Abstract: Discrete wavelet transform (DWT) has been widely adopted in biomedical signal processing for denoising, compression
and so on. Choosing a suitable decomposition level (DL) in DWT is of paramount importance to its performance. In this paper, we propose to exploit sparseness of the transformed signals to determine the appropriate DL. Simulation results have shown that the sparseness of transformed signals after DWT increases with the increasing DLs. Additional Monte-Carlo simulation results have verified the effectiveness of sparseness measure in determining the DL.
Abstract: In this paper, a low-power digital controller for DC-DC power conversion was presented. The controller generates the pulse-width modulated (PWM) signal from digital inputs provided by analog-to-digital converter (ADC). An efficient and simple design scheme to develop the control unit was discussed. This method allows minimization of the consumed resources of the chip and it is based on direct digital design approach. In this application, with the proposed scheme, nearly half area and two-third of the power consumption was saved compared to the conventional schemes. This work illustrates the possibility of implementing low-power and area-efficient power management circuit using direct digital design based approach.
Abstract: This study presents a new method for detecting the
cutting tool wear based on the measured cutting force signals using
the regression model and I-kaz method. The detection of tool wear
was done automatically using the in-house developed regression
model and 3D graphic presentation of I-kaz 3D coefficient during
machining process. The machining tests were carried out on a CNC
turning machine Colchester Master Tornado T4 in dry cutting
condition, and Kistler 9255B dynamometer was used to measure the
cutting force signals, which then stored and displayed in the DasyLab
software. The progression of the cutting tool flank wear land (VB)
was indicated by the amount of the cutting force generated. Later, the
I-kaz was used to analyze all the cutting force signals from beginning
of the cut until the rejection stage of the cutting tool. Results of the IKaz
analysis were represented by various characteristic of I-kaz 3D
coefficient and 3D graphic presentation. The I-kaz 3D coefficient
number decreases when the tool wear increases. This method can be
used for real time tool wear monitoring.
Abstract: In this paper, an extreme learning machine with an automatic segmentation algorithm is applied to heart disorder classification by heart sound signals. From continuous heart sound signals, the starting points of the first (S1) and the second heart pulses (S2) are extracted and corrected by utilizing an inter-pulse histogram. From the corrected pulse positions, a single period of heart sound signals is extracted and converted to a feature vector including the mel-scaled filter bank energy coefficients and the envelope coefficients of uniform-sized sub-segments. An extreme learning machine is used to classify the feature vector. In our cardiac disorder classification and detection experiments with 9 cardiac disorder categories, the proposed method shows significantly better performance than multi-layer perceptron, support vector machine, and hidden Markov model; it achieves the classification accuracy of 81.6% and the detection accuracy of 96.9%.
Abstract: This paper presents an evolutionary method for designing
electronic circuits and numerical methods associated with
monitoring systems. The instruments described here have been used
in studies of weather and climate changes due to global warming, and
also in medical patient supervision. Genetic Programming systems
have been used both for designing circuits and sensors, and also for
determining sensor parameters. The authors advance the thesis that
the software side of such a system should be written in computer
languages with a strong mathematical and logic background in order
to prevent software obsolescence, and achieve program correctness.
Abstract: Mostly, pedestrian-car accidents occurred at a
signalized interaction is because pedestrians cannot across the
intersection safely within the green light. From the viewpoint of
pedestrian, there might have two reasons. The first one is pedestrians
cannot speed up to across the intersection, such as the elders. The other
reason is pedestrians do not sense that the signal phase is going to
change and their right-of-way is going to lose. Developing signal logic
to protect pedestrian, who is crossing an intersection is the first
purpose of this study. Another purpose of this study is improving the
reliability and reduce delay of public transportation service. Therefore,
bus preemption is also considered in the designed signal logic. In this
study, the traffic data of the intersection of Chong-Qing North Road
and Min-Zu West Road, Taipei, Taiwan, is employed to calibrate and
validate the signal logic by simulation. VISSIM 5.20, which is a
microscopic traffic simulation software, is employed to simulate the
signal logic. From the simulated results, the signal logic presented in
this study can protect pedestrians crossing the intersection
successfully. The design of bus preemption can reduce the average
delay. However, the pedestrian safety and bus preemptive signal will
influence the average delay of cars largely. Thus, whether applying the
pedestrian safety and bus preemption signal logic to an isolated
intersection or not should be evaluated carefully.
