No description available.

No description available.

The philosophy of research at the University of Texas – Robotics Research Group (RRG) is towards creating a foundation for an open architecture, reconfigurable intelligent machines to meet wide breadth of operational needs. An intelligent system is the one which has complete knowledge of its operating characteristics at all times (updated in real-time) and it can make on-the-fly decisions to adapt itself to the different conditions or present the best possible options to the human decision maker under specified and ranked criteria. The reality of all complex system is that they are inherently non-linear with coupled parameters. The traditional approach dealing with such systems assumes linearized models, imposing conservative bounds on the operational domain and thus limiting performance capability of the system. Recent advancements in sensor technology and availability of computational resources (embedded processing) at low cost have made real-time intelligent control feasible for complex systems. The computational intelligence envisioned in modern intelligent machines will enhance the system performance and will provide capabilities such as criteria based control, identification of incipient faults, condition based maintenance, fault tolerance, and ability to monitor performance parameters in real-time. The first step in this process is to equip a system with a comprehensive suite of sensors. These sensors will provide real-time data and awareness about both, the internal system states and the external/environmental operating conditions. The aim of this work is to establish an argument in favor of using multiple sensors in all complex electro-mechanical systems. The report discusses numerous benefits of a multi-sensor environment with suitable examples and attempts to justify its pressing need in all the existing complex mechanical systems. Case studies for a multi-sensor environment in railroad freight cars and vehicle systems are presented. Sensing requirements in freight train and vehicle systems are evaluated and suitable sensor technology and commercial sensor options are suggested for decision makers. In addition to benefits, challenges in a multi-sensor environment such as sensor noise, cabling complexities, signal processing, communication, data validation and data management, sensor fusion, information integration, maintenance etc. are addressed and best practices to alleviate these complexities are discussed in the report. This effort lays out a foundation for developing a multi-sensor system and will enable computational intelligence and structured decision making in the system. / text

No description available.

Wireless sensor networks (WSN) have been largely used in various applications due to its ease of deployment and scalability. The throughput of such networks, however, suffers from high bit error rates mainly because of medium characteristics. Maximizing bandwidth utilization while maintaining low frame error rate has been an interesting problem. Frame fragmentation into small blocks with dedicated error detection codes per block can reduce the unnecessary retransmission of the correctly received blocks. The optimal block size, however, varies based on the wireless channel conditions. In addition, blocks within a frame can have different optimal sizes based on the variations on interference patterns. This thesis studies two dynamic partial packet recovery approaches experimentally over several interference intensities with various transmission-power levels. It also proposes a dynamic data link layer protocol: Hybrid Seda (H-Seda). H-Seda effectively addresses the challenges associated with dynamic partitioning of blocks while taking the observed error patterns into consideration. The design of H-Seda is discussed in details and compared to other previous approaches, namely Seda+ and Seda. The implementation of H-Seda shows substantial enhancements over fixed-size partial packet recovery protocols, achieving up to 2.5x improvement in throughput when the channel condition is noisy, while delay experienced decreases to only 14 % of the delay observed in Seda. On average, it shows 35% gain in goodput across all channel conditions used in our experiments. This significant improvement is due to the selective nature of H-Seda which minimizes retransmission overhead by selecting the appropriate number of blocks in each data frame. Additionally, H-Seda successfully reduces block overhead by 50% through removing block number field reaching to better performance when channel conditions are identical.

This work describes the development, validation and application of sensor systems to monitor phase transition events of cryoprotectant mixtures in samples and cryopreservation profiles and post-thaw recovery of Lactobacillus delbrueckii subsp. bulgaricus CFL1. Ice nucleation and glass transition (Tg) temperatures influence cell viability during cryopreservation. Knowledge of these phase changes for cryoprotectant mixtures is an essential step in optimising cryopreservation protocols for cell survival. Differential scanning calorimetry (DSC) is used to determine Tg, but the expensive nature of such instrumentation limits its widespread use. Cost-effective sensor systems have been designed to monitor ice-initiation and Tg events in small volume samples of cryoprotectants solutions. Tg values were measured for glycerol, sucrose and Me2SO (with and without NaCl supplement and ice-nucleators) in cryotubes and cryostraws, using temperature and screen-printed impedance sensors. The effect of changes to ice-initiation temperature on Tg was also investigated at different cooling and warming rates by using a Grant Asymptote (EF600) controlled rate freezer. The resulting Tg values obtained by single-channel transition monitoring system (TMS 1) were not significantly different from the values obtained by DSC reported in the literature. However multiple channelled transition monitoring system (TMS 2) requires further circuit modification and multiple screen-printed temperature probes to study the phase-change temperatures and to determine transition events in more than one sample at a time. The lactic acid bacterium (LAB) Lactobacillus delbrueckii was investigated as a model system to monitor the effect of different cryopreservation protocols on post-thaw cell metabolic activity. An important parameter for monitoring the post-thaw quality of LAB for starter culture preparation is the change in pH of the culture medium during incubation at 40 oC. Glass pH combination electrodes are the most common and widely used sensors. However, they are fragile, must be conditioned before use and are not disposable. An alternative to conventional glass electrodes are screen-printed carbon-metal electrodes. Different percentage mixtures of ruthenium and antimony pastes were tested and 54.5% carbon-antimony electrodes gave the best sensitivity and consistency in potentials at fixed pH with a screen-printed salt-bridged Ag/AgCl reference. LAB cultures were cryo-preserved at very rapid, moderate and very slow cooling rates and their post-thaw metabolic activity after overnight incubation in MRS broth was determined using screen-printed pH electrodes. Back to back testing with conventional glass pH sensors was performed to compare responses. Results indicated that early ice-initiation (by means of nucleators) prevents the cells from extensive dehydration (during cooling) and enables maximum post-thaw recovery after incubation (due to equilibrium ice formation and ice melting). In future, screen-printed pH sensors require development with integrated salt-bridged Ag/AgCl reference to make it robust in signalling response. The availability of low cost, disposable, non-fragile sensors and sensor systems to monitor transition events allows the determination of Tg of cryopreservation media during both cooling and warming cycles. A combined screen-printed (impedance + temperature) sensor is proposed for this purpose. A combined screen-printed (pH + reference) sensor would allow the monitoring of metabolic activity in post-thaw and fresh starter cultures of LAB. At present the salt-bridged pH reference is manually attached to the screen-printed pH working electrode but it requires further modifications to the method of attachment. The two sensor systems would enable optimisation of cryopreservation protocols for LAB and could enable such measurements to become routine at commercial scale.

