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The automotive industry is in the midst of a shift towards electrification, with an increasing prominence of by-wire technologies. This thesis is dedicated to investigating the modeling, control, and parameter optimization of brake-by-wire actuators, with applications for both future autonomous and non-autonomous vehicles. To this aim, a modular control architecture is proposed to facilitate the integration of these actuators into vehicles.The actuators are initially modeled through bond graph methodology. Subsequently, a cascaded control approach is employed to govern the intelligent actuators, with individual controllers designed using the Youla parameterization technique. Two distinct physical parameter optimization strategies are employed to enhance actuator responsiveness and energy efficiency. One approach is based on linear system optimization using transfer function, while the other centers around nonlinear system optimization. Comparative evaluations of the actuators are conducted using step and ramp tests. Finally, a direct comparison of the actuators within the proposed control architecture is carried out through a straight-line braking test scenario.

The emergence of data-intensive applications, such as Deep Neural Networks (DNNs), exacerbates the well-known memory bottleneck in computer systems and demands early attention in the design flow. Electronic System-Level (ESL) design using Transaction Level Modeling (TLM) enables early performance estimation, efficient design space exploration, and gradual refinement. In this dissertation, we present our exploratory modeling framework for hardware-software codesign based on IEEE SystemC TLM with particular focus on exposing parallelism and memory contention. We demonstrate the effectiveness of our approach for representative large DNNs such as GoogLeNet and Single Shot MultiBox Detector.First, we study the impact of communication mechanisms on the available parallelism in TLM models. Specifically, we demonstrate the impact of varying synchronization mechanisms and buffering schemes on the exposed parallelism using different modeling styles of a DNN. We measure the performance of aggressive out-of-order parallel discrete event simulation and analyze the available parallelism in the models. Our study suggests that increased parallel simulation performance indicates better models with higher amounts of parallelism exposed.Second, we explore the critical aspects of modeling and analysis of timing accuracy with the respect to memory contention. A major hurdle in tackling the memory bottleneck is the detection of memory contention late in the design cycle when detailed timed or cycle-accurate models are developed. A bottleneck detected at such a late stage can severely limit the available design choices or even require costly redesign. To explore new architectures prior to RTL implementation, we propose a novel TLM-2.0 loosely-timed contention-aware (LT-CA) modeling style that offers high-speed simulation close to traditional loosely-timed (LT) models, yet shows the same accuracy for memory contention as low level approximately-timed (AT) models.Finally, we further refine the TLM-2.0 AT model by adding a cycle-accurate model of a memory subsystem. This model provides a higher timing accuracy for contention analysis. Hence it provides more accurate estimation of the performance. We revise our LT-CA memory delay modeling to provide further accuracy comparable to the cycle-accurate AT model of the shared memory subsystem. The high amount of contention on the shared memory suggests the need to move toward new processor architectures with local memories.

One of the biggest shortcomings of traditional quadcopters is that they are underactuatedwith control over only 4 out of a possible 6 degrees of freedom. The user may control translations in all three axes as well as a single rotation in the Z axis, otherwise known as yaw. A handful of designs have been introduced in the field to gain control of the two remaining degrees of freedom, as gaining control of them would mean complete control over the quadcopter’s attitude and position, a useful feature in certain applications. This work delves deeper into a design introduced in a previous work, of an overactuated quadcopter design with twelve system inputs, where each arm has 3 control inputs – propeller speed, a twist angle, and a tilt angle. A system identification of the quadcopter is completed using an experimental setup that dissects the quadcopter into a modular part – a single- arm. Furthermore, a multibody dynamic simulation of the complete quadcopter is created which accounts for the system’s mass distribution, moments of inertia, and low level motor dynamics which were simplified or ignored in previous work. In addition to the previously proposed control scheme, a new scheme is also introduced which simplifies the system into a 6 input to 6 output system, thus creating a more simple and elegant control scheme. Both control schemes are tested in simulation and experiment with success.

