Exploiting parallelism within multidimensional multirate digital signal processing systems

The intense requirements for high processing rates of multidimensional Digital Signal Processing systems in practical applications justify the Application Specific Integrated Circuits designs and parallel processing implementations. In this dissertation, we propose novel theories, methodologies and architectures in designing high-performance VLSI implementations for general multidimensional multirate Digital Signal Processing systems by exploiting the parallelism within those applications. To systematically exploit the parallelism within the multidimensional multirate DSP algorithms, we develop novel transformations including (1) nonlinear I/O data space transforms, (2) intercalation transforms, and (3) multidimensional multirate unfolding transforms. These transformations are applied to the algorithms leading to systematic methodologies in high-performance architectural designs. With the novel design methodologies, we develop several architectures with parallel and distributed processing features for implementing multidimensional multirate applications. Experimental results have shown that those architectures are much more efficient in terms of execution time and/or hardware cost compared with existing hardware implementations

Exploiting parallelism within multidimensional multirate digital signal processing systems

The intense requirements for high processing rates of multidimensional Digital Signal Processing systems in practical applications justify the Application Specific Integrated Circuits designs and parallel processing implementations. In this dissertation, we propose novel theories, methodologies and architectures in designing high-performance VLSI implementations for general multidimensional multirate Digital Signal Processing systems by exploiting the parallelism within those applications. To systematically exploit the parallelism within the multidimensional multirate DSP algorithms, we develop novel transformations including (1) nonlinear I/O data space transforms, (2) intercalation transforms, and (3) multidimensional multirate unfolding transforms. These transformations are applied to the algorithms leading to systematic methodologies in high-performance architectural designs. With the novel design methodologies, we develop several architectures with parallel and distributed processing features for implementing multidimensional multirate applications. Experimental results have shown that those architectures are much more efficient in terms of execution time and/or hardware cost compared with existing hardware implementations

Physiologically-Aware Communication Architecture for Transmission of Biomedical Signals in BASNs for Emerging IoT Applications

This research work proposes a novel physiologicallyaware communication architecture for the transmission of biomedical signals in BASNs (Body Area Sensor Networks) and wearables for emerging IoT applications. The architecture to fulfill the following objectives: • Reduce volume of biomedical data in IoT networks and Internet infrastructure. • Minimize the required computational load of biomedical data on the cloud side. • Extend the lifetime of the mobile wearable/BASN through improved energy savings. This architecture is generalizable to class of biomedical signals. It prevents large volume of biomedical data to be generated; uses patient state to control almost any system parameter; achieves significant energy savings, helping to extend the lifetime of the device for medical-IoT applications

The dynamic predictive power of company comparative networks for stock sector performance

As economic integration and business connections increase, companies actively interact with each other in the market in cooperative or competitive relationships. To understand the market network structure with company relationships and to investigate the impacts of market network structure on stock sector performance, we propose the construct of a company comparative network based on public media data and sector interaction metrics based on the company network. All the market network structure metrics are integrated into a vector autoregression model with stock sector return and risk. Several findings demonstrate the dynamic relationships that exist between sector interactions and sector performance. First, sector interaction metrics constructed based on company networks are significant leading indicators of sector performance. Interestingly, the interactions between sectors have greater predictive power than those within sectors. Second, compared with the company closeness network, the company comparative network, which labels the cooperative or competitive relationships between companies, is a better construct to understand and predict sector interactions and performance. Third, competitive company interactions between sectors impact sector performance in a slower manner than cooperative company interactions. The findings enrich financial studies regarding asset pricing by providing additional explanations of company/sector interactions and insights into company management using industry-level strategies

Framework for Extracting and Characterizing Load Profile Variability Based on a Comparative Study of Different Wavelet Functions

The penetration of distributed energy resources (DERs) on the electric power system is changing traditional power flow and analysis studies. DERs may cause the systems\u27 protection and control equipment to operate outside their intended parameters, due to DERs\u27 variability and dispatchability. As this penetration grows, hosting capacity studies as well as protection and control impact mitigation become critical components to advance this penetration. In order to conduct such studies accurately, the electric power system\u27s distribution components should be modeled correctly, and will require realistic time series loads at varying temporal and spatial conditions. The load component consists of the built environment and its load profiles. However, large-scale building load profiles are scarce, expensive, and hard to obtain. This article proposes a framework to fill this gap by developing detailed and scalable synthesized building load profile data sets. Specifically, a framework to extract load variability characteristics from a subset of buildings\u27 empirical load profiles is presented. Thirty-four discrete wavelet transform functions with three levels of decomposition are used to extract a taxonomy of load variability profiles. The profiles are then applied to modeled building load profiles, developed using the energy simulation program EnergyPlus® , to generate synthetic load profiles. The synthesized load profiles are variations of realistic representations of measured load profiles, containing load variabilities observed in actual buildings served by the electric power system. The paper focuses on the framework development with emphasis on variability extraction and application to develop 750 synthesized load profiles at a 15-minute time resolution

