Occupancy Estimation in Smart Building using Hybrid CO2/Light Wireless Sensor Network

Smart building, which delivers useful services to residents at lowest cost and maximum comfort, has gained increasing attention in recent years. A variety of emerging information technologies have been adopted in modern buildings, such as wireless sensor networks, internet of things, big data analytics, deep machine learning, etc. Most people agree that a smart building should be energy efficient, and consequently, much more affordable to building owners. Building operation accounts for major portion of energy consumption in the United States. HVAC (heating, ventilating, and air conditioning) equipment is a particularly expensive and energy consuming of building operation. As a result, the concept of “demand-driven HVAC control” is currently a growing research topic for smart buildings. In this work, we investigated the issue of building occupancy estimation by using a wireless CO2 sensor network. The concentration level of indoor CO2 is a good indicator of the number of room occupants, while protecting the personal privacy of building residents. Once indoor CO2 level is observed, HVAC equipment is aware of the number of room occupants. HVAC equipment can adjust its operation parameters to fit demands of these occupants. Thus, the desired quality of service is guaranteed with minimum energy dissipation. Excessive running of HVAC fans or pumps will be eliminated to conserve energy. Hence, the energy efficiency of smart building is improved significantly and the building operation becomes more intelligent. The wireless sensor network was selected for this study, because it is tiny, cost effective, non-intrusive, easy to install and flexible to configure. In this work, we integrated CO2 and light senors with a wireless sensor platform from Texas Instruments. Compare with existing occupancy detection methods, our proposed hybrid scheme achieves higher accuracy, while keeping low cost and non-intrusiveness. Experimental results in an office environment show full functionality and validate benefits. This study paves the way for future research, where a wireless CO2 sensor network is connected with HVAC systems to realize fine-grained, energy efficient smart building

Barn Owls (\u3cem\u3eTyto alba\u3c/em\u3e) Crossing the Road - Examining the Interplay Among Occupancy, Behavior, Habitat Selection, and Roadway Mortality in Southern Idaho

Barn Owls (Tyto alba) are killed by vehicle collisions in greater numbers than any other North American bird of prey. Interstate-84 (I-84) in southern Idaho, USA has among the world’s highest known rates of Barn Owl-vehicle collisions. Little is known about Barn Owl occupancy in this region, so it is unclear if owls are killed in proportion to their abundance, or if they are equally abundant in segments with lower mortality and somehow escape collisions. Furthermore, studies of Barn Owl movements and behavior are limited. I was interested in understanding (1) factors that affect Barn Owl occupancy in two seasons: early- and post-breeding, (2) Barn Owl colonization of sites from early-breeding to post-breeding season, (3) examining the relationship between model-predicted occupancy, based on the factors I assessed, and roadway mortality established from observed Barn Owl mortality locations, and (4) examining Barn Owl behavior, movements and habitat selection, particularly in relation to I-84. I conducted nighttime point counts for Barn Owls in southern Idaho during the early- and post-breeding seasons (Jan – Mar and Sept – Nov 2014, respectively). I detected Barn Owls during 52 of 666 (7.8 %) point counts and at 39 of 222 (17.6 %) locations in the early-breeding season and during 50 of 201 (24.8 %) point counts and at 31 of 67 (46.3 %) locations in the post-breeding season. During the early-breeding season, probability of Barn Owl detection was 0.32 ± 0.06 (SE). Detection increased with playback of Barn Owl calls and with increasing Julian date, percent moon illumination, and cloud cover. Barn Owl occupancy increased with increased proportion of crops and presence of trees, and it decreased as background noise level (dBA) increased. Probability of detection of Barn Owls was higher in the post-breeding season (0.45 ± 0.07). Detection increased with playback of Barn Owl vocalizations, increasing Julian date, and decreasing background noise. During the post-breeding season, Barn Owl occupancy was positively related to stream length and negatively related to proportion development and increasing distance from the Snake River. Of the potential models I assessed to describe colonization between seasons, there were two top models. The first model indicated that colonization of sites from the early- to post-breeding season declined with increasing terrain roughness while the second suggested that colonization increased with increasing cumulative stream length but decreased with distance to the Snake River and proportion development. Understanding factors influencing occupancy of Barn Owls will facilitate more effective conservation of this species in southern Idaho, especially in light of potential population declines related to roadway mortality. Using data from standardized roadkill surveys, I also compared road mortality locations of Barn Owls to model-predicted occupancy estimates to understand how occupancy may be influencing mortality along I-84. Using the previously created occupancy models, I generated predicted occupancy at point-count locations which I then paired with the nearest interstate segment (1- and 5-km lengths) to examine the potential effects of occupancy and season on the likelihood of dead Barn Owls. The likelihood that 1-km segments near point count locations included a dead Barn Owl increased with occupancy and was greater during the early-breeding season. For 5-km segments, there was an interaction between occupancy and season, with occupancy having a greater positive effect on mortality during the early-breeding season than in the post-breeding season. However, a substantial proportion of variation in roadway mortality at both scales (96 % and 56% at 1 and 5 km scale respectively) was not explained by occupancy and season, so factors such as geometric roadway features, traffic patterns, fluctuations in small mammal abundance, and owl behavior near the interstate likely also influence mortality rates and locations. Finally, to address questions of Barn Owl behavior and movements in relation to I-84, as well as habitat use I studied four adult male Barn Owls that were tending nests during February 2015. Two of these nests were within 3 km of the interstate, whereas the other two were more than 25 km away. I examined the efficacy of GPS data loggers for tracking Barn Owls and assessed six recapture methods to retrieve the data loggers. I obtained location data that spanned approximately two weeks of activity for each owl during the nesting season. I recaptured all instrumented males and found that manual- or laser-break triggered trap doors mounted on the nest box were most effective for recapturing Barn Owls. Within home ranges, the probability a Barn Owl used a site was higher near trees and minor roads and lower as distance from the nearest stream and the nearest major road increased. Barn Owl use of areas also increased as terrain roughness increased. Relative use of development, hay/pasture, and sage steppe land cover were less than for cultivated crops, while owls used grassland/herbaceous and wetland land cover more than cultivated crops. The two male Barn Owls that nested within 3 km of the interstate never moved closer than 1 km even though their maximum movements ranged up to 3 km. Thus, it is possible major roadways function as barriers to adult owl movements during the breeding season because they avoid roads, but they remain susceptible to road mortality in their more rare attempts to cross