Two sub-systems that present a significant challenge in the development of highperformanceair vehicle exceeding speeds of Mach 5 are the power generation and thermalmanagement sub-systems. The air friction experienced at high speeds, particularly aroundthe engine, generates large thermal loads that need to be managed. In addition, traditionaljet engines do not operate at speeds greater than Mach 3, therefore eliminating thepossibility of a rotating power generator. A multi-mode water-based Rankine cycle is aninnovative method to address both of these constraints of generating power and providingcooling. Implementing a Rankine cycle-based system allows for the waste heat from thevehicle to be used to meet the onboard power requirements. This application of a Rankinecycle differs from standard power plant applications because the transient system dynamicsbecome important due to rapid changes in thermal loads and electrical power requirements.Both an experimental and computational investigation is presented. An experimentalsteady state energy balance was used to determine a 5.1% and 11.5% thermal and SecondLaw efficiency, respectively. Transient testing showed an increase in power generation of283% in 30.5 seconds when starting from idle, with a steady state power generation of 230W. In addition to the power generation, the experimental system removed 10.7 kW fromthe hot oil loop which emulates a typical aircraft cooling fluid. Experimental results were used in the development of dynamic computational models using OpenModelica, an opensourcemodeling tool. Deviation between model and experimental results was within 5%for component models and 3.5% when analyzing the system energy balance. Testing of thevehicle level model included steady state, transient, and simulated mission, which was usedto characterize performance and develop the system controls. During transient testing, thesystem controls demonstrated the ability to meet both the cooling and power requirementsof the system through rapid response times and minimal temperature overshoot (2.72%).The development and testing of this model provides an opportunity for scaling andoptimization of a combined power and thermal management system across a wide range ofvehicle sizes and operating conditions.

Due to the battery capacity, a ground-based Airborne Wind Energy has a limited flight time. An on-board power generation system could eliminate this problem and therefore increase the annual power production drastically. The main objective of this thesis is to investigate the feasibility of an on-board power generation system for a ground-based AWE-aircraft to recharge the batteries mid flight. A main focus hereby lies on using the existing main motors and propellers of the aircraft. To investigate the defining parameters and the power output of the system, a matlab model was developed and different measurements with a generator test rig and in a wind tunnel were conducted. The used carbon folding propellers unfold themselves to an open position in the wind tunnel. The measured power is sufficient to recharge the batteries in a reasonable time. The camber of the propeller has a big influence on the generated power. Conclusions Using the existing flight hardware is a very attractive alternative to using additional hardware since it results in a good power output while minimizing additional weight and drag when the system is not in use.

A series of single-physics heat and mass transfer models of a rotary hearth forging furnace are studied for design, selection, and implementation into a multi-physics full system model with a mind towards accuracy and computational efficiency. The transfer modes studied are fluid mass flow, mass-coupled enthalpy transfer, thermal conduction, thermal convection, radiative heat transfer, and steel oxidation. Each mode has one or more associated submodels which are compared for accuracy and efficiency against baseline high fidelity models. A novel model of zonal radiative heat transfer calculations is presented and compared to the radiative heat transfer of a full furnace geometry solved by finite element analysis. A full system model was developed by combining the optimal single-physics submodels according to criteria that favored both speed and accuracy. This system model was then put through a parameter sensitivity analysis for the purpose of identifying which uncontrollable parameters require further accuracy and which controllable parameters are best modified for temperature set point optimization. The system model was also put through an optimization function which determined locally optimal input parameters within a selected parameter space. These optima were compared for the purpose of maximizing furnace efficiency.

A series of single-physics heat and mass transfer models of a rotary hearth forging furnace are studied for design, selection, and implementation into a multi-physics full system model with a mind towards accuracy and computational efficiency. The transfer modes studied are fluid mass flow, mass-coupled enthalpy transfer, thermal conduction, thermal convection, radiative heat transfer, and steel oxidation. Each mode has one or more associated submodels which are compared for accuracy and efficiency against baseline high fidelity models. A novel model of zonal radiative heat transfer calculations is presented and compared to the radiative heat transfer of a full furnace geometry solved by finite element analysis. A full system model was developed by combining the optimal single-physics submodels according to criteria that favored both speed and accuracy. This system model was then put through a parameter sensitivity analysis for the purpose of identifying which uncontrollable parameters require further accuracy and which controllable parameters are best modified for temperature set point optimization. The system model was also put through an optimization function which determined locally optimal input parameters within a selected parameter space. These optima were compared for the purpose of maximizing furnace efficiency.

