VIABILITY OF TIME-MEMORY TRADE-OFFS IN LARGE DATA SETS

The main hypothesis of this paper is whether compression performance – both hardware and software – is at, approaching, or will ever reach a point where real-time compression of cached data in large data sets will be viable to improve hit ratios and overall throughput. The problem identified is: storage access is unable to keep up with application and user demands, and cache (RAM) is too small to contain full data sets. A literature review of several existing techniques discusses how storage IO is reduced or optimized to maximize the available performance of the storage medium. However, none of the techniques discovered preclude, or are mutually exclusive with, the hypothesis proposed herein. The methodology includes gauging three popular compressors which meet the criteria for viability: zlib, lz4, and zstd. Common storage devices are also benchmarked to establish costs for both IO and compression operations to help build charts and discover break-even points under various circumstances. The results indicate that modern CISC processors and compressors are already approaching tradeoff viability, and that FPGA and ASIC could potentially reduce all overhead by pipelining compression – nearly eliminating the cost portion of the tradeoff, leaving mostly benefit

MobileNetV2: Inverted Residuals and Linear Bottlenecks

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameter

The dislocation behaviour and GND development in a nickel based superalloy during creep

In the current study, dislocation activity and storage during creep deformation in a nickel based superalloy (Waspaloy) were investigated, focussing on the storage of geometrically necessary (GND) and statistically stored (SSD) dislocations. Two methods of GND density calculation were used, namely, EBSD Hough Transformation and HR-EBSD Cross Correlation based methods. The storage of dislocations, including SSDs, was investigated by means of TEM imaging. Here, the concept of GND accumulation in soft and hard grains and the effect of neighbouring grain orientation on total dislocation density was examined. Furthermore, the influence of applied stress (below and above the yield stress of Waspaloy) during creep on deformation micro-mechanism and dislocation density was studied. It was demonstrated that soft grains provided pure shear conditions on at least two octahedral (111) slip systems for easy dislocation movement. This allowed dislocations to reach the grain boundary without significant geometrically necessary dislocation accumulation in the centre of the grain. Hence, the majority of the soft grains appeared to have minimum GND density in the centre of the grain with high GND accumulation in the vicinity of the grain boundaries. However, the values and width of accumulated GND depended on the surrounding grain orientations. Furthermore, it was shown that the hard grains were not favourably oriented for octahedral slip system activation leading to a grain rotation in order to activate any of the available slip systems. Eventually, (i) the hard grain resistance to deformation and (ii) neighbouring grain resistance for the hard grain reorientation caused high GND density on a number of octahedral (111) slip systems. The results also showed that during creep below the yield stress of Waspaloy (500 MPa/700 °C), the GND accumulation was relatively low due to the insufficient macroscopic stress level. However, the regions near grain boundaries showed high GND density. At 800 MPa/700 °C (above yield at this temperature), in addition to the movement of pre-existing dislocations (SSD and GND) at a higher mobility rate, large numbers of dislocations were generated and moved toward the grain boundaries. This resulted in a much higher GND density but narrower width of high intensity GNDs near the grain boundaries. It is concluded that although GND measurement by means of EBSD can provide great insight into dislocation accumulation and its behaviour, it is critical to consider SSD type which also contributes to the strain hardening of the material

Dislocation Density-Based Finite Element Method Modeling of Ultrasonic Consolidation

A dislocation density-based constitutive model has been developed and implemented into a crystal plasticity quasi-static finite element framework. This approach captures the statistical evolution of dislocation structures and grain fragmentation at the bonding interface when sufficient boundary conditions pertaining to the Ultrasonic Consolidation (UC) process are prescribed. The hardening is incorporated using statistically stored and geometrically necessary dislocation densities (SSDs and GNDs), which are dislocation analogs of isotropic and kinematic hardening, respectively. Since the macroscopic global boundary conditions during UC involves cyclic sinosuidal simple shear loading along with constant normal pressure, the cross slip mechanism has been included in the evolution equation for SSDs. The inclusion of cross slip promotes slip irreversibility, dislocation storage, and hence, cyclic hardening during the UC. The GND considers strain-gradient and thus renders the model size-dependent. The model is calibrated using experimental data from published refereed literature for simple shear deformation of single crystalline pure aluminum alloy and uniaxial tension of polycrystalline Aluminum 3003-H18 alloy. The model also incorporates various local and global effects such as (1) friction, (2) thermal softening, (3) acoustic softening, (4) surface texture of the sonotrode and initial mating surfaces, and (6) presence of oxide-scale at the mating surfaces, which further contribute significantly specifically to the grain substructure evolution at the interface and to the anisotropic bulk deformation away from the interface during UC in general. The model results have been predicted for Al-3003 alloy undergoing UC. A good agreement between the experimental and simulated results has been observed for the evolution of linear weld density and anisotropic global strengths macroscopically. Similarly, microscopic observations such as embrittlement due to grain substructure evolution at the UC interface have been also demonstrated by the simulation. In conclusion, the model was able to predict the effects of macroscopic global boundary conditions on bond quality. It has been found that the normal pressure enhances good bonding characteristics at the interface, though beyond a certain magnitude enhances dynamic failure. Similarly, lower oscillation amplitudes result in a lower rate of gap closure, whereas higher oscillation amplitude results in an enhanced rate of gap relaxation at the interface. Henceforth, good bonding characteristics between the constituent foils are found at an optimum oscillation amplitude. A similar analogy is also true for weld speed where the longitudinal locations behind the sonotrode rip open when higher weld speeds are implemented in the UC machine, leading to lower linear weld density and poor bonding characteristics

