International Journal on Advanced Science, Engineering and Information Technology
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The Assesment of Capital Flow and Technology Transfer in Asparagus Production
Get PDFThe industrial sector has marginalized the agribusiness sector. The tight competition in the industrial sector has led to efforts by businesses to develop the agribusiness sector. The agribusiness sector has a vital role as state revenue and food security of rural households. Asparagus is one of the agribusiness commodities. Management of investment capital flows and technology transfer is an obstacle in improving the quality and quantity of asparagus production. Asparagus as a food crop commodity is sought after by the world community as a counterweight to food needs. This study aims to identify and analyze the development of Asparagus cultivation, investment capital flows, and technology transfer in asparagus production. The analytical method used is the assessment of investment capital flows and comparative studies. The research findings show that asparagus production is influenced by integrated development, capital flows, investment value, and technology transfer. The results of the analysis and assessment of investment capital flows, technology transfer show net B/C 1.01-2.21, IRR 24-35.87%, payback period 3.7-4.0-month, maximum production life of 10 years. The maximum production value of 1 Ha of land is IDR 108,000,000 /year; costs are IDR 68,800,000/year. The research findings show that (1) the ability to supply asparagus and the fulfillment of market needs tend to be unbalanced; (2) asparagus producers have a comparative advantage due to production technology
Efficient Supervised Features Learning for Remote Sensing Image Classification
Get PDFThe features extracted from the fully connected (FC) layers of a convolutional neural network (ConvNet or CNN) can provide accurate classification results as long as the labelled datasets are large enough. On the other end, high accuracy remote sensing image (RSI) classification is demanded various implementations such as urban planning, environmental monitoring, and geographic image retrieval. Many studies have been presented in this domain; however, satisfactory classification accuracy is yet to be achieved. In this study, the proposed method of fine-tuning the pre-trained ConvNets (GoogleNet, VGG16, and ResNet50) on RSI, extracting features from the last fine-tuned FC layer of these networks and reprocess the extracted features for classification by SVM, produced high classification accuracy. Extensive experiments have been conducted on three RSI datasets: the NWPU, AID, and PatternNet. Comparative results over the selected datasets demonstrate that our method considerably outperforms the state-of-the-art best-stated results. Also, the overall accuracy (OA) and confusion matrix report quantitative evaluation. Our best outcomes from the first part were 99.54%, 94.60%, and 94.83% on the PatternNet, NWPU, and AID datasets, respectively, achieved by fine-tuned ResNet50. Moreover, the best classification accuracies with training ratios 20% and 50% on the AID dataset, 10% and 20% on the NWPU dataset, and the 10%, 20%, 50% and 80% on PatternNet dataset were 95.72%, 97.53%, 96.19%, 96.85%, 99.60%, 99.56%, 99.75% and 99.80% respectively. The classification performance of each class was estimated using a confusion matrix for the three datasets
Transfer Data from PC to PC Based on Li-Fi Communication Using Arduino
Get PDFLight Fidelity (Li-Fi) is a new technology that has been developed in the last few years and still needs more investigation and experiments to prove its perfection to be an alternative solution for wireless fidelity (Wi-Fi) technology. Li-Fi utilizes light as a medium of communication instead of traditional radio frequencies as in Wi-Fi. Li-Fi technology has the essential features compared to Wi-Fi, such as its ideal for high-density wireless data coverage in a confined area and reducing radio interferences issues. In this paper, a PC-to-PC wireless data transfer system is proposed based on Li-Fi technology. Data is transmitted from transmitter Pc via the light of an array of high-power white LEDs connected to Arduino UNO. The data is then received on the PC receiver using a photodiode, connected to another Arduino UNO device, to sense the light and decode it to its original format. This work aims to improve the transmission and reception mechanism by increasing the data bit rate. After experimenting, the evaluation results showed that the data bit rate was improved using the proposed transmission mechanism and reached up to 147 bps with an accuracy of 100%, over 20 cm as a distance between the transmitter and receiver
A Robust Embedded Non-Linear Acoustic Noise Cancellation (ANC) Using Artificial Neural Network (ANN) for Improving the Quality of Voice Communications