Adaptation and Stochasticity of Natural Complex Systems

The methods that fueled the microscale revolution (top-down design/fabrication, combined with application of forces large enough to overpower stochasticity) constitute an approach that will not scale down to nanoscale systems. In contrast, in nanotechnology, we strive to embrace nature’s quite different paradigms to create functional systems, such as self-assembly to create structures, exploiting stochasticity, rather than overwhelming it, in order to create deterministic, yet highly adaptable, behavior. Nature’s approach, through billions of years of evolutionary development, has achieved self-assembling, self-duplicating, self-healing, adaptive systems. Compared to microprocessors, nature’s approach has achieved eight orders of magnitude higher memory density and three orders of magnitude higher computing capacity while utilizing eight orders of magnitude less power. Perhaps the most complex of functions, homeostatis by a biological cell – i.e., the regulation of its internal environment to maintain stability and function – in a fluctuating and unpredictable environment, emerges from the interactions between perhaps 50M molecules of a few thousand different types. Many of these molecules (e.g. proteins, RNA) are produced in the stochastic processes of gene expression, and the resulting populations of these molecules are distributed across a range of values. So although homeostasis is maintained at the system (i.e. cell) level, there are considerable and unavoidable fluctuations at the component (protein, RNA) level. While on at least some level, we understand the variability in individual components, we have no understanding of how to integrate these fluctuating components together to achieve complex function at the system level. This thesis will explore the regulation and control of stochasticity in cells. In particular, the focus will be on (1) how genetic circuits use noise to generate more function in less space; (2) how stochastic and deterministic responses are co-regulated to enhance function at a system level; and (3) the development of high-throughput analytical techniques that enable a comprehensive view of the structure and distribution of noise on a whole organism level

Spectrum measurement, sensing, analysis and simulation in the context of cognitive radio

