The dynamic decode-and-forward channel: SNR allocation.

In the half-duplex relay channel applying the decode-and-forward protocol the relay introduces energy over random time intervals into the channel as observed at the destination. Consequently, during simulation the average signal power seen at the destination becomes known at run-time only. Therefore, in order to obtain specific performance measures at the signal-to-noise ratio (SNR) of interest, strategies are required to adjust the noise variance during simulation run-time. It is necessary that these strategies result in the same performance as measured under real-world conditions. This paper introduces three noise power allocation strategies and demonstrates their applicability using numerical and simulation results.

A touchless passive Infrared gesture sensor.

A sensing device for a touchless, hand gesture, user interface based on an inexpensive passive infrared pyroelectric detector array is presented. The 2 x 2 element sensor responds to changing infrared radiation generated by hand movement over the array. The sensing range is from a few millimetres to tens of centimetres. The low power consumption (< 50 μW) enables the sensor’s use in mobile devices and in low energy applications. Detection rates of 77% have been demonstrated using a prototype system that differentiates the four main hand motion trajectories – up, down, left and right. This device allows greater non-contact control capability without an increase in size, cost or power consumption over existing on/off devices.

CFAR Adaptive PN Code acquisition for DSSS Systems.

Constant false alarm rate (CFAR) techniques can be used in Pseudo-Noise (PN) code acquisition in Spread Spectrum (SS) communication systems, and all the CFAR techniques perform well in homogeneous background PN code acquisition. However, in non-homogeneous background, some CFAR techniques suffer rapid degradation. GO/SO (Greatest-of/Smallest-of) CFAR and adaptive censored mean level detector (ACMLD) are two adaptive CFAR techniques, which are analyzed and compared with other CFAR techniques. The simulation results show that GO/SO CFAR is superior to other CFAR techniques, it maintains short mean acquisition time (MAT) even at environment with strong clutter noise, and ACMLD is suitable for background with strong interfering targets

Mid-infrared photonic sensors based on metamaterial structures

In this work three different metallic metamaterials (MMs) structures such as asymmetric split ring resonators (A-SRRs), dipole and split H-shaped (ASHs) structures that support plasmonic resonances have been developed. The aim of the work involves the optimization of photonic sensor based on plasmonic resonances and surface enhanced infrared absorption (SEIRA) from the MM structures. The MMs structures were designed to tune their plasmonic resonance peaks in the mid-infrared region. The plasmonic resonance peaks produced are highly dependent on the structural dimension and polarisation of the electromagnetic (EM) source. The ASH structure particularly has the ability to produce the plasmonic resonance peak with dual polarisation of the EM source. The double resonance peaks produced due to the asymmetric nature of the structures were optimized by varying the fundamental parameters of the design. These peaks occur due to hybridization of the individual elements of the MMs structure. The presence of a dip known as a trapped mode in between the double plasmonic peaks helps to narrow the resonances. A periodicity greater than twice the length and diameter of the metallic structure was applied to produce narrow resonances for the designed MMs. A nanoscale gap in each structure that broadens the trapped mode to narrow the plasmonic resonances was also used. A thickness of 100 nm gold was used to experimentally produce a high quality factor of 18 in the mid-infrared region. The optimised plasmonic resonance peaks was used for detection of an analyte, 17β-estradiol. 17β-estradiol is mostly responsible for the development of human sex organs and can be found naturally in the environment through human excreta. SEIRA was the method applied to the analysis of the analyte. The work is important in the monitoring of human biology and in water treatment. Applying this method to the developed nano-engineered structures, enhancement factors of 10^5 and a sensitivity of 2791 nm/RIU was obtained. With this high sensitivity a figure of merit (FOM) of 9 was also achieved from the sensors. The experiments were verified using numerical simulations where the vibrational resonances of the C-H stretch from 17β-estradiol were modelled. Lastly, A-SRRs and ASH on waveguides were also designed and evaluated. These patterns are to be use as basis for future work.

