Performance of Angle of Arrival Detection Using MUSIC Algorithm in Inter-Satellite Link

An attitude of satellite is not always static, sometimes it moves randomly and the antenna pointing of satellite is harder to achieve line of sight communication to other satellite when it is outage by tumbling effect. In order to determine an appropriate direction of satellite antenna in inter-satellite link, this paper analyze estimation performance of the direction of arrival (DoA) using MUSIC algorithm from connected satellite signal source. It differs from optical measurement, magnetic field measurement, inertial measurement, and global positioning system (GPS) attitude determination. The proposed method is characterized by taking signal source from connected satellites, after that the main satellite processed the information to obtain connected satellites antenna direction. The simulation runs only on the direction of azimuth. The simulation result shows that MUSIC algorithm processing time is faster than satellite movement time in orbit on altitude of 830 km with the period of 101 minutes. With the use of a 50 elements array antenna in spacing of 0.5 wavelength, the total of 20 angle of arrival (AoA) can be detected in 0.98 seconds of processing time when using MUSIC algorithm

Registrasi Citra Dental Menggunakan Feature From Accelerated Segment Test dan Local Gabor Texture For Iterative Point Correspondence

AbstrakRegistrasi citra di bidang periodontal telah dikembangkan untuk melakukan evaluasi terhadap tulang alveolar. Masalah yang disebabkan oleh kesalahan saat ekstraksi fitur atau oleh degradasi gambar bisa timbul pada proses pencocokan fitur. Selain itu, teknik registrasi citra yang didasarkan pada fitur seperti titik, identifikasi tepian (edges), kontur, atau fitur yang lain yang biasa digunakan untuk membandingkan gambar dan kemudian memetakannya merupakan teknik yang sangat sensitif terhadap keakuratan pada tahap ekstraksi fitur. Dari kedua argumen ini, maka diperlukan teknik ekstraksi fitur yang tangguh untuk mencegah terjadinya kesalahan pada proses pencocokan fitur sehingga mendapatkan hasil registrasi citra yang akurat. Pada penelitian ini, diusulkan metode baru untuk registrasi citra. Metode yang diusulkan menggunakan metode ekstraksi fitur yang efektif terhadap akurasi dan efisien terhadap waktu komputasi dengan menerapkan Learning Features, yaitu Feature from Accelerated Segment Test (FAST) sebagai metode ekstraksi fitur. Selain itu, akan dilakukan pengembangan terhadap proses pencocokan fitur dengan menerapkan Local Gabor Texture (LGT) pada algoritma Iterative Point Correspondence (IPC) untuk melakukan registrasi pada citra dental periapikal. Uji coba dilakukan terhadap 8 citra grayscale dental periapikal dan berhasil melakukan registrasi citra  pada citra dental periapikal dengan nilai akurasi rata-rata diatas 93% dengan jumlah iterasi minimal mulai dari 400 iterasi.Kata kunci: registrasi citra, learning feature, local gabor texture, iterative point correspondence, citra dental periapikalAbstractImage registration in the periodontal field has been developed to evaluate alveolar bones. Problems caused by errors during feature extraction or by image degradation can arise in feature matching process. In addition, image registration techniques that are based on features such as points, identification of edges, contours, or other features commonly used to compare images and map them are very sensitive techniques for accuracy at the feature extraction stage. From both of these arguments, a robust feature extraction technique is needed to prevent mistakes in the feature matching process to get image registration results accurately. In this study, a new method for image registration is proposed. The proposed method uses an effective feature extraction method for accuracy and efficient computing time by applying learning features, which is Feature from Accelerated Segment Test (FAST) as a feature extraction method. In addition, a feature-matching process will be developed by applying Local Gabor Texture (LGT) to the Iterative Point Correspondence (IPC) algorithm to register on the periapical dental images. The experiments were conducted on 8 grayscale dental periapical images and successfully registered the image in periapical dental image with an average accuracy more than 93% with a minimum iteration count starting from 400 iterations.Keywords: image registration, learning feature, local gabor texture, iterative point correspondence, dental periapical image

Extreme Learning Machine Weights Optimization Using Genetic Algorithm In Electrical Load Forecasting