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated locally by national agencies. Spectrum has been assigned to different services and it is very difficult for emerging wireless technologies to gain access due to rigid spectmm policy and heavy opportunity cost. Current spectrum management by licensing causes artificial spectrum scarcity. Spectrum monitoring shows that many frequencies and times are unused. Dynamic spectrum access (DSA) is a potential solution to low spectrum efficiency. In DSA, an unlicensed user opportunistically uses vacant licensed spectrum with the help of cognitive radio. Cognitive radio is a key enabling technology for DSA. In a cognitive radio system, an unlicensed Secondary User (SU) identifies vacant licensed spectrum allocated to a Primary User (PU) and uses it without harmful interference to the PU. Cognitive radio increases spectrum usage efficiency while protecting legacy-licensed systems. The purpose of this thesis is to bring together a group of CR concepts and explore how we can make the transition from conventional radio to cognitive radio. Specific goals of the thesis are firstly the measurement of the radio spectrum to understand the current spectrum usage in the Humber region, UK in the context of cognitive radio. Secondly, to characterise the performance of cyclostationary feature detectors through theoretical analysis, hardware implementation, and real-time performance measurements. Thirdly, to mitigate the effect of degradation due to multipath fading and shadowing, the use of -wideband cooperative sensing techniques using adaptive sensing technique and multi-bit soft decision is proposed, which it is believed will introduce more spectral opportunities over wider frequency ranges and achieve higher opportunistic aggregate throughput.Understanding spectrum usage is the first step toward the future deployment of cognitive radio systems. Several spectrum usage measurement campaigns have been performed, mainly in the USA and Europe. These studies show locality and time dependence. In the first part of this thesis a spectrum usage measurement campaign in the Humber region, is reported. Spectrum usage patterns are identified and noise is characterised. A significant amount of spectrum was shown to be underutilized and available for the secondary use. The second part addresses the question: how can you tell if a spectrum channel is being used? Two spectrum sensing techniques are evaluated: Energy Detection and Cyclostationary Feature Detection. The performance of these techniques is compared using the measurements performed in the second part of the thesis. Cyclostationary feature detection is shown to be more robust to noise. The final part of the thesis considers the identification of vacant channels by combining spectrum measurements from multiple locations, known as cooperative sensing. Wideband cooperative sensing is proposed using multi resolution spectrum sensing (MRSS) with a multi-bit decision technique. Next, a two-stage adaptive system with cooperative wideband sensing is proposed based on the combination of energy detection and cyclostationary feature detection. Simulations using the system above indicate that the two-stage adaptive sensing cooperative wideband outperforms single site detection in terms of detection success and mean detection time in the context of wideband cooperative sensing

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

Characterization and management of voltage noise in multi-core, multi-threaded processors

textReliability is one of the important issues of recent microprocessor design. Processors must provide correct behavior as users expect, and must not fail at any time. However, unreliable operation can be caused by excessive supply voltage fluctuations due to an inductive part in a microprocessor power distribution network. This voltage fluctuation issue is referred to as inductive or di/dt noise, and requires thorough analysis and sophisticated design solutions. This dissertation proposes an automated stressmark generation framework to characterize di/dt noise effect, and suggests a practical solution for management of di/dt effects while achieving performance and energy goals. First, the di/dt noise issue is analyzed from theory to a practical view. Inductance is a parasitic part in power distribution network for microprocessor, and its characteristics such as resonant frequencies are reviewed. Then, it is shown that supply voltage fluctuation from resonant behavior is much harmful than single event voltage fluctuations. Voltage fluctuations caused by standard benchmarks such as SPEC CPU2006, PARSEC, Linpack, etc. are studied. Next, an AUtomated DI/dT stressmark generation framework, referred to as AUDIT, is proposed to identify maximum voltage droop in a microprocessor power distribution network. The di/dt stressmark generated from AUDIT framework is an instruction sequence, which draws periodic high and low current pulses that maximize voltage fluctuations including voltage droops. AUDIT uses a Genetic Algorithm in scheduling and optimizing candidate instruction sequences to create a maximum voltage droop. In addition, AUDIT provides with both simulation and hardware measurement methods for finding maximum voltage droops in different design and verification stages of a processor. Failure points in hardware due to voltage droops are analyzed. Finally, a hardware technique, floating-point (FP) issue throttling, is examined, which provides a reduction in worst case voltage droop. This dissertation shows the impact of floating point throttling on voltage droop, and translates this reduction in voltage droop to an increase in operating frequency because additional guardband is no longer required to guard against droops resulting from heavy floating point usage. This dissertation presents two techniques to dynamically determine when to tradeoff FP throughput for reduced voltage margin and increased frequency. These techniques can work in software level without any modification of existing hardware.Electrical and Computer Engineerin