Spatial and temporal measurements using polyoxometalate, enzymatic and biofilm layers on a CMOS 0.35 μm 64 X 64-pixel I.S.F.E.T. array sensor

This thesis presents the achievements and scientific work conducted using a previously designed and fabricated 64 x 64-pixel ion camera with the use of a 0.35 μm CMOS technology. We used an array of Ion Sensitive Field Effect Transistors (ISFETs) to monitor and measure chemical and biochemical reactions in real time. The area of our observation was a 4.2 x 4.3 mm silicon chip while the actual ISFET array covered an area of 715.8 x 715.8 μm consisting of 4096 ISFET pixels in total with a 1 μm separation space among them. The ion sensitive layer, the locus where all reactions took place was a silicon nitride layer, the final top layer of the austriamicrosystems 0.35 μm CMOS technology used. Our final measurements presented an average sensitivity of 30 mV/pH. With the addition of extra layers we were able to monitor a 65 mV voltage difference during our experiments with glucose and hexokinase, whereas a difference of 85 mV was detected for a similar glucose reaction mentioned in literature, and a 55 mV voltage difference while performing photosynthesis experiments with a biofilm made from cyanobacteria, whereas a voltage difference of 33.7 mV was detected as presented in literature for a similar cyanobacterial species using voltamemtric methods for detection. To monitor our experiments PXIe-6358 measurement cards were used and measurements were controlled by LabVIEW software. The chip was packaged and encapsulated using a PGA-100 chip carrier and a two-component commercial epoxy. Printed circuit board (PCB) has also been previously designed to provide interface between the chip and the measurement cards.

The development of planar high-K/III-V p-channel MOSFETs for post-silicon CMOS

Conventional Si complementary-metal-oxide-semiconductor (CMOS) scaling is fast approaching its limits. The extension of the logic device roadmap for future enhancements in transistor performance requires non-Si materials and new device architectures. III-V materials, due to their superior electron transport properties, are well poised to replace Si as the channel material beyond the 10nm technology node to mitigate the performance loss of Si transistors from further reductions in supply voltage to minimise power dissipation in logic circuits. However several key challenges, including a high quality dielectric/III-V gate stack, a low-resistance source/drain (S/D) technology, heterointegration onto a Si platform and a viable III-V p-metal-oxide-semiconductor field-effect-transistor (MOSFET), need to be addressed before III-Vs can be employed in CMOS. This Thesis specifically addressed the development and demonstration of planar III-V p-MOSFETs, to complement the n-MOSFET, thereby enabling an all III-V CMOS technology to be realised. This work explored the application of InGaAs and InGaSb material systems as the channel, in conjunction with Al2O3/metal gate stacks, for p-MOSFET development based on the buried-channel flatband device architecture. The body of work undertaken comprised material development, process module development and integration into a robust fabrication flow for the demonstration of p-channel devices. The parameter space in the design of the device layer structure, based around the III-V channel/barrier material options of In_x≥0.53Ga_1-xAs/In_0.52Al_0.48As and In_x≥0.1Ga_1-xSb/AlSb, was systematically examined to improve hole channel transport. A mobility of 433 cm²/Vs, the highest room temperature hole mobility of any InGaAs quantum-well channel reported to date, was obtained for the In_0.85Ga_0.15As (2.1% strain) structure. S/D ohmic contacts were developed based on thermally annealed Au/Zn/Au metallisation and validated using transmission line model test structures. The effects of metallisation thickness, diffusion barriers and de-oxidation conditions were examined. Contacts to InGaSb-channel structures were found to be sensitive to de-oxidation conditions. A fabrication process, based on a lithographically-aligned double ohmic patterning approach, was realised for deep submicron gate-to-source/drain gap (L_side) scaling to minimise the access resistance, thereby mitigating the effects of parasitic S/D series resistance on transistor performance. The developed process yielded gaps as small as 20nm. For high-k integration on GaSb, ex-situ ammonium sulphide ((NH₄)₂S) treatments, in the range 1%-22%, for 10min at 295K were systematically explored for improving the electrical properties of the Al₂O₃/GaSb interface. Electrical and physical characterisation indicated the 1% treatment to be most effective with interface trap densities in the range of 4 - 10×10¹²cm^-2eV^-1 in the lower half of the bandgap. An extended study, comprising additional immersion times at each sulphide concentration, was further undertaken to determine the surface roughness and the etching nature of the treatments on GaSb. A number of p-MOSFETs based on III-V-channels with the most promising hole transport and integration of the developed process modules were successfully demonstrated in this work. Although the non-inverted InGaAs-channel devices showed good current modulation and switch-off characteristics, several aspects of performance were non-ideal; depletion-mode operation, modest drive current (I_d,sat=1.14mA/mm), double peaked transconductance (g_m=1.06mS/mm), high subthreshold swing (SS=301mV/dec) and high on-resistance (R_on=845kΩ.μm). Despite demonstrating substantial improvement in the on-state metrics of I_d,sat (11×), g_m (5.5×) and R_on (5.6×), inverted devices did not switch-off. Scaling gate-to-source/drain gap (L_side) from 1μm down to 70nm improved I_d,sat (72.4mA/mm) by a factor of 3.6 and gm (25.8mS/mm) by a factor of 4.1 in inverted InGaAs-channel devices. Well-controlled current modulation and good saturation behaviour was observed for InGaSb-channel devices. In the on-state In_0.3Ga_0.7Sb-channel (I_d,sat=49.4mA/mm, g_m=12.3mS/mm, R_on=31.7kΩ.μm) and In_0.4Ga_0.6Sb-channel (I_d,sat=38mA/mm, g_m=11.9mS/mm, R_on=73.5kΩ.μm) devices outperformed the InGaAs-channel devices. However the devices could not be switched off. These findings indicate that III-V p-MOSFETs based on InGaSb as opposed to InGaAs channels are more suited as the p-channel option for post-Si CMOS.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.