Get PDFEmbedded Acoustic Noise Cancellation (ANC) has enjoyed remarkable success in the telecommunication field, and it becomes an essential component in various communications applications, such as digital transmission. So, it is an efficient method used to enhance the quality of communications against noise phenomena which is a problem in communication systems. This paper contributes towards a new non-linear embedded ANC based Artificial Neural Network (ANN) in digital signal processing and backpropagation (BP) of the gradient algorithm. This system is usually required for non-linear adaptive processing digital signals. The neuronal ANC estimates the noise path and subtracting noise from a received signal by minimizing a cost function. It is the mean square error. Thus, also the filter weights are adaptively updated. In this work, we designed and simulated our intelligent embedded ANC model with the help of MATLAB\Simulink software. The proposed system was designed by using embedded functions in Simulink. In addition, all simulation results are performed and verified using Signal Noise to Ratio (SNR) and Mean Square Error (MSE), number of iteration, neuronal architecture, criteria and it has been compared in various scenarios. Â Finally, a study and analysis on convergence of neuronal ANC based backpropagation of the gradient algorithm demonstrate that our proposed system can effectively improve the quality of voice communications against the undesired noise. It also provides faster convergence during the back propagation of the gradient. Furthermore, the best values of SNR and MSE show the effectiveness of the proposed model
Polyisoprenoid Profiling of Mangrove Litters–based Zonations and Salinity Groups in North Sumatra, Indonesia
Get PDFThe polyprenols and dolichols in mangrove litter–based salinity groups and zonations in Lubuk Kertang, North Sumatra, Indonesia, was performed using two-dimensional thin-layer chromatography. Eight sites with twenty-four samples consisting of 0, 2, and 3% salt concentrations and five zonations (Avicennia spp, Bruguiera spp, Nypa fruticans community, Rhizophora spp, and Sonneratia spp) were analyzed. In the zonations, two types concerning the distribution of polyprenols and dolichols were detected. Type-I, showing predominance of dolichols over polyprenols, was observed in Avicennia spp, Bruguiera spp, Nypa fruticans, and Rhizophora spp. Type-II, having both polyprenols and dolichols, was observed in Sonneratia spp. In contrast, no type-I distribution was found in the salinity group. A type-II distribution was also observed in 0, 2, and 3% salt concentrations. The diversity of polyisoprenoid composition in the mangrove litters of salinity groups was noted, whereas dolichols predominated in the zonations (80%). In Avicennia spp litter, dolichols were found to be longer than other types of community litter (Bruguiera spp, Nypa, and Rhizophora spp). These conditions can be caused by leaf litter factors that have different ages and environments. A dendrogram was constructed using the Unweighted-Pair Group Method with Arithmetic mean (UPGMA) method to confirm these findings. The dendrogram demonstrated that the zonations and salinity groups were generally clustered according to appropriate species and families. The study suggested that dominated dolichols function as chemotaxonomic markers, useful in identifying and classifying mangroves, and in phylogenetic studies
The Comparison of Grayscale Image Enhancement Techniques for Improving the Quality of Marker in Augmented Reality
Get PDFNatural Feature Tracking (NFT) in Augmented Reality (AR) applications use feature detection and a feature matching approach to aligning virtual objects in a real environment. Thus, this tracking detects and compares features that are naturally found in the image (query of image) with the visible feature in the real environment. Therefore, the query of an image must contain good features to track. One of the representing natural features that is easily found in the image is in the corner, and a feature from Accelerated Segment Test (FAST) is one of the fastest corner detectors. However, the FAST corner uses the intensity of the grayscale pixel to determine the candidate corner. Hence, the intensity greatly affects the detection result. Therefore, FAST corner uses the grayscale conversion process to changes the color image into a grayscale image. Thus, the conversion process can lose some details of the images, such as sharpness, shadow, and color image structure. Hence, this process will affect the result of FAST corner to find the feature corner. Besides, Contrast Enhancement also can improve the quality of low contrast grayscale image. In this paper, there are three techniques of the Contrast Enhancement (CE) method were compared, which are Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Colormap. As a result, Colormap is better than HE and CLAHE to extract conner and others feature accurately
Characterization of River Sediments in Loja-Ecuador
Get PDFThis research's main objective is to delineate areas with a high concentration of "pollutant" elements that imply a risk for the ecosystem and inhabitants' health in the Cordillera Real, south of Ecuador. To this end, a survey was carried out applying statistical analysis of the data. Specifically, the method of ordinary Kriging and Lepeltier is used to sort the data in populations according to their concentration. Previously, the information was compared with national (TULAS) and international regulations (Environmental Canada). These metals' spatial distribution is shown in concentration maps for each element (Hg, Pb, Zn, As, and Cu), where the potential villages exposed to these anomalies are displayed. In this sense, the samples' chemical digestion was conducted to quantify the atomic emission technique's before-mentioned pollutants' concentration. It was also correlated with geology, mineral occurrences, and metallogeny evidence to conclude that Pb and Zn anomalies are related to San Lucas granodiorite's intrusion, whereas Cu and Hg with local mineralization of sulfides, and as may be with domestic and industrial discharges. Finally, even though the anomalous concentrations of metallic elements were found to be characteristic of the lithology, cautions should be taken to safeguard the health of people and agriculture because there is evidence of elements such as As and Hg bioaccumulation in species that are part of the food chain
