Adaptive chebyshev fusion of vegetation imagery based on SVM classifier

A novel adaptive image fusion method by using Chebyshev polynomial analysis (CPA), for applications in vegetation satellite imagery, is introduced in this paper. Fusion is a technique that enables the merging of two satellite cameras: panchromatic and multi-spectral, to produce higher quality satellite images to address agricurtural and vegetation issues such as soiling, floods and crop harvesting. Recent studies show Chebyshev polynomials to be effective in image fusion mainly in medium to high noise conditions, as per real-life satellite conditions. However, its application was limited to heuristics. In this research, we have proposed a way to adaptively select the optimal CPA parameters according to user specifications. Support vector machines (SVM) is used as a classifying tool to estimate the noise parameters, from which the appropriate CPA degree is utilised to perform image fusion according to a look-up table. Performance evaluation affirms the approach’s ability in reducing the computational complexity to perform fusion. Overall, adaptive CPA fusion is able to optimize an image fusion system’s resources and processing time. It therefore may be suitably incorporated onto real hardware for use on vegetation satellite imagery

Sticker systems over monoids

Molecular computing has gained many interests among researchers since Head introduced the first theoretical model for DNA based computation using the splicing operation in 1987. Another model for DNA computing was proposed by using the sticker operation which Adlemanused in his successful experiment for the computation of Hamiltonian paths in a graph: a double stranded DNA sequence is composed by prolonging to the left and to the right a sequence of (single or double) symbols by using given single stranded strings or even more complex dominoes with sticky ends, gluing these ends together with the sticky ends of the current sequence according to a complementarity relation. According to this sticker operation, a language generative mechanism, called a sticker system, can be defined: a set of (incomplete) double-stranded sequences (axioms) and a set of pairs of single or double-stranded complementary sequences are given. The initial sequences are prolonged to the left and to the right by using sequences from the latter set, respectively. The iterations of these prolongations produce “computations” of possibly arbitrary length. These processes stop when a complete double stranded sequence is obtained. Sticker systems will generate only regular languages without restrictions. Additional restrictions can be imposed on the matching pairs of strands to obtain more powerful languages. Several types of sticker systems are shown to have the same power as regular grammars; one type is found to represent all linear languages whereas another one is proved to be able to represent any recursively enumerable language. The main aim of this research is to introduce and study sticker systems over monoids in which with each sticker operation, an element of a monoid is associated and a complete double stranded sequence is considered to be valid if the computation of the associated elements of the monoid produces the neutral element. Moreover, the sticker system over monoids is defined in this study

Investigating machine learning techniques for detection of depression using structural MRI volumetric features

Structural MRI offers anatomical details and high sensitivity to pathological changes. It can demonstrate certain patterns of brain changes present at a structural level. Research to date has shown that volumetric analysis of brain regions has importance in depression detection. However, such analysis has had very minimal use in depression detection studies at individual level. Optimally combining various brain volumetric features/attributes, and summarizing the data into a distinctive set of variables remain difficult. This study investigates machine learning algorithms that automatically identify relevant data attributes for depression detection. Different machine learning techniques are studied for depression classification based on attributes extracted from structural MRI (sMRI) data. The attributes include volume calculated from whole brain, white matter, grey matter and hippocampus. Attributes subset selection is performed aiming to remove redundant attributes using three filtering methods and one hybrid method, in combination with ranker search algorithms. The highest average classification accuracy, obtained by using a combination of both SVM-EM and IG-Random Tree algorithms, is 85.23%. The classification approach implemented in this study can achieve higher accuracy than most reported studies using sMRI data, specifically for detection of depression

Lighting Analysis at Access Zone of Tunnel Entrance of Hong Kong-Zuhai-Macao Bridge (HZMB)

The study aimed to evaluate several shading schemes at access zone of the tunnel entrance of HZMB in order to find the best scenario to increase the user lighting comfort. The study analysed the luminance at the tunnel entrance (L20) and energy saving in the thereshold zone. For these analyses, four shading schemes have been simulated. The schemes were the Original Scheme (Zebra, 50% solid & 50% transparent), Option 1 (Gradation Glass), Option 2 (Perforated Material), and Option 3 (No Shading). The results shows that the Original scheme has the lowest L20 luminance and consequently the highest lighting energy saving. While the Option 3 (No Shading) has the highest L20 luminance and consequently the lowest lighting energy saving

PENGGUNAAN BUSINESS MODEL CANVAS PADA PERUSAHAAN SEPATU CUSTOMADE

Penelitian ini bertujuan untuk membuat rancangan model perusahaan Customade yang menggunakan framework business model canvas (BMC). Customade sendiri hadir sebagai model pengembangan system pelayanan penjualan sendal dan sepatu kulit yang menawarkan harga murah dan mempunyai kualitas yang bagus serta memberikan merchandise dan sampai saat ini para pengusaha sepatu dan sendal kulit terus tumbuh tetapi system pelayanan yang ditawarkan tetap menggunkan sistem yang sama saja. Dimana para calon pelanggan membeli produk yang berkualitas tetapi harganya mahal. Hal tersebut menjadi hal yang melatar belakangi peneliti membuat rancangan model bisnis perusahaan sepatu yang bernama Customade

EFFECTS OF THE TEMPERATURE ON THE OUTPUT VOLTAGE OF MONO-CRYSTALLINE AND POLY-CRYSTALLINE SOLAR PANELS

People can make solar energy alternative energy by employing solar panels to generate electricity. The utilization of solar energy on a solar panel to generate electricity is affected by the weather and the duration of the radiation, and they will affect the solar panel’s temperature. There are various types of solar panels that can be found on the market today, including Mono-Crystalline and Poly-Crystalline. The difference in the material used needs to be observed in terms of temperature changes in the solar module. Our study’s findings showed that a change in the temperature would impact the solar panel’s output voltage, and the solar panel’s output voltage would change when it was connected to the load although the measured temperatures were almost the same