Algoritmos de otimização e o problema inverso aplicados à identificação de danos estruturais com o uso do MEF

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2015.

Surface acoustic wave streaming in a PDMS microfluidic system: effect of frequency and fluid geometry & A remote ultrasonic glucose sensor

This thesis describes two separate projects. The first is a theoretical and experimental investigation of surface acoustic wave streaming in microfluidics. The second is the development of a novel acoustic glucose sensor. A separate abstract is given for each here. Optimization of acoustic streaming in microfluidic channels by SAWs Surface Acoustic Waves, (SAWs) actuated on flat piezoelectric substrates constitute a convenient and versatile tool for microfluidic manipulation due to the easy and versatile interfacing with microfluidic droplets and channels. The acoustic streaming effect can be exploited to drive fast streaming and pumping of fluids in microchannels and droplets (Shilton et al. 2014; Schmid et al. 2011), as well as size dependant sorting of particles in centrifugal flows and vortices (Franke et al. 2009; Rogers et al. 2010). Although the theory describing acoustic streaming by SAWs is well understood, very little attention has been paid to the optimisation of SAW streaming by the correct selection of frequency. In this thesis a finite element simulation of the fluid streaming in a microfluidic chamber due to a SAW beam was constructed and verified against micro-PIV measurements of the fluid flow in a fabricated device. It was found that there is an optimum frequency that generates the fastest streaming dependent on the height and width of the chamber. It is hoped this will serve as a design tool for those who want to optimally match SAW frequency with a particular microfluidic design. An acoustic glucose sensor Diabetes mellitus is a disease characterised by an inability to properly regulate blood glucose levels. In order to keep glucose levels under control some diabetics require regular injections of insulin. Continuous monitoring of glucose has been demonstrated to improve the management of diabetes (Zick et al. 2007; Heinemann & DeVries 2014), however there is a low patient uptake of continuous glucose monitoring systems due to the invasive nature of the current technology (Ramchandani et al. 2011). In this thesis a novel way of monitoring glucose levels is proposed which would use ultrasonic waves to ‘read’ a subcutaneous glucose sensitive-implant, which is only minimally invasive. The implant is an acoustic analogy of a Bragg stack with a ‘defect’ layer that acts as the sensing layer. A numerical study was performed on how the physical changes in the sensing layer can be deduced by monitoring the reflection amplitude spectrum of ultrasonic waves reflected from the implant. Coupled modes between the skin and the sensing layer were found to be a potential source of error and drift in the measurement. It was found that by increasing the number of layers in the stack that this could be minimized. A laboratory proof of concept system was developed using a glucose sensitive hydrogel as the sensing layer. It was possible to monitor the changing thickness and speed of sound of the hydrogel due to physiological relevant changes in glucose concentration.

