Quasiconjugates of functions, duality relationship between Quasiconvex Minimization under a Reverse Convex Constraint and Quasiconvex Maximization under a Convex Constraint, and applications

AbstractIn this paper we introduce a concept of quasiconjugate for functions defined on Rn whose values are in −R. The conjugacy correspondence between functions and their quasiconjugates is one-to-one and symmetric in a class of quasiconvex functions whose minimizer on Rn is located at the origin. By using the concept of quasiconjugate we obtain a duality relationship between Quasiconvex Minimization under a Reverse Convex Constraint and Quasiconvex Maximization under a Convex Constraint. This duality relationship allows us to establish a primal-dual pair in a class of nonconvex optimization problems without the duality gap. Several applications are given

The shear-driven Rayleigh problem for generalised Newtonian fluids

We consider a variant of the classical ‘Rayleigh problem’ (‘Stokes’s first problem’) in which a semi-infinite region of initially quiescent fluid is mobilised by a shear stress applied suddenly to its boundary. We show that self-similar solutions for the fluid velocity are available for any generalised Newtonian fluid, regardless of its constitutive law. We demonstrate how these solutions may be used to provide insight into some generic questions about the behaviour of unsteady, non-Newtonian boundary layers, and in particular the effect of shear thinning or thickening on the thickness of a boundary layer

Strategies for improving efficiency and efficacy of image quality assessment algorithms

Image quality assessment (IQA) research aims to predict the qualities of images in a manner that agrees with subjective quality ratings. Over the last several decades, the major impetus in IQA research has focused on improving prediction efficacy globally (across images) of distortion-specific types or general types; very few studies have explored local image quality (within images), or IQA algorithm for improved JPEG2000 coding. Even fewer studies have focused on analyzing and improving the runtime performance of IQA algorithms. Moreover, reduced-reference (RR) IQA is also a new field to be explored, when the transmitting bandwidth is limited, side information about original image was received with distorted image at the receiver. This report explored these four topics. For local image quality, we provided a local sharpness database, and we analyzed the database along with current sharpness metrics. We revealed that human highly agreed when rating sharpness of small blocks. Overall, this sharpness database is a true representation of human subjective ratings and current sharpness algorithms could reach 0.87 in terms of SROCC score. For JPEG2000 coding using IQA, we provided a new JPEG2000 image database, which includes only same total distortion images. Analysis of existing IQA algorithms on this database revealed that even though current algorithms perform reasonably well on JPEG2000-compressed images in popular image-quality databases, they often fail to predict the correct rankings on our database's images. Based on the framework of Most Apparent Distortion (MAD), a new algorithm, MADDWT is then proposed using local DWT coefficient statistics to predict the perceived distortion due to subband quantization. MADDWT outperforms all others algorithms on this database, and shows a promising use in JPEG2000 coding. For efficiency of IQA algorithms, this paper is the first to examine IQA algorithms from the perspective of their interaction with the underlying hardware and microarchitectural resources, and to perform a systematic performance analysis using state-of-the-art tools and techniques from other computing disciplines. We implemented four popular full-reference IQA algorithms and two no-reference algorithms in C++ based on the code provided by their respective authors. Hotspot analysis and microarchitectural analysis of each algorithm were performed and compared. Despite the fact that all six algorithms share common algorithmic operations (e.g., filterbanks and statistical computations), our results revealed that different IQA algorithms overwhelm different microarchitectural resources and give rise to different types of bottlenecks. For RR IQA, we also provide a new framework based on multiscale sharpness map. This framework employs multiscale sharpness maps as reduced information. As we will demonstrate, our framework with 2% reduced information can outperform other frameworks, which employ from 2% to 3% reduced information. Our framework is also competitive to current state-of-the-art FR algorithms

Performance-analysis-based Acceleration of Image Quality Assessment

Algorithms for image/video quality assessment (QA) aim to predict the qualitiesof images in a manner that agrees with subjective quality ratings. Over the lastseveral decades, the major impetus in QA research has focused on improving predictiveperformance; very few studies have focused on analyzing and improving theruntime performance of QA algorithms. Modern algorithms of image/video qualityassessment commonly employed two stages: (1) a local frequency-based decomposition, and (2) block-based statistical comparisons between the frequency coefficients of the reference and distorted images. These two stages constitute the bulk of the computation and runtime required for QA. This research thesis presents a performance analysis of and techniques for accelerating these stages. We also specifically analyze and accelerate one representative QA algorithm, Most Apparent Distortion (MAD), which was developed by Eric Larson and Damon Chandler in 2010 [1]. We identify the bottlenecks from the above-mentioned stages, and we present methods of acceleration using generalized integral image, inline expansion, a GPGPU implementation, and other code modifications. We show how a combination of these approaches can yield a speedup of 47x.The content of the report is divided into five different chapters. In Chapter 1,we present a general overview of QA algorithms, current work on improving the computational performance and execution time of QA algorithms, and an introduction toour work. In Chapter 2, we describe MAD algorithm, the first performance analysis,and the systems used to test the performance. In Chapter 3, we present generalizedintegral image and inline expansion techniques. In this chapter, we also providethe results of each technique in terms of speeding up running time. Chapter 4 providesGPGPU and some other code optimization techniques with the timing results.Finally, the conclusion are proposed in the Chapter 5 to summarize the report.Electrical Engineerin

