Andriyan Bayu Suksmono

Compressive Stepped-Frequency Continuous-Wave Ground-Penetrating Radar

Data acquisition speed is an inherent problem of stepped-frequency continuous-wave (SFCW) radars, which may discourage further usage and development of this technology. We propose an emerging paradigm called compressed sensing (CS) to overcome this problem. In CS, a signal can be reconstructed exactly based on only a few samples below the Nyquist rate. Accordingly, the data acquisition speed can be increased significantly. A novel design of an SFCW ground-penetrating radar (GPR) with high acquisition speed is proposed and evaluated. Simulation by a monocycle waveform and actual measurement by a vector network analyzer at a GPR test range indicate the applicability of the proposed system.

A Modified Bow-Tie Antenna for Improved Pulse Radiation

The analysis, design, and realization of a modified bow-tie antenna optimized for impulse ground penetrating radar (GPR) applications is described. The proposed antenna shows improved properties important for GPR, which include its compact size (in comparison with a conventional bow-tie antenna) and ability to radiate UWB pulses with increased amplitude and very small late-time ringing. A substantial increase in the amplitude of the transmitted pulse is achieved by utilizing radiation from discontinuities introduced by the resistive loading employed in the antenna to suppress late-time ringing. By choosing an optimal distance between the antennas feed point and the location of the resistive loading, radiations that occur from the antennas feed point and the mentioned discontinuities at the resistive loading will combine constructively in the boreside direction of the antenna. As a result, one will observe a substantial increase of the amplitude of the transmitted pulse in the boreside direction. Furthermore, an analytical expression describing approximate time-harmonic current distribution is derived to indicate an optimal resistive loading profile for the proposed antenna. Additionally, the traveling-wave current distribution of the antenna is theoretically analyzed to examine the applicability of the obtained time-harmonic expression for pulse excitation. It has been found that when the antenna is resistively loaded both the time-harmonic and traveling-wave currents decay to approach nearly the same value at the end section of the antenna. As the amount of current at the antenna ends corresponds to the level of reflection which occurs there, the derived expression is found to be useful to indicate an optimal loading profile for the proposed antenna. A theoretical model of the proposed antenna has been developed to perform numerical analysis using a modified NEC-2 code. In addition, an experimental verification has been carried out and both the simulation and experiment confirmed the improved properties of the proposed antenna.

Color retinal image enhancement using CLAHE

Common method in image enhancement thats often use is histogram equalization, due to this method is simple and has low computation load. In this research, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the color retinal image. To reduce this noise effect in color retinal image due to the acquisition process, we need to enhance this image. Color retinal image has unique characteristic than other image, that is, this image has important in green (G) channel. Image enhancement has important contribution in ophthalmology. In this paper, we propose new enhancement method using CLAHE in G channel to improve the color retinal image quality. The enhancement process conduct in G channel is appropriate to enhance the color retinal image quality.

GNU Radio based software-defined FMCW radar for weather surveillance application

In this paper, a GNU Radio based software-defined FMCW (Frequency Modulated — Continuous Wave) radar is studied for weather surveillance application. The FMCW radar that has been gaining popularity due to the use of solid state microwave amplifier to generate a signal source is proposed for the design since the current weather surveillance radar is usually using a pulse-radar type that needs high power in pulse generation and high cost in the deployment. In addition, by using software-defined radar, therefore the designed FMCW radar can be implemented and configured at a reduced cost and complexity. The FMCW radar prototype is implemented using both open sources of software and hardware. The software part of the radar is realized using GNU Radio, whilst the hardware part is implemented using USRP (Universal Software Radio Peripheral) N210. Based on the design specification, an FMCW radar prototype based on GNU Radio is then realized and examined. From the performance-test, the prototype that works at a center frequency of 2.1GHz with a bandwidth of 750kHz is able to perform range detection of targets after utilizing the FFT (Fast Fourier Transform) function using MATLAB. In addition, the discussion of system design of software defined FMCW radar and performance-test of its prototype are presented.

Progressive Transform-Based Phase Unwrapping Utilizing a Recursive Structure

We propose a progressive transform-based phase unwrapping (PU) technique that employs a recursive structure. Each stage, which is identical with others in the construction, performs PU by FFT method that yields a solution and a residual phase error as well. The residual phase error is then reprocessed by the following stages. This scheme effectively improves the gradient estimate of the noisy wrapped phase image, which is unrecoverable by conventional global PU methods. Additionally, by incorporating computational strength of the transform PU method in a recursive system, we can realize a progressive PU system for prospective near real-time topographic-mapping radar and near real-time medical imaging system (such as MRI thermometry and MRI flow imager). PU performance of the proposed system and the conventional PU methods are evaluated by comparing their residual error quantitatively with a fringe-density-related error metric called FZX (fringes zero-crossing) number. Experimental results for simulated and real InSAR phase images show significant, progressive improvement over conventional ones of a single-stage system, which demonstrates the high applicability of the proposed method.

