Search results for: weak signal detection.

Authors: Mohammad Akbarpour Sekeh, Mohd. Aizaini Maarof, Mohd. Foad Rohani, Malihe Motiei

Abstract:

Duplicated region detection is a technical method to expose copy-paste forgeries on digital images. Copy-paste is one of the common types of forgeries to clone portion of an image in order to conceal or duplicate special object. In this type of forgery detection, extracting robust block feature and also high time complexity of matching step are two main open problems. This paper concentrates on computational time and proposes a local block matching algorithm based on block clustering to enhance time complexity. Time complexity of the proposed algorithm is formulated and effects of two parameter, block size and number of cluster, on efficiency of this algorithm are considered. The experimental results and mathematical analysis demonstrate this algorithm is more costeffective than lexicographically algorithms in time complexity issue when the image is complex.

Keywords: Copy-paste forgery detection, Duplicated region, Timecomplexity, Local block matching, Sequential block clustering.

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Authors: I. Asanuma, T. Yamaguchi, J. Park, K. J. Mackin

Abstract:

Fishery lights on the surface could be detected by the Day and Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi-NPP). The DNB covers the spectral range of 500 to 900 nm and realized a higher sensitivity. The DNB has a difficulty of identification of fishing lights from lunar lights reflected by clouds, which affects observations for the half of the month. Fishery lights and lights of the surface are identified from lunar lights reflected by clouds by a method using the DNB and the infrared band, where the detection limits are defined as a function of the brightness temperature with a difference from the maximum temperature for each level of DNB radiance and with the contrast of DNB radiance against the background radiance. Fishery boats or structures on islands could be detected by the Synthetic Aperture Radar (SAR) on the polar orbit satellites using the reflected microwave by the surface reflecting targets. The SAR has a difficulty of tradeoff between spatial resolution and coverage while detecting the small targets like fishery boats. A distribution of fishery boats and island activities were detected by the scan-SAR narrow mode of Radarsat-2, which covers 300 km by 300 km with various combinations of polarizations. The fishing boats were detected as a single pixel of highly scattering targets with the scan-SAR narrow mode of which spatial resolution is 30 m. As the look angle dependent scattering signals exhibits the significant differences, the standard deviations of scattered signals for each look angles were taken into account as a threshold to identify the signal from fishing boats and structures on the island from background noise. It was difficult to validate the detected targets by DNB with SAR data because of time lag of observations for 6 hours between midnight by DNB and morning or evening by SAR. The temporal changes of island activities were detected as a change of mean intensity of DNB for circular area for a certain scale of activities. The increase of DNB mean intensity was corresponding to the beginning of dredging and the change of intensity indicated the ending of reclamation and following constructions of facilities.

Keywords: Day night band, fishery, SAR, South China Sea.

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Authors: L. Khoshnevisan, H. R. Momeni, A. Ashraf-Modarres

Abstract:

One of the robust fault detection filter (RFDF) designing method is based on sliding-mode theory. The main purpose of our study is to introduce an innovative simplified reference residual model generator to formulate the RFDF as a sliding-mode observer without any manipulation package or transformation matrix, through which the generated residual signals can be evaluated. So the proposed design is more explicit and requires less design parameters in comparison with approaches requiring changing coordinates. To the best author's knowledge, this is the first time that the sliding mode technique is applied to detect actuator and sensor faults in a real boiler. The designing procedure is proposed in a drum boiler in Synvendska Kraft AB Plant in Malmo, Sweden as a multivariable and strongly coupled system. It is demonstrated that both sensor and actuator faults can robustly be detected. Also sensor faults can be diagnosed and isolated through this method.

Keywords: Boiler, fault detection, robustness, simplified sliding-mode observer.

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Authors: Lae-Jeong Park

Abstract:

The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.

Keywords: Pedestrian detection, color segmentation, false positives, feature extraction.

