and many more issues that have been discussed in this chapter.

Artificial intelligence driving technology has become the main direction of technological progress in intelligent vehicles. To address the issue of road recognition and traffic sign recognition response in the field of intelligent vehicle autonomous driving, a vehicle autonomous driving algorithm based on HSV road detection and yolov5 traffic sign recognition is proposed and applied to micro intelligent vehicles. The hardware of the mini smart car is controlled by NVIDIA Jetson AGX Orin, and the software method is embedded in the Ubuntu system. The yolov5 neural network is used to solve the classification and location problems of traffic signs. The results of experimental testing prove that micro intelligent vehicles can drive according to regulations on simulated roads and accurately recognize road signs, and can make corresponding actions accurately. The research results provide certain reference value for the application and development of vehicle autonomous driving.

Manned and unmanned aircraft are commonly manufactured from lightweight composite materials, replacing heavier metallic aircraft materials. Here, we describe a collaborative industrial-academic effort to develop novel microvascular composites that offer thermal and structural multifunctionality via embedded micro-capillary array-based heat exchangers for thermal management embedded within aerospace-grade carbon prepreg material. The current research establishes a route to create prepreg materials suitable for heat exchanger network manufacture by automated fiber placement processes. Potential network geometries are investigated with analytical fluid models to predict flow characteristics and heat distribution for pre-defined heat exchanger configurations within a carbon composite panel. Based on these predictions, two geometries are fabricated for thermal management experiments. The mechanical effect of the embedded microvascular networks on the panel structural properties is also investigated analytically.