LHC main dipole magnet circuits: sustaining near-nominal beam energies

Crossing the Franco-Swiss border, the Large Hadron Collider (LHC), designed to collide 7 TeV proton beams, is the world's largest and most powerful particle accelerator the operation of which was originally intended to commence in 2008. Unfortunately, due to an interconnect discontinuity in one of the main dipole circuit's 13 kA superconducting busbars, a catastrophic quench event occurred during initial magnet training, causing significant physical system damage. Furthermore, investigation into the cause found that such discontinuities were not only present in the circuit in question, but throughout the entire LHC. This prevented further magnet training and ultimately resulted in the maximum sustainable beam energy being limited to approximately half that of the design nominal, 3.5-4 TeV, for the first three years of operation (Run 1, 2009-2012) and a major consolidation campaign being scheduled for the first long shutdown (LS 1, 2012-2014). Throughout Run 1, a series of studies attempted to predict the amount of post-installation training quenches still required to qualify each circuit to nominal-energy current levels. With predictions in excess of 80 quenches (each having a recovery time of 8-12+ hours) just to achieve 6.5 TeV and close to 1000 quenches for 7 TeV, it was decided that for Run 2, all systems be at least qualified for 6.5 TeV operation. However, even with all interconnect discontinuities scheduled to be repaired during LS 1, numerous other concerns regarding circuit stability arose. In particular, observations of an erratic behaviour of magnet bypass diodes and the degradation of other potentially weak busbar sections, as well as observations of seemingly random millisecond spikes in beam losses, known as unidentified falling object (UFO) events, which, if persist at 6.5 TeV, may eventually deposit sufficient energy to quench adjacent magnets. In light of the above, the thesis hypothesis states that, even with the observed issues, the LHC main dipole circuits can safely support and sustain near-nominal proton beam energies of at least 6.5 TeV. Research into minimising the risk of magnet training led to the development and implementation of a new qualification method, capable of providing conclusive evidence that all aspects of all circuits, other than the magnets and their internal joints, can safely withstand a quench event at near-nominal current levels, allowing for magnet training to be carried out both systematically and without risk. This method has become known as the Copper Stabiliser Continuity Measurement (CSCM). Results were a success, with all circuits eventually being subject to a full current decay from 6.5 TeV equivalent current levels, with no measurable damage occurring. Research into UFO events led to the development of a numerical model capable of simulating typical UFO events, reproducing entire Run 1 measured event data sets and extrapolating to 6.5 TeV, predicting the likelihood of UFO-induced magnet quenches. Results provided interesting insights into the involved phenomena as well as confirming the possibility of UFO-induced magnet quenches. The model was also capable of predicting that such events, if left unaccounted for, are likely to be commonplace or not, resulting in significant long-term issues for 6.5+ TeV operation. Addressing the thesis hypothesis, the following written works detail the development and results of all CSCM qualification tests and subsequent magnet training as well as the development and simulation results of both 4 TeV and 6.5 TeV UFO event modelling. The thesis concludes, post-LS 1, with the LHC successfully sustaining 6.5 TeV proton beams, but with UFO events, as predicted, resulting in otherwise uninitiated magnet quenches and being at the forefront of system availability issues.

Locating the maximum power point for thermoelectric generators for constant heat operation

Thermoelectric generators (TEGs) are solid-state devices that can be used for the direct conversion between heat and electricity. These devices are an attractive option for generating clean energy from heat. There are two modes of operation for TEGs; constant heat and constant temperature. It is a well-known fact that for constant temperature operation, TEGs have a maximum power point lying at half the open circuit voltage of the TEG, for a particular temperature. This work aimed to investigate the position of the maximum power point for Bismuth Telluride TEGs working under constant heat conditions i.e. the heat supply to the TEG is fixed however the temperature across the TEG can vary depending upon its operating conditions. It was found that for constant heat operation, the maximum power point for a TEG is greater than half the open circuit voltage of the TEG.

