Comparison of Various Mean Field Formulations for Retrieving Refractive Indices of Aerosol Particles Containing Inclusions

Application of effective medium approximation (EMA) methods to two-component systems are presented. Systems studied are composed of water, sulfate, soot, and dust as these are commonly encountered atmospheric aerosol components. Atmospheric models often employ EMAs to include internally mixed aerosols without the computational burden of exact theory. In the current work, several types of mixing rules (Maxwell-Garnet, Bruggeman, and coherent potential approximation) have been applied to various two-component internally mixed particles at 550 nm using volume fractions of the minor component below 0.1. As expected, results show that the formulations tested produce very similar effective refractive indices indicating that electric field interactions between inclusions is negligible at the tested volume fractions. This indicates that the differences in component refractive index has only a minor effect on the validity of the EMA at the tested volume fractions. In all cases considered, the linear average of the refractive indices of the two components provides an upper limit for the EMAs

Bandwidth Borrowing Schemes for Instantaneous Video-on-Demand Systems

A controlled multicast scheme provides instantaneous service, but limited server bandwidth causes some user requests to be either delayed or rejected when insufficient free bandwidth is available. Two borrowing schemes are proposed for instantaneous video-on-demand (VOD) that reduce the user request blocking rate by borrowing bandwidth from ongoing video streams when there is insufficient free bandwidth for the server to deliver a new video stream. Both these new schemes have proved to be successful in reducing blocking rate and increasing bandwidth utilization at the expense of temporarily degrading the video quality

Analysis of fuzzy clustering and a generic fuzzy rule-based image segmentation technique

Many fuzzy clustering based techniques when applied to image segmentation do not incorporate spatial relationships of the pixels, while fuzzy rule-based image segmentation techniques are generally application dependent. Also for most of these techniques, the structure of the membership functions is predefined and parameters have to either automatically or manually derived. This paper addresses some of these issues by introducing a new generic fuzzy rule based image segmentation (GFRIS) technique, which is both application independent and can incorporate the spatial relationships of the pixels as well. A qualitative comparison is presented between the segmentation results obtained using this method and the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms using an empirical discrepancy method. The results demonstrate this approach exhibits significant improvements over these popular fuzzy clustering algorithms for a wide range of differing image types

A new species of Dialeurodes Cockerell (Hemiptera: Aleyrodidae) on Schefflera Forst and Forst in Florida

Descriptions of pupal cases of Dialeurodes schefflerae, new species, as well as distribution records are presented. This species is known to occur in Florida, Hawaii and Puerto Rico appearing to feed only on species of Schefflera Forst and Forst. This restriction to plant hosts in the Asian genus Schefflera, along with its affinities with Dialeurodes agalmae Takahashi, Dialeurodes citri (Ashmead) and Dialeurodes kirkaldyi (Kotinsky), suggests it is an invasive species, probably endemic to the Asian region

A Modified Distortion Measurement Algorithm for Shape Coding

Efficient encoding of object boundaries has become increasingly prominent in areas such as content-based storage and retrieval, studio and television post-production facilities, mobile communications and other real-time multimedia applications. The way distortion between the actual and approximated shapes is measured however, has a major impact upon the quality of the shape coding algorithms. In existing shape coding methods, the distortion measure do not generate an actual distortion value, so this paper proposes a new distortion measure, called a modified distortion measure for shape coding (DMSC) which incorporates an actual perceptual distance. The performance of the Operational Rate Distortion optimal algorithm [1] incorporating DMSC has been empirically evaluated upon a number of different natural and synthetic arbitrary shapes. Both qualitative and quantitative results confirm the superior results in comparison with the ORD lgorithm for all test shapes, without any increase in computational complexity

Automatic analysis of magnetic resonance (MR) sequences for the diagnosis of ligament lesions

To date the diagnosis of carpal instabilities due to ligament lesions relies upon a qualitative examination of the patient's wrist. This paper presents a novel system where sequences of magnetic resonance images (MRI) are automatically analysed to measure the motion of several wrist bones. Resulting motion graphs provide a quantitative basis for diagnostic as well as scientific purposes. As the imaging method is non-invasive up to twelve different wrist positions can be measured giving a detailed insight into the motion of the bone

Distributed and Load-Adaptive Self Configuration in Sensor Networks

Proactive self-configuration is crucial for MANETs such as sensor networks, as these are often deployed in hostile environments and are ad hoc in nature. The dynamic architecture of the network is monitored by exchanging so-called Network State Beacons (NSBs) between key network nodes. The Beacon Exchange rate and the network state define both the time and nature of a proactive action to combat network performance degradation at a time of crisis. It is thus essential to optimize these parameters for the dynamic load profile of the network. This paper presents a novel distributed adaptive optimization Beacon Exchange selection model which considers distributed network load for energy efficient monitoring and proactive reconfiguration of the network. The results show an improvement of 70% in throughput, while maintaining a guaranteed quality-of- service for a small control-traffic overhead

Image segmentation using fuzzy clustering incorporating spatial information

Effective image segmentation cannot be achieved for a fuzzy clustering algorithm based on using only pixel intensity, pixel locations or a combination of the two. Often if both pixel intensity and pixel location are combined, one feature tends to minimize the effect of other, thus degrading the resulting segmentation. This paper directly addresses this problem by introducing a new algorithm called image segmentation using fuzzy clustering incorporating spatial information (FCSI), which merges the segmented results independently generated by fuzzy clustering-based on pixel intensity and the location of pixels. Qualitative results show the superiority of the FCSI algorithm compared with the fuzzy c-means (FCM) algorithm for all three alternatives, clustering using only pixel intensity, pixel locations and a combination of the two

A survey of fuzzy rule-based image segmentation techniques

This paper describes the various fuzzy rule based techniques for image segmentation. Fuzzy rule based segmentation techniques can incorporate domain expert knowledge and manipulate numerical as well as linguistic data. They are also capable of drawing partial inference using fuzzy IF-THEN rules. For these reasons they have been extensively applied in medical imaging. But these rules are application domain specific and it is very difficult to define the rules either manually or automatically so that the segementation can be achieved successfully

Fuzzy Image Segmentation using Suppressed Fuzzy C-Means Clustering (SFCM)

Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having SSV satisfactorily. To improve the effectiveness of FSOS in segmenting objects with SSV, this paper introduces a new fuzzy image segmentation using suppressed fuzzy c-means clustering (FSSC) algorithm, which directly considers object SSV and incorporates the use of suppressed-FCM (SFCM) using pixel location. The algorithm also perceptually selects the threshold within the range of human visual perception. Both qualitative and quantitative results confirm the improved segmentation performance of FSSC compared with other algorithms including FSOS, FCM, possibilistic c-means (PCM) and SFCM for many different images