Ubiquitous and Pervasive Sensor Nets even powered by AI and Deep Learning
Develop and commercialize pervasive and ubiquitous sensing products and infrastructures based on Cyber Physical Systems (CSPs), even powered by AI technologies including Deep Learning, to support demands of intelligent quasi-autonomous control coming from emergent green and blue markets, e.g., home solar energy management, elderly care and people security, DOP food in agriculture, and sustainable mobility. These sensing platforms are conceived to be included in the CPS model to guarantee technical, semantic and user interoperability.
Bio-data are sensed by either available BLE sensors on the market or by DIY edge devices. They deal with Body temperature, Blood pressure, Oxygen in the Blood, Dehydration, Heart Rate Variability (HRV), Mental Stress, Breathing Rate. Such sensors are able to process data to carry out the first real time control and are provided with internal gateway to send data to brokers resident on remote control centers. Currently such intelligent sensors are tested in the Projects IHOSP and SELCA supported by EC Regional Funds.
Hospitals and Clinical Laboratories make available the devices that are needed by remote patients, and vice-versa patients make available to a remote hospital the home devices or the ones they wear while are walking or driving to monitor/control their health status or pathology from distance. Currently this monitoring and control system is tested in the Projects IHOSP and SELCA supported by EC Regional Funds.
Thermal and optical IP cameras are used to control the facial temperature and the posture of a people. Also people identities are checked when they pass closer to the camera. Images and video are stored on SD or remote servers for statistical processing. Currently these cameras are tested in the Project IHOSP and BioTrak supported by EC Regional Funds.
Such low cost smart system aims at controlling that the energy produced by the domestic solar plant is completely exploited by the domestic appliances, otherwise the exceeding energy is used to charge some service batteries exploited in the evening or during the night to decrease the electrical consumption of the house. Two main sensing devices measure in real time the electrical consumption of the house and the energy produced by the solar plant. The energy coming from the solar plant is consumed by the house appliances, but when it exceeds the consumption demand, the exceeding collected energy is used to charge progressively some service batteries connected to the home electrical circuit. The batteries can be located in every part of the house. The plant status can be monitored and controlled via internet from everywhere by using a simple APP . The system is sold preferably to the electrical installers.
Our platform for Domotics deals mainly with the surveillance of the typical aspects related to home security, e.g, the detection of unauthorized people, water leakage, bad weather conditions, sudden disconnection of the power supply, low voltage of the service batteries. All these aspects are monitored by low cost devices that can be integrated with relevant technologies available on the market, e.g. Arlo and Reolink Cameras, Samsung Smatthings, and Sonoff Bridges. As much as possible of integration will be accomplished case by case by using suitable technologies (e.g., MQTT, IFTT, etc.). Alerts are sent to the users for every suspicious event detected by the sensing devices. Control actions are taken immediately, e.g., the water supply from tanks or from the mains is switched off in case of water leakage, the siren is turned on in case of intrusion. The home status can be monitored and controlled via internet by using a simple APP. The system is sold to either the end user or to electrical installers depending on the complexity of the desired control.
Automotive applications deal with surveillance systems from remote to improve people security on both cars and boats. Some domotics control is implemented also in the automative field e.g, the detection of unauthorized people, water leakage, bad weather conditions, sudden disconnection of the power supply, low voltage of the service batteries. Further specific controls deal with monitoring the collisions between adjacent moored boats or the low pressure of the car tires. All these aspects are monitored by simple low cost devices that make available the relevant information to the users through internet APP. Alerts are sent autonomously to the users and control actions are taken immediately, e.g., the water supply from tanks or from the mains is switched off, further safety fenders between moored boats are automatically inflated in case of bad weather conditions, battery charge is powered by the dock of the port in case of cloudy weather. The system is sold to the end users or to electrical installers depending on the control complexity.
Deepsensing is a member of the NVIDIA Inception Program for Startups and of the Oracle Startup Program to improve the technologies offered to their clients in the field of Artificial Intelligence and AIoT (Artificial Intelligence of Things), and to offer intensive computing services and "data as service" platforms to the clients. This is obtained by using intelligent and powerful edge devices based on Raspberry and NVIDIA Jetson boards, and by means of deep learning technologies running on super computing nodes such as the NVIDIA A100 available at the Oracle Cloud. DeepSensing Company operates to transfer deep sensing based services and products
available at research labs as proof of concepts to market. Current technology transfer projects deal with elderly care from remote and knowledge discovery in bioinformatics , identifying genetic markers for DOP foods and computer vision based sensors in agriculture. In all such projects we plan to use not only basic sensing technologies but also GPUs with the aim: a) to develop very large
and pervasive sensor nets powered by real time AI algorithms embedded in both
the leaf sensing nodes and in intermediate coordination nodes, and b) to learn
off line how to improve the monitored system based on the collected data, thus
supporting complex diagnosis and control decisions to be injected into
the sensing net. Prototypes and experimental studies are currently tested in the projects IHOSP, SELCA, and BIOTRAK supported by EC Regional Funds.
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