A hydro-pneumatic pulse forming network is an energy storage and conditioning system. It converts the highly variable power provided by renewable sources into a cyclical form demanded by practical loads. In an affiliated project, a laboratory testbed has been developed for demonstrating the HPPFN. This thesis studies the implementation of a cyber-physical system architecture that monitors and controls the HPPFN to enhance its reliability and flexibility. The thesis explains the testbed design including assessment of the components and their characteristics. It also explains the physical system modeling and simulation design process, the functionalities of the model as well as the difficulties encountered during the modeling process. CPS implementation and its decision-making logics are then explained in detail and its performance is evaluated based on the successfulness of its design purpose realization. The evolution of CPS model design is included to provide a clear overview of the CPS performance improvements. Possible future works to improve the CPS implementation are also discussed in the thesis. With the implementation of CPS, the HPPFN can adjust its charging and discharging strategy according to varying input conditions and load requirements to ensure optimal and efficient operation. This research demonstrates one way for how CPS connects a physical system to users by allowing them to monitor and control a system using online simulation models. It provides a basis for a control solution to the HPPFN where other control theories are hard to apply.

A hydro-pneumatic pulse forming network is an energy storage and conditioning system. It converts the highly variable power provided by renewable sources into a cyclical form demanded by practical loads. In an affiliated project, a laboratory testbed has been developed for demonstrating the HPPFN. This thesis studies the implementation of a cyber-physical system architecture that monitors and controls the HPPFN to enhance its reliability and flexibility. The thesis explains the testbed design including assessment of the components and their characteristics. It also explains the physical system modeling and simulation design process, the functionalities of the model as well as the difficulties encountered during the modeling process. CPS implementation and its decision-making logics are then explained in detail and its performance is evaluated based on the successfulness of its design purpose realization. The evolution of CPS model design is included to provide a clear overview of the CPS performance improvements. Possible future works to improve the CPS implementation are also discussed in the thesis. With the implementation of CPS, the HPPFN can adjust its charging and discharging strategy according to varying input conditions and load requirements to ensure optimal and efficient operation. This research demonstrates one way for how CPS connects a physical system to users by allowing them to monitor and control a system using online simulation models. It provides a basis for a control solution to the HPPFN where other control theories are hard to apply.

Managing large-scale dynamical systems (e.g., transportation systems, complex information systems, and power networks, etc.) in real-time is very challenging considering their complicated system dynamics, intricate network interactions, large scale, and especially the existence of various uncertainties. To address this issue, intelligent techniques which can quickly design decision-making strategies that are robust to uncertainties are needed. This dissertation aims to conquer these challenges by exploring a data-driven decision-making framework, which leverages big-data techniques and scalable uncertainty evaluation approaches to quickly solve optimal control problems. In particular, following techniques have been developed along this direction: 1) system modeling approaches to simplify the system analysis and design procedures for multiple applications; 2) effective simulation and analytical based approaches to efficiently evaluate system performance and design control strategies under uncertainty; and 3) big-data techniques that allow some computations of control strategies to be completed offline. These techniques and tools for analysis, design and control contribute to a wide range of applications including air traffic flow management, complex information systems, and airborne networks.

Current online risk monitors provide a point-in-time estimate of the system risk given the current plant configuration (e.g., equipment availability, operational regime, environmental conditions). However, these risk monitors do not account for plant- specific normal, abnormal, and deteriorating states of active components and systems. The lack of operating experience with proposed advanced reactor designs limits our ability to estimate the probability of failure (POF) of key components. Incorporation of unit-specific estimates of POF into dynamic probabilistic risk assessment (PRA) has the potential to enable real-time decisions about stress relief and to support effective maintenance planning while ensuring investment protection. The enhanced risk monitor (ERM) supports the safe and economic operation goals of advanced reactor by providing a dynamic assessment of system risk with real-time estimates of POF and event probability based on equipment condition assessment. A simulation framework for a prototypical advanced reactor (PAR) was developed in this work to provide a platform to demonstrate the ERM. A Simulink model of the PAR was developed, including the primary system, intermediate heat transport loop, steam generator, and balance of plant (BOP). To ensure accuracy across a large range of operating conditions, a nonlinear model for the primary system, including reactor kinetics and heat transfer, was used. A perturbation model of the steam generator showed good performance across the range of conditions and was thus employed. The PAR power block features two independent primary systems, each with dedicated intermediate heat exchangers and steam generators. These two modules are connected to a common BOP through a steam header. To balance the power output of each unit to meet overall power demand, fuzzy control is implemented in the primary system. Degradation of the primary and intermediate sodium pumps is numerically simulated to investigate the effect on overall plant performance. The results indicate that the core power decreases as pump degradation leads to reduced flow in either primary or intermediate loops. The developed PAR model provides simulated power block performance data under component degradation, which can be used to develop and demonstrate the ERM framework.

