Search results for: Color Histogram

Authors: Sunglim Lee, Susumu Fujii, Koji Okamura

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Smart grid is a new approach for electric power grid that uses information and communications technology to control the electric power grid. Smart grid provides real-time control of the electric power grid, controlling the direction of power flow or time of the flow. Control devices are installed on the power lines of the electric power grid to implement smart grid. The number of the control devices should be determined, in relation with the area one control device covers and the cost associated with the control devices. One approach to determine the number of the control devices is to use the data on the surplus power generated by home solar generators. In current implementations, the surplus power is sent all the way to the power plant, which may cause power loss. To reduce the power loss, the surplus power may be sent to a control device and sent to where the power is needed from the control device. Under assumption that the control devices are installed on a lattice of equal size squares, our goal is to figure out the optimal spacing between the control devices, where the power sharing area (the area covered by one control device) is kept small to avoid power loss, and at the same time the power sharing area is big enough to have no surplus power wasted. To achieve this goal, a simulation using landscape ecology method is conducted on a sample area. First an aerial photograph of the land of interest is turned into a mosaic map where each area is colored according to the ratio of the amount of power production to the amount of power consumption in the area. The amount of power consumption is estimated according to the characteristics of the buildings in the area. The power production is calculated by the sum of the area of the roofs shown in the aerial photograph and assuming that solar panels are installed on all the roofs. The mosaic map is colored in three colors, each color representing producer, consumer, and neither. We started with a mosaic map with 100 m grid size, and the grid size is grown until there is no red grid. One control device is installed on each grid, so that the grid is the area which the control device covers. As the result of this simulation we got 350m as the optimal spacing between the control devices that makes effective use of the surplus power for the sample area.

Keywords: Landscape ecology, IT, smart grid, aerial photograph, simulation.

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra, Abdus Sobur

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of artificial intelligence (AI), specifically deep learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images, representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our approach presents a hybrid model, amalgamating the strengths of two renowned convolutional neural networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: Artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging.

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Authors: Daniela N. Correa-Llantén, Sebastián A. Muñoz-Ibacache, Mathilde Maire, Jenny M. Blamey

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The biosynthesis of nanoparticles by microorganisms, on the contrary to chemical synthesis, is an environmentally-friendly process which has low energy requirements. In this investigation, we used the microorganism Geobacillus wiegelii, strain GWE1, an aerobic thermophile belonging to genus Geobacillus, isolated from a drying oven. This microorganism has the ability to reduce selenite evidenced by the change of color from colorless to red in the culture. Elemental analysis and composition of the particles were verified using transmission electron microscopy and energy-dispersive X-ray analysis. The nanoparticles have a defined spherical shape and a selenium elemental state. Previous experiments showed that the presence of the whole microorganism for the reduction of selenite was not necessary. The results strongly suggested that an intracellular NADPH/NADH-dependent reductase mediates selenium nanoparticles synthesis under aerobic conditions. The enzyme was purified and identified by mass spectroscopy MALDI-TOF TOF technique. The enzyme is a 1-pyrroline-5-carboxylate dehydrogenase. Histograms of nanoparticles sizes were obtained. Size distribution ranged from 40-160 nm, where 70% of nanoparticles have less than 100 nm in size. Spectroscopic analysis showed that the nanoparticles are composed of elemental selenium. To analyse the effect of pH in size and morphology of nanoparticles, the synthesis of them was carried out at different pHs (4.0, 5.0, 6.0, 7.0, 8.0). For thermostability studies samples were incubated at different temperatures (60, 80 and 100 ºC) for 1 h and 3 h. The size of all nanoparticles was less than 100 nm at pH 4.0; over 50% of nanoparticles have less than 100 nm at pH 5.0; at pH 6.0 and 8.0 over 90% of nanoparticles have less than 100 nm in size. At neutral pH (7.0) nanoparticles reach a size around 120 nm and only 20% of them were less than 100 nm. When looking at temperature effect, nanoparticles did not show a significant difference in size when they were incubated between 0 and 3 h at 60 ºC. Meanwhile at 80 °C the nanoparticles suspension lost its homogeneity. A change in size was observed from 0 h of incubation at 80ºC, observing a size range between 40-160 nm, with 20% of them over 100 nm. Meanwhile after 3 h of incubation at size range changed to 60-180 nm with 50% of them over 100 nm. At 100 °C the nanoparticles aggregate forming nanorod structures. In conclusion, these results indicate that is possible to modulate size and shape of biologically synthesized nanoparticles by modulating pH and temperature.

