Abstract: A robust sequential nonparametric method is proposed
for adaptation to background noise parameters for real-time. The
distribution of background noise was modelled like to Huber
contamination mixture. The method is designed to operate as an
adaptation-unit, which is included inside a detection subsystem of an
integrated multichannel monitoring system. The proposed method
guarantees the given size of a nonasymptotic confidence set for noise
parameters. Properties of the suggested method are rigorously
proved. The proposed algorithm has been successfully tested in real
conditions of a functioning C-OTDR monitoring system, which was
designed to monitor railways.
Abstract: Multiple User Interference (MUI) considers the
primary problem in Optical Code-Division Multiple Access
(OCDMA), which resulting from the overlapping among the users. In
this article we aim to mitigate this problem by studying an
interference cancellation scheme called successive interference
cancellation (SIC) scheme. This scheme will be tested on two
different detection schemes, spectral amplitude coding (SAC) and
direct detection systems (DS), using partial modified prime (PMP) as
the signature codes. It was found that SIC scheme based on both SAC
and DS methods had a potential to suppress the intensity noise, that is
to say it can mitigate MUI noise. Furthermore, SIC/DS scheme
showed much lower bit error rate (BER) performance relative to
SIC/SAC scheme for different magnitude of effective power. Hence,
many more users can be supported by SIC/DS receiver system.
Abstract: Optic disk segmentation plays a key role in the mass
screening of individuals with diabetic retinopathy and glaucoma
ailments. An efficient hardware-based algorithm for optic disk
localization and segmentation would aid for developing an automated
retinal image analysis system for real time applications. Herein,
TMS320C6416DSK DSP board pixel intensity based fractal analysis
algorithm for an automatic localization and segmentation of the optic
disk is reported. The experiment has been performed on color and
fluorescent angiography retinal fundus images. Initially, the images
were pre-processed to reduce the noise and enhance the quality. The
retinal vascular tree of the image was then extracted using canny
edge detection technique. Finally, a pixel intensity based fractal
analysis is performed to segment the optic disk by tracing the origin
of the vascular tree. The proposed method is examined on three
publicly available data sets of the retinal image and also with the data
set obtained from an eye clinic. The average accuracy achieved is
96.2%. To the best of the knowledge, this is the first work reporting
the use of TMS320C6416DSK DSP board and pixel intensity based
fractal analysis algorithm for an automatic localization and
segmentation of the optic disk. This will pave the way for developing
devices for detection of retinal diseases in the future.
Abstract: Background subtraction and temporal difference are
often used for moving object detection in video. Both approaches are
computationally simple and easy to be deployed in real-time image
processing. However, while the background subtraction is highly
sensitive to dynamic background and illumination changes, the
temporal difference approach is poor at extracting relevant pixels of
the moving object and at detecting the stopped or slowly moving
objects in the scene. In this paper, we propose a simple moving object
detection scheme based on adaptive background subtraction and
temporal difference exploiting dynamic background updates. The
proposed technique consists of histogram equalization, a linear
combination of background and temporal difference, followed by the
novel frame-based and pixel-based background updating techniques.
Finally, morphological operations are applied to the output images.
Experimental results show that the proposed algorithm can solve the
drawbacks of both background subtraction and temporal difference
methods and can provide better performance than that of each method.
Abstract: The work aims to develop a robot in the form of
autonomous vehicle to detect, inspection and mapping of
underground pipelines through the ATmega328 Arduino platform.
Hardware prototyping is very similar to C / C ++ language that
facilitates its use in robotics open source, resembles PLC used in
large industrial processes. The robot will traverse the surface
independently of direct human action, in order to automate the
process of detecting buried pipes, guided by electromagnetic
induction. The induction comes from coils that send the signal to the
Arduino microcontroller contained in that will make the difference in
intensity and the treatment of the information, and then this
determines actions to electrical components such as relays and
motors, allowing the prototype to move on the surface and getting the
necessary information. This change of direction is performed by a
stepper motor with a servo motor. The robot was developed by
electrical and electronic assemblies that allowed test your application.
The assembly is made up of metal detector coils, circuit boards and
microprocessor, which interconnected circuits previously developed
can determine, process control and mechanical actions for a robot
(autonomous car) that will make the detection and mapping of buried
pipelines plates. This type of prototype can prevent and identifies
possible landslides and they can prevent the buried pipelines suffer an
external pressure on the walls with the possibility of oil leakage and
thus pollute the environment.
Abstract: In this paper a novel color image compression
technique for efficient storage and delivery of data is proposed. The
proposed compression technique started by RGB to YCbCr color
transformation process. Secondly, the canny edge detection method is
used to classify the blocks into the edge and non-edge blocks. Each
color component Y, Cb, and Cr compressed by discrete cosine
transform (DCT) process, quantizing and coding step by step using
adaptive arithmetic coding. Our technique is concerned with the
compression ratio, bits per pixel and peak signal to noise ratio, and
produce better results than JPEG and more recent published schemes
(like CBDCT-CABS and MHC). The provided experimental results
illustrate the proposed technique that is efficient and feasible in terms
of compression ratio, bits per pixel and peak signal to noise ratio.
Abstract: This study suggests the estimation method of stress
distribution for the beam structures based on TLS (Terrestrial Laser
Scanning). The main components of method are the creation of the
lattices of raw data from TLS to satisfy the suitable condition and
application of CSSI (Cubic Smoothing Spline Interpolation) for
estimating stress distribution. Estimation of stress distribution for the
structural member or the whole structure is one of the important
factors for safety evaluation of the structure. Existing sensors which
include ESG (Electric strain gauge) and LVDT (Linear Variable
Differential Transformer) can be categorized as contact type sensor
which should be installed on the structural members and also there are
various limitations such as the need of separate space where the
network cables are installed and the difficulty of access for sensor
installation in real buildings. To overcome these problems inherent in
the contact type sensors, TLS system of LiDAR (light detection and
ranging), which can measure the displacement of a target in a long
range without the influence of surrounding environment and also get
the whole shape of the structure, has been applied to the field of
structural health monitoring. The important characteristic of TLS
measuring is a formation of point clouds which has many points
including the local coordinate. Point clouds are not linear distribution
but dispersed shape. Thus, to analyze point clouds, the interpolation is
needed vitally. Through formation of averaged lattices and CSSI for
the raw data, the method which can estimate the displacement of
simple beam was developed. Also, the developed method can be
extended to calculate the strain and finally applicable to estimate a
stress distribution of a structural member. To verify the validity of the
method, the loading test on a simple beam was conducted and TLS
measured it. Through a comparison of the estimated stress and
reference stress, the validity of the method is confirmed.
Abstract: In this paper, we propose moving object detection
method which is helpful for driver to safely take his/her car out of
parking lot. When moving objects such as motorbikes, pedestrians,
the other cars and some obstacles are detected at the rear-side of host
vehicle, the proposed algorithm can provide to driver warning. We
assume that the host vehicle is just before departure. Gaussian
Mixture Model (GMM) based background subtraction is basically
applied. Pre-processing such as smoothing and post-processing as
morphological filtering are added. We examine “which color space
has better performance for detection of moving objects?” Three color
spaces including RGB, YCbCr, and Y are applied and compared, in
terms of detection rate. Through simulation, we prove that RGB
space is more suitable for moving object detection based on
background subtraction.
Abstract: This paper presents a new automatic vehicle detection
method from very high resolution aerial images to measure traffic
density. The proposed method starts by extracting road regions from
image using road vector data. Then, the road image is divided into
equal sections considering resolution of the images. Gradient vectors
of the road image are computed from edge map of the corresponding
image. Gradient vectors on the each boundary of the sections are
divided where the gradient vectors significantly change their
directions. Finally, number of vehicles in each section is carried out
by calculating the standard deviation of the gradient vectors in each
group and accepting the group as vehicle that has standard deviation
above predefined threshold value. The proposed method was tested in
four very high resolution aerial images acquired from Istanbul,
Turkey which illustrate roads and vehicles with diverse
characteristics. The results show the reliability of the proposed
method in detecting vehicles by producing 86% overall F1 accuracy
value.