Abstract: Construction labour productivity (CLP) significantly influences the profitability of construction companies; however, CLP exhibits the highest variability among project resources and is a major source of project risk. The construction industry is thus constantly searching for ways to improve labour productivity. Unfortunately, despite long-term, continued research and industry practice, predicting and improving CLP remains a challenge. Previous productivity studies mainly focus on factor and activity models, using factor models to model productivity with context-specific influencing parameters (factors and practices), and activity models to model the relationship between productivity and work sampling proportions (WSP). However, modeling CLP remains a challenge as for a given context, the complex impact of the multiple subjective and objective variables, made up of critical factors, practices, and WSP; have to be considered simultaneously, while maintaining a high accuracy and interpretability in developed models. To address these challenges, this thesis presents advanced frameworks for the development of a series of interpretable and accurate fuzzy inference based context-specific CLP models, which are then abstracted to develop the universal CLP models, and facilitate a better understanding of the variables that influence CLP. The development of the CLP models included identifying, classifying, quantifying, and documenting the variables influencing CLP. By analyzing existing literature in the field of CLP analysis and modeling, the influencing variables, made up of 169 parameters and 7 work sampling categories, were identified and quantified. The research conducted extensive field data collection from 11 construction projects across Alberta, Canada, spanning over a time period of 29-months; and documented information using factor survey, factors and practices documentation, work sampling studies, foreman delay surveys, craftsman questionnaires, and productivity measurements. First, the research identified the key variables influencing CLP using expert and data-driven approaches in order to reduce the large feature space of the variables. Next, the role of work sampling proportions in CLP modeling was formulated by testing the fundamental assumption of activity models—that CLP improves if more time is spent on direct work activities—and analysis results showed that using work sampling proportions alone, it is not possible to accurately predict CLP. Thus, a system-based modeling framework to incorporate work sampling proportions with factors and practices leading to improved CLP modeling and analysis was developed. Then, an operational definition of context for CLP modeling was formulated and associated context attributes were developed, based on the 5W1H (Who, What, Where, When, Why, and How) question and answers approach, and employed together with the system-based CLP modeling framework for the development of a series of context-specific CLP models after combining projects sharing similar contexts. Using a hybrid fuzzy multi-objective optimization framework, the learning ability of the developed fuzzy inference system CLP models was improved. Finally, a context adaptation framework for transferring knowledge among contexts was developed using linear and non-linear adaptation on the membership functions of the context-specific fuzzy CLP models, and a framework for developing universal CLP models is established. The main contributions of this research to the state of art of CLP modeling and analysis are: (1) evaluation of the usefulness of relying on work sampling proportions like direct work or tool time to predict CLP, (2) development of a system model framework for CLP, which provides a better understanding of CLP and the variables influencing CLP, (3) addressing the challenges faced in past CLP models by developing and optimizing fuzzy inference CLP models, (4) presenting an operational definition of context for CLP modeling for characterizing and classifying construction projects and assisting in the process of grouping similar projects for more accurate context-specific CLP model development, and (5) developing frameworks for adaptation and abstraction of context-specific CLP models. The developed frameworks and findings of this study are of a value to researchers and industry practitioners and provide a better understanding of CLP, the variables influencing CLP, and how work-study methods like work sampling can be integrated to provide an accurate CLP analysis tool.

Grid parity for alternative energy resources occurs when the cost of electricity generated from the source is lower than or equal to the purchasing price of power from the electricity grid. This thesis aims to quantitatively analyze the evolution of hybrid stand-alone microgrids in the US, Germany, Pakistan and South Africa to determine grid parity for a solar PV/Diesel/Battery hybrid system. The Energy System Model (ESM) and NREL's Hybrid Optimization of Multiple Energy Resources (HOMER) software are used to simulate the microgrid operation and determine a Levelized Cost of Electricity (LCOE) figure for each location. This cost per kWh is then compared with two distinct estimates of future retail electricity prices at each location to determine grid parity points. Analysis results reveal that future estimates of LCOE for such hybrid stand-alone microgrids range within the 35-55 cents/kWh over the 25 year study period. Grid parity occurs earlier in locations with higher power prices or unreliable grids. For Pakistan grid parity is already here, while Germany hits parity between the years 2023-2029. Results for South Africa suggest a parity time range of the years 2040-2045. In the US, places with low grid prices do not hit parity during the study period. Sensitivity analysis results reveal the significant impact of financing and the cost of capital on these grid parity points, particularly in developing markets of Pakistan and South Africa. Overall, the study helps conclude that variations in energy markets may determine the fate of emerging energy technologies like microgrids. However, policy interventions have a significant impact on the final outcome, such as the grid parity in this case. Measures such as eliminating uncertainty in policies and improving financing can help these grids overcome barriers in developing economies, where they may find a greater use much earlier in time.