Detecting malfunction in wireless sensor networks

The objective of this thesis is to detect malfunctioning sensors in wireless sensor networks. The ability to detect abnormality is critical to the security of any sensor network. However, the ability to detect a faulty wireless sensor is not trivial. Controlled repeatable experiments are difficult in wireless channels. A Redhat Linux. 7.0 Wireless Emulation Dynamic Switch software was used to solve this problem. Six nodes were configured with a node acting as a base station. The nodes were all part of a cell. This means that every node could communicate with all other nodes. A client-server program simulated the background traffic. Another program simulated a faulty node. A node was isolated as the faulty node while all other nodes were good. The experiment ran for several hours and the data was captured with tcpdump. The data was analyzed to conclusions based on a statistical comparison of good node versus bad node. The statistical delay on the good node was an average of 0.69 ms while the standard deviation was 0.49. This was much better than the delay on the bad node that was 0.225192 s with a standard deviation of 0.89. This huge difference in the delay indicated that the faulty node was detected statistically. A threshold value of I ms was chosen. The good node was within this value about 98% of the time. The bad node on the other hand was far out of this range and was definitely detected. The channel utilization data provided the same conclusion

RFiof: An RF Approach to I/O-pin and Memory Controller Scalability for Off-chip Memories

Given the maintenance of Moore's law behavior, core count is expected to continue growing, which keeps demanding more memory bandwidth destined to feed them. Memory controller (MC) scalability is crucial to achieve these bandwidth needs, but constrained by I/O pin scaling. In this study, we introduce RFiof, a radio-frequency (RF) memory approach to address I/O pin constraints which restrict MC scalability in off-chip-memory systems, while keeping interconnection energy at lower levels. In this paper, we model, design, and demonstrate how RFiof achieves high MC I/O pin scalability for different memory technology generations, while evaluating its area and power/energy impact. By introducing the novel concept of RFpins -- to replace traditional MC I/O pins, and using RFMCs - MCs coupled to RF transmitters (TX)/receivers (RX), while employing a minimal RF-path between RFMC and ranks, we demonstrate that for a 32-out-of-order multicore configured with off-chip ranks with a 1:1 core-to-MC ratio, RFiof presents scalable 4 RFpins per RFMC -comparable to pin-scalable optical solutions - and is able to respectively improve bandwidth and performance by up to 7.2x and 8.6x, compared to the traditional baseline -- constrained to MC I/O pin counts. Furthermore, RFiof reduces about 65.6% of MC area usage, and 80% of memory path energy interconnection

Development of low-cost indoor air quality monitoring devices: Recent advancements

The use of low-cost sensor technology to monitor air pollution has made remarkable strides in the last decade. The development of low-cost devices to monitor air quality in indoor environments can be used to understand the behaviour of indoor air pollutants and potentially impact on the reduction of related health impacts. These user-friendly devices are portable, require low-maintenance, and can enable near real-time, continuous monitoring. They can also contribute to citizen science projects and community-driven science. However, low-cost sensors have often been associated with design compromises that hamper data reliability. Moreover, with the rapidly increasing number of studies, projects, and grey literature based on low-cost sensors, information got scattered. Intending to identify and review scientifically validated literature on this topic, this study critically summarizes the recent research pertinent to the development of indoor air quality monitoring devices using low-cost sensors. The method employed for this review was a thorough search of three scientific databases, namely: ScienceDirect, IEEE, and Scopus. A total of 891 titles published since 2012 were found and scanned for relevance. Finally, 41 research articles consisting of 35 unique device development projects were reviewed with a particular emphasis on device development: calibration and performance of sensors, the processor used, data storage and communication, and the availability of real-time remote access of sensor data. The most prominent finding of the study showed a lack of studies consisting of sensor performance as only 16 out of 35 projects performed calibration/validation of sensors. An even fewer number of studies conducted these tests with a reference instrument. Hence, a need for more studies with calibration, credible validation, and standardization of sensor performance and assessment is recommended for subsequent research