The purpose of this thesis is to study security for digital home visits, where traditional home visits are replaced by digital home visits using digital technology. The report examines the safety aspects for welfare technology solutions where data is collected from sensor systems and digital platforms and examines di↵erent Swedish laws that implies on a digital home visit. The study proposes an implementation of a prototype application to support users, relatives, and healthcare professionals to conduct digital home visits in a safe manner. The chosen scenario of the digital home visit was to check whether the person has eaten food during the day or not. This was done in a lab kitchen at Lule°a University of Technology with help of Z-wave sensors and a implemented systems. The result is displayed on a secure website. The solution is discussed and compared to other technical solutions of this problem and also to several Swedish laws. This paper finishes with a section aimed to provide a variety of recommendations when implementing a similar system.

Complex engineered systems evolve, with a tendency toward self-organization, which can, paradoxically, frustrate the aims of those seeking to develop them. The systems engineer, seeking to promote the development in the context of changing and uncertain requirements, is challenged by conceptual gaps that emerge within engineering projects, particularly as they scale up, that inhibit communication among the various stakeholders. Overall optimization, involving multiple criterion, is often expressed in the language of the individual parties, increasing the complexity of the overall situation, subsuming the participants within the evolution of the complex engineered system, containing the objective and subjective in counterproductive or inefficient ways that can arrest healthy development. The conventional pragmatic systems engineering approach to the resolution of such situations is to introduce architectural discipline by way of separation of concerns. In complex engineered systems projects, the crucial interface, at any level of abstraction, is between the technical domain experts and higher level decision makers. Bridging the ensuing conceptual gap requires models and methods that provide communication tools promoting a convergence of the conversation between these parties on a common "common sense" of the underlying reality of the evolving engineered system. In the interest of conceptual clarity, we confine our investigation to a restricted, but important general class of evolving engineered system, information gathering and utilizing systems. Such systems naturally resolve the underlying domain specific measures by reduction into common plausible information measures aimed at an overall sense of informativeness. For concreteness, we further restrict the investigation and the demonstration to a species that is well documented in the open literature: weather radar networks, and in particular to the case of the currently emerging system referred to as CASA. The multiobjective problem of objectively exploring the high dimensionality of the decision space is done using multiobjective genetic algorithms (MOGA), specifically the John Eddy genetic algorithms (JEGA), resulting in well-formed Pareto fronts and sets containing Pareto optimal points within 20% of the ideal point. A visualization technique ensures a clear separation of the subjective criterion provided by the decision makers by superficially adding preferences to the objective optimal solutions. To identify the integrative objective functions and test patterns utilized in the MOGA analysis, explorations of networked weather radar technologies and configuration are completed. The explorations identify trends within and between network topologies, and captures both the robustness and fragility of network based measurements. The information oriented measures of fusion accuracy and precision are used to evaluate pairs of networked weather radars against a standardized low order vortex test pattern, resulting in a metrics for characterizing the performance of dual-Doppler weather radar pairs. To define integrative measures, information oriented measures abstracting over sensor estimators and parameters used to estimate the radial velocity and returned signal from distributed targets, specifically precipitation, are shown to capture the single radar predicted performance against standardized test patterns. The methodology bridges the conceptual gap, based on plausible information oriented measures, standardized with test patterns, and objectively applied to a concrete case with high dimensionality, allowed the conversation to converge between the systems engineer, decision makers, and domain experts. The method is an informative objective process that can be generalized to enable expansion within the technology and to other information gathering and utilizing systems and sensor technologies.