Keywords: Genus Geobacillus, NADPH/NADH-dependent reductase, Selenium nanoparticles.

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Authors: Disciglio G., Lops F., Carlucci A., Gatta G., Tarantino A., Frabboni L., Carriero F., Cibelli F., Raimondo M. L., Tarantino E.

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The tomato is a very important crop, whose cultivation in the Mediterranean basin is severely affected by the phytoparasitic weed Phelipanche ramosa. The semiarid regions of the world are considered the main areas where this parasitic weed is established causing heavy infestation as it is able to produce high numbers of seeds (up to 500,000 per plant), which remain viable for extended period (more than 20 years). In this paper the results obtained from eleven treatments in order to control this parasitic weed including chemical, agronomic, biological and biotechnological methods compared with the untreated test under two plowing depths (30 and 50 cm) are reported. The split-plot design with 3 replicates was adopted. In 2014 a trial was performed in Foggia province (southern Italy) on processing tomato (cv Docet) grown in the field infested by Phelipanche ramosa. Tomato seedlings were transplant on May 5, on a clay-loam soil. During the growing cycle of the tomato crop, at 56-78 and 92 days after transplantation, the number of parasitic shoots emerged in each plot was detected. At tomato harvesting, on August 18, the major quantity-quality yield parameters were determined (marketable yield, mean weight, dry matter, pH, soluble solids and color of fruits). All data were subjected to analysis of variance (ANOVA) and the means were compared by Tukey's test. Each treatment studied did not provide complete control against Phelipanche ramosa. However, among the different methods tested, some of them which Fusarium, gliphosate, radicon biostimulant and Red Setter tomato cv (improved genotypes obtained by Tilling technology) under deeper plowing (50 cm depth) proved to mitigate the virulence of the Phelipanche ramose attacks. It is assumed that these effects can be improved combining some of these treatments each other, especially for a gradual and continuing reduction of the “seed bank” of the parasite in the soil.

Keywords: Control methods, Phelipanche ramosa, tomato crop.

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Authors: Hira Lal Gope, Hidekazu Fukai

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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: Convolutional neural networks, coffee bean, peaberry, sorting, support vector machine.

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Authors: Nicole M. Martino

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Reinforced concrete bridge deck condition assessments primarily use visual inspection methods, where an inspector looks for and records locations of cracks, potholes, efflorescence and other signs of probable deterioration. Sounding is another technique used to diagnose the condition of a bridge deck, however this method listens for damage within the subsurface as the surface is struck with a hammer or chain. Even though extensive procedures are in place for using these inspection techniques, neither one provides the inspector with a comprehensive understanding of the internal condition of a bridge deck – the location where damage originates from.  In order to make accurate estimates of repair locations and quantities, in addition to allocating the necessary funding, a total understanding of the deck’s deteriorated state is key. The research presented in this paper collected infrared thermography and ground penetrating radar data from reinforced concrete bridge decks without an asphalt overlay. These decks were of various ages and their condition varied from brand new, to in need of replacement. The goals of this work were to first verify that these nondestructive evaluation methods could identify similar areas of healthy and damaged concrete, and then to see if combining the results of both methods would provide a higher confidence than if the condition assessment was completed using only one method. The results from each method were presented as plan view color contour plots. The results from one of the decks assessed as a part of this research, including these plan view plots, are presented in this paper. Furthermore, in order to answer the interest of transportation agencies throughout the United States, this research developed a step-by-step guide which demonstrates how to collect and assess a bridge deck using these nondestructive evaluation methods. This guide addresses setup procedures on the deck during the day of data collection, system setups and settings for different bridge decks, data post-processing for each method, and data visualization and quantification.

Keywords: Bridge deck deterioration, ground penetrating radar, infrared thermography, NDT of bridge decks.

