In this thesis the novel concept of Sycophant Wireless Sensors (SWS) is introduced. A SWS network is a static ectoparasitic clandestine sensor network mounted incognito on a mobile agent using only the agent&rsquo / s mobility without intervention. SWS networks not only communicate with each other through mobileWireless Sensor Networks (WSN) but also cooperate with them to form a global hybrid Wireless Sensor Network. Such a hybrid network has its own problems and opportunities, some of which have been studied in this thesis work. Assuming that direct position measurements are not always feasible tracking performance of the sycophant using range only measurements for various communication intervals is studied. Then this framework was used to create a hybrid 2D map of the environment utilizing the capabilities of the mobile network the sycophant. In order to show possible applications of a sycophant deployment, the sycophant sensor node was equipped with a laser ranger as its sensor, and it was let to create a 2D map of its environment. This 2D map, which corresponds to a height dierent than the follower network, was merged with the 2D map of the mobile network forming a novel rough 3D map. Then by giving up from the need to properly localize the sycophant even when it is disconnected to the rest of the network, a full 3D map of the environment is obtained by fusing 2D map and tracking capabilities of the mobile network with the 2D vertical scans of the environment by the sycophant. And finally connectivity problems that arise from the hybrid sensor/actuator network were solved. For this 2 new connectivity maintenance algorithms, one based on the helix structures of the proteins, and the other based on the acute triangulation of the space forming a Gabriel Graph, were introduced. In this new algorithms emphasis has been given to sparseness in order to increase fault tolerance to regional problems. To better asses sparseness a new measure, called Resistance was introduced, as well as another called updistance.

Finding lost persons, collecting information in disturbed communities, efficiently traversing urban areas after a blast or similar catastrophic events have motivated researchers to develop intelligent sensor frameworks to aid law enforcement, first responders, and military personnel with situational awareness. This dissertation consists of a two-part framework for providing situational awareness using both acoustic ground sensors and aerial sensing modalities. Ground sensors in the field of data-driven detection and classification approaches typically rely on computationally expensive inputs such as image or video-based methods [6, 91]. However, the information given by an acoustic signal offers several advantages, such as low computational needs and possible classification of occluded events including gunshots or explosions. Once an event is identified, responding to real-time events in urban areas is difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous sounds given context, then the sensors can communicate with an autonomous UAV that can navigate in a GPS-denied urban environment for investigation of the event; this could offer several advantages for time-critical and precise, localized response information necessary for life-saving decision-making. Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements to GPS-degraded localization has become apparent for applications such as anomaly detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations. Distributed AAUS networks could provide end-users with near real-time actionable information for large urban environments with limited resources. Complete ISR mission profiles require a UAV to fly in GPS challenging or denied environments such as natural or urban canyons, at least in a part of a mission. This dissertation addresses, 1) the development of intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification and 2) GPS impaired localization through a formal framework for trajectory-based flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban airspace. Our AAUS sensor method utilizes monophonic sound event detection in which the sensor detects, records, and classifies each event utilizing supervised machine learning techniques [90]. We propose a simulated framework to enhance the performance of localization in GPS-denied environments. We do this by using a new representation of 3D geospatial data using planar features that efficiently capture the amount of information required for sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation would impact both military and civilian areas of research with the ability to react to events and navigate in an urban environment. / Doctor of Philosophy / Emergency scenarios such as missing persons or catastrophic events in urban areas require first responders to gain situational awareness motivating researchers to investigate intelligent sensor frameworks that utilize drones for observation prompting questions such as: How can responders detect and classify acoustic anomalies using unattended sensors? and How do they remotely navigate in GPS-denied urban environments using drones to potentially investigate such an event? This dissertation addresses the first question through the development of intelligent WSN systems that can provide time-critical and precise, localized environmental information necessary for decision-making. At Virginia Tech, we have developed a static ground Acoustic Autonomous Unattended Sensor (AAUS) capable of machine learning for audio feature classification. The prior arts of intelligent AAUS and network architectures do not account for network failure, jamming capabilities, or remote scenarios in which cellular data wifi coverage are unavailable [78, 90]. Lacking a framework for such scenarios illuminates vulnerability in operational integrity for proposed solutions in homeland security applications. We address this through data ferrying, a communication method in which a mobile node, such as a drone, physically carries data as it moves through the environment to communicate with other sensor nodes on the ground. When examining the second question of navigation/investigation, concerns of safety arise in urban areas regarding drones due to GPS signal loss which is one of the first problems that can occur when a drone flies into a city (such as New York City). If this happens, potential crashes, injury and damage to property are imminent because the drone does not know where it is in space. In these GPS-denied situations traditional methods use point clouds (a set of data points in space (X,Y,Z) representing a 3D object [107]) constructed from laser radar scanners (often seen in a Microsoft Xbox Kinect sensor) to find itself. The main drawback from using methods such as these is the accumulation of error and computational complexity of large data-sets such as New York City. An advantage of cities is that they are largely flat; thus, if you can represent a building with a plane instead of 10,000 points, you can greatly reduce your data and improve algorithm performance. This dissertation addresses both the needs of an intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification as well as GPS-impaired localization through a formal framework for trajectory-based flight navigation for UAS operating BVLOS in low altitude urban and suburban environments.