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Authors: Ahmad M. Khalil, Ahmad M. Alshamali, Marwan H. Gagaa

Abstract:

We investigated oxidative DNA damage caused by radio frequency radiation using 8-oxo-7, 8-dihydro-2'- deoxyguanosine (8-oxodG) generated in mice tissues after exposure to 900 MHz mobile phone radio frequency in three independent experiments. The RF was generated by a Global System for Mobile Communication (GSM) signal generator. The radio frequency field was adjusted to 25 V/m. The whole body specific absorption rate (SAR) was 1.0 W/kg. Animals were exposed to this field for 30 min daily for 30 days. 24 h post-exposure, blood serum, brain and spleen were removed and DNA was isolated. Enzyme-linked immunosorbent assay (ELISA) was used to measure 8-oxodG concentration. All animals survived the whole experimental period. The body weight of animals did not change significantly at the end of the experiment. No statistically significant differences observed in the levels of oxidative stress. Our results are not in favor of the hypothesis that 900 MHz RF induces oxidative damage.

Keywords: Mice, Mobile phone radiation, oxidative stress, 8-oxo-7, 8-dihydro-2'-deoxyguanosine

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Authors: Farshad Arvin, Shyamala Doraisamy

Abstract:

This paper presents a new hardware interface using a microcontroller which processes audio music signals to standard MIDI data. A technique for processing music signals by extracting note parameters from music signals is described. An algorithm to convert the voice samples for real-time processing without complex calculations is proposed. A high frequency microcontroller as the main processor is deployed to execute the outlined algorithm. The MIDI data generated is transmitted using the EIA-232 protocol. The analyses of data generated show the feasibility of using microcontrollers for real-time MIDI generation hardware interface.

Keywords: Signal processing, MIDI, Microcontroller, EIA-232.

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Authors: J. Sagouis, A. Chamel, E. Carre, C. Casasreales, G. Rudnik, M. Cerdan

Abstract:

Robotics provides answers to amputees. The most expensive solutions surgically connect the prosthesis to nerve endings. There are also several types of non-invasive technologies that recover nerve messages passing through the muscles. After analyzing these messages, myoelectric prostheses perform the desired movement. The main goal is to avoid all surgeries, which can be heavy and offer cheaper alternatives. For an amputee, we use valid muscles to recover the electrical signal involved in a muscle movement. EMG sensors placed on the muscle allows us to measure a potential difference, which our program transforms into control for a robotic arm with two degrees of freedom. We have shown the feasibility of non-invasive prostheses with two degrees of freedom. Signal analysis and an increase in degrees of freedom is still being improved.

Keywords: Prosthesis, electromyography (EMG), robotic arm, nerve message.

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Authors: May Fadheel Estephan, Richard Perks

Abstract:

Cancer is a serious health concern that affects millions of people worldwide. Early detection and treatment are essential for improving patient outcomes. However, current methods for cancer detection have limitations, such as low sensitivity and specificity. The aim of this study was to develop an optical sensor for cancer detection using elastic light scattering spectroscopy (ELSS). ELSS is a non-invasive optical technique that can be used to characterize the size and concentration of particles in a solution. An optical probe was fabricated with a 100-μm-diameter core and a 132-μm centre-to-centre separation. The probe was used to measure the ELSS spectra of polystyrene spheres with diameters of 2 μm, 0.8 μm, and 0.413 μm. The spectra were then analysed to determine the size and concentration of the spheres. The results showed that the optical probe was able to differentiate between the three different sizes of polystyrene spheres. The probe was also able to detect the presence of polystyrene spheres in suspension concentrations as low as 0.01%. The results of this study demonstrate the potential of ELSS for cancer detection. ELSS is a non-invasive technique that can be used to characterize the size and concentration of cells in a tissue sample. This information can be used to identify cancer cells and assess the stage of the disease. The data for this study were collected by measuring the ELSS spectra of polystyrene spheres with different diameters. The spectra were collected using a spectrometer and a computer. The ELSS spectra were analysed using a software program to determine the size and concentration of the spheres. The software program used a mathematical algorithm to fit the spectra to a theoretical model. The question addressed by this study was whether ELSS could be used to detect cancer cells. The results of the study showed that ELSS could be used to differentiate between different sizes of cells, suggesting that it could be used to detect cancer cells. The findings of this research show the utility of ELSS in the early identification of cancer. ELSS is a non-invasive method for characterizing the number and size of cells in a tissue sample. To determine cancer cells and determine the disease's stage, this information can be employed. Further research is needed to evaluate the clinical performance of ELSS for cancer detection.

