Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States

The lack of continuous soil moisture fields at large spatial scales, based on observations, has hampered hydrologists from understanding its role in weather and climate. The most readily available observations from which a surface wetness state could be derived is the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations at 10.65 GHz. This paper describes the first attempt to map daily soil moisture from space over an extended period of time. Methods to adjust for diurnal changes associated with this temporal variability and how to mosaic these orbits are presented. The algorithm for deriving soil moisture and temperature from TMI observations is based on a physical model of microwave emission from a layered soil-vegetation-atmosphere medium. An iterative, least-squares minimization method, which uses dual polarization observations at 10.65 GHz, is employed in the retrieval algorithm. Soil moisture estimates were compared with ground measurements over the U.S. Southern Great Plains (SGP) in Oklahoma and the Little River Watershed, Georgia. The soil moisture experiment in Oklahoma was conducted in July 1999 and Little River in June 2000. During both the experiments, the region was dry at the onset of the experiment, and experienced moderate rainfall during the course of the experiment. The regions experienced a quick dry-down before the end of the experiment. The estimated soil moisture compared well with the ground observations for these experiments (standard error of 2.5%). The TMI-estimated soil moisture during 6-22 July over Southern U.S. was analyzed and found to be consistent with the observed meteorological conditions.     

Concurrent optimisation of a computer vision system’s multiple responses

A number of recent research works have focused on how to improve the performance of production systems. This paper examines the system based on a simulation model with two manufacturing cells under a re-entrant environment. With the model a set of experiments has been carried out to study how the factors influence the system performance. Different release times and lot sizes have been compared, and scheduling heuristics for both bottleneck and non-bottleneck have been discussed to capture the essence of the production system. We used ANOVA to analyze the experimental results and achieved the conclusions that: interval releasing is better than beginning releasing; lot size can improve one of the performance indicators, but deteriorate the other under interval releasing; NC policy can obtain both good due-date performance and high throughput.     

Impact of hydrogen generated by splitting water with nano-silicon and nano-aluminum on diesel engine performance

Efficient utilization of hydrogen generated during the reactions of nano-silicon/water and nano-aluminum/water in internal combustion engine has been investigated in the current work. Engine performance and emission studies of formulated and stabilized nanoemulsion fuels (water in diesel W/D), nano-aluminum in water/diesel (W/DA) and water in nano-silicon/diesel (W/DS) have been compared with those of diesel. Experimental investigations showed reduction in brake specific fuel consumption (BSFC) by 21% and 37%; rise in brake thermal efficiency (BTE) by 16% and 14% when engine was fueled with W/DA and W/DS respectively. For nanoemulsion fuels an increase in induced power was also recorded. Brake mean effective pressure, BTE and NOx emission dropped for W/D due to reduced exhaust gas temperatures. Nevertheless due to elevated peak cylinder pressures and exhaust gas temperatures a marginal rise in NOx, CO, HC and radiative heat emissions was observed with W/DA and W/DS.     

Rolling element bearing design through genetic algorithms

The design of rolling element bearings has been a challenging task in the field of mechanical engineering. While most of the real aspects of the design are never disclosed by bearing manufacturers, the common engineer is left with no other alternative than to refer to standard tables and charts containing the bearing performance characteristics. This paper presents a more viable method to solve this problem using genetic algorithms (GAs). Since the algorithm is basically a guided random search, it weakens the chances of getting trapped in local maxima or minima. The method used has yielded improved performance parameters than those catalogued in standard tables.     

Selective greedy routing: exploring the path diversity in backbone mesh networks

This paper proposes a new routing protocol for IEEE 802.11s wireless mesh network to overcome the problems associated with the conventional proactive and reactive routing approaches. The proposed ‘Selective Greedy’ (SelG) routing protocol operates in two phases. In the first phase it exploits the proactive mode of Hybrid Wireless Mesh Protocol, the standard routing protocol for IEEE 802.11s mesh, and constructs a set of potential forwarders for every mesh point, that can act as the next-hop relay. In the second phase, during the actual data communication, a candidate is selected from the set of potential forwarders based on a local optimization. The optimization procedure considers the statistical effect of local link quality fluctuation and interference over the global routing path selection. This way the SelG protocol reduces the control packet flooding in the network (a major drawback for reactive protocols). At the same time, the optimization procedure captures the network dynamics, and thus avoids the possibility of routing based on stale information (a drawback for proactive protocols). The routing properties and the correctness of the SelG protocol is established theoretically, and the performance of the protocol is analyzed through simulation results. The proposed protocol is implemented in an indoor wireless mesh testbed, and the performance is evaluated and compared with other traditional approaches.     

Data-Driven DPPM Estimation and Adaptive Fault Coverage Calibration Using MATLAB?

A manufacturing defect is a finite chip area with electrically malfunctioning circuitry caused by fabrication errors. The fraction of defective chips that escapes to the customer is called the defect level, also known as defective parts per million (DPPM, or simply PPM) when normalized to one million units. This paper demonstrates a technique used to correlate coverage goals to DPPM based on test fallout data using a MATLAB^?-based error function minimization approach. This analysis is explained using regression models for DPPM yield versus fault/defect coverage. This approach is beneficial to semiconductor companies for calibrating their fault coverage goals to meet DPPM requirements from automotive or other customers that have very aggressive (i.e., ultra-low) DPPM demands.