Performance Analysis of Massive MIMO Networks with Random Unitary Pilot Matrices

A common approach to obtain channel state information for massive MIMO networks is to use the same orthogonal training sequences in each cell. We call this the full-pilot reuse (FPR) scheme. In this paper, we study an alternative approach where each cell uses different sets of orthogonal pilot (DOP) sequences. Considering uplink communications with matched filter (MF) receivers, we first derive the SINR in the large system regime where the number of antennas at the base station, the number of users in each cell, and training duration grow large with fixed ratios. For tractability in the analysis, the orthogonal pilots are drawn from Haar distributed random unitary matrices. The resulting expression is simple and easy to compute. As shown by the numerical simulations, the asymptotic SINR approximates the finite-size systems accurately. Secondly, we derive the user capacity of the DOP scheme under a simple power control and show that it is generally better than that of the FPR scheme.Comment: Draf

Deteksi Pemalsuan Citra dengan Teknik Copy-Move Menggunakan Metode Ordinal Measure dari Koefisien Discrete Cosine Transform

This article discusses a new method for the detection of forgery images generated by copy-move technique. Copy-move technique is one of image forgery techniques which taking a particular object from its original image and add it on that image for the purpose of increasing the number of or changing the same object in the original image. This study aims to detect the forged image generated by the copy-move techniques and copy-move forged image that has been modified by the rotation operation and histogram equalization. Detection feature used is Ordinal Measure of Discrete Cosine Transform coefficient (OM-DCT). Detection starts with division of the image into a block size of BXB (B = 16x16, 32x32 and 64x64) and two-dimensional DCT was performed to each of blocks. The feature distance from the original to the fake image, was calculated by the Euclidian distance and each feature has a distance of less than or equal to the threshold value (T) according to the observations will be marked as a forged part. The results show that there are blocks detected on the copy-move image, whether on the unmodified copy-move forge image or those which modified by the rotation operation and histogram equalization. The number of blocks that are found in the copy-move object varies according to the size of the detection block used.Key words: Discrete Cosine Transform (DCT), ordinal measure of DCT Coefficient, copy-move, rotation, histogram equalization.Abstrak— Artikel ini membahas tentang metode baru untuk deteksi citra palsu yang dihasilkan dari teknik copy-move. Teknik copy-move merupakan salah satu teknik pemalsuan citra dengan cara mengambil objek tertentu dari citra asli dan menambahkannya pada citra tersebut dengan tujuan untuk menambah jumlah atau merubah objek yang sama pada citra asli. Penelitian ini bertujuan untuk mendeteksi citra palsu yang dihasilkan oleh teknik copy-move dan citra palsu copy-move yang telah dimodifikasi dengan operasi rotasi dan ekualisasi histogram. Fitur deteksi yang digunakan adalah Ordinal Measure dari koefisien Discrete Cosine Transform (OM-DCT). Pendeteksian dimulai dengan membagi citra ke dalam blok berukuran BxB (B = 16x16, 32x32 dan 64x64) dan DCT 2 dimensi dilakukan pada setiap blok tersebut. Jarak fitur citra asli dengan palsu dihitung dengan persamaan jarak Ecluidian dan setiap fitur yang memiliki jarak lebih kecil atau sama dengan nilai threshold (T) menurut pengamatan akan ditandai sebagai bagian yang dipalsukan. Hasil pendeteksian menunjukkan bahwa ada blok-blok yang terdeteksi pada objek citra yang di-copy-move baik pada citra palsu copy-move yang tidak dimodifikasi ataupun yang telah dimodifikasi dengan operasi rotasi dan ekualisasi histogram. Jumlah blok yang ditemukan pada objek copy-move bervariasi sesuai ukuran blok pendeteksian yang digunakan.Kata kunci : Discrete Cosine Transform (DCT), ordinal measure dari koefisien DCT, copy-move, rotasi, ekualisasi histogram

Effectiveness of MPEG-7 Color Features in Clothing Retrieval

Clothing is a human used to cover the body. Clothing consist of dress, pants, skirts, and others. Clothing usually consists of various colors or a combination of several colors. Colors become one of the important reference used by humans in determining or looking for clothing according to their wishes. Color is one of the features that fit the human vision. Content Based Image Retrieval (CBIR) is a technique in Image Retrieval that give index to an image based on the characteristics contained in image such as color, shape, and texture. CBIR can make it easier to find something because it helps the grouping process on image based on its characteristic. In this case CBIR is used for the searching process of Muslim fashion based on the color features. The color used in this research is the color descriptor MPEG-7 which is Scalable Color Descriptor (SCD) and Dominant Color Descriptor (DCD). The SCD color feature displays the overall color proportion of the image, while the DCD displays the most dominant color in the image. For each image of Muslim women's clothing, the extraction process utilize SCD and DCD. This study used 150 images of Muslim women's clothing as a dataset consistingclass of red, blue, yellow, green and brown. Each class consists of 30 images. The similarity between the image features is measured using the eucludian distance. This study used human perception in viewing the color of clothing.The effectiveness is calculated for the color features of SCD and DCD adjusted to the human subjective similarity. Based on the simulation of effectiveness DCD result system gives higher value than SCD

Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG

The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories

Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG

The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories

Penghematan Daya pada Sistem Komunikasi Kooperatif Two-Way dengan Pengaturan Rasio Data Rate

Currently, many communication technologies use wireless media because it can provide seamless connectivity and mobile access. In the implementation, wireless communication system faces many challenges, one of them is fading. The effect of fading on a wireless channel will also add power consumption on mobile devices and can reduce the information signal power. However, fading can be overcome by using a cooperative communication system which is a method that utilizes antenna from other users (relays) with the principle of diversity, so the performance of wireless communication system can be improved. This paper proposes power saving on two-way cooperative communication system based on data rate ratio. The method of setting the value of this data rate ratio aims to minimize power consumption in a two-way cooperative communication system, i.e., a full-duplex communication system with quantized and forward (QF) relay protocol. To obtain a minimum power consumption, the ratio of the data rate must be set on the assumption that the value of the transmit power of each source and the relay is equal. The result shows that the system performance is improved, the system SNR value becomes lower, and the power is more efficient by adjusting the ratio of data rate compared to the conventional system without power control

Klasifikasi Otomatis Motif Tekstil Menggunakan Support Vector Machine Multi Kelas

Tekstur merupakan pola atau motif tertentu yang tersusun secara berulang-ulang pada citra. Tekstur mudah dikenali/dikelompokkan oleh manusia, tetapi sulit bagi mesin. Klasifikasi tekstur secara otomatis berguna dan dibutuhkan pada banyak bidang seperti industri tekstil, pendaratan pesawat otomatis, fotografi dan seni. Pada industri tekstil, klasifikasi tekstur otomatis dapat meningkatkan efisiensi proses desain motif. Motif tekstil terdiri dari banyak kelompok, sehingga diperlukan metode klasifikasi multi kelas untuk mengelompokkan motif-motif tersebut. Artikel ini memaparkan kinerja tiga metode Support Vector Machine (SVM) multi kelas: One Against One (OAO), Directed Acyclic Graph (DAG) dan One Against All (OAA) pada klasifikasi motif dari citra tekstil, dimana Wavelet Gabor digunakan sebagai pengekstraksi fitur. Kinerja SVM diukur berdasarkan parameter akurasi dan fitur Gabor diekstraksi dengan skala dan orientasi yang berbeda. Tujuan penelitian ini adalah menentukan kinerja SVM dan pengaruh jumlah skala dan orientasi Gabor yang digunakan pada klasifikasi motif tekstil. Pada simulasi digunakan 120 citra tekstil yang terbagi menjadi tiga kategori motif: bunga, kotak dan polkadot. Akurasi pengelompokan SVM mencapai kisaran 90%-100%, bahkan untuk citra yang terpotong. Pengujian dengan k-fold validation menunjukkan bahwa SVM DAG lebih baik daripada SVM OAO dan SVM OAA, dengan akurasi mencapai 78%. AbstractTexture is a repetition of a specific pattern concatenation in an image. The Texture can be defined as a repetition of pattern in an image.  The texture is easy for the human to classify, but it is not easy for a machine. Automatic texture classification is useful and required in many fields such as textile industry, automatic aircraft landing, photography and art. In the textile industry, automatic texture classification can enhance the efficiency of motif designing process. The textile motif is various and should be grouped into more than two classes; therefore a multiclass classification is required. This article discusses the performance of multiclass Support Vector Machine (SVM): One Against One (OAO), Directed Acyclic Graph (DAG) and One Against All (OAA) in classifying textile motifs, in which the Gabor Filter was used to extract the texture features. The SVM performance was measured in terms of accuracy, while the Gabor features were extracted in a different combination of scales and orientations. The purpose of the work is to measure the SVM performance and determine the effect of using various Gabor scales and orientations in textile motifs classification. We used 120 textile images with three motifs: flower, boxes and polka dot. The SVM accuracy of 90%-100% was achieved; even for cropped textile images. Using the k-fold validation, the accuracy of SVM DAG was 78%, higher than those of SVM OAO and SVM OA

Effectiveness of MPEG-7 Color Features in Clothing Retrieval

Clothing is a human used to cover the body. Clothing consist of dress, pants, skirts, and others. Clothing usually consists of various colors or a combination of several colors. Colors become one of the important reference used by humans in determining or looking for clothing according to their wishes. Color is one of the features that fit the human vision. Content Based Image Retrieval (CBIR) is a technique in Image Retrieval that give index to an image based on the characteristics contained in image such as color, shape, and texture. CBIR can make it easier to find something because it helps the grouping process on image based on its characteristic. In this case CBIR is used for the searching process of Muslim fashion based on the color features. The color used in this research is the color descriptor MPEG-7 which is Scalable Color Descriptor (SCD) and Dominant Color Descriptor (DCD). The SCD color feature displays the overall color proportion of the image, while the DCD displays the most dominant color in the image. For each image of Muslim women\u27s clothing, the extraction process utilize SCD and DCD. This study used 150 images of Muslim women\u27s clothing as a dataset consistingclass of red, blue, yellow, green and brown. Each class consists of 30 images. The similarity between the image features is measured using the eucludian distance. This study used human perception in viewing the color of clothing.The effectiveness is calculated for the color features of SCD and DCD adjusted to the human subjective similarity. Based on the simulation of effectiveness DCD result system gives higher value than SCD