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Authors: Barot Jagruti

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The textile industry plays a major role in the economy of India and on the other side of the coin it is the major source for water pollution. As azo dyes is the largest dye class they are extensively used in many fields such as textile industry, leather tanning industry, paper production, food, color photography, pharmaceuticals and medicine, cosmetic, hair colorings, wood staining, agricultural, biological and chemical research etc. In addition to these, they can have acute and/or chronic effects on organisms depending on their concentration and length of exposure when they discharged as effluent in the environment. The aim of this study was to assess the genotoxic and histotoxic potentials of environmentally relevant concentrations of C. I. Reactive Red 120 (RR 120) on Catla catla, important edible freshwater fingerlings. For this, healthy Catla catla fingerlings were procured from the Government Fish Farm and acclimatized in 100 L capacity and continuously aerated glass aquarium in laboratory for 15 days. According to APHA some physic-chemical parameters were measured and maintained such as temperature, pH, dissolve oxygen, alkalinity, total hardness. Water along with excreta had been changed every 24 hrs. All fingerlings were fed artificial food palates once a day @ body weight. After 15 days fingerlings were grouped in 5 (10 in each) and exposed to various concentrations of RR 120 (Control, 10, 20, 30 and 40 mg.l-1) and samples (peripheral blood and gills, kidney) were collected and analyzed at 96 hrs. All results were compared with the control. Micronuclei (MN), nuclear buds (NB), fragmented-apoptotic (FA) and bi-nucleated (BN) cells in blood smears and in tissues (gills and kidney cells) were observed. Prominent histopathological alterations were noticed in gills such as aneurism, hyperplasia, degenerated central axis, lifting of gill epithelium, curved secondary gill lamellae etc. Similarly kidney showed some detrimental changes like shrunken glomeruli with increased periglomerular space, degenerated renal tubules etc. Both haematological and histopathological changes clearly reveal the toxic potential of RR 120. This work concludes that water pollution assessment can be done by these two biomarkers which provide baseline to the further chromosomal or molecular work.

Keywords: Catla catla, genotoxicity, histopathlogicalchanges, RR 120azo dye.

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Authors: Dor Oppenheim, Yael Edan, Guy Shani

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This paper presents an image analysis algorithm to detect and count yellow tomato flowers in a greenhouse with uneven illumination conditions, complex growth conditions and different flower sizes. The algorithm is designed to be employed on a drone that flies in greenhouses to accomplish several tasks such as pollination and yield estimation. Detecting the flowers can provide useful information for the farmer, such as the number of flowers in a row, and the number of flowers that were pollinated since the last visit to the row. The developed algorithm is designed to handle the real world difficulties in a greenhouse which include varying lighting conditions, shadowing, and occlusion, while considering the computational limitations of the simple processor in the drone. The algorithm identifies flowers using an adaptive global threshold, segmentation over the HSV color space, and morphological cues. The adaptive threshold divides the images into darker and lighter images. Then, segmentation on the hue, saturation and volume is performed accordingly, and classification is done according to size and location of the flowers. 1069 images of greenhouse tomato flowers were acquired in a commercial greenhouse in Israel, using two different RGB Cameras – an LG G4 smartphone and a Canon PowerShot A590. The images were acquired from multiple angles and distances and were sampled manually at various periods along the day to obtain varying lighting conditions. Ground truth was created by manually tagging approximately 25,000 individual flowers in the images. Sensitivity analyses on the acquisition angle of the images, periods throughout the day, different cameras and thresholding types were performed. Precision, recall and their derived F1 score were calculated. Results indicate better performance for the view angle facing the flowers than any other angle. Acquiring images in the afternoon resulted with the best precision and recall results. Applying a global adaptive threshold improved the median F1 score by 3%. Results showed no difference between the two cameras used. Using hue values of 0.12-0.18 in the segmentation process provided the best results in precision and recall, and the best F1 score. The precision and recall average for all the images when using these values was 74% and 75% respectively with an F1 score of 0.73. Further analysis showed a 5% increase in precision and recall when analyzing images acquired in the afternoon and from the front viewpoint.

Keywords: Agricultural engineering, computer vision, image processing, flower detection.

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Authors: G. A. Oladosu, P. O. Ogbodogbo, C. I. Makinde, M. O. Tijani, O. A. Adegboyega