Keywords: Elastic Light Scattering Spectroscopy, Polystyrene spheres in suspension, optical probe, fibre optics.

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Authors: Supattra Aomumpai, Chutima Prommak

Abstract:

This paper presents a studyof the impact of reference node locations on the accuracy of the indoor positioning systems. In particular, we analyze the localization accuracy of the RSSI database mapping techniques, deploying on the IEEE 802.15.4 wireless networks. The results show that the locations of the reference nodes used in the positioning systems affect the signal propagation characteristics in the service area. Thisin turn affects the accuracy of the wireless indoor positioning system. We found that suitable location of reference nodes could reduce the positioning error upto 35 %.

Keywords: Indoor positioning systems, IEEE 802.15.4 wireless networks, Signal propagation characteristics

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Authors: Fatemeh Amiri, Caro Lucas, Nasser Yazdani

Abstract:

As the network based technologies become omnipresent, demands to secure networks/systems against threat increase. One of the effective ways to achieve higher security is through the use of intrusion detection systems (IDS), which are a software tool to detect anomalous in the computer or network. In this paper, an IDS has been developed using an improved machine learning based algorithm, Locally Linear Neuro Fuzzy Model (LLNF) for classification whereas this model is originally used for system identification. A key technical challenge in IDS and LLNF learning is the curse of high dimensionality. Therefore a feature selection phase is proposed which is applicable to any IDS. While investigating the use of three feature selection algorithms, in this model, it is shown that adding feature selection phase reduces computational complexity of our model. Feature selection algorithms require the use of a feature goodness measure. The use of both a linear and a non-linear measure - linear correlation coefficient and mutual information- is investigated respectively

Keywords: anomaly Detection, feature selection, Locally Linear Neuro Fuzzy (LLNF), Mutual Information (MI), liner correlation coefficient.

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Authors: Aníbal Rodríguez Fuentes, Juan V. Lorenzo Ginori, Ricardo Grau Ábalo

Abstract:

Identifying protein coding regions in DNA sequences is a basic step in the location of genes. Several approaches based on signal processing tools have been applied to solve this problem, trying to achieve more accurate predictions. This paper presents a new predictor that improves the efficacy of three techniques that use the Fourier Transform to predict coding regions, and that could be computed using an algorithm that reduces the computation load. Some ideas about the combination of the predictor with other methods are discussed. ROC curves are used to demonstrate the efficacy of the proposed predictor, based on the computation of 25 DNA sequences from three different organisms.

Keywords: Bioinformatics, Coding region prediction, Computational load reduction, Digital Signal Processing, Fourier Transform.

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Authors: Adnan Al-Smadi

Abstract:

One of the primary uses of higher order statistics in signal processing has been for detecting and estimation of non- Gaussian signals in Gaussian noise of unknown covariance. This is motivated by the ability of higher order statistics to suppress additive Gaussian noise. In this paper, several methods to test for non- Gaussianity of a given process are presented. These methods include histogram plot, kurtosis test, and hypothesis testing using cumulants and bispectrum of the available sequence. The hypothesis testing is performed by constructing a statistic to test whether the bispectrum of the given signal is non-zero. A zero bispectrum is not a proof of Gaussianity. Hence, other tests such as the kurtosis test should be employed. Examples are given to demonstrate the performance of the presented methods.

Keywords: Non-Gaussian, bispectrum, kurtosis, hypothesistesting, histogram.

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Authors: Farzaneh Ahmadzadeh

Abstract:

Quality control charts are very effective in detecting out of control signals but when a control chart signals an out of control condition of the process mean, searching for a special cause in the vicinity of the signal time would not always lead to prompt identification of the source(s) of the out of control condition as the change point in the process parameter(s) is usually different from the signal time. It is very important to manufacturer to determine at what point and which parameters in the past caused the signal. Early warning of process change would expedite the search for the special causes and enhance quality at lower cost. In this paper the quality variables under investigation are assumed to follow a multivariate normal distribution with known means and variance-covariance matrix and the process means after one step change remain at the new level until the special cause is being identified and removed, also it is supposed that only one variable could be changed at the same time. This research applies artificial neural network (ANN) to identify the time the change occurred and the parameter which caused the change or shift. The performance of the approach was assessed through a computer simulation experiment. The results show that neural network performs effectively and equally well for the whole shift magnitude which has been considered.

Keywords: Artificial neural network, change point estimation, monte carlo simulation, multivariate exponentially weighted movingaverage

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Authors: S. Aisyah Azizan, M. Syafiq Azmi, Yuki Harada, Yoshinobu Maeda, Takaomi Matsutani

Abstract:

We introduced an all-optical multicasting characteristics with wavelength conversion based on a novel all-optical triode using negative feedback semiconductor optical amplifier. This study was demonstrated with a transfer speed of 10 Gb/s to a non-return zero 231-1 pseudorandom bit sequence system. This multi-wavelength converter device can simultaneously provide three channels of output signal with the support of non-inverted and inverted conversion. We studied that an all-optical multicasting and wavelength conversion accomplishing cross gain modulation is effective in a semiconductor optical amplifier which is effective to provide an inverted conversion thus negative feedback. The relationship of received power of back to back signal and output signals with wavelength 1535 nm, 1540 nm, 1545 nm, 1550 nm, and 1555 nm with bit error rate was investigated. It was reported that the output signal wavelengths were successfully converted and modulated with a power penalty of less than 8.7 dB, which the highest is 8.6 dB while the lowest is 4.4 dB. It was proved that all-optical multicasting and wavelength conversion using an optical triode with a negative feedback by three channels at the same time at a speed of 10 Gb/s is a promising device for the new wavelength conversion technology.

Keywords: Cross gain modulation, multicasting, negative feedback optical amplifier, semiconductor optical amplifier.

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Authors: Chun-Cheng Lin

Abstract:

Noise level has critical effects on the diagnostic performance of signal-averaged electrocardiogram (SAECG), because the true starting and end points of QRS complex would be masked by the residual noise and sensitive to the noise level. Several studies and commercial machines have used a fixed number of heart beats (typically between 200 to 600 beats) or set a predefined noise level (typically between 0.3 to 1.0 μV) in each X, Y and Z lead to perform SAECG analysis. However different criteria or methods used to perform SAECG would cause the discrepancies of the noise levels among study subjects. According to the recommendations of 1991 ESC, AHA and ACC Task Force Consensus Document for the use of SAECG, the determinations of onset and offset are related closely to the mean and standard deviation of noise sample. Hence this study would try to perform SAECG using consistent root-mean-square (RMS) noise levels among study subjects and analyze the noise level effects on SAECG. This study would also evaluate the differences between normal subjects and chronic renal failure (CRF) patients in the time-domain SAECG parameters. The study subjects were composed of 50 normal Taiwanese and 20 CRF patients. During the signal-averaged processing, different RMS noise levels were adjusted to evaluate their effects on three time domain parameters (1) filtered total QRS duration (fQRSD), (2) RMS voltage of the last QRS 40 ms (RMS40), and (3) duration of the low amplitude signals below 40 μV (LAS40). The study results demonstrated that the reduction of RMS noise level can increase fQRSD and LAS40 and decrease the RMS40, and can further increase the differences of fQRSD and RMS40 between normal subjects and CRF patients. The SAECG may also become abnormal due to the reduction of RMS noise level. In conclusion, it is essential to establish diagnostic criteria of SAECG using consistent RMS noise levels for the reduction of the noise level effects.

Keywords: Signal-averaged electrocardiogram, Ventricular latepotentials, Chronic renal failure, Noise level effects.

