Microwave responses of the western North Atlantic

Features and objects in the Western North Atlantic Ocean - the Eastern Seaboard of the United States - are observed from Earth orbit by passive microwaves. The intensities of their radiated flux signatures are measured and displayed in color as a microwave flux image. The features of flux emitting objects such as the course of the Gulf Stream and the occurrence of cold eddies near the Gulf Stream are identified by contoured patterns of relative flux intensities. The flux signatures of ships and their wakes are displayed and discussed. Metal data buoys and aircraft are detected. Signal to clutter ratios and probabilities of detection are computed from their measured irradiances. Theoretical models and the range equations that explain passive microwave detection using the irradiances of natural sources are summarized

Microwave properties of a quiet sea

The microwave flux responses of a quiet sea are observed at five microwave frequencies and with both horizontal and vertical polarizations at each frequency--a simultaneous 10 channel receiving system. The measurements are taken from Earth orbit with an articulating antenna. The 10 channel responses are taken simultaneously since they share a common articulating collector with a multifrequency feed. The plotted flux responses show: (1) the effects of the relative, on-axis-gain of the collecting aperture for each frequency; (2) the effects of polarization rotation in the output responses of the receive when the collecting aperture mechanically rotates about a feed that is fixed; (3) the difference between the flux magnitudes for the horizontal and vertical channels, at each of the five frequencies, and for each pointing position, over a 44 degree scan angle; and (4) the RMS value of the clutter--as reckoned over the interval of a full swath for each of the 10 channels. The clutter is derived from the standard error of estimate of the plotted swath response for each channel. The expected value of the background temperature is computed for each of the three quiet seas. The background temperature includes contributions from the cosmic background, the downwelling path, the sea surface, and the upwelling path

Persepsi pelajar sarjana muda kejuruteraan elektrik terhadap program latihan industri, Kolej Universiti Teknologi Tun Hussein Onn

Kajian ini dijalankan bertujuan untuk mengetahui persepsi Pelajar Sarjana Muda Kejuruteraan Elektrik Terhadap Program Latihan Industri, KUiTTHO berdasarkan kepada 4 faktor iaitu kesesuaian penempatan program latihan industri, kesesuaian pendedahan pelajaran teori di KUiTTHO dan amali di tempat program latihan industri, tahap kerjasama yang diberikan oleh pihak industri kepada pelajar d a n kesediaan pelajar melakukan kerja yang diberi semasa program latihan industri. Sampel kajian adalah terdiri daripada pelajar-pelajar Sarjana Mud a Kejuruteraan Elektrik di KUITTHO yang telah menjalani program latihan industri. Set soal selidik terdiri daripada 3 bahagian iaitu bahagian A yang bertujuan untuk mendapatkan maklumat diri responden manakala bahagian Bertujuan untuk mengetahui kesesuaian program latihan industri yang telah diikuti oleh pelajar dan bahagian C adalah cadangan untuk meningkatkan mutu program latihan industri. Data - data yang diperolehi dianalisis menggunakan perisisan SPSS 10.0 for Windows (Statistical Package for the Social Science version 10) dan dipersembahkan dalam bentuk peratusan, carta dan keterangan analisis. Dapatan kajian secara umumnya menunjukkan reaksi positif dimana bagi semua aspek menunjukkan min keseluruhan yang tingg

Infrared Image Enhancement Using Wavelet Transform

In Infrared Image Enhancement using Wavelet Transform, two enhancement algorithms namely spatial and spatiotemporal homomorphic filtering (SHF and STHF) have been given for enhancement of the far infrared images based upon a far infrared imaging model. Although spatiotemporal homomorphic filtering may reduce the number of iterations greatly in comparison to spatial one for a similar degree of convergence by making explicit use of the additional information provided temporally, the enhanced results from SHF are in general better than those from STHF. In this dissertation work an additive wavelet transform will be proposed for enhancement and filtration of homomorphic infrared images. Keywords: Infrard Images, Additive Wavelet transform, Homomorphic Image Enhancement

Adjacent Infrared Multitarget Detection Using Robust Background Estimation

Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).111Ysciescopu

Filter design for small target detection on infrared imagery using normalized-cross-correlation layer

In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similarly to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on mid-wave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept

Structural matching by discrete relaxation

This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we locus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations ai the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The arti