Phenotypic and genomics-assisted breeding of soybean for Central Europe : from environmental adaptation to tofu traits

Soybean (Glycine max Merr.) is one of the major crops in the world providing an important source of protein and oil for food and feed; however it is still a minor crop in Central Europe. Soybean cultivation can play an important role in a more sustainable agricultural system by increasing local and regional protein production in Europe. The demand for locally produced soybean products is still growing in Europe. The key for a successful establishment of soybean cultivation in Europe is adaptation of soybean varieties to the Central European growing conditions. For the latitudinal adaptation to long-day conditions in Central to Northern Europe, an adapted early flowering and maturity time is of crucial importance for a profitable cultivation. The key traits flowering and maturity are quantitatively inherited and mainly affected by photoperiod responsiveness and temperature sensitivity. The most important loci for an early flowering and maturity are E1-E4 and the various allelic combinations condition soybean flowering and maturity time and therefore strongly contribute to the wide adaptability (Jiang et al., 2014; Tsubokura et al., 2014; M. Xu et al., 2013). Besides the main usage as protein source for animal feeding, soybean is also a very valuable source for human consumption. Tofu is enjoying ever greater popularity in Europe, as it is one of the best sources of plant protein with additional health benefits, rich in essential amino acids, beneficial lipids, vitamins, and minerals, as well as other bioactive compounds, such as isoflavones, soyasaponin, and others, (Lima et al., 2017; Zhang et al., 2018). Thus, plant breeding has to provide not only well-adapted varieties with good agronomic and quality properties, but also provide varieties well-suited to the further processing into soymilk and tofu. Therefore, a good knowledge about the breeding target, how to assess it and how it is inherited is crucial. The conducted studies covered a broad range of aspects relevant to improve a soybean breeding program. By combining environmental analysis, E-gene analysis, genomic approaches (QTL mapping and genomic prediction), and tofu phenotyping, breeder decisions become more accurate and targeted in the way of selection thereby increasing the genetic gain. In addition, combining the results of the different aspects helps to optimize the resources of a breeding program. Increasing the knowledge about the different aspects from environment to tofu QTL enables a breeder to be more precise and focused. But the more targeted and specific, the more complex a breeding program gets, which requires adequate tools to handle all the different information in a meaningful and efficient way to enable a quick and precise breeding decision.Die Sojabohne (Glycine max Merr.) ist eine der wichtigsten Nutzpflanzen der Welt und stellt eine wichtige Protein- und Ölquelle für Lebens- und Futtermittel dar; in Mitteleuropa spielt die Sojabohne jedoch immer noch eine untergeordnete Rolle im Anbau. Der Sojabohnenanbau kann eine wichtige Rolle in einem nachhaltigeren Agrarsystem spielen, indem er die lokale und regionale Proteinproduktion in Europa steigert. Die Nachfrage nach lokal produzierten Sojabohnenprodukten wächst in Europa weiter. Der Schlüssel für eine erfolgreiche Etablierung des Sojaanbaus in Europa ist die Anpassung der Sojasorten an die mitteleuropäischen Anbaubedingungen. Für die Breitenanpassung an Langtagbedingungen in Mittel- bis Nordeuropa ist eine angepasste frühe Blüte- und Reifezeit von entscheidender Bedeutung für einen ertragreichen Anbau. Die Schlüsselmerkmale Blüte und Reife werden quantitativ vererbt und hauptsächlich durch die Photoperioden- und Temperaturempfindlichkeit beeinflusst. Die wichtigsten Genorte für eine frühe Blüte und Reife sind E1-E4. Die verschiedenen Allelkombinationen bedingen die Sojabohnenblüte und Reifezeit und tragen daher stark zur breiten Anpassungsfähigkeit bei (Jiang et al., 2014; Tsubokura et al., 2014; M. Xu et al., 2013). Neben der Hauptverwendung als Proteinquelle für die Tierfütterung ist Soja auch eine sehr wertvolle Quelle für die menschliche Ernährung. Lebensmittel auf Sojabasis spielen eine zentrale Rolle in der asiatischen Küche, die sehr unterschiedliche Produkte anbietet, wobei Tofu das wichtigste Produkt ist. Tofu erfreut sich in Europa immer größerer Beliebtheit, da er eine der besten pflanzlichen Proteinquellen mit zusätzlichem Gesundheitsnutzen ist, reich an essentiellen Aminosäuren, nützlichen Lipiden, Vitaminen und Mineralstoffen sowie anderen bioaktiven Verbindungen wie Isoflavonen, Sojasaponin und andere (Lima et al., 2017; Zhang et al., 2018). Daher muss die Pflanzenzüchtung nicht nur gut angepasste Sorten mit guten agronomischen und qualitativen Eigenschaften liefern, sondern auch Sorten, die sich für die Weiterverarbeitung zu Sojamilch und Tofu gut eignen. Gute Kenntnisse über das Zuchtziel, wie es zu beurteilen ist und wie es vererbt wird, sind daher entscheidend. Die durchgeführten Studien deckten ein breites Spektrum von Aspekten ab, die für die Verbesserung eines Sojabohnenzuchtprogramms relevant sind. Durch die Kombination von Umweltanalyse, E-Gen-Analyse, genomischen Ansätzen (QTL-Mapping und genomische Vorhersage) und Tofuphänotypisierung werden Züchterentscheidungen genauer und zielgerichteter in der Selektion, wodurch der Zuchtfortschritt erhöht wird. Darüber hinaus hilft die Kombination der Ergebnisse der verschiedenen Aspekte, die Ressourcen eines Zuchtprogramms zu optimieren. Die Erweiterung des Wissensstands über die verschiedenen Aspekte von der Umwelt bis zum Tofu-QTL ermöglicht es einem Züchter, präziser und fokussierter zu sein. Doch je gezielter und spezifischer, desto komplexer wird ein Zuchtprogramm, das adäquate Werkzeuge benötigt, um mit all den unterschiedlichen Informationen sinnvoll und effizient umzugehen, um damit dann eine schnelle und präzise Zuchtentscheidung zu ermöglichen