Abstract: Adopting Most Advantageous Tender (MAT) for the
government procurement projects has become popular in Taiwan. As
time pass by, the problems of MAT has appeared gradually. People
condemn two points that are the result might be manipulated by a
single committee member’s partiality and how to make a fair decision
when the winner has two or more. Arrow’s Impossibility Theorem
proposed that the best scoring method should meet the four reasonable
criteria. According to these four criteria this paper constructed an
“Illegitimate Scores Checking Scheme” for a scoring method and used
the scheme to find out the illegitimate of the current evaluation method
of MAT. This paper also proposed a new scoring method that is called
the “Standardizing Overall Evaluated Score Method”. This method
makes each committee member’s influence tend to be identical. Thus,
the committee members can scoring freely according to their partiality
without losing the fairness. Finally, it was examined by a large-scale
simulation, and the experiment revealed that the it improved the
problem of dictatorship and perfectly avoided the situation of cyclical
majorities, simultaneously. This result verified that the Standardizing
Overall Evaluated Score Method is better than any current evaluation
method of MAT.
Abstract: The aim of this investigation is to elaborate nearinfrared
methods for testing and recognition of chemical components
and quality in “Pannon wheat” allied (i.e. true to variety or variety
identified) milling fractions as well as to develop spectroscopic
methods following the milling processes and evaluate the stability of
the milling technology by different types of milling products and
according to sampling times, respectively. These wheat categories
produced under industrial conditions where samples were collected
versus sampling time and maximum or minimum yields. The changes
of the main chemical components (such as starch, protein, lipid) and
physical properties of fractions (particle size) were analysed by
dispersive spectrophotometers using visible (VIS) and near-infrared
(NIR) regions of the electromagnetic radiation. Close correlation
were obtained between the data of spectroscopic measurement
techniques processed by various chemometric methods (e.g. principal
component analysis [PCA], cluster analysis [CA]) and operation
condition of milling technology. It is obvious that NIR methods are
able to detect the deviation of the yield parameters and differences of
the sampling times by a wide variety of fractions, respectively. NIR
technology can be used in the sensitive monitoring of milling
technology.
Abstract: A sensory network consists of multiple detection
locations called sensor nodes, each of which is tiny, featherweight
and portable. A single path routing protocols in wireless sensor
network can lead to holes in the network, since only the nodes
present in the single path is used for the data transmission. Apart
from the advantages like reduced computation, complexity and
resource utilization, there are some drawbacks like throughput,
increased traffic load and delay in data delivery. Therefore, multipath
routing protocols are preferred for WSN. Distributing the traffic
among multiple paths increases the network lifetime. We propose a
scheme, for the data to be transmitted through a dominant path to
save energy. In order to obtain a high delivery ratio, a basic route
reconstruction protocol is utilized to reconstruct the path whenever a
failure is detected. A basic reconstruction routing (BRR) algorithm is
proposed, in which a node can leap over path failure by using the
already existing routing information from its neighbourhood while
the composed data is transmitted from the source to the sink. In order
to save the energy and attain high data delivery ratio, data is
transmitted along a multiple path, which is achieved by BRR
algorithm whenever a failure is detected. Further, the analysis of
how the proposed protocol overcomes the drawback of the existing
protocols is presented. The performance of our protocol is compared
to AOMDV and energy efficient node-disjoint multipath routing
protocol (EENDMRP). The system is implemented using NS-2.34.
The simulation results show that the proposed protocol has high
delivery ratio with low energy consumption.