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Authors: A. J. Babajanyan, T. A. Abrahamyan, H. A. Minasyan, Kh. V. Nerkararyan

Abstract:

Optical radiation emitted from a metal-coated fiber tip apex at liquid-air interface was measured. The intensity of the output radiation was strongly depend on the relative position of the tip to a liquid-air interface and varied with surface fluctuations. This phenomenon permits in-situ real-time investigation of nano-metric vibrations of the liquid surface and provides a basis for development of various origin ultrasensitive vibration detecting sensors. The described method can be used for detection of week seismic vibrations.

Keywords: Fiber-tip, Liquid-air interface, Nano vibration, Opto-mechanical sensor.

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Authors: Krzysztof Wawryn, Robert Suszyński, Bogdan Strzeszewski

Abstract:

This paper presents two prototypes of low power low voltage current mode 9 bit pipelined a/d converters. The first and the second converters are configured of 1.5 bit and 2.5 bit stages, respectively. The a/d converter structures are composed of current mode building blocks and final comparator block which converts the analog current signal into digital voltage signal. All building blocks have been designed in CMOS AMS 0.35μm technology, then simulated to verify proposed concept. The performances of both converters are compared to performances of known current mode and voltage mode switched capacitance converter structures. Low power consumption and small chip area are advantages of the proposed converters.

Keywords: Pipelined converter, a/d converter, low power, lowvoltage, current mode.

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Authors: Fattah Alizadeh, Saeed Nalousi, Chiman Savari

Abstract:

Because of increasing demands for security in today-s society and also due to paying much more attention to machine vision, biometric researches, pattern recognition and data retrieval in color images, face detection has got more application. In this article we present a scientific approach for modeling human skin color, and also offer an algorithm that tries to detect faces within color images by combination of skin features and determined threshold in the model. Proposed model is based on statistical data in different color spaces. Offered algorithm, using some specified color threshold, first, divides image pixels into two groups: skin pixel group and non-skin pixel group and then based on some geometric features of face decides which area belongs to face. Two main results that we received from this research are as follow: first, proposed model can be applied easily on different databases and color spaces to establish proper threshold. Second, our algorithm can adapt itself with runtime condition and its results demonstrate desirable progress in comparison with similar cases.

Keywords: face detection, skin color modeling, color, colorfulimages, face recognition.

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Authors: Eklas Hossain, Sheroz Khan, Ahad Ali

Abstract:

Power line communications may be used as a data communication channel in public and indoor distribution networks so that it does not require the installing of new cables. Industrial low voltage distribution network may be utilized for data transfer required by the on-line condition monitoring of electric motors. This paper presents a pilot distribution network for modeling low voltage power line as data transfer channel. The signal attenuation in communication channels in the pilot environment is presented and the analysis is done by varying the corresponding parameters for the signal attenuation.

Keywords: Data communication, indoor distribution networks, low voltage, power line.

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Authors: Eric Filiol

Abstract:

This paper presents a formalisation of the different existing code mutation techniques (polymorphism and metamorphism) by means of formal grammars. While very few theoretical results are known about the detection complexity of viral mutation techniques, we exhaustively address this critical issue by considering the Chomsky classification of formal grammars. This enables us to determine which family of code mutation techniques are likely to be detected or on the contrary are bound to remain undetected. As an illustration we then present, on a formal basis, a proof-of-concept metamorphic mutation engine denoted PB MOT, whose detection has been proven to be undecidable.

Keywords: Polymorphism, Metamorphism, Formal Grammars, Formal Languages, Language Decision, Code Mutation, Word Problem

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Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

Abstract:

Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: Fake news detection, types of fake news, machine learning, natural language processing, classification techniques.

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Authors: Prakash K. Aithal, U. Dinesh Acharya, Rajesh Gopakumar

Abstract:

Edge is variation of brightness in an image. Edge detection is useful in many application areas such as finding forests, rivers from a satellite image, detecting broken bone in a medical image etc. The paper discusses about finding edge of multiple aerial images in parallel. The proposed work tested on 38 images 37 colored and one monochrome image. The time taken to process N images in parallel is equivalent to time taken to process 1 image in sequential. Message Passing Interface (MPI) and Open Computing Language (OpenCL) is used to achieve task and pixel level parallelism respectively.

Keywords: Edge detection, multicore, GPU, openCL, MPI.

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Authors: N. Chaitawittanun, M. Munlin

Abstract:

Due to rapid advancement of powerful image processing software, digital images are easy to manipulate and modify by ordinary people. Lots of digital images are edited for a specific purpose and more difficult to distinguish form their original ones. We propose a clustering method to detect a copy-move image forgery of JPEG, BMP, TIFF, and PNG. The process starts with reducing the color of the photos. Then, we use the clustering technique to divide information of measuring data by Hausdorff Distance. The result shows that the purposed methods is capable of inspecting the image file and correctly identify the forgery.

Keywords: Image detection, forgery image, copy-paste.

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Authors: Anwar Jarndal

Abstract:

In this paper, a genetic-neural-network (GNN) based large-signal model for GaN HEMTs is presented along with its parameters extraction procedure. The model is easy to construct and implement in CAD software and requires only DC and S-parameter measurements. An improved decomposition technique is used to model self-heating effect. Two GNN models are constructed to simulate isothermal drain current and power dissipation, respectively. The two model are then composed to simulate the drain current. The modeling procedure was applied to a packaged GaN-on-Si HEMT and the developed model is validated by comparing its large-signal simulation with measured data. A very good agreement between the simulation and measurement is obtained.

Keywords: GaN HEMT, computer-aided design & modeling, neural networks, genetic optimization.

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Authors: Koushlendra Kumar Singh, Manish Kumar Bajpai, Rajesh K. Pandey

Abstract:

The edges of low contrast images are not clearly distinguishable to human eye. It is difficult to find the edges and boundaries in it. The present work encompasses a new approach for low contrast images. The Chebyshev polynomial based fractional order filter has been used for filtering operation on an image. The preprocessing has been performed by this filter on the input image. Laplacian of Gaussian method has been applied on preprocessed image for edge detection. The algorithm has been tested on two test images.

Keywords: Chebyshev polynomials, Fractional order differentiator, Laplacian of Gaussian (LoG) method, Low contrast image.

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Authors: Hsiao-Wei Wang, Jung-Tang Huang, Chun-Chiang Lin

Abstract:

Aptamers are useful tools in microorganism researches, diagnoses, and treatment. Aptamers are specific target molecules formed by oligonucleic acid molecules, and are not decomposed by alcohol. Aptamers used to detect Mycobacterium tuberculosis (MTB) have been proved to have specific affinity to the outer membrane proteins of MTB. This article presents a biosensor chip set with aptamers for early detection of MTB with high specificity and sensitivity, even in very low concentration. Meanwhile, we have already made a modified hydrophobic facial mask module with internal rendering hydrophobic for effectively collecting M. tuberculosis.

Keywords: Aptamers, CNTFET, Mycobacterium tuberculosis, early detection.

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Authors: Yi-Feng Su, Chia-Tseng Chen, Hsueh-Lung Liao

Abstract:

The objective of this research is to develop an advanced driver assistance system characterized with the functions of lane departure warning (LDW), forward collision warning (FCW) and adaptive front-lighting system (AFS). The system is mainly configured a CCD/CMOS camera to acquire the images of roadway ahead in association with the analysis made by an image-processing unit concerning the lane ahead and the preceding vehicles. The input image captured by a camera is used to recognize the lane and the preceding vehicle positions by image detection and DROI (Dynamic Range of Interesting) algorithms. Therefore, the system is able to issue real-time auditory and visual outputs of warning when a driver is departing the lane or driving too close to approach the preceding vehicle unwittingly so that the danger could be prevented from occurring. During the nighttime, in addition to the foregoing warning functions, the system is able to control the bending light of headlamp to provide an immediate light illumination when making a turn at a curved lane and adjust the level automatically to reduce the lighting interference against the oncoming vehicles driving in the opposite direction by the curvature of lane and the vanishing point estimations. The experimental results show that the integrated vehicle image system is robust to most environments such as the lane detection and preceding vehicle detection average accuracy performances are both above 90 %.