Abstract: In this paper we propose a new classification method for automatic sleep scoring using an artificial neural network based decision tree. It attempts to treat sleep scoring progress as a series of two-class problems and solves them with a decision tree made up of a group of neural network classifiers, each of which uses a special feature set and is aimed at only one specific sleep stage in order to maximize the classification effect. A single electroencephalogram (EEG) signal is used for our analysis rather than depending on multiple biological signals, which makes greatly simplifies the data acquisition process. Experimental results demonstrate that the average epoch by epoch agreement between the visual and the proposed method in separating 30s wakefulness+S1, REM, S2 and SWS epochs was 88.83%. This study shows that the proposed method performed well in all the four stages, and can effectively limit error propagation at the same time. It could, therefore, be an efficient method for automatic sleep scoring. Additionally, since it requires only a small volume of data it could be suited to pervasive applications.
Abstract: The present work involves measurements to examine
the effects of initial conditions on aerodynamic and acoustic
characteristics of a Jet at M=0.8 by changing the orientation of sharp
edged orifice plate. A thick plate with chamfered orifice presented divergent and convergent openings when it was flipped over. The centerline velocity was found to decay more rapidly for divergent
orifice and that was consistent with the enhanced mass entrainment
suggesting quicker spread of the jet compared with that from the convergent orifice. The mixing layer region elucidated this effect of
initial conditions at an early stage – the growth was found to be comparatively more pronounced for the divergent orifice resulting in
reduced potential core size. The acoustic measurements, carried out in the near field noise region outside the jet within potential core
length, showed the jet from the divergent orifice to be less noisy. The frequency spectra of the noise signal exhibited that in the initial
region of comparatively thin mixing layer for the convergent orifice,
the peak registered a higher SPL and a higher frequency as well. The noise spectra and the mixing layer development suggested a direct correlation between the coherent structures developing in the initial
region of the jet and the noise captured in the surrounding near field.
Abstract: Fault detection determines faultexistence and detecting
time. This paper discusses two layered fault detection methods to
enhance the reliability and safety. Two layered fault detection methods
consist of fault detection methods of component level controllers and
system level controllers. Component level controllers detect faults by
using limit checking, model-based detection, and data-driven
detection and system level controllers execute detection by stability
analysis which can detect unknown changes. System level controllers
compare detection results via stability with fault signals from lower
level controllers. This paper addresses fault detection methods via
stability and suggests fault detection criteria in nonlinear systems. The
fault detection method applies tothe hybrid control unit of a military
hybrid electric vehicleso that the hybrid control unit can detect faults
of the traction motor.
Abstract: In this paper we introduce an effective ECG compression algorithm based on two dimensional multiwavelet transform. Multiwavelets offer simultaneous orthogonality, symmetry and short support, which is not possible with scalar two-channel wavelet systems. These features are known to be important in signal processing. Thus multiwavelet offers the possibility of superior performance for image processing applications. The SPIHT algorithm has achieved notable success in still image coding. We suggested applying SPIHT algorithm to 2-D multiwavelet transform of2-D arranged ECG signals. Experiments on selected records of ECG from MIT-BIH arrhythmia database revealed that the proposed algorithm is significantly more efficient in comparison with previously proposed ECG compression schemes.
Abstract: This paper presents a new Quality-Controlled, wavelet based, compression method for electrocardiogram (ECG) signals. Initially, an ECG signal is decomposed using the wavelet transform. Then, the resulting coefficients are iteratively thresholded to guarantee that a predefined goal percent root mean square difference (GPRD) is matched within tolerable boundaries. The quantization strategy of extracted non-zero wavelet coefficients (NZWC), according to the combination of RLE, HUFFMAN and arithmetic encoding of the NZWC and a resulting look up table, allow the accomplishment of high compression ratios with good quality reconstructed signals.
Abstract: In this paper, an automatic system of diagnosis was
developed to detect and locate in real time the defects of the wound
rotor asynchronous machine associated to electronic converter. For
this purpose, we have treated the signals of the measured parameters
(current and speed) to use them firstly, as indicating variables of the
machine defects under study and, secondly, as inputs to the Artificial
Neuron Network (ANN) for their classification in order to detect the
defect type in progress. Once a defect is detected, the interpretation
system of information will give the type of the defect and its place of
appearance.
Abstract: This paper presents an algorithm for reconstructing phase and magnitude responses of the impulse response when only the output data are available. The system is driven by a zero-mean independent identically distributed (i.i.d) non-Gaussian sequence that is not observed. The additive noise is assumed to be Gaussian. This is an important and essential problem in many practical applications of various science and engineering areas such as biomedical, seismic, and speech processing signals. The method is based on evaluating the bicepstrum of the third-order statistics of the observed output data. Simulations results are presented that demonstrate the performance of this method.