The growth of electrical consumers in Indonesia continues to increases every year, but it is not matched by the provision of adequate infrastructure that available. This causes the available electrical capacity can't fulfill the demand for electricity.  In this study, a smart computing system is build to solves the problem. Electrical load data per hour is being used as an input to do the electrical load forecasting with Extreme Learning Machine method. Extreme Learning Machine method uses random input weight within range -1 to 1. Before the electric load prediction process runs, genetic algorithms first optimizing the input weight.  According to the test results with weight optimization, MAPE average error rate is 0.799% while without weight optimization the rate rise to 1.1807%. Thus this study implies that Extreme Learning Machine (ELM) method with weight optimization using Genetics Algorithm (GA) can be used in electrical load forecasting problem and give better prediction resul

Movie recommender systems using hybrid model based on graphs with co-rated, genre, and closed caption features

A movie recommendation is a long-standing challenge. Figuring out the viewer’s interest in movies is still a problem since a huge number of movies are released in no time. In the meantime, people cannot enjoy all available new releases or unseen movies due to their limited time. They also still need to choose which movies to watch when they have spare time. This situation is not good for the movie business too. In order to satisfy people in choosing what movies to watch and to boost movie sales, a system that can recommend suitable movies is required, either unseen in the past or new releases. This paper focuses on the hybrid approach, a combination of content-based and collaborative filtering, using a graph-based model. This hybrid approach is proposed to overcome the drawbacks of combination in the content-based and collaborative filtering. The graph database, Neo4j is used to store the collaborative features, such as movies with its genres, and ratings. Since the movie’s closed caption is rarely considered to be used in a recommendation, the proposed method evaluates the impact of using this syntactic feature. From the early test, the combination of collaborative filtering and content-based using closed caption gives a slightly better result than without closed caption, especially in finding similar movies such as sequel or prequel

Fitur Length Of Edge Dan Moment Invarian Untuk Gesture Recognition Dengan Menggunakan Kinect Untuk Kontrol Lampu

AbstrakTeknologi Kinect adalah teknologi yang dikembangkan untuk game. Kinect memungkinkan pemain game mengontrol permainan dengan menggunakan gerakan dan suara. Hal ini dikarenakan di dalam Kinect terdapat 3 hardware yang bekerja bersama-sama. Tiga hardware tersebut yaitu color VGA video camera, sensor kedalaman, dan multi array microphones. Karena itu, penelitian ini mencoba mengembangkan sensor Kinect untuk keperluan mendeteksi telapak tangan dan gesturnya untuk digunakan sebagai kontrol lampu. Hal ini dilakukan dengan menambahkan beberapa proses pengolahan citra. Pengenalan telapak tangan, menggunakan sensor VGA camera dan depth camera dalam Kinect. Ketika seorang pengguna menjulurkan tangannya kearah sensor, kemudian mengangkat jarinya maka program akan bereaksi. Jika 2 jari yang diangkat maka program akan mengaktifkan saklar padam. Sedangkan jika 5 jari yang diangkat, maka program akan mengaktifkan saklar hidup. Dalam penelitian ini ada 2 fitur yang digunakan dan dibandingkan hasilnya. Fitur yang digunakan adalah fitur Moment Invariant dan Length of Edge. Hasil lebih baik diberikan jika pengenalan dilakukan dengan menggunakan Length of Edge. Dari seluruh data uji yang dipakai, untuk pengenalan dengan menggunakan fitur Length of Edge, akurasi maksimal diperoleh sebesar 100%, sedangkan  dengan menggunakan fitur Moment Invariant, akurasi maksimal diperoleh sebesar 80%.Kata kunci: Moment Invarian, Length of Edge, gesture telapak tanganAbstractKinect is a technology developed for game. Kinect allows players to control game play by using movement and sound. This is because Kinect has 3 hardware that work together. The three hardware are VGA video camera, depth sensor and multi-array microphones. Therefore, this study attempts to use Kinect sensor for detecting the palm's gestures and use them as a light control. This is done by adding some image processing technique. The palm's gesture detection is done by using the VGA camera and depth camera in Kinect Sensor. When a user sticking his hand out and raise his fingers, then program will gives response. If 2 fingers are raised, program will activate off switch, while if 5 fingers are raised, program will activate on switch. There are two features are used in this study, and the results are compared. The features are Moment Invariant and Length of Edge. Better results given by recognition using the Length of Edge. From all the test data used, recognition using Length of Edge give 100% accuracy, while recognition using Moment Invariant features, give 80% in acuracy.Keywords: Moment Invarian, Length of Edge, palm gestur