Accelerometers deployed within smart structures produce a wealth of vibration data that can be analyzed to infer information about the types of acceleration events that are occurring within the structure. In the case of monitored smart buildings, some of these acceleration events are linked to occupant behavior, such as walking, operating machinery, closing doors, etc. The identification and classification of such events has many potential applications within a smart structure or city. Understanding occupant patterns could be beneficial for operations, retail, or HVAC management, as it could be used to monitor occupancy flow with a relatively sparse sensor network. It may also have detrimental implications in terms of cybersecurity, where such information could be mined for malicious practices if unauthorized access to the data was obtained. This work presents methods for the detection and classification of vibration events in an experimental smart building, Goodwin Hall at Virginia Tech. Goodwin Hall's 200+ accelerometer network is used to gather acceleration data, from which vibration events are automatically detected and clustered. The presence of a vibration event is detected from a raw acceleration signal with an adaptive RMS threshold method. A feature vector is then created for each extracted event as areas under regions of the FFT of the event's acceleration signal. The feature vectors are then mapped into a low-dimensional space using principal component analysis, where they are clustered with various unsupervised algorithms. These processes have shown to be successful when gathering vibration events from a single-sensor setup, but pose challenges when expanded to a multi-sensor network. Because of this, expanded applications such as a semi-supervised classifier for events detected anywhere in the building are currently still under development. This semi-supervised process, combined with the known location of each sensor would allow inferences to be drawn about the frequency of different activity types in regions of the building not captured in the labeled data. Future work intends to address these multi-sensor challenges with adjustments to the algorithm process. / Master of Science / All objects experience vibrations when they are disturbed by some force. In the case of this work, the object is complex, a classroom building, but the principle still stands. When the building is disturbed by a force it will vibrate, even if the force is small, such as a person walking down a hallway or closing a door. The vibrations caused by these 'events' are unique to the type of event, that is, footstep vibrations will be different from door vibrations. These vibrations are observed with accelerometers, and the corresponding signal is used to determine what type of event caused the vibration. First, an event is automatically detected within the signal and separated from it. Second, characteristics unique to the signal are identified, a process known as 'feature extraction.' Finally, those features are used to distinguish the event from others and to identify what had caused it based on previous experimental data. The ability to detect these events and classify them introduces many interesting applications, including any that would stem from occupant detection, including improved security or operations, retail, or HVAC management. The methods here may also be applicable to other applications, such as monitoring bridges and machinery, or for developing cutting-edge smartphone applications with the accelerometer that is built in.

In this dissertation, the design, implementation, and educational applications of a real-time water and weather monitoring system, developed to enhance water sustainability education and research, are discussed. This unique system, called LabVIEW Enabled Watershed Assessment System (LEWAS), is a real- world extension of various data acquisition modules that were successfully implemented using LabVIEW into a freshman engineering course (Engineering Exploration, ENGE 1024) at Virginia Tech. The outdoor site location measures water quality and quantity data including flow rate, pH, dissolved oxygen, conductivity, and temperature -- as indicators of stream health - for an on-campus impaired stream in real-time. In addition, weather parameters (temperature, barometric pressure, relative humidity and precipitation) are measured at the LEWAS outdoor site. The measured parameters can be accessed by remote users in a real-time through a web-based interface for education and research. LEWAS is solar powered and uses the campus wireless network through a high-gain antenna to transmit data to remote clients in real-time. Its power budget consisting of consumption (14 W), electrical storage, and generation (80 W, peak) is balanced to enable 24/7 operation regardless of weather conditions. An embedded computer with low power consumption and modules for communicating and storing data are installed in the field and it is programmed to process measured environmental parameters to be delivered to remote users. This computer is programmed both using a field programmable gate array (FPGA, for low power consumption and robust operation) and traditional microprocessor programming (for more flexibility). The environmental sensors of the system are routinely calibrated using established procedures. A LEWAS Development Platform was established to develop and test the system and to train and mentor several undergraduate and graduate students who helped in its implementation. A number of design and implementation challenges were overcome including extending campus Internet access to a location not included on the network and integrating hardware and software from three different sensor manufacturers into a unified software platform accessible over the Internet. To study the educational applications of LEWAS, an observational study was conducted as the system was gradually introduced to students in ENGE 1024 between 2009 and 2011. Positive student attitudes on the role of LEWAS to enhance their environmental awareness informed an experimental design implemented to study the motivational outcomes associated with the system. Accordingly, appropriate educational interventions and a hands-on activity on the importance of environmental monitoring were developed for both control and experiment groups, with only the latter given access to LEWAS to retrieve the environmental parameters for the activity. An instrument was developed on the theoretical foundation of the expectancy value theory of motivation and was administered to control and experimental groups in ENGE 1024. Altogether, 150 students participated in the study. Exploratory Factor Analysis (EFA) was applied which resulted in factors that group questions together based on interest, importance, real-time access, and cost (feasibility of monitoring). After conducting parametric and nonparametric statistical analyses, it was determined that there exists a statistically significant difference between control and experimental groups in interest, real-time, and cost factors. This finding implies that providing real-time access to environmental parameters can increase student interest and their perception of feasibility of environmental monitoring -- both major components of motivation to learn about the environment. Future extensions and applications of the system at Virginia Tech and beyond are discussed. / Ph. D.