Bio-Inspired Soft Artificial Muscles for Robotic and Healthcare Applications

Soft robotics and soft artificial muscles have emerged as prolific research areas and have gained substantial traction over the last two decades. There is a large paradigm shift of research interests in soft artificial muscles for robotic and medical applications due to their soft, flexible and compliant characteristics compared to rigid actuators. Soft artificial muscles provide safe human-machine interaction, thus promoting their implementation in medical fields such as wearable assistive devices, haptic devices, soft surgical instruments and cardiac compression devices. Depending on the structure and material composition, soft artificial muscles can be controlled with various excitation sources, including electricity, magnetic fields, temperature and pressure. Pressure-driven artificial muscles are among the most popular soft actuators due to their fast response, high exertion force and energy efficiency. Although significant progress has been made, challenges remain for a new type of artificial muscle that is easy to manufacture, flexible, multifunctional and has a high length-to-diameter ratio. Inspired by human muscles, this thesis proposes a soft, scalable, flexible, multifunctional, responsive, and high aspect ratio hydraulic filament artificial muscle (HFAM) for robotic and medical applications. The HFAM consists of a silicone tube inserted inside a coil spring, which expands longitudinally when receiving positive hydraulic pressure. This simple fabrication method enables low-cost and mass production of a wide range of product sizes and materials. This thesis investigates the characteristics of the proposed HFAM and two implementations, as a wearable soft robotic glove to aid in grasping objects, and as a smart surgical suture for perforation closure. Multiple HFAMs are also combined by twisting and braiding techniques to enhance their performance. In addition, smart textiles are created from HFAMs using traditional knitting and weaving techniques for shape-programmable structures, shape-morphing soft robots and smart compression devices for massage therapy. Finally, a proof-of-concept robotic cardiac compression device is developed by arranging HFAMs in a special configuration to assist in heart failure treatment. Overall this fundamental work contributes to the development of soft artificial muscle technologies and paves the way for future comprehensive studies to develop HFAMs for specific medical and robotic requirements

Development of new functional food traits in peanuts

Two categories of functional food traits were researched: dietary minerals and antioxidants. The primary objectives were to (1) characterise diverse peanut phenotypes using established methods or developing and validating analytical methods if necessary; (2) estimate genotypic variation in the functional food trait; and (3) investigate the stability of the functional food trait through studies of genotype-by-environment (G × E) interaction. Essential mineral concentrations in kernels were analysed by inductively coupled plasma-optical emission spectroscopy (ICP-OES) and ICP-mass spectrometry (ICP-MS) with the use of a dynamic reaction cell (DRC) after preparation of samples by microwave-assisted closed acid digestion. Antioxidant capacity was assessed using ABTS +, DPPH , Folin-Ciocalteu total phenolics, and ORAC assays adapted to a 96-well microplate format for high-throughput analysis. The phytochemical profile was quantitatively analysed by high performance liquid chromatography (HPLC) with the use of a photodiode array (PDA) detector, after ultrasound- and enzyme-assisted extraction and solid phase extraction to purify and concentrate the extracts. Genotypic variation for essential minerals and antioxidant capacity was estimated by analysis of 32 full-season maturity and 24 ultra-early maturity genotypes from the Australian Peanut Breeding Program (APBP). The studies established useful levels of variation of more than 10% relative standard deviation (RSD) among the genotypes in concentrations of most of the tested essential minerals, and of more than 20% RSD in antioxidant capacity, although only the ORAC assay distinguished statistically significant differences between genotypes. Studies of G × E interaction affecting the essential mineral and antioxidant capacity traits revealed that genotype, environment, and G × E interaction all significantly affected trait expression. The results confirmed that there was substantial genetic control of essential mineral concentrations and antioxidant capacity in peanut kernels, but that it will be important to characterise environmental interaction to enable plant/seed selection in the APBP and potentially manipulate the interaction through agronomic or postharvest management. The essential minerals data were used to develop approximately predictive calibrations for Ca, K, Mg, and P by near-infrared reflectance spectroscopy (NIRS) of sufficient accuracy to be useful as plant/seed selection tools in plant breeding. Techniques that enable high-throughput, non-destructive, time/cost-effective analysis of trait segregation are valuable due to the extremely large number of samples that are generated in breeding programs. Five peanut genotypes with diverse antioxidant capacity phenotypes were quantitatively profiled for p coumaric acid, salicylic acid, resveratrol, and daidzein. The co-eluting compounds, caffeic/vanillic acid and ferulic/sinapic acid, were quantified on caffeic acid equivalent and ferulic acid equivalent bases, respectively. The HPLC analysis established significant genotypic differences in phytochemical concentrations and also the importance of the bound (e.g., conjugated and matrix-embedded) fraction. Fractions of the HPLC eluate were evaluated by ORAC assay to evaluate relative contributions to antioxidant capacity, and allowed identification of a number of unknown compounds that made important contributions to antioxidant capacity. HPLC analysis of kernels subjected to various roasting treatments (150 °C, 0-70 min and 160 °C, 0-32.5 min) showed that ferulic/sinapic acid concentrations declined with roasting duration, but that most other tested analytes were relatively thermo-stable

Imposition of physical parameters in dissipative particle dynamics

In the mesoscale simulations by the dissipative particle dynamics (DPD), the motion of a fluid is modelled by a set of particles interacting in a pairwise manner, and it has been shown to be governed by the Navier–Stokes equation, with its physical properties, such as viscosity, Schmidt number, isothermal compressibility, relaxation and inertia time scales, in fact its whole rheology resulted from the choice of the DPD model parameters. In this work, we will explore the response of a DPD fluid with respect to its parameter space, where the model input parameters can be chosen in advance so that (i) the ratio between the relaxation and inertia time scales is fixed; (ii) the isothermal compressibility of water at room temperature is enforced; and (iii) the viscosity and Schmidt number can be specified as inputs. These impositions are possible with some extra degrees of freedom in the weighting functions for the conservative and dissipative forces. Numerical experiments show an improvement in the solution quality over conventional DPD parameters/weighting functions, particularly for the number density distribution and computed stresses