A Case Study to Quantify Variability in Building Load Profiles

Recent technology development and penetration of advanced metering infrastructure (AMI), advanced building control systems, and the internet-of-things (IoT) in the built environment are providing detailed information on building operation, performance, and user\u27s comfort and behavior. Building owners can obtain a wide range of energy consumption details at various levels of time granularity to augment their decisions as they manage the building operation and interact with the grid. AMI data are providing a new level of detail and visibility that may enhance building services and assets in the smart grid domain and make buildings inch closer to becoming a grid-interactive energy efficient buildings (GEB). While utility-installed AMI typically records energy consumption at a 15, 30, or 60-minute resolution, building- owner-installed metering can record energy consumption at one-minute or sub-minute time scales, providing information about how much the energy consumption varies from one sub-minute to the other (i.e. variability) at a finer time resolutions than typically available from AMI. This paper examines one-minute building load profile data sets and presents a framework to study, define, extract, quantify and analyze variability in buildings\u27 load profiles. The discussion of variability and its analysis is based on a case study of an actual sub-minute time-resolution data set, collected in 2019, for two buildings in a Midwest state in the USA. The result shows that for the case studies, the level of variability in an end-use category is not simply proportional to its consumption. Furthermore, distinct and predictable daily variability patterns emerge in end-use load categories. This information is useful for a host of applications including prediction, forecasting, and modeling

Intelligent Controls for Net-Zero Energy Buildings

The goal of this project is to develop and demonstrate enabling technologies that can empower homeowners to convert their homes into net-zero energy buildings in a cost-effective manner. The project objectives and expected outcomes are as follows: • To develop rapid and scalable building information collection and modeling technologies that can obtain and process “as-built” building information in an automated or semiautomated manner. • To identify low-cost measurements and develop low-cost virtual sensors that can monitor building operations in a plug-n-play and low-cost manner. • To integrate and demonstrate low-cost building information modeling (BIM) technologies. • To develop decision support tools which can empower building owners to perform energy auditing and retrofit analysis. • To develop and demonstrate low-cost automated diagnostics and optimal control technologies which can improve building energy efficiency in a continual manner

Effect of Preharvest Spraying of Diethyl Aminoethyl Hexanoate on Membrane Lipid Metabolism of Grapes during Storage

In order to investigate the effect of preharvest spraying of diethyl aminoethyl hexanoate (DA-6) on membrane lipid metabolism in ‘Kyoho’ grapes during postharvest storage, distilled water (control) and 50 mg/L DA-6 were used to spray grapes at the veraison stage. The grapes were harvested when they were ripe, stored at (0 ± 1) ℃ and relative humidity of 65%–70%, and evaluated for cell membrane related indicators of grape skin after 0, 20, 30, 40, 50, and 60 days. The results showed that compared with the control group, DA-6 treatment effectively inhibited the increase in the relative permeability of cell membrane during postharvest storage, maintained the activities of lipoxygense (LOX), lipase and phospolipase D (PLD), inhibited the decrease of phosphatidylcholine and phosphatidylinositol content and the increase of phosphatidyl acid content, and maintained a high relative content of unsaturated acids such as linoleic acid and linolenic acid as well as saturated fatty acids such as stearic acid, behenic acid, arachidic acid and palmitoleic acid, consequently maintaining the degree of unsaturation of fatty acids at a high level. Additionally, DA-6 treatment suppressed the expression of the LOX, Lipase, and PLD genes. In summary, preharvest spraying of 50 mg/L DA-6 can delay the senescence and prolong the storage period of ‘Kyoho’ grapes by effectively relieving the membrane lipid metabolism during storage

Humidity-Induced Charge Leakage and Field Attenuation in Electric Field Microsensors

The steady-state zero output of static electric field measuring systems often fluctuates, which is caused mainly by the finite leakage resistance of the water film on the surface of the electric field microsensor package. The water adsorption has been calculated using the Boltzmann distribution equation at various relative humidities for borosilicate glass and polytetrafluoroethylene surfaces. At various humidities, water film thickness has been calculated, and the induced charge leakage and field attenuation have been theoretically investigated. Experiments have been performed with microsensors to verify the theoretical predictions and the results are in good agreement