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Saprolegniasis is a major pathogenic infection that contributes significantly to poor hatching rates in incubated fish eggs in the African catfish hatchery in Nigeria. Malachite green, known to be very effective against this condition, has been banned because it is carcinogenic. There is therefore the need for other effective, yet safer method of controlling saprolegniasis in incubated fish eggs. A total of 50 ml crude, chloroform extract of pyocyanin from which solvent was removed to attain 30 ml, having a concentration of 12.16 ug/ml was produced from 700 ml broth culture of Pseudomonas aeruginosa isolated from a previous study. In vitro susceptibility of the fungus was investigated by exposing fungal infected eggs to two different time-concentration ratios of pyocyanin; 0.275 ug/ml and 2.75 ug/ml for 1 and 24 h, and 5 mg/L malachite green as positive control while normal saline was the control. Efficacy of pyocyanin was evaluated using the degree of mycelial growth inhibition in the different treatments. Fertilized Clarias gariepinus eggs (between 45 to 64 eggs) were then incubated in 20 ml of medium containing the similar concentrations of pyocyanin and malachite green, with freshwater as control for 24 hours. Hatching rates of the incubated eggs were observed. Three samples of un-hatched eggs were taken from each medium and observed for the presence of fungal pathogens using microscopy. Another batch of three samples of un-hatched eggs from each treatment was also inoculated on Sabourand dextrose agar (SDA) using Egg-Agar Transfer technique to observe for fungal growth. Mycelial growth was inhibited in fungal infected eggs treated with 2.75 ug/ml for 24 h and the 5 mg/L malachite green for both 1 h and 24 h. The mortality rate was 100% in fertilized C. gariepinus eggs exposed for 24 h to 0.275 and 2.75 ug/ml of pyocyanin. The mortality rate was least in the malachite green followed by the control treatment. Embryonic development was observed to be arrested in the eggs treated with the two pyocyanin concentrations as they maintain their color but showed no development beyond the gastrula stage, whereas viable eggs in the control and malachite green treatments developed fully into healthy hatchlings. Furthermore, microscopy of the un-hatched eggs revealed the presence of a protozoan ciliate; Colpidium sp. (Tetrahymenidae), as well as a pathogenic fungus; Saprolegnia sp. in the control, but not in the malachite green and pyocyanin treatments. Growth of Saprolegnia sp. was also observed in SDA culture of un-hatched eggs from the control, but not from pyocyanin and malachite green treated eggs. Pyocyanin treatment of incubated eggs of Clarias gariepinus effectively prevented fungal infection in the eggs, but also arrested the development of the embryo. Therefore, crude chloroform extract of pyocyanin from Pseudomonas aeruginosa cannot be used in the control of Saprolegniasis in incubated Clarias gariepinus eggs at the concentration and duration tested in this study.

Keywords: African catfish, bioprophylaxis, embryo, saprolegniasis.

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Authors: M. Arun, A. Kannan

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Textile industries cater to varied customer preferences and contribute substantially to the economy. However, these textile industries also produce a considerable amount of effluents. Prominent among these are the azo dyes which impart considerable color and toxicity even at low concentrations. Azo dyes are also used as coloring agents in food and pharmaceutical industry. Despite their applications, azo dyes are also notorious pollutants and carcinogens. Popular techniques like photo-degradation, biodegradation and the use of oxidizing agents are not applicable for all kinds of dyes, as most of them are stable to these techniques. Chemical coagulation produces a large amount of toxic sludge which is undesirable and is also ineffective towards a number of dyes. Most of the azo dyes are stable to UV-visible light irradiation and may even resist aerobic degradation. Adsorption has been the most preferred technique owing to its less cost, high capacity and process efficiency and the possibility of regenerating and recycling the adsorbent. Adsorption is also most preferred because it may produce high quality of the treated effluent and it is able to remove different kinds of dyes. However, the adsorption process is influenced by many variables whose inter-dependence makes it difficult to identify optimum conditions. The variables include stirring speed, temperature, initial concentration and adsorbent dosage. Further, the internal diffusional resistance inside the adsorbent particle leads to slow uptake of the solute within the adsorbent. Hence, it is necessary to identify optimum conditions that lead to high capacity and uptake rate of these pollutants. In this work, commercially available activated carbon was chosen as the adsorbent owing to its high surface area. A typical azo dye found in textile effluent waters, viz. the monoazo Acid Orange 10 dye (CAS: 1936-15-8) has been chosen as the representative pollutant. Adsorption studies were mainly focused at obtaining equilibrium and kinetic data for the batch adsorption process at different process conditions. Studies were conducted at different stirring speed, temperature, adsorbent dosage and initial dye concentration settings. The Full Factorial Design was the chosen statistical design framework for carrying out the experiments and identifying the important factors and their interactions. The optimum conditions identified from the experimental model were validated with actual experiments at the recommended settings. The equilibrium and kinetic data obtained were fitted to different models and the model parameters were estimated. This gives more details about the nature of adsorption taking place. Critical data required to design batch adsorption systems for removal of Acid Orange 10 dye and identification of factors that critically influence the separation efficiency are the key outcomes from this research.

Keywords: Acid Orange 10, Activated carbon, Optimum conditions, Statistical design.

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