Illumination Invariant Outdoor Perception

This thesis proposes the use of a multi-modal sensor approach to achieve illumination invariance in images taken in outdoor environments. The approach is automatic in that it does not require user input for initialisation, and is not reliant on the input of atmospheric radiative transfer models. While it is common to use pixel colour and intensity as features in high level vision algorithms, their performance is severely limited by the uncontrolled lighting and complex geometric structure of outdoor scenes. The appearance of a material is dependent on the incident illumination, which can vary due to spatial and temporal factors. This variability causes identical materials to appear differently depending on their location. Illumination invariant representations of the scene can potentially improve the performance of high level vision algorithms as they allow discrimination between pixels to occur based on the underlying material characteristics. The proposed approach to obtaining illumination invariance utilises fused image and geometric data. An approximation of the outdoor illumination is used to derive per-pixel scaling factors. This has the effect of relighting the entire scene using a single illuminant that is common in terms of colour and intensity for all pixels. The approach is extended to radiometric normalisation and the multi-image scenario, meaning that the resultant dataset is both spatially and temporally illumination invariant. The proposed illumination invariance approach is evaluated on several datasets and shows that spatial and temporal invariance can be achieved without loss of spectral dimensionality. The system requires very few tuning parameters, meaning that expert knowledge is not required in order for its operation. This has potential implications for robotics and remote sensing applications where perception systems play an integral role in developing a rich understanding of the scene

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Management of hypertension: Insights into prescribing behavior with focus on angiotensin receptor blockers

Introduction: Angiotensin receptor blockers (ARBs) are emerging as an attractive first choice antihypertensive as recommended by various guidelines. However, choice among the first-line antihypertensive classes and among ARBs differs between practicing physicians. Aims: This survey aimed to understand the usage preferences of ARBs and its place in for treating hypertension (HTN) among physicians from various clinical settings in India. Methods: A cross-sectional survey was conducted with a prevalidated survey questionnaire consisting of 25 questions for HTN management. Practicing general physicians and cardiologists were approached for seeking their perception, opinions, and prescribing behavior. Results: Responses of 594 physicians and cardiologists were received. As opined by 90.1% of physicians, newly diagnosed HTN represented more than 10% of their overall patient load. As a monotherapy, 59.9% of the physicians preferred ARB as the first choice in newly diagnosed HTN patients, followed by calcium channel blocker (12.3%) and angiotensin-converting-enzyme inhibitor (8.1%). Of all ARBs, telmisartan is preferred by 73% of physicians. Most physicians prefer telmisartan among all ARBs for 24 h blood pressure (BP) control, including morning BP surge (76.4%) and for prevention of cardiovascular morbidity and mortality (78.8%) followed by olmesartan and losartan. Predominantly, majority of physicians (89.1%) agreed for the beneficial role of telmisartan in preventing onset of microalbuminuria and nephropathy. Conclusion: Indian physicians prefer ARBs as the first choice in most hypertensive patients, which shows agreement with the guideline recommendations followed globally. Telmisartan has emerged as the most preferred ARB among all, for most of the HTN patients including those with comorbidities

Unsupervised Feature-Learning for Hyperspectral Data with Autoencoders

This paper proposes novel autoencoders for unsupervised feature-learning from hyperspectral data. Hyperspectral data typically have many dimensions and a significant amount of variability such that many data points are required to represent the distribution of the data. This poses challenges for higher-level algorithms which use the hyperspectral data (e.g., those that map the environment). Feature-learning mitigates this by projecting the data into a lower-dimensional space where the important information is either preserved or enhanced. In many applications, the amount of labelled hyperspectral data that can be acquired is limited. Hence, there is a need for feature-learning algorithms to be unsupervised. This work proposes unsupervised techniques that incorporate spectral measures from the remote-sensing literature into the objective functions of autoencoder feature learners. The proposed techniques are evaluated on the separability of their feature spaces as well as on their application as features for a clustering task, where they are compared against other unsupervised feature-learning approaches on several different datasets. The results show that autoencoders using spectral measures outperform those using the standard squared-error objective function for unsupervised hyperspectral feature-learning

Perspectives and Challenges for Sustainable Management of Fungal Diseases of Mungbean [Vigna radiata (L.) R. Wilczek var. radiata]: A Review