Abstract: In this paper, we present a non-blind technique of
adding the watermark to the Fourier spectral components of audio
signal in a way such that the modified amplitude does not exceed the
maximum amplitude spread (MAS). This MAS is due to individual
Discrete fourier transform (DFT) coefficients in that particular frame,
which is derived from the Energy Spreading function given by
Schroeder. Using this technique one can store double the information
within a given frame length i.e. overriding the watermark on the
host of equal length with least perceptual distortion. The watermark
is uniformly floating on the DFT components of original signal.
This helps in detecting any intentional manipulations done on the
watermarked audio. Also, the scheme is found robust to various signal
processing attacks like presence of multiple watermarks, Additive
white gaussian noise (AWGN) and mp3 compression.
Abstract: We investigate sonic cues for binaural sound localization within classrooms and present a structural model for the same. Two of the primary cues for localization, interaural time difference (ITD) and interaural level difference (ILD) created between the two ears by sounds from a particular point in space, are used. Although these cues do not lend any information about the elevation of a sound source, the torso, head, and outer ear carry out elevation dependent spectral filtering of sounds before they reach the inner ear. This effect is commonly captured in head related transfer function (HRTF) which aids in resolving the ambiguity from the ITDs and ILDs alone and helps localize sounds in free space. The proposed structural model of HRTF produces well controlled horizontal as well as vertical effects. The implemented HRTF is a signal processing model which tries to mimic the physical effects of the sounds interacting with different parts of the body. The effectiveness of the method is tested by synthesizing spatial audio, in MATLAB, for use in listening tests with human subjects and is found to yield satisfactory results in comparison with existing models.
Abstract: This document shows a software that shows different chaotic generator, as continuous as discrete time. The software gives the option for obtain the different signals, using different parameters and initial condition value. The program shows then critical parameter for each model. All theses models are capable of encrypter information, this software show it too.
Abstract: A Space Vector based Pulse Width Modulation
control technique for the three-phase PWM converter is proposed in
this paper. The proposed control scheme is based on a synchronous
reference frame model. High performance and efficiency is obtained
with regards to the DC bus voltage and the power factor
considerations of the PWM rectifier thus leading to low losses.
MATLAB/SIMULINK are used as a platform for the simulations and
a SIMULINK model is presented in the paper. The results show that
the proposed model demonstrates better performance and properties
compared to the traditional SPWM method and the method improves
the dynamic performance of the closed loop drastically.
For the Space Vector based Pulse Width Modulation, Sine signal
is the reference waveform and triangle waveform is the carrier
waveform. When the value sine signal is large than triangle signal,
the pulse will start produce to high. And then when the triangular
signals higher than sine signal, the pulse will come to low. SPWM
output will changed by changing the value of the modulation index
and frequency used in this system to produce more pulse width. The
more pulse width produced, the output voltage will have lower
harmonics contents and the resolution increase.
Abstract: We show that in a two-channel sampling series expansion
of band-pass signals, any finitely many missing samples can
always be recovered via oversampling in a larger band-pass region.
We also obtain an analogous result for multi-channel oversampling
of harmonic signals.
Abstract: In this paper, several improvements are proposed to
previous work of automated classification of alcoholics and nonalcoholics.
In the previous paper, multiplayer-perceptron neural
network classifying energy of gamma band Visual Evoked Potential
(VEP) signals gave the best classification performance using 800
VEP signals from 10 alcoholics and 10 non-alcoholics. Here, the
dataset is extended to include 3560 VEP signals from 102 subjects:
62 alcoholics and 40 non-alcoholics. Three modifications are
introduced to improve the classification performance: i) increasing
the gamma band spectral range by increasing the pass-band width of
the used filter ii) the use of Multiple Signal Classification algorithm
to obtain the power of the dominant frequency in gamma band VEP
signals as features and iii) the use of the simple but effective knearest
neighbour classifier. To validate that these two modifications
do give improved performance, a 10-fold cross validation
classification (CVC) scheme is used. Repeat experiments of the
previously used methodology for the extended dataset are performed
here and improvement from 94.49% to 98.71% in maximum
averaged CVC accuracy is obtained using the modifications. This
latest results show that VEP based classification of alcoholics is
worth exploring further for system development.
Abstract: The Mobile IP Standard has been developed to support mobility over the Internet. This standard contains several drawbacks as in the cases where packets are routed via sub-optimal paths and significant amount of signaling messages is generated due to the home registration procedure which keeps the network aware of the current location of the mobile nodes. Recently, a dynamic hierarchical mobility management strategy for mobile IP networks (DHMIP) has been proposed to reduce home registrations costs. However, this strategy induces a packet delivery delay and increases the risk of packet loss. In this paper, we propose an enhanced version of the dynamic hierarchical strategy that reduces the packet delivery delay and minimizes the risk of packet loss. Preliminary results obtained from simulations are promising. They show that the enhanced version outperforms the original dynamic hierarchical mobility management strategy version.