362 research outputs found

    Vision-Based Incoming Traffic Estimator Using Deep Neural Network on General Purpose Embedded Hardware

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    Traffic management is a serious problem in many cities around the world. Even the suburban areas are now experiencing regular traffic congestion. Inappropriate traffic control wastes fuel, time, and the productivity of nations. Though traffic signals are used to improve traffic flow, they often cause problems due to inappropriate or obsolete timing that does not tally with the actual traffic intensity at the intersection. Traffic intensity determination based on statistical methods only gives the average intensity expected at any given time. However, to control traffic accurately, it is required to know the real-time traffic intensity. In this research, image processing and machine learning have been used to estimate actual traffic intensity in real time. General-purpose electronic hardware has been used for in-situ image processing based on the edge-detection method. A deep neural network (DNN) was trained to infer traffic intensity in each image in real time. The trained DNN estimated traffic intensity accurately in 90% of the real-time images during road tests. The electronic system was implemented on a Raspberry Pi single-board computer; hence, it is cost-effective for large-scale deployment.Comment: 6 pages, 11 figures, journa

    A virtual odometer for a Quadrotor Micro Aerial Vehicle

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    This paper describes the synthesis and evaluation of a "virtual odometer" for a Quadrotor Micro Aerial Vehicle. Availability of a velocity estimate has the potential to enhance the accuracy of mapping, estimation and control algorithms used with quadrotors, increasing the effectiveness of their applications. As a result of the unique dynamic characteristics of the quadrotor, a dual axis accelerometer mounted parallel to the propeller plane provides measurements that are directly proportional to vehicle velocities in that plane. Exploiting this insight, we encapsulate quadrotor dynamic equations which relate acceleration, attitude and the aerodynamic propeller drag in an extended Kalman filter framework for the purpose of state estimation. The result is a drift free estimation of lateral and longitudinal components of translational velocity and roll and pitch components of attitude of the quadrotor. Real world data sets gathered from two different quadrotor platforms, together with ground truth data from a Vicon system, are used to evaluate the effectiveness of the proposed algorithm and demonstrate that drift free estimates for the velocity and attitude can be obtained

    Feasibility of valuing credit risk in the financial market in Sri Lanka: a case study

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    The Sri Lankan financial market uses non analytical techniques to quantify credit risk. Credit derivatives are not used to transfer credit risk. A Credit Default Swap (CDS) is the most widely used credit derivative to manage credit risk. To evaluate the price of CDS, various sophisticated methods are used. This research paper focuses on techniques to hedge credit risk in the Sri Lankan financial market, the behaviours of CDS in derivative markets, calculating a fair value of CDS, the main advantages of using credit derivatives, and major imperfections to use the pricing process of CDS in the Sri Lankan marke

    Multi-Scaling of Correlation Functions in Single Species Reaction-Diffusion Systems

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    We derive the multi-fractal scaling of probability distributions of multi-particle configurations for the binary reaction-diffusion system A+AA+A \to \emptyset in d2d \leq 2 and for the ternary system 3A3A \to \emptyset in d=1d=1. For the binary reaction we find that the probability Pt(N,ΔV)P_{t}(N, \Delta V) of finding NN particles in a fixed volume element ΔV\Delta V at time tt decays in the limit of large time as (lntt)N(lnt)N(N1)2(\frac{\ln t}{t})^{N}(\ln t)^{-\frac{N(N-1)}{2}} for d=2d=2 and t^{-Nd/2}t^{-\frac{N(N-1)\epsilon}{4}+\mathcal{O}(\ep^2)} for d<2d<2. Here \ep=2-d. For the ternary reaction in one dimension we find that Pt(N,ΔV)(lntt)N/2(lnt)N(N1)(N2)6P_{t}(N,\Delta V) \sim (\frac{\ln t}{t})^{N/2}(\ln t)^{-\frac{N(N-1)(N-2)}{6}}. The principal tool of our study is the dynamical renormalization group. We compare predictions of \ep-expansions for Pt(N,ΔV)P_{t}(N,\Delta V) for binary reaction in one dimension against exact known results. We conclude that the \ep-corrections of order two and higher are absent in the above answer for Pt(N,ΔV)P_{t}(N, \Delta V) for N=1,2,3,4N=1,2,3,4. Furthermore we conjecture the absence of \ep^2-corrections for all values of NN.Comment: 10 pages, 6 figure

    A holistic approach to dissecting SPARC family protein complexity reveals FSTL-1 as an inhibitor of pancreatic cancer cell growth.

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    SPARC is a matricellular protein that is involved in both pancreatic cancer and diabetes. It belongs to a wider family of proteins that share structural and functional similarities. Relatively little is known about this extended family, but evidence of regulatory interactions suggests the importance of a holistic approach to their study. We show that Hevin, SPOCKs, and SMOCs are strongly expressed within islets, ducts, and blood vessels, suggesting important roles for these proteins in the normal pancreas, while FSTL-1 expression is localised to the stromal compartment reminiscent of SPARC. In direct contrast to SPARC, however, FSTL-1 expression is reduced in pancreatic cancer. Consistent with this, FSTL-1 inhibited pancreatic cancer cell proliferation. The complexity of SPARC family proteins is further revealed by the detection of multiple cell-type specific isoforms that arise due to a combination of post-translational modification and alternative splicing. Identification of splice variants lacking a signal peptide suggests the existence of novel intracellular isoforms. This study underlines the importance of addressing the complexity of the SPARC family and provides a new framework to explain their controversial and contradictory effects. We also demonstrate for the first time that FSTL-1 suppresses pancreatic cancer cell growth