Keywords: Lane mark detection, lane departure warning (LDW), dynamic range of interesting (DROI), forward collision warning (FCW), adaptive front-lighting system (AFS).

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Authors: P. Siriarchawatana, K. Leungchavaphongse, N. Covavisaruch, K. Rojananuangnit, P. Boondaeng, N. Panyayingyong

Abstract:

Glaucoma is a disease that causes visual loss in adults. Glaucoma causes damage to the optic nerve and its overall pathophysiology is still not fully understood. Vasculopathy may be one of the possible causes of nerve damage. Photographic imaging of retinal vessels by fundus camera during eye examination may complement clinical management. This paper presents an innovation for measuring central light reflex width-to-retinal vessel diameter ratio (CRR) from digital retinal photographs. Using our edge detection technique, CRRs from glaucoma and normal eyes were compared to examine differences and associations. CRRs were evaluated on fundus photographs of participants from Mettapracharak (Wat Raikhing) Hospital in Nakhon Pathom, Thailand. Fifty-five photographs from normal eyes and twenty-one photographs from glaucoma eyes were included. Participants with hypertension were excluded. In each photograph, CRRs from four retinal vessels, including arteries and veins in the inferotemporal and superotemporal regions, were quantified using edge detection technique. From our finding, mean CRRs of all four retinal arteries and veins were significantly higher in persons with glaucoma than in those without glaucoma (0.34 vs. 0.32, p < 0.05 for inferotemporal vein, 0.33 vs. 0.30, p < 0.01 for inferotemporal artery, 0.34 vs. 0.31, p < 0.01 for superotemporal vein, and 0.33 vs. 0.30, p < 0.05 for superotemporal artery). From these results, an increase in CRRs of retinal vessels, as quantitatively measured from fundus photographs, could be associated with glaucoma.

Keywords: Glaucoma, retinal vessel, central light reflex, image processing, fundus photograph, edge detection.

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Authors: Florin Gorunescu

Abstract:

Diagnosis can be achieved by building a model of a certain organ under surveillance and comparing it with the real time physiological measurements taken from the patient. This paper deals with the presentation of the benefits of using Data Mining techniques in the computer-aided diagnosis (CAD), focusing on the cancer detection, in order to help doctors to make optimal decisions quickly and accurately. In the field of the noninvasive diagnosis techniques, the endoscopic ultrasound elastography (EUSE) is a recent elasticity imaging technique, allowing characterizing the difference between malignant and benign tumors. Digitalizing and summarizing the main EUSE sample movies features in a vector form concern with the use of the exploratory data analysis (EDA). Neural networks are then trained on the corresponding EUSE sample movies vector input in such a way that these intelligent systems are able to offer a very precise and objective diagnosis, discriminating between benign and malignant tumors. A concrete application of these Data Mining techniques illustrates the suitability and the reliability of this methodology in CAD.

Keywords: Endoscopic ultrasound elastography, exploratorydata analysis, neural networks, non-invasive cancer detection.

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Authors: Seiichi Gohshi, Hiroyuki Sekiguchi, Yoshiyasu Shimizu, Takeshi Ikenaga

Abstract:

Super resolution (SR) technologies are now being applied to video to improve resolution. Some TV sets are now equipped with SR functions. However, it is not known if super resolution image reconstruction (SRR) for TV really works or not. Super resolution with non-linear signal processing (SRNL) has recently been proposed. SRR and SRNL are the only methods for processing video signals in real time. The results from subjective assessments of SSR and SRNL are described in this paper. SRR video was produced in simulations with quarter precision motion vectors and 100 iterations. These are ideal conditions for SRR. We found that the image quality of SRNL is better than that of SRR even though SRR was processed under ideal conditions.

Keywords: Super Resolution Image Reconstruction, Super Resolution with Non-Linear Signal Processing, Subjective Assessment, Image Quality

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