Mungbean (Vigna radiata var. radiata) is a key legume crop grown predominantly in South and Southeast Asia. Biotic and abiotic stresses cause significant yield reduction in mungbean, and among these, fungal diseases are particularly important. Although disease management practices, including physical, chemical, and biological methods have been researched and described in the literature, few of these are available or have been used by growers. Here we review the economic impact, and sustainable management options for the soil-borne and foliar fungal diseases of mungbean as well as major challenges to manage these diseases. Potential use of all possible components of integrated management practices including host resistance, fungicides, biocontrol agents, natural plant products, and cultural practices etc. are discussed. Major diseases include powdery mildew, anthracnose, Cercospora leaf spot, Fusarium wilt, Rhizoctonia root rot and web blight, Macrophomina charcoal rot/dry root rot and blight. Review of the literature indicated an absence of resistance to Rhizoctonia root rot, little sources of resistance for dry root rot and anthracnose. Major resistant genes (R genes) and quantitative trait loci (QTL) were identified for powdery mildew and Cercospora leaf spot, which may be potentially used in Marker assisted selection (MAS). Although the mechanisms of induced systemic resistance (ISR) by biocontrol agents have been studied with Macrophomina blight, there is little information on the mechanisms and use of systemic acquired resistance (SAR) in managing fungal diseases of mungbean. Several studies targeted exploiting biological control for soil-borne root rot diseases. Botanical products, such as plant extracts, are also found effective to manage root and foliar diseases. However, many of these studies were limited to laboratory and/or green house experiments. Thus, long-term field studies are required for further exploitation of biological methods and commercial applications

Anisotropic Ferromagnetic Organic Nanoflowers

We report a weak anisotropic ferromagnetic behavior in a purely organic molecule at room temperature, a property rarely reported in organic nanomaterials. The reported 1,2-bis(tritylthio)ethane, forming plate- and organic-flower-like morphologies at the nanolevel, is the first organic crystal with an inherent magnetic property at 300 and 2 K. However, at low temperatures, the magnetization value [Mmax(T) ∼116 emu/mol at 2 K] increases drastically at 3 orders higher compared to 300 K. Interestingly, the system exhibits strong anisotropy with an anisotropic constant, K1 ∼3.25 7 103 erg/cc, and anisotropy field, HK ∼3.25 kOe. Below 10 K, this system displays unusual temperature dependence of the coercive field [HC(T)] and remanence magnetization [MR(T)] with a hysteresis-peak anomaly (T∗ ∼10-15 K) due to the enhanced spin-orbit coupling. The maximum HC and MR at T∗ were HC = 220 Oe and MR ∼12 emu/mol, respectively. Beyond T*, HC(T) and MR(T) drop continuously and become negligible as the measurement temperature approaches 300 K. Our results demonstrate that the triphenyl molecules can be further exploited for the design and synthesis of organic magnets for possible applications in spintronics and memory storage devices

Mode of application influences the biofertilizing efficacy of cyanobacterial biofilm formulations in chrysanthemum varieties under protected cultivation

Availability of nutrients in soil plays an important role in the productivity and quality of flowers in chrysanthemum. A set of novel biofilm inoculants- Anabaena-Azotobacter, Anabaena-Pseudomonas fluorescens (An-Psf) and Anabaena-Trichoderma (An-Tr) were applied as carrier based dry formulation or soil drench and their performance compared in two varieties of chrysanthemum (White Star and Zembla), in a climate-controlled greenhouse. Both the An-Psf and (An-Tr) inoculants enhanced glomalin related soil proteins in the rhizosphere of White Star, while in terms of polysaccharide content of soil, both these inoculants performed better in Zembla variety. Significant increases in the availability of selected macro and micronutrients in rhizosphere soil samples, in both chrysanthemum varieties were recorded, particularly when the inoculants were applied as soil drench. Principal Component analysis illustrated the significant interaction among soil and plant parameters, more specifically, the distinct effect of the inoculants, as compared to the application of carrier alone or control treatment. This investigation demonstrated the varietal effects on soil biological activities and significance of mode of application of microbial inoculants in influencing plant growth and rhizospheric metabolic activities