    Factors Associated with Production Input Difference of a Manufacturing Plant in Sri Lanka: A Case Study

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    AbstractThe supply chain is a system of organizations, peoples, activities, information, and resources involved in moving a product or service from supplier to customer. As the whole supply chain is linked together, any inconsistency in one link can badly affect the overall supply chain. Each organization in the supply chain has its own internal individual supply chains. The internal supply chain is mainly based on the production demand and material supply to the production. Any inconsistency between the demand and the supply, directly affects the status of internal supply chain. Only few studies have been done on internal supply demand variance, and this study is one of the few approaches into this area. The main objective of this study is to identify the factors associated with production input difference of a manufacturing plant. This is an explanatory research, which is done using appropriate sampling methods and Vector Auto regressive (VAR) modeling. Eviews (7.0.0.1) version is used to analyze the data. First of all the data has been checked for stationary property and the related lag length has been selected. Then the VAR modeling techniques has been applied and later the diagnostic tests have been performed on the resulted models. In briefing the results, it is stated that style of the product (Style) does not impact the input variance models or downtime models. Considering input variance models, it is found that downtime at lag 1 does not have any impact on the input variance. Furthermore, the previous day input variance has a significant impact to the next day input variance. The style and previous day downtime influence the demand variance only in special cases. As heteroskedasticity is present in some of the models, exponential & power transformations have been done in order to avoid heteroskedasticity. But the results do not dramatically change due to transformations.Keywords: Factors, Internal Supply Chain, Manufacturing Plant, Vector Auto Regressiv

    Production of biochemicals and biofuels with no CO2 production and improved product yields

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    Traditional fermentation processes for the production of the majority of biochemicals and biofuels produce CO2 because of decarboxylation reactions, which limits the final mass yields of products. To overcome this limitation, we have developed a fermentation technology called MixoFerm™ (also known as anaerobic, non-photosynthetic mixotrophy), which uses microorganisms capable of simultaneously consuming both organic (e.g., sugars) and inorganic (e.g., CO2, CO, or H2) substrates. With this technology, product mass yields for almost any biochemical or biofuel can be increased by at least 50%, and processes can be designed that result in no CO2 production. In order to achieve zero CO2 emissions for most products, exogenous reductant must be added to the system, since sugar lacks the necessary reducing energy to both fix CO2 and produce the product of interest. Here, we demonstrate concurrent consumption of both sugars and exogenously added reducing gases (CO and/or H2) to produce products of interest at enhanced mass yields and with no CO2 emissions. In addition, we have screened a library of acetogenic bacteria in order to find an optimal MixoFerm™ host strain, one that consumes both a broad range of carbohydrates and gases. From this library, we identified strains with a broader carbohydrate consumption range than traditional acetogens like C. ljungdahlii or C. autoethanogenum, and characterized their ability to grow under a variety of MixoFerm™ conditions to produce biochemicals at enhanced mass yields. With the ability to improve product yields for reduced products, especially for ethanol and other potential biofuels, MixoFerm™ is a robust and flexible platform technology to improve process economics and product life-cycle analysis

    Referral patterns to primary mental health services in Western Sydney (Australia) : an analysis of routinely collected data (2005-2018)

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    Background: Regionally-specific approaches to primary mental health service provision through Primary Health Networks (PHNs) have been a feature of recent national mental health reforms. No previous studies have been conducted to investigate local patterns of primary mental health care (PMHC) services in Western Sydney. This study is designed to (i) understand the socio-demographic and economic profiles (ii) examine the inequalities of service access, and (iii) investigate the service utilisation patterns, among those referred to PMHC services in Western Sydney, Australia. Methods: This study used routinely collected PMHC data (2005–2018), population-level general practice and Medicare rebates data (2013–2018) related to mental health conditions, for the population catchment of the Western Sydney PHN. Sex- and age-specific PMHC referrals were examined by socio-demographic, diagnostic, referral- and service-level factors, and age-specific referrals to PMHC services as a percentage of total mental health encounters were investigated. Results: There were 27,897 referrals received for 20,507 clients, of which, 79.19% referrals resulted in follow-up services with 138,154 sessions. Overall, 60.09% clients were female, and median age was 31 years with interquartile ranged 16–46 years. Anxiety and depression were the predominant mental health condition, and 9.88% referred for suicidal risk. Over two-thirds of referrals started treatments during the first month of the referral and 95.1% of the total sessions were delivered by face to face. The younger age group (0–24) had greater referral opportunities as a percentage of total visits to a general practitioner and Medicare rebates, however demonstrating poor attendance rates with reduced average sessions per referral compared with older adults. Conclusion: Children and young adults were more likely to be referred to PMHC services than older adults, but were less likely to attend services. Further research is needed to identify the strategies to address these differences in access to PMHC services to/10.1186/s13033-020-00368-5 optimise the effectiveness of services

    Accurate Crop Spraying with RTK and Machine Learning on an Autonomous Field Robot

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    The agriculture sector requires a lot of labor and resources. Hence, farmers are constantly being pressed for technology and automation to be cost-effective. In this context, autonomous robots can play a very important role in carrying out agricultural tasks such as spraying, sowing, inspection, and even harvesting. This paper presents one such autonomous robot that is able to identify plants and spray agro-chemicals precisely. The robot uses machine vision technologies to find plants and RTK-GPS technology to navigate the robot along a predetermined path. The experiments were conducted in a field of potted plants in which successful results have been obtained.Comment: 7 pages, 12 figures, Journa
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