Mendeteksi Jenis Burung Berdasarkan Pola Suaranya

AbstrakIlmuwan biologi terutama di bidang biodifersitas, terus melakukan penelitian tentang spesies hewan yang ada di dunia. salah satu hewan yang spesiesnya memiliki banyak variasi adalah burung. Tiap jenis burung memiliki perbedaan-perbedaan, mulai dari bentuk anggota tubuhnya, prilakunya, makanannya hingga suaranya. Ilmuwan sering juga mengalami kesulitan untuk melakukan pengamatan di alam. Misalnya, untuk mengetahui spesies burung apa saja yang ada di suatu daerah, mereka harus hadir di suatu wilayah, dan menelusuri setiap pelosok. kadang kala kehadiran mereka di tempat tersebut dalam jangka waktu lama, malah mengusik burung yang ada, dan burung-burung malah pergi meninggalkan tempat, sebelum berhasil diamati. Salah satu cara untuk mendeteksi burung apa saja yang ada di suatu wilayah, tanpa harus mengusik keberadaan burung adalah dengan menggunakan alat bantu. Bisa dengan menggunakan kamera video untuk mengambil gambar lingkungan sekitar, atau dengan perekam suara, untuk merekam suara burung yang ada di sana. Untuk itu penelitian ini ditujukan untuk membuat sebuah pengklasifikasi suara burung secara otomatis. Fitur yang digunakan adalah rhythm, pitch, mean, varian, min, max, dan delta  dari suara burungnya. dari hasil klasifikasi 4 jenis burung, didapatkan hasil rata-rata akurasi terbaik sebesar 88.82%. Kata Kunci : suara burung, klasifikasi, rhythm, pitchAbstractMany of Biologi scientist, especially in the field of biodiversity, conduct research on the animal species that exist in the world. One of the animal which is largely diverse in species is bird. Each species of birds have differences, from the shape of his body, his behavior, his food to it's voice. Scientists often find it difficult to make observations in nature. For example, to determine which species of birds present in an area, they should be present in an area, and explore every corner. sometimes their presence in that place for a long time, even disturb the bird, and they leaving the place, before been observed. One way to detect any bird that is in an area, without having to disturb the presence of birds is to use the automatic tools. For example to use a video camera to take pictures of the surrounding environment, or with voice recorders to record the sound of the birds that were there. This study is aimed to create a classifier bird sound automatically. Features used are rhythm, pitch, mean, variance, min, max, and delta of the bird sound samples. of the results of the classification of four types of birds, showed the best average of accuracy is 88.82%. Key Word : bird song, classification, rhythm, pitch

A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams

The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of interest are among the main reasons for a smart technology to work incorrectly or even to completely fail. This paper presents an attempt to resolve the imbalance problem in sensor sequential (time-series) data through training data augmentation. An Unrolled Generative Adversarial Networks (Unrolled GAN)-powered framework is developed and successfully used to balance the training data of smartphone accelerometer and gyroscope sensors in different contexts of road surface monitoring. Experiments with other sensor data from an open data collection are also conducted. It is demonstrated that the proposed approach allows for improving the classification performance in the case of heavily imbalanced data (the F1 score increased from 0.69 to 0.72, p<0.01, in the presented case study). However, the effect is negligible in the case of slightly imbalanced or inadequate training sets. The latter determines the limitations of this study that would be resolved in future work aimed at incorporating mechanisms for assessing the training data quality into the proposed framework and improving its computational efficiency

A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams

The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of interest are among the main reasons for a smart technology to work incorrectly or even to completely fail. This paper presents an attempt to resolve the imbalance problem in sensor sequential (time-series) data through training data augmentation. An Unrolled Generative Adversarial Networks (Unrolled GAN)-powered framework is developed and successfully used to balance the training data of smartphone accelerometer and gyroscope sensors in different contexts of road surface monitoring. Experiments with other sensor data from an open data collection are also conducted. It is demonstrated that the proposed approach allows for improving the classification performance in the case of heavily imbalanced data (the F1 score increased from 0.69 to 0.72, p&lt;0.01, in the presented case study). However, the effect is negligible in the case of slightly imbalanced or inadequate training sets. The latter determines the limitations of this study that would be resolved in future work aimed at incorporating mechanisms for assessing the training data quality into the proposed framework and improving its computational efficiency