In this dissertation we propose several DFT techniques specific to 3D stacked IC systems. The goal has explicitly been to create techniques that integrate easily with existing IC test systems. Specifically, this means utilizing scan- and wrapper-based techniques, two foundations of the digital IC test industry. First, we describe a general test architecture for 3D ICs. In this architecture, each tier of a 3D design is wrapped in test control logic that both manages tier test pre-bond and integrates the tier into the large test architecture post-bond. We describe a new kind of boundary scan to provide the necessary test control and observation of the partial circuits, and we propose a new design methodology for test hardcore that ensures both pre-bond functionality and post-bond optimality. We present the application of these techniques to the 3D-MAPS test vehicle, which has proven their effectiveness. Second, we extend these DFT techniques to circuit-partitioned designs. We find that boundary scan design is generally sufficient, but that some 3D designs require special DFT treatment. Most importantly, we demonstrate that the functional partitioning inherent in 3D design can potentially decrease the total test cost of verifying a circuit. Third, we present a new CAD algorithm for designing 3D test wrappers. This algorithm co-designs the pre-bond and post-bond wrappers to simultaneously minimize test time and routing cost. On average, our algorithm utilizes over 90% of the wires in both the pre-bond and post-bond wrappers. Finally, we look at the 3D vias themselves to develop a low-cost, high-volume pre-bond test methodology appropriate for production-level test. We describe the shorting probes methodology, wherein large test probes are used to contact multiple small 3D vias. This technique is an all-digital test method that integrates seamlessly into existing test flows. Our experimental results demonstrate two key facts: neither the large capacitance of the probe tips nor the process variation in the 3D vias and the probe tips significantly hinders the testability of the circuits. Taken together, this body of work defines a complete test methodology for testing 3D ICs pre-bond, eliminating one of the key hurdles to the commercialization of 3D technology.

Il controllo in agricoltura delle malattie causate da patogeni fungini può essere effettuato attraverso l’uso di modelli di previsione che si basano comunemente sul monitoraggio in tempo reale di una serie di variabili di input. Queste informazioni generalmente combinano dati metereologici locali con modelli matematici costruiti allo scopo di predire il rischio di malattie. Il processo decisionale si attiva quando un avvertimento sul potenziale rischio viene riconosciuto da parte dei modelli. Diversi modelli epidemiologici sono stati sviluppati e validati nel mondo. Negli Stati Uniti d’America, ad esempio, l’università della California ha sviluppato un supporto decisionale on-line per gestire la coltura secondo i principi della lotta integrata (Integrated Pest Management - IPM). Ciascun agricoltore può consultare il proprio database informativo e prendere decisioni sui trattamenti da effettuare basandosi su dati sito-specifici. Le difficoltà sorgono quando non sono disponibili dati meteorologici da stazioni poste nelle vicinanze del sito in studio o per le zone montane caratterizzate da una forte variabilità altimetrica. Inoltre i dati meteorologici disponibili possono presentarsi in formato non adeguato rispetto alle esigenze del modello previsionale. Con l’intento di avere una visione regionale e una maggiore accuratezza nella gestione del controllo delle malattie, l’obiettivo della tesi è stato l’utilizzo contemporaneo di modelli epidemiologici (Lobesia botrana e Erysiphe necator, agente causale dell’oidio della vite) con modelli fenologici (cultivar di vite Chardonnay) utilizzando parametri meteorologici come la temperatura per creare mappe a livello regionale, a frequenza giornaliera e con una risoluzione spaziale di 200 metri. L’utilizzo contemporaneo di entrambi i modelli aiuta ad essere più precisi nel consigliare interventi colturali nel periodo di sensibilità dell’ospite nei confronti del patogeno o della malattia in modo da poterne stimare il reale rischio di diffusione o insorgenza. Dopo aver calibrato e validato i modelli in Trentino-Alto Adige (Nord Italia) con dati metereologici locali, basandoci sul modello del cambiamento climatico HadAM3 dell’Hadley Centre (Pope et al., 2000),l’andamento climatico previsto è stato proiettato e statisticamente portato. in scala, utilizzando lo scenario A2 e B2. L’algoritmo statistico utilizzato per ridurre la scala giornaliera di risoluzione è chiamato “transfer function” (Eccel et al., 2009). Per completare l’analisi, è stato inoltre utilizzato lo scenario ridimensionato di