In support of hyperspectral imaging system design and parameter trade-off research, an analytical end-to-end model to simulate the remote sensing system pipeline and to forecast remote sensing system performance has been implemented. It is also being made available to the remote sensing community through a website. Users are able to forecast hyperspectral imaging system performance by defining an observational scenario along with imaging system parameters. For system modeling, the implemented analytical model includes scene, sensor and target characteristics as well as atmospheric features, background spectral reflectance statistics, sensor specifications and target class reflectance statistics. The sensor model has been extended to include the airborne ProspecTIR instrument. To validate the analytical model, experiments were designed and conducted. The predictive system model has been verified by comparing the forecast results to ones obtained using real world data collected during the RIT SHARE 2012 collection. Results include the use of large calibration panels to show the predicted radiance consistent with the collected data. Grass radiance predicted from ground truth reflectance data also compare well with the real world collected data, and an eigenvector analysis also supports the validity of the predictions. Two examples of subpixel target detection scenario are presented. One is to detect subpixel wood yellow painted planks in an asphalt playground, and the other is to detect subpixel green painted wood planks in grass. To validate our system performance, the detection performance are analyzed using receiver operating characteristic (ROC) curves in a comprehensive scenario setting. The predicted ROC result of the yellow planks matches well the ROC derived from collected data. However, the predicted ROC curve of green planks differs from collected data ROC curve. Additional experiments were conducted and analyzed to discuss the possible reasons of the mismatch including scene characterization inaccuracy. Several subpixel target detection parameter trade-off analyses are given, including relative calibration error vs SNR, the relationship among probability of detection, meteorological range, pixel fill factor, relative calibration error and false alarm rate. These trade-off analyses explain the utility of this model for hyperspectral imaging system design and research.

Typically, researchers underestimate the effect of temperature, amongst other inputs such asload, creep and shrinkage, in a structure. Even when this is not the case, it is treated ascompletely independent and worthy of generating error-free predictions about the current stateof a structure. There have been various methods used, under the broader context of StructuralHealth Monitoring in literature, and most have been feature-extraction-based solutions.This thesis introduces Eigen Residuals, which is mathematically derived from PCA. I shall showthat knowledge from this, and Eigen Slope, better models a truss bridge member's response tochanges in strain as a result of driving inputs. There are instances where existing systemmodels for a structure's members are inaccurate, and PCA System Modeling solves thischallenge.Next, I establish a monitoring threshold using equi-probability contours, and incorporateknowledge of expected structural relationships between members. I proceed to develop asystem-wide multi-member bridge monitoring algorithm, using Eigen Residuals in a multivariateapproach. Members validate each other (via a voting system for events of significance), andprovide an extra layer of intelligence.Our final results show an improved system model for individual members of a truss bridge. Inaddition, the multi-member algorithm eliminated false alarms that were generated, and providedextra information to the Ohio Department of Transportation, for bridge maintenance purposes.

Abstract: Information granules regarded as the key components of knowledge representation help generalize information while the level of granularity of information granules becomes crucial to the problem description and an overall strategy of problem solving in system modeling. The ultimate challenge is to develop a comprehensive model within which information granules serving as an important design asset helps realize problem solving. In this study, we propose a system modeling framework of granular architectures and evaluate its effectiveness. We develop a new approach to build functional rule-based fuzzy models by focusing on the reduction of input space which is realized by genetic algorithms. A concept and practice of a granular fuzzy model is established with interval results of global character by aggregating some collective sources of knowledge (local models). The granular fuzzy model based on interval analysis directly reflects upon the diversity of the local sources of knowledge in which intervals are constructed through the use of the principle of justifiable granularity. Two approaches are proposed to design granular neural networks. The first method is concerned with a formation of a global granular neural network whose architecture is formed as a result of reconciliation of outcomes produced by local neural networks. The second method is aimed at the realization of granular neural networks through the formation of interval (granular) connections around numeric connections of the original neural networks where single- and multiple-objective particle swarm optimization is used. We develop a granular analytic hierarchy process (AHP), which provides decision--makers a significant level of flexibility (expressed by the granular nature of the underling construct) so that their initial preferences can be adjusted within a certain interval to achieve higher level of consensus within the group. Moreover, a granulation of linguistic information used in the AHP model is adopted to elevate the consistency of the obtained solution. Throughout the overall study, particle swarm optimization is used as a comprehensive optimization framework to realize the design of granular constructs.

