Sensor Data Fusion e applicazioni

Un trattato che illustra tutto quello che riguarda il Sensor Data Fusion sin dagli strumenti basilari fino alle applicazioni di specializzate

Towards a Smart Campus Digital Twin: Promoting Awareness and Sustainability Through Wayfinding and Real-Time Environmental Data

The newly inaugurated Navile District of the University of Bologna is a complex created along the Navile canal, that now houses various teaching and research activities for the disciplines of Chemistry, Industrial Chemistry, Pharmacy, Biotechnology and Astronomy. A Building Information Modeling system (BIM) gives staff of the Navile campus several ways to monitor buildings in the complex throughout their life cycle, one of which is the ability to access real-time environmental data such as room temperature, humidity, air composition, and more, thereby simplifying operations like finding faults and optimizing environmental resource usage. But smart features at Navile are not only available to the staff: AlmaMap Navile is a web application, whose development is documented in this thesis, that powers the public touch kiosks available throughout the campus, offering maps of the district and indications on how to reach buildings and spaces. Even if these two systems, BIM and AlmaMap, don't seem to have many similarities, they share the common intent of promoting awareness for informed decision making in the campus, and they do it while relying on web standards for communication. This opens up interesting possibilities, and is the idea behind AlmaMap Navile 2.0, an app that interfaces with the BIM system and combines real-time sensor data with a comfort calculation algorithm, giving users the ability not just to ask for directions to a space, but also to see its comfort level in advance and, should they want to, check environmental measurements coming from each sensor in a granular manner. The end result is a first step towards building a smart campus Digital Twin, that can support all the people who are part of the campus life in their daily activities, improving their efficiency and satisfaction, giving them the ability to make informed decisions, and promoting awareness and sustainability.

Fault diagnosis of an automotive suspension system

With the development of the embedded application and driving assistance systems, it becomes relevant to develop parallel mechanisms in order to check and to diagnose these new systems. In this thesis we focus our research on one of this type of parallel mechanisms and analytical redundancy for fault diagnosis of an automotive suspension system. We have considered a quarter model car passive suspension model and used a parameter estimation, ARX model, method to detect the fault happening in the damper and spring of system. Moreover, afterward we have deployed a neural network classifier to isolate the faults and identifies where the fault is happening. Then in this regard, the safety measurements and redundancies can take into the effect to prevent failure in the system. It is shown that The ARX estimator could quickly detect the fault online using the vertical acceleration and displacement sensor data which are common sensors in nowadays vehicles. Hence, the clear divergence is the ARX response make it easy to deploy a threshold to give alarm to the intelligent system of vehicle and the neural classifier can quickly show the place of fault occurrence.

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.

Implementation of a VLSI amplifier interfaced with biomedical sensors for ultra wide band data transmission

This thesis presents a CMOS Amplifier with High Common Mode rejection designed in UMC 130nm technology. The goal is to achieve a high amplification factor for a wide range of biological signals (with frequencies in the range of 10Hz-1KHz) and to reject the common-mode noise signal. It is here presented a Data Acquisition System, composed of a Delta-Sigma-like Modulator and an antenna, that is the core of a portable low-complexity radio system; the amplifier is designed in order to interface the data acquisition system with a sensor that acquires the electrical signal. The Modulator asynchronously acquires and samples human muscle activity, by sending a Quasi-Digital pattern that encodes the acquired signal. There is only a minor loss of information translating the muscle activity using this pattern, compared to an encoding technique which uses astandard digital signal via Impulse-Radio Ultra-Wide Band (IR-UWB). The biological signals, needed for Electromyographic analysis, have an amplitude of 10-100μV and need to be highly amplified and separated from the overwhelming 50mV common mode noise signal. Various tests of the firmness of the concept are presented, as well the proof that the design works even with different sensors, such as Radiation measurement for Dosimetry studies.

Mac protocols for linear wireless (sensor) networks

Wireless sensor networks (WSNs) consist of a large number of sensor nodes, characterized by low power constraint, limited transmission range and limited computational capabilities [1][2].The cost of these devices is constantly decreasing, making it possible to use a large number of sensor devices in a wide array of commercial, environmental, military, and healthcare fields. Some of these applications involve placing the sensors evenly spaced on a straight line for example in roads, bridges, tunnels, water catchments and water pipelines, city drainages, oil and gas pipelines etc., making a special class of these networks which we define as a Linear Wireless Network (LWN). In LWNs, data transmission happens hop by hop from the source to the destination, through a route composed of multiple relays. The peculiarity of the topology of LWNs, motivates the design of specialized protocols, taking advantage of the linearity of such networks, in order to increase reliability, communication efficiency, energy savings, network lifetime and to minimize the end-to-end delay [3]. In this thesis a novel contention based Medium Access Control (MAC) protocol called L-CSMA, specifically devised for LWNs is presented. The basic idea of L-CSMA is to assign different priorities to nodes based on their position along the line. The priority is assigned in terms of sensing duration, whereby nodes closer to the destination are assigned shorter sensing time compared to the rest of the nodes and hence higher priority. This mechanism speeds up the transmission of packets which are already in the path, making transmission flow more efficient. Using NS-3 simulator, the performance of L-CSMA in terms of packets success rate, that is, the percentage of packets that reach destination, and throughput are compared with that of IEEE 802.15.4 MAC protocol, de-facto standard for wireless sensor networks. In general, L-CSMA outperforms the IEEE 802.15.4 MAC protocol.

UWB radar sensor networks: Detection algorithms design and experimental analysis

In the last years radar sensor networks for localization and tracking in indoor environment have generated more and more interest, especially for anti-intrusion security systems. These networks often use Ultra Wide Band (UWB) technology, which consists in sending very short (few nanoseconds) impulse signals. This approach guarantees high resolution and accuracy and also other advantages such as low price, low power consumption and narrow-band interference (jamming) robustness. In this thesis the overall data processing (done in MATLAB environment) is discussed, starting from experimental measures from sensor devices, ending with the 2D visualization of targets movements over time and focusing mainly on detection and localization algorithms. Moreover, two different scenarios and both single and multiple target tracking are analyzed.