A practical MEMS gravimeter

The ability to measure tiny variations in the local gravitational acceleration allows – amongst other applications – the detection of hidden hydrocarbon reserves, magma build-up before volcanic eruptions, and subterranean tunnels. Several technologies are available that achieve the sensitivities required (tens of μGal/√Hz), and stabilities required (periods of days to weeks) for such applications: free-fall gravimeters, spring-based gravimeters, superconducting gravimeters, and atom interferometers. All of these devices can observe the Earth tides; the elastic deformation of the Earth’s crust as a result of tidal forces. This is a universally predictable gravitational signal that requires both high sensitivity and high stability over timescales of several days to measure. All present gravimeters, however, have limitations of excessive cost (£70 k) and high mass (<8 kg). In this thesis, the building of a microelectromechanical system (MEMS) gravimeter with a sensitivity of 40 μGal/√Hz in a package size of only a few cubic centimetres is discussed. MEMS accelerometers – found in most smart phones – can be mass-produced remarkably cheaply, but most are not sensitive enough, and none have been stable enough to be called a ‘gravimeter’. The remarkable stability and sensitivity of the device is demonstrated with a measurement of the Earth tides. Such a measurement has never been undertaken with a MEMS device, and proves the long term stability of the instrument compared to any other MEMS device, making it the first MEMS accelerometer that can be classed as a gravimeter. This heralds a transformative step in MEMS accelerometer technology. Due to their small size and low cost, MEMS gravimeters could create a new paradigm in gravity mapping: exploration surveys could be carried out with drones instead of low-flying aircraft; they could be used for distributed land surveys in exploration settings, for the monitoring of volcanoes; or built into multi-pixel density contrast imaging arrays.

Inside standard and hyperspectral low-dose CT variational imaging: parameter identification and material decomposition

The main contribution of this thesis is the proposal of novel strategies for the selection of parameters arising in variational models employed for the solution of inverse problems with data corrupted by Poisson noise. In light of the importance of using a significantly small dose of X-rays in Computed Tomography (CT), and its need of using advanced techniques to reconstruct the objects due to the high level of noise in the data, we will focus on parameter selection principles especially for low photon-counts, i.e. low dose Computed Tomography. For completeness, since such strategies can be adopted for various scenarios where the noise in the data typically follows a Poisson distribution, we will show their performance for other applications such as photography, astronomical and microscopy imaging. More specifically, in the first part of the thesis we will focus on low dose CT data corrupted only by Poisson noise by extending automatic selection strategies designed for Gaussian noise and improving the few existing ones for Poisson. The new approaches will show to outperform the state-of-the-art competitors especially in the low-counting regime. Moreover, we will propose to extend the best performing strategy to the hard task of multi-parameter selection showing promising results. Finally, in the last part of the thesis, we will introduce the problem of material decomposition for hyperspectral CT, which data encodes information of how different materials in the target attenuate X-rays in different ways according to the specific energy. We will conduct a preliminary comparative study to obtain accurate material decomposition starting from few noisy projection data.

Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications

Ill-conditioned inverse problems frequently arise in life sciences, particularly in the context of image deblurring and medical image reconstruction. These problems have been addressed through iterative variational algorithms, which regularize the reconstruction by adding prior knowledge about the problem's solution. Despite the theoretical reliability of these methods, their practical utility is constrained by the time required to converge. Recently, the advent of neural networks allowed the development of reconstruction algorithms that can compute highly accurate solutions with minimal time demands. Regrettably, it is well-known that neural networks are sensitive to unexpected noise, and the quality of their reconstructions quickly deteriorates when the input is slightly perturbed. Modern efforts to address this challenge have led to the creation of massive neural network architectures, but this approach is unsustainable from both ecological and economic standpoints. The recently introduced GreenAI paradigm argues that developing sustainable neural network models is essential for practical applications. In this thesis, we aim to bridge the gap between theory and practice by introducing a novel framework that combines the reliability of model-based iterative algorithms with the speed and accuracy of end-to-end neural networks. Additionally, we demonstrate that our framework yields results comparable to state-of-the-art methods while using relatively small, sustainable models. In the first part of this thesis, we discuss the proposed framework from a theoretical perspective. We provide an extension of classical regularization theory, applicable in scenarios where neural networks are employed to solve inverse problems, and we show there exists a trade-off between accuracy and stability. Furthermore, we demonstrate the effectiveness of our methods in common life science-related scenarios. In the second part of the thesis, we initiate an exploration extending the proposed method into the probabilistic domain. We analyze some properties of deep generative models, revealing their potential applicability in addressing ill-posed inverse problems.