Effects of Environmental Conditions on Photovoltaic Generation System Performance with Polycrystalline Panels
Get PDFPhotovoltaic solar energy is the third most widely used renewable source worldwide, after hydroelectric and wind energy, and this energy source requires experimental and theoretical development in specific topics such as the effect of environmental conditions on energy performance. Thus, this study's main objective was to determine the influence that meteorological conditions have on the performance of solar photovoltaic systems, based on measurements from a measurement station installed in the city of Barranquilla-Colombia, to determine the factors that significantly affect the system’s energy efficiency deviation. The experimental results show a dependence of the solar panel energy performance on some weather conditions, which is an uncontrolled phenomenon such as the ambient temperature and the atmosphere's humidity. Also, solar panel temperature and irradiance were the parameters with greater importance in the systems power generation. Also, the panel temperature must be controlled to obtain the desired response, because the panel temperature is inversely proportional to the voltage and directly proportional to the current. However, the negative effect of increased panel temperature in sunny climates is compensated by increased solar hours, so the summer system has less instantaneous efficiency, but it has higher solar output throughout the day. Therefore, solar energy production study should be related to total daily production.Â
Measuring Dynamic Capabilities of IT Resources
Get PDFThe concept of Dynamic Capabilities (DC) has been popular for almost two decades. Dynamic capabilities were the ability of an organization to adapt to a changing environment. Dynamic capabilities are organizations that managed resources and competencies, both internally and externally, would survive with environmental changes. Many studies have reviewed the concept of dynamic capabilities. Some studies attempted to investigate how dynamic capabilities impact business, dynamic capabilities, and dynamic capabilities. However, limited research suggests how to measure an organization's dynamic capabilities, especially the capabilities of resources that drive an organization to have dynamic capabilities. Therefore, this study tries to provide a proposed method for measuring organizational resources' dynamic capabilities, especially IT. Information technology was chosen because there was a general belief that IT could strengthen the organization's position in business competition. The model was developed with a conceptual research and case study approach. Conceptual research was carried out to develop a theoretical framework through the study of literature. At the same time, the case study was carried out for model validation. The proposed method was adopted from the IT portfolio management and portfolio mapping. Based on the validation processes, the model proposed was suitable for measuring IT resources' capabilities and describing how IT resources could support dynamic capabilities. The result shows that more dynamic IT resources could improve the dynamic capabilities of an organization
Recognition of Emotion in Indian Classical Dance Using EMG Signal
Get PDFIndian classical dance forms like Kathak are an enrichment of Indian culture and tradition. These dance forms glorify its beauty by expressing nine emotions (Navras) such as Adbhut (amazed), Bhayanaka (fearful), Hasya (humorous), Karuna (tragic), Raudra (fierce), Shringar (loving smile), Veer (heroic), Bibhatsa (disgusted), and Shant (peaceful). Identifying correct emotions is an important task. The objective of this research work is to recognize Navras in the Kathak dance. Proposed research work can assist dance teachers in an accurate and unbiased evaluation process of dance examination. This research work analyzed the Electromyogram (EMG) signals acquired from eleven subjects. The EMG signals collected from the various locations on the face and neck represent the emotions and head movement. The EMG signals are processed to extract integrated EMG (IEMG) features. This research introduced a new feature named 'difference in IEMG feature' for improving the accuracy of emotion recognition. For the classification of nine emotions, the Least Square Support Vector Machine (LSSVM), Nonlinear Autoregressive Exogenous Network (NARX), and Long- and Short-Term Memory (LSTM) classifiers were used. The classifiers' performance is judged with head motion and without head motion. The classification accuracies are calculated using a maximum, variance, and mean of the feature. LSSVM, NARX, and LSTM classifiers achieved 60.80%, 81.67%, and 92.28% classification accuracies, respectively, using the IEMG feature and head motion. Using the new feature, LSSVM, NARX, and LSTM classifiers achieved 64.29%, 81.27%, and 93.63% classification accuracies, respectively. The overall classification accuracy improved by 1.46% by using the new feature