This thesis constitutes an effort of verifying and validating a safety system designed for a specific research facility. An initial comprehensive review of the system design is presented, detailing all the relevant aspects of the system and investigating the way its design development interrelates to the formal "safety analysis" procedures proposed in the literature. The verification process includes the development of a complete formal specification for the system and the investigation of how well the original design follows its formal requirements. The validation process details the system’s hardware and software implementations, discusses the testing approach, and evaluates the final outcomes. In summary, this work can be considered as an effort to prove that the operation of the laboratory in question, within the designed safety system’s scope, is safe.

A multi-unit tracked vehicle such as a continuous haulage system is widely used in underground mining applications due to its high mobility and payload capacity on rugged and soft terrain. To automate such a system, a high fidelity model of a tracked vehicle is essential in designing a controller for each tracked vehicle in the system, and a system model is required to simulate its response to input commands. This thesis presents the 2-D mathematical models of a tracked vehicle and a multi-unit tracked vehicle. All existing track-terrain interaction models are investigated and modified. By employing the modified track-terrain interaction model and applying Newton's second law of motion, the equations of motion of both single and multi-unit tracked vehicles can be derived. Computer programs for simulating the motions of these tracked vehicles on level ground have been implemented on a digital computer based on the derived system of differential equations. The fourth-order Runge-Kutta and Keun's methods are adopted to numerically integrate these differential equations. The simulation results clearly show that the programs can accurately predict the motion of a tracked vehicle maneuvered on horizontal plane, and closely predict the response of a multi-unit tracked vehicle operated on level ground its command inputs.

In the past twenty five years, efforts have been made to develop Compton Single Photon Emission Computed Tomography (SPECT) cameras for medical imaging. The Compton camera consists of a pair of position sensitive detectors, a Compton scatter detector and a detector to absorb the scattered photons. The energy and position information from these detectors gives information about the energy, position, and incident direction of the incoming gamma-ray. This electronic collimation is superior to conventional mechanical collimation since it utilizes as many emitted photons as possible from all directions, improves the solid angle of detection and therefore provides improved detection efficiency and increased sensitivity. Better sensitivity will have a positive impact on image noise and resolution. Development of practical Compton SPECT faces many new challenges. First, Compton SPECT acquires the projection data directly in 3D and requires storage of three sets of coordinates, two spatial coordinates and the angular coordinates. Therefore Compton SPECT cameras have to deal with very large amount of data leading to difficulties in computation. Hence, simplification of Compton SPECT camera is necessary. Second, new reconstruction methods need to be developed for the Compton SPECT conical projection geometry. This dissertation presents a method for reducing storage and computation which is based on an analytical model that has the potential to permit tractable fully 3D reconstructions. A mathematical model is proposed for the camera which exploits hemispherical symmetries by using an adapted spatial sampling pattern in the object domain. For each projection angle, the sampling pattern is uniform over a set of equispaced nested hemispheres. By using this sampling pattern the system matrix is reduced to a product of an (approximately) block circulant matrix and a sparse interpolation matrix. This representation reduces the very high storage and computation requirement inherent to 3D reconstruction. We consider a simple method for designing the detector pair trajectory around the field of view using a sinogram sampling diagram to guarantee proper object sampling. As the exploitation of hemispherical symmetries requires interpolation, we develop a 3D volumetric interpolation between hemispherical and cartesian coordinates. Finally, we present a 3D image reconstruction method using the 2D Fourier transform for which there exists a fast algorithm because of the block circulant structure of the transition matrix. These methods are simply illustrated for the noiseless case with implementation of a fully 3D penalized least squares reconstruction algorithm.