Classification of internally damaged almond nuts using hyperspectral imagery

Hyperspectral transmission spectra of almond nuts are studied for discriminating internally damaged almond nuts from normal ones. We introduce a novel internally damaged almond detection method that requires only two sets of ratio features (the ratio of the responses at two different spectral bands) for classification. Our proposed method avoids exhaustively searching the whole feature space by first ordering the set of ratio features and then choosing the best ratio features based on the ordered set. Use of two sets of ratio features for classification is attractive, since it can be used in real-time practical multispectral sensor systems. Experimental results demonstrate that our method gives a higher classification rate than does use of the best feature selection subset of separate wavebands or than does use of feature extraction algorithms using all wavelength data.     

Fusion algorithm for poultry skin tumor detection using hyperspectral data

We consider a feature selection method to detect skin tumors on chicken carcasses using hyperspectral (HS) reflectance data. Detection of chicken tumors is difficult because the tumors vary in size and shape; some tumors are small, early-stage tumor spots. We make use of the fact that a chicken skin tumor consists of a lesion region surrounded by a region of thickened skin and that the spectral responses of the lesion and the thickened-skin regions of tumors are considerably different and train our feature selection algorithm to separately detect lesion regions and thickened-skin regions; we then fuse the two HS detection results to reduce false alarms. To the best of our knowledge, these techniques are new. Our forward selection and modified branch and bound algorithm is used to select a small number of lambda spectral features that are useful for discrimination. Initial results show that our method offers promise for a good tumor detection rate and a low false alarm rate.     

Fast spatial averaging: an efficient algorithm for 2D mean filtering

We present a new fast spatial averaging technique that efficiently implements operations for spatial averaging or two-dimensional mean filtering. To perform spatial averaging of an M×N image with an averaging filter of size m×n, our proposed method requires approximately 4MN additions and no division. This is very promising, since the major computations required by our algorithm depend only on the size of the original image but not on the size of the averaging filter. To our knowledge, this technique requires the smallest number of additions for mean filtering. Experimental results on various image sizes using different filter sizes confirm that our fast spatial averaging algorithm is significantly faster than other spatial averaging algorithms, especially when the size of the input image is very large.     

An improvement on floating search algorithms for feature subset selection

A new improved forward floating selection (IFFS) algorithm for selecting a subset of features is presented. Our proposed algorithm improves the state-of-the-art sequential forward floating selection algorithm. The improvement is to add an additional search step called “replacing the weak feature” to check whether removing any feature in the currently selected feature subset and adding a new one at each sequential step can improve the current feature subset. Our method provides the optimal or quasi-optimal (close to optimal) solutions for many selected subsets and requires significantly less computational load than optimal feature selection algorithms. Our experimental results for four different databases demonstrate that our algorithm consistently selects better subsets than other suboptimal feature selection algorithms do, especially when the original number of features of the database is large.     

Modeling Mode and Route Similarities in Network Equilibrium Problem with Go-Green Modes

Environmental sustainability is a common requirement on the development of various real-world systems, especially on road transportation systems. Motorized vehicles generate a large amount of harmful emissions, which have adverse effects to the environment and human health. Environmental sustainability requires more promotions of ‘go-green’ transportation modes such as public transit and bicycle to realize the increasing travel demands while keeping the environmental expenses low. In this paper, we make use of recent advances in discrete choice modeling to develop equivalent mathematical programming formulations for the combined modal split and traffic assignment (CMSTA) problem that explicitly considers mode and route similarities under congested networks. Specifically, a nested logit model is adopted to model the modal split problem by accounting for mode similarity among the available modes, and a cross-nested logit model is used to account for route overlapping in the traffic assignment problem. This new CMSTA model has the potential to enhance the behavioral modeling of travelers’ mode shift between private motorized mode and ‘go-green’ modes as well as their mode-specific route choices, and to assist in quantitatively evaluating the effectiveness of different ‘go-green’ promotion policies.     

Comparison of antioxidant capacities and cytotoxicities of certain fruit peels

This work was undertaken to explore the potential of fruit waste materials as sources of powerful natural antioxidants. The peels of eight kinds of fruits commonly consumed and grown in Thailand were used. The ethanolic fruit peel extracts were subjected to the scavenging tests of DPPH and ABTS radicals. Results from both assays were in good agreement that the top three markedly high free radical-scavenging power was from the peel extracts of Punica granatum (pomegranate), Nephelium lappaceum (rambutan), and Garcinia mangostana (mangosteen). The IC₅₀ values to quench the DPPH free radicals of these three extracts were 0.003, 0.006, and 0.023 mg/ml and the trolox equivalent antioxidant capacity (TEAC) values from ABTS assay were 4.066, 3.074, and 3.001 mM/mg, respectively. The extract of mangosteen peel showed moderate toxicity to Caco-2 cells and high toxicity to peripheral blood mononuclear cells (PBMC) with the IC₅₀ values of 32.0 and 4.9 μg/ml, respectively. Pomegranate peel extract stimulated Caco-2 cell and PBMC proliferation with the ED₅₀ of 4.7 and 44.4 μg/ml, respectively. Peel extract of rambutan exhibited extremely high value of IC₅₀ (>100 μg/ml) against both cell types indicating non-toxic activity to the cells. It was concluded that the peel of rambutan may be considered potentially useful as a source of natural antioxidants for food or drug product because of its high antioxidant activity and non-toxic property to normal cells.