Assistive technology for the visually impaired: How to provide independence and ease to meal preparation?

Disabilities are a reality many don’t think about too much unless one, or someone one knows, is affected by them. This work is motivated by this fact and is focused on attempting to improve the quality of life (QoL) of persons with visual impairment or blindness by facilitating the execution of one of the activities of daily living (ADL) – cooking independently. As this work will show, cooking with severe sight loss could prove easier or more difficult depending on various factors, the main one of which is time spent living with limited sight and, of course, proclivity for this activity. Easier, however, not easy. This is why the subject of this work is to provide a set of reliable, simple, and easy to use dispensers for oil and spices equipped with elements for identification of the various products. In order to, offer adequate results, this work began with contextualizing the target market by presenting statistics. This was followed by a glimpse of the reality of living with the limitations of visual impairment by examining the neural pathways that allow for multisensory processing (MP) and how that applies in the investigation and development of sensory substitution devices (SSD). Leaving the purely scientific and experimental part, this work proceeded to examining the evolution of assistive technology throughout the centuries and concluded by analyzing the current products on the market today. This analysis led to the compiling of plausible hypotheses for the solutions proposed by the author followed by final proposals and the development of proof-of-concept prototypes. The work concluded by presenting a business plan around the proposed products

Automatic vision based fault detection on electricity transmission components using very highresolution

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations. Fault identification is one of the most significant bottlenecks faced by Electricity transmission and distribution utilities in developing countries to deliver credible services to customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial resolution to monitor four major Electric power transmission network (EPTN) components condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks (CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage object detection model on the electric transmission power line imagery to localize, classify and inspect faults present. The components fault considered include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision of 89.61%. All the developed SSD based models achieve a high precision rate and low recall rate in detecting the faulty components, thus achieving acceptable balance levels F1-score and representation. Finally, comparable to other works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood