Orthogonal Filter Banks Research Articles

Novel method for liver cancer detection using super pixel and ANN algorithm

Nowadays most of the people are affected by liver cancer that can be the leading cause of cancer death. To analyze the liver cancer detection using super pixel segmentation and artificial neural network (ANN) Algorithm and it reduces the time complexity. An automatic 3D segmentation method for high energy sound waves in ultrasound liver images, which is based on a fusion of accurate time-consuming method and statistical texture prior. Three orthogonal linear filter banks are used to extract texture features from each image in the region around the point of the database. The atlas database includes registered liver images from previous patients and their segmented liver surfaces. To train the problem of patient–specific frequency content in the image features from the atlas database are using ANN algorithm and then to segment the super pixel segmentation the liver image from a new patient. The dataset of liver cancer detection are benign and malign.

Air‐interface virtualization using filter bank multicarrier and orthogonal frequency division multiplexing configurations

AbstractMobile networks have to cater for diverse services that have distinct requirements in terms of bandwidth, latency, and so on. Network virtualization is a key technology that efficiently utilizes the network resources to meet these service requirements. In this work, we investigate radio virtualization for creating fine‐grained network slices using orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC) combinations; the two are individually referred to as virtual radios (VRs). Using universal software radio peripherals (USRPs), we experimentally analyze multiple configurations of VRs including OFDM‐OFDM, OFDM‐FBMC, and FBMC‐FBMC. An extensive performance comparison is done on the basis of error rate, spectral efficiency, interference power, and computational complexity of the VR configurations that are confined within an operational bandwidth. An increase in transmit power of the VRs is theoretically expected to decrease the error rate, but there is also an increase in adjacent channel interference. Therefore, after a certain decrease in error rate with increase in transmit power, an inflection point is reached after which the error rate starts to increase. Of the three combinations, FBMC‐FBMC gives the lowest error rate (at the highest transmit power), that is, 10% and 18% lower than OFDM‐FBMC and OFDM‐OFDM, respectively. However, FBMC‐FBMC also has the highest complexity. We conclude that this air‐interface virtualization framework allows the network to use the waveform configuration that is best suited to a particular set of services, while considering the pros and cons of the individual waveforms.

Automated detection of abnormal EEG signals using localized wavelet filter banks

Epilepsy is a neural disorder that is associated with the central nervous system (CNS) in which the brain activity sometimes becomes abnormal, which may lead to seizures, loss of awareness, unusual sensations, and behavior. Electroencephalograms (EEG) are widely used to detect epilepsy accurately. However, the interpretation of a particular type of abnormality using the EEG signal is a subjective affair and may vary from clinician-to-clinician. Visual inspection of the EEG signal by observing a change in frequency or amplitude in long-duration signals is an arduous task for the clinicians. It may lead to an erroneous classification of EEGs. The proposed methodology focuses on automated detection of epilepsy using a novel stop-band energy (SBE) minimized orthogonal wavelet filter bank. Using the wavelet decomposition, we obtain subbands (SBs) of EEG signals. Subsequently, fuzzy entropy, logarithmic of the squared norm, and fractal dimension are computed for each SB. The different combinations of the extracted features were supplied to various classifiers for the classification of normal and abnormal EEG signals. In the proposed method, we have used a single-channel EEG dataset of Temple University Hospital. The dataset is the most substantial EEG data publicly available, which contains an EEG recording of 2130 distinct subjects. Our proposed system obtained the highest classification accuracy (CACC) of 78.4% and 79.34% during training and evaluation using the SVM classifier. We achieved the highest F1-score of 0.88.

Lossless Wideband RF Compression via Lifting-Based IIR Subband Decomposition

This correspondence presents a scheme for the lossless compression of wideband spectrally and/or temporally sparse radio-frequency intercepts. It decomposes intercepts into time/frequency partitions using an adaptive invertible channelizer based on infinite impulse response orthogonal wavelet filter banks. The resulting subband decomposition is compressed via arithmetic coding.

Technologies Assisting the Paradigm Shift from 4G to 5G

Due to exponential increase in the capacity demands raised by the smart devices and multimedia applications of new generations, existing cellular networks are facing significant burden. To provide a solution to the challenges faced by 4th generation (4G) networks, there is utmost need of improving existing technologies as well as developing new technologies to meet the key requirements of 5th generation (5G) networks as well as the Next Generation Mobile Networks (NGMNs). Networks having high capacity, low latency, faster data rates and better quality of service is the vision of 5G mobile networks. In order to achieve this, we will need wider bandwidths as offered by millimeter wave bands, more spatial diversity as offered by Massive multiple input multiple output technology, denser networks as designed in dense small cell deployment technology, new waveforms using efficient coding techniques such as filtered orthogonal frequency division multiplexing and filter bank multiple carrier, a new architecture supporting virtualization and advance computing techniques as in network function virtualization, software defined networks, cloud radio access networks. Many more technologies are yet to be introduced. This tutorial gives an insight to the candidate technologies proposed by researchers till date and the progress made, for defining the standards to meet the requirements of 5G and NGMNs. It explains the basic concept of the technology and the latest development in the industry to support this technology.

Comparison between Orthogonal and Bi-Orthogonal Wavelets

Theoretical aspects and analysis of wavelets have applications in mathematical modeling, artificial neural networks, digital signal processing, and image processing and numerical methods. The term orthogonal deals with the mathematical part which covers a wide area of digital signal processing and image processing. An orthogonal wavelet generates the wavelet whose nature is orthogonal. This means an inverse or transpose wavelet transform is nothing but the adjoint of a wavelet transform. If this condition fails by missing orthogonality it may result in biorthogonal wavelets. Single scaling functions and single wavelets are generated but orthogonal wavelet filter bank. A biorthogonal wavelet associated with the wavelet transformation is invertible. There is no need that if it is invertible so it should be orthogonal. The biorthogonal wavelet allows maximum freedom in the case of designing the orthogonal wavelet. It also supports the construction of symmetric wavelet functions. In biorthogonal wavelets, as the name indicates, two scaling factors or functions are responsible for the generation of the various multi-resolutions on the basis of different wavelets. For the image and signal reconstruction purpose we need that wavelet. We get a better result in the presence of biorthogonal wavelets. In the present work, we analyzed the performance of orthogonal and biorthogonal wavelet filters for image processing. We test the image and observed that the filter coefficient and image quality for the orthogonal and biorthogonal wavelet. On the basis of performance analysis it is concluded that biorthogonal wavelets are better than orthogonal wavelets.

Time-Varying Time–Frequency Tilings Using Non-Uniform Orthogonal Filterbanks Based on MDCT Analysis/Synthesis and Time Domain Aliasing Reduction

Time Domain Aliasing Reduction (TDAR) is a method to improve the impulse response compactness of non-uniform orthogonal Modified Discrete Cosine Transforms (MDCT). Previously, TDAR was only possible between frames of identical time-frequency tilings, however in this letter we describe a method to overcome this limitation. This method enables the use of TDAR between two consecutive frames of different time-frequency tilings by introducing another subband merging or subband splitting step. Consecutively, this method allows more flexible and adaptive filterbank tilings while retaining compact impulse responses, two attributes needed for efficient perceptual audio coding.

Automated Detection of Sleep Stages Using Energy-Localized Orthogonal Wavelet Filter Banks

Sleep is an integral part of human life which provides the body with much-needed rest which facilitates recovery and promotes health. Sleep disorders, however, lead to a reduced quality of sleep and as a result, affect the standard of human life. It is important to classify sleep stages in order to detect sleep disorders. Electroencephalogram (EEG) signals are obtained from patients under observation. But, classifying these EEG signals into various sleep stages is an arduous task. It becomes more difficult when one tries to classify EEG signals visually. Even sleep specialists struggle to classify the EEG signals into different sleep stages by visual inspection. Several approaches have been adopted by scientists across the world to mitigate these errors by using EEG and polysomnogram signals. In this paper, an automated method has been proposed for scoring various sleep stages employing EEG signals. We have employed a two-band energy-localized filter in the time-frequency domain, which decomposed six sub-bands using five-level wavelet decomposition. Subsequently, we compute discriminatory features namely fuzzy entropy and log energy from the decomposed coefficients. The extracted features are fed to various supervised machine learning classifiers. Our proposed approach yielded an accuracy of 91.5% and 88.5% for six-class classification task using small and large datasets, respectively.

Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank.

Hypertension (HT) is an extreme increment in blood pressure that can prompt a stroke, kidney disease, and heart attack. HT does not show any symptoms at the early stage, but can lead to various cardiovascular diseases. Hence, it is essential to identify it at the beginning stages. It is tedious to analyze electrocardiogram (ECG) signals visually due to their low amplitude and small bandwidth. Hence, to avoid possible human errors in the diagnosis of HT patients, an automated ECG-based system is developed. This paper proposes the computerized segregation of low-risk hypertension (LRHT) and high-risk hypertension (HRHT) using ECG signals with an optimal orthogonal wavelet filter bank (OWFB) system. The HRHT class is comprised of patients with myocardial infarction, stroke, and syncope ECG signals. The ECG-data are acquired from physionet’s smart health for accessing risk via ECG event (SHAREE) database, which contains recordings of a total 139 subjects. First, ECG signals are segmented into epochs of 5 min. The segmented epochs are then decomposed into six wavelet sub-bands (WSBs) using OWFB. We extract the signal fractional dimension (SFD) and log-energy (LOGE) features from all six WSBs. Using Student’s t-test ranking, we choose the high ranked WSBs of LOGE and SFD features. We develop a novel hypertension diagnosis index (HDI) using two features (SFD and LOGE) to discriminate LRHT and HRHT classes using a single numeric value. The performance of our developed system is found to be encouraging, and we believe that it can be employed in intensive care units to monitor the abrupt rise in blood pressure while screening the ECG signals, provided this is tested with an extensive independent database.

Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features

Malignant arrhythmia can lead to sudden cardiac death (SCD). Shockable arrhythmia can be terminated with device electrical shock therapies. Ventricular-tachycardia (VT) and ventricular fibrillation (VF) are responsive to electrical anti-tachycardia pacing therapy and defibrillation which help to restore normal electrical and mechanical function of the heart. In contrast, non-shockable arrhythmia like asystole and bradycardia are not responsive to electric shock therapy. Distinguishing between shockable and non-shockable arrhythmia is an important diagnostic challenge that has practical clinical relevance. It is difficult to accurately differentiate between these two types of arrhythmia by manual inspection of electrocardiogram (ECG) segments within the short time duration before triggering the device for electrical therapy. Automated defibrillators are equipped with automatic shockable arrhythmia detection algorithms based on ECG morphological features