Abstract: Surf is an increasingly popular sport and its performance evaluation is often qualitative. This work aims at using a smartphone to collect and analyze the GPS and inertial sensors data in order to obtain quantitative metrics of the surfing performance. Two approaches are compared for detection of wave rides, computing the number of waves rode in a surfing session, the starting time of each wave and its duration. The first approach is based on computing the velocity from the Global Positioning System (GPS) signal and finding the velocity thresholds that allow identifying the start and end of each wave ride. The second approach adds information from the Inertial Measurement Unit (IMU) of the smartphone, to the velocity thresholds obtained from the GPS unit, to determine the start and end of each wave ride. The two methods were evaluated using GPS and IMU data from two surfing sessions and validated with similar metrics extracted from video data collected from the beach. The second method, combining GPS and IMU data, was found to be more accurate in determining the number of waves, start time and duration. This paper shows that it is feasible to use smartphones for quantification of performance metrics during surfing. In particular, detection of the waves rode and their duration can be accurately determined using the smartphone GPS and IMU.
Abstract: Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).
Abstract: Image segmentation and edge detection is a fundamental section in image processing. In case of noisy images Edge Detection is very less effective if we use conventional Spatial Filters like Sobel, Prewitt, LOG, Laplacian etc. To overcome this problem we have proposed the use of Stochastic Gradient Mask instead of Spatial Filters for generating gradient images. The present study has shown that the resultant images obtained by applying Stochastic Gradient Masks appear to be much clearer and sharper as per Edge detection is considered.
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.
Abstract: Driver fatigue is an important factor in the increasing
number of road accidents. Dynamic template matching method was
proposed to address the problem of real-time driver fatigue detection
system based on eye-tracking. An effective vision based approach
was used to analyze the driver’s eye state to detect fatigue. The driver
fatigue system consists of Face detection, Eye detection, Eye
tracking, and Fatigue detection. Initially frames are captured from a
color video in a car dashboard and transformed from RGB into YCbCr
color space to detect the driver’s face. Canny edge operator was used
to estimating the eye region and the locations of eyes are extracted.
The extracted eyes were considered as a template matching for eye
tracking. Edge Map Overlapping (EMO) and Edge Pixel Count
(EPC) matching function were used for eye tracking which is used to
improve the matching accuracy. The pixel of eyeball was tracked
from the eye regions which are used to determine the fatigue state of
the driver.
Abstract: Optical biosensors have become a powerful detection
and analysis tool for wide-ranging applications in biomedical research,
pharmaceuticals and environmental monitoring. This study carried out
the computational fluid dynamics (CFD)-based simulations to explore
the dispersion phenomenon in the micro channel of an optical
biosensor. The predicted time sequences of concentration contours
were utilized to better understand the dispersion development occurred
in different geometric shapes of micro channels. The simulation results
showed the surface concentrations at the sensing probe (with the best
performance of a grating coupler) in respect of time to appraise the
dispersion effect and therefore identify the design configurations
resulting in minimum dispersion.
Abstract: Cooperative spectrum sensing is a crucial challenge in
cognitive radio networks. Cooperative sensing can increase the
reliability of spectrum hole detection, optimize sensing time and
reduce delay in cooperative networks. In this paper, an efficient
central capacity optimization algorithm is proposed to minimize
cooperative sensing time in a homogenous sensor network using OR
decision rule subject to the detection and false alarm probabilities
constraints. The evaluation results reveal significant improvement in
the sensing time and normalized capacity of the cognitive sensors.
Abstract: Image Processing is a structure of Signal Processing
for which the input is the image and the output is also an image or
parameter of the image. Image Resolution has been frequently
referred as an important aspect of an image. In Image Resolution
Enhancement, images are being processed in order to obtain more
enhanced resolution. To generate highly resoluted image for a low
resoluted input image with high PSNR value. Stationary Wavelet
Transform is used for Edge Detection and minimize the loss occurs
during Downsampling. Inverse Discrete Wavelet Transform is to get
highly resoluted image. Highly resoluted output is generated from the
Low resolution input with high quality. Noisy input will generate
output with low PSNR value. So Noisy resolution enhancement
technique has been used for adaptive sub-band thresholding is used.
Downsampling in each of the DWT subbands causes information loss
in the respective subbands. SWT is employed to minimize this loss.
Inverse Discrete wavelet transform (IDWT) is to convert the object
which is downsampled using DWT into a highly resoluted object.
Used Image denoising and resolution enhancement techniques will
generate image with high PSNR value. Our Proposed method will
improve Image Resolution and reached the optimized threshold.