ENSEMBLES attraverso l’uso di set di dati provenienti da 49 stazioni meteorologiche della FEM e dal pacchetto “RMAWGEN” (Cordano et al., 2012) creato con il software statistico R. (Gentleman et al., 1997). Per mappare i modelli è stata sviluppata una semplice piattaforma modulare WEB-GIS chiamata ENVIRO. I moduli sono “Open Source” e seguono gli standard internazionali dell’“Open Geospatial Consortium” (OGC) e sono stati implementati come segue: i) enviDB è il database per i dati spazio-temporali, ii) enviGRID permette agli utenti di navigare attraverso i dati e i modelli nello spazio e nel tempo, iii) enviMapper è l’interfaccia web per prendere le decisioni, consiste in uno stato dell’arte per mappare la vulnerabilità del cambiamento climatico a diverse scale di aggregazione nello spazio e nel tempo, iv) enviModel è l’interfaccia web per i ricercatori a cui viene fornita una piattaforma per processare e condividere modelli di rischio ambientali utilizzando il “web processing Technologies” (WPS) seguendo gli standard OGC. Con l’obiettivo di diventare ancora più accurati nelle previsioni dei volumi per i trattamenti contro insetti e malattie, in accordo con la direttiva 2009/128/EC, il seguente lavoro dimostra che il sensore LIDAR può essere utilizzato per caratterizzare la geometria della pianta della vite e stimare l’area fogliare (LAI) ad ogni stadio di crescita. Inoltre permette di calcolare il volume da applicare (Tree Row Volume -TRV) visualizzato nelle mappe 3D in GRASS. (Neteler et al., 2008, Neteler et al., 2012). / Control of agricultural pests and diseases is often based on forecasting models commonly based on real time monitoring of inputs variables. This information generally combines meteorological local databases and mathematical models designed to forecast pest and disease risk. The decision process starts when an alert or a potential risk event from the outputs of the models is issued. Epidemiological models based on local datasets have been created and validated worldwide, for example in USA, the University of California developed the online Integrated Pest Management (IPM) program where each farmer can consult with his own database and make the pest management decision based on site-specific conditions. Difficulties arise when no data from a close weather station are available, in mountain areas where weather conditions highly depend on the altimetry, or if data are not in a standard format to feed the model. In a view of having a regional vision and an increased accuracy in the pest control management, the goal of this thesis was to run contemporaneously epidemiological (the pest Lobesia botrana and the pathogen causing Powdery mildew Erysiphe necator) and phenological models (grapevine cv. chardonnay) using environmental variables as temperature and to create maps at regional level, with 200 meters of resolution and daily scale or frequency. Running both models together helps to be more precise in the sensibility period of the host versus the pest or the disease and to understand the real final risk. After calibrating and validating the models in the Trentino-Alto Adige Region (Italy) with local weather data, the forecasted climate was projected and statistically downscaled, based on the output of the Hadley Centre climate model - HadAM3 (Pope et al., 2000) under scenarios A2 and B2. The statistical downscaling algorithm was “transfer function method” (Eccel et al., 2009) at daily resolution. In order to complete the analysis, the downscaled scenario from ENSEMBLES was also used with the datasets of 49 weather stations from FEM and the “RMAWGEN” packages (Cordano et al., 2012) created for this project in R statistical open source software (Gentleman et al., 1997). In order to map the models, a friendly modular WEB-GIS platform called ENVIRO was developed. Modules are Open Source, follow international Open Geospatial Consortium (OGC) standards and were implemented as follows: i) enviDB is the database for spatial temporal data, ii) enviGRID allows users to navigate through data and model in space and time, iii) enviMapper is the web interface for decision makers, a state of the art client to map vulnerability to climate change at different aggregation scales in time and space; finally, iv) enviModel is the web interface for researchers that provides a platform to process and share environmental risk models using web geo-processing technologies (WPS) following OGC standards. With the aim of being even more accurate in pests and diseases spraying volumes and according with the Directive 2009/128/EC, the current work shows that the LIDAR sensor can be used to characterize the geometry of the grapevine and the Leaf Area Index (LAI) at each growth stage and calculate the Tree Row Volume (TRV) visualized in 3D maps in GRASS (Neteler et al., 2008, Neteler et al., 2012).