Y (2011). Evaluating of drought stress tolerance based on selection indices in Turkish bread wheat landraces

ABSTRACT The objective of this study was to evaluate the ability of several selection indices to identify drought tolerant/resistant genotypes under different conditions of Konya. Thirty six bread wheat genotypes (twenty four pure lines from Turkish bread wheat landraces and twelve modern bread wheat cultivars) were evaluated under both moisture stress (rainfed) and non stress (irrigated) conditions using a "randomized complete block design" with three replications. Nine drought tolerance indices including yield stability index (YSI), yield index (YI), superiority index (P i ), stress tolerance index (STI), geometric mean productivity (GMP), stress susceptibility index (SSI), mean productivity (MP), stress tolerance (TOL), harmonic mean (HM) and linear regression coefficient (b i ) were calculated. The indices were adjusted based on grain yield under drought condition (Ys) and normal conditions (Yp). SSI is suggested as useful indicator for wheat breeding where the stress is severe while MP, GMP, TOL HM and STI are suggested if the stress is less severe. Biplot analysis was used to explain relationship between grain yield and drought indices. Karahan-99, Bayraktar-2000, Gerek-79, PL-7, Sönmez-2000, Kıraç-66 and PL-5 were the superior genotypes for both rainfed and irrigated conditions with high PC1 and low PC2. PL-25, PL-13 and PL-23 with high PC2 were more suitable for rainfed than irrigated conditions

Performance and energy-efficient implementation of a smart city application on FPGAs

The continuous growth of modern cities and the request for better quality of life, coupled with the increased availability of computing resources, lead to an increased attention to smart city services. Smart cities promise to deliver a better life to their inhabitants while simultaneously reducing resource requirements and pollution. They are thus perceived as a key enabler to sustainable growth. Out of many other issues, one of the major concerns for most cities in the world is traffic, which leads to a huge waste of time and energy, and to increased pollution. To optimize traffic in cities, one of the first steps is to get accurate information in real time about the traffic flows in the city. This can be achieved through the application of automated video analytics to the video streams provided by a set of cameras distributed throughout the city. Image sequence processing can be performed both peripherally and centrally. In this paper, we argue that, since centralized processing has several advantages in terms of availability, maintainability and cost, it is a very promising strategy to enable effective traffic management even in large cities. However, the computational costs are enormous, and thus require an energy-efficient High-Performance Computing approach. Field Programmable Gate Arrays (FPGAs) provide comparable computational resources to CPUs and GPUs, yet require much lower amounts of energy per operation (around 6$$\times$$ and 10$$\times$$ for the application considered in this case study). They are thus preferred resources to reduce both energy supply and cooling costs in the huge datacenters that will be needed by Smart Cities. In this paper, we describe efficient implementations of high-performance algorithms that can process traffic camera image sequences to provide traffic flow information in real-time at a low energy and power cost