Beamforming of ultra-wideband pulses by a complex-valued spatio-temporal multilayer neural network

We present a neuro-beam former of ultra-wideband (UWB) pulses employing complex-valued spatio-temporal multilayer neural network, where complex-valued back propagation through time (CV-BPTT) is used as a learning algorithm. The system performance is evaluated with a UWB mono-cycle pulse. Simulation results in suppressing multiple UWB interferes and in steering to multiple desired UWB pulses, demonstrates the applicability of the proposed system.

Automatic classification of tuberculosis bacteria using neural network

Sputum smear microscopy analysis is the important thing for early diagnosis tuberculosis diseases. A lot of patients in tuberculosis medical center cause the doctors and the technicians have heavy duty. Our research result can be used to reduce technician involvement in screening for tuberculosis and would be useful in laboratories. This research is early step to find appropriate method for identifying tuberculosis bacteria. The analysis of sputum smear requires highly trained to avoid high errors. It needs an appropriate pattern recognition and classification of tuberculosis bacteria. Before classification, geometric features of tuberculosis cell image are found from its binary image. The geometric features of tuberculosis cell image consist of circularity, compactness, eccentricity, and tortuosity. These geometric features would become inputs to the neural network trained with backpropagation method. The 100 samples would be divided into 75 training samples and 25 testing samples. After getting optimum weights and architectures of neural network with 20 neurons hidden layer, 0.05 learning rate, and 0.9 momentum, this network is used for classifying tuberculosis bacteria into two categories: tuberculosis bacteria or not. Results presented for several image taken from different binary cell image show that neural network classifies the presence of tuberculosis bacteria image accurately with mean square error 0.000368, error classification zero in training and testing processes for the data that is used in this research.

Improved Bow-Tie Antenna for Pulse Radiation and Its Implementation in a GPR Survey

In our previous works we developed an improved UWB bow-tie antenna for high-resolution GPR applications. The antenna has been designed to detect small shallow-buried objects for which it should be able to transmit short pulses with very small late-time ringing. It has been shown theoretically that in comparison with conventional bow-tie or dipole antennas, this antenna radiates significantly stronger pulses in its broadside direction with very small late-time ringing. In this paper, experimentally we compare the antenna with a resistively-loaded planar dipole which is commonly used for GPR. It has been found that the antenna exhibits superior characteristics in terms of significantly larger amplitude of the transmitted pulses and much smaller late-time ringing. Furthermore, the antenna has been implemented in a commercial GPR system and tested in real GPR surveys. In this paper we demonstrate that the implementation of this antenna results in clear B-scan images which allow one to easily observe the detail of the shallow subsurface.

Deconvolution of VLBI images based on compressive sensing

Direct inversion of incomplete visibility samples in VLBI (Very Large Baseline Interferometry) radio telescopes produces images with convolutive artifacts. Since proper analysis and interpretations of astronomical radio sources require a non-distorted image, and because filling all of sampling points in the uv-plane is an impossible task, image deconvolution has been one of central issues in the VLBI imaging. Up to now, the most widely used deconvolution algorithms are based on least-squares-optimization and maximum entropy method. In this paper, we propose a new algorithm that is based on an emerging paradigm called compressive sensing (CS). Under the sparsity condition, CS capable to exactly reconstructs a signal or an image, using only a few number of random samples. We show that CS is well-suited with the VLBI imaging problem and demonstrate that the proposed method is capable to reconstruct a simulated image of radio galaxy from its incomplete visibility samples taken from elliptical trajectories in the uv-plane. The effectiveness of the proposed method is also demonstrated with an actual VLBI measured data of 3C459 asymmetric radio-galaxy observed by the VLA (Very Large Array).

A Graphical Representation of the Multi-Domain Signal Processing

A new framework on the representation of signal processing that considers multi-domain information flow of the signal is proposed. Elementary information processing and their diagrams are identified to analyze the components of an algorithm, which is to be translated to the diagram. Intraand extra-domain relationships are represented as graphs that connect the corresponding domains by suitable mappings or transforms and corresponding emissions or absorption of information. Various examples: convolution, wavelet filtering, projection imaging and coding in noisy channel; are presented to show the ubiquity and to illustrate the usability of the proposed framework