Microstrip Coupled Band Pass Filter using Parallel Coupled Lines used for EMI Reduction

The use of BPF is in telecommunications wireless systems. The signal to be transmitted and which they are received are filtered at a center frequency having some significant bandwidth. This paper comprises a brief knowledge related to designing of a band pass filter (BPF) using microstrip parallel coupled line structure for reduction of noise and EMI. The band pass filter has a center frequency of 2.45GHz having less insertion loss and more than 20dB return loss in its pass band having more than 5% moderate bandwidth is successfully designed.The center frequency is selected such that it is mainly used in WLAN network or high speed wireless broadband is configured to transmit data voice and video IP because system requires more bandwidth. The layout is designed such that centre frequency is 2.45GHz with a fractional bandwidth of 200MHz and impedance resonator length of each coupled line is seperated such that impedance is adjusted to 50Ω exactly.Two sections are mainly given in design: two coupled lines distinguished by a non-uniform line resonator.The impedance resonators gives a separate resonance to obtain the passband region or response.The simulation is perform out on a HFSS software

Review on Efficient Contrast Enhancement Technique for Low Illumination Color Images

A digital color image, as its fundamental purpose requires, is to provide a perception of the scene to a human viewer or a computer for carrying out automation tasks such as object recognition. An image of high quality that could truly represent the captured object and the scene is hence in great demand.Contrast is an important factor in any subjective evaluation of image quality. It is the difference in visual properties that makes an object distinguishable from other object and background. On the contrary, the human visual perception is interested in hue (H), saturation (S) and intensity (I) attributes that are carried by the color image. Therefore, when the image has to be processed, most approaches convert the RGB space into some convenient working signal spaces that are close to human perceptions

Segmentation of Optic Disc in Fundus Images using Convolutional Neural Networks for Detection of Glaucoma

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a difficult task due to various anatomical structures like blood vessel, optic cup, optic disc, macula and fovea. Blood vessel segmentation can assist in the detection of pathological changes which are possible indicators for arteriosclerosis, retinopathy, microaneurysms and macular degeneration. The segmentation of optic disc and optic cup from retinal images is used to calculate an important indicator, cup-to disc ratio( CDR) accurately to help the professionals in the detection of Glaucoma in fundus images.In this proposed work, an automated segmentation of anatomical structures in fundus images such as blood vessel and optic disc is done using Convolutional Neural Networks (CNN) . A Convolutional Neural Network is a composite of multiple elementary processing units, each featuring several weighted inputs and one output, performing convolution of input signals with weights and transforming the outcome with some form of nonlinearity. The units are arranged in rectangular layers (grids), and their locations in a layer correspond to pixels in an input image. The spatial arrangement of units is the primary characteristics that makes CNNs suitable for processing visual information; the other features are local connectivity, parameter sharing and pooling of hidden units. The advantage of CNN is that it can be trained repeatedly so more features can be found. An average accuracy of 95.64% is determined in the classification of blood vessel or not. Optic cup is also segmented from the optic disc by Fuzzy C Means Clustering (FCM). This proposed algorithm is tested on a sample of hospital images and CDR value is determined. The obtained values of CDR is compared with the given values of the sample images and hence the performance of proposed system in which Convolutional Neural Networks for segmentation is employed, is excellent in automated detection of healthy and Glaucoma images

Ekspresi Arsitektur Berwawasan Ekowisata di Kawasan Boulevard

                  Perkembangan Kota Manado sebagai kota jasa dan perdagangan yang merupakan pintu gerbang serta beranda provinsi Sulawesi Utara saat ini sedang berpacu dengan pengaruh pertumbuhan ekonomi di kawasan pasifik, yang merupakan pertumbuhan ekonomi salah satu yang berkembang pesat di dunia.             Kejelasan fungsi bangunan di Kawasan BOB melalui ekspresi arsitektur, dapat memperkuat tingkat kejelasan suatu wilayah kota dimana bangunan tersebut berada. Kawasan BOB ini berpeluang menjadi Landmark kota Manado karena menjadi pusat tujuan masyarakat manado khususnya, Sulawesi Utara umumnya dan para wisatawan nasional dan manca negara.          Hasil dari penelitian ini dengan mempertimbangkan parameter penelitian, diharapkan dapat menjadi dasar pertimbangan dan masukan dalam konsep proses perencanaan dan penataan bangunan (guide lines) di Kota Manado agar sesuai dengan Branded Kota Manado yaitu Kota Model Ekowisata. Berdasarkan Rencana Pembangunan Jangka Panjang Daerah (RPJPD) Kota Manado Tahun 2005-2025, Visi Kota Manado adalah: “Manado Pariwisata Dunia”   Keywords : Manado Pariwisata Dunia, Guide Lines

Hybrid Algorithmic Approach for Medical Image Compression Based on Discrete Wavelet Transform (DWT) and Huffman Techniques for Cloud Computing

As medical imaging facilities move towards complete filmless imaging and also generate a large volume of image data through various advance medical modalities, the ability to store, share and transfer images on a cloud-based system is essential for maximizing efficiencies. The major issue that arises in teleradiology is the difficulty of transmitting large volume of medical data with relatively low bandwidth. Image compression techniques have increased the viability by reducing the bandwidth requirement and cost-effective delivery of medical images for primary diagnosis.Wavelet transformation is widely used in the fields of image compression because they allow analysis of images at various levels of resolution and good characteristics. The algorithm what is discussed in this paper employs wavelet toolbox of MATLAB. Multilevel decomposition of the original image is performed by using Haar wavelet transform and then image is quantified and coded based on Huffman technique. The wavelet packet has been applied for reconstruction of the compressed image. The simulation results show that the algorithm has excellent effects in the image reconstruction and better compression ratio and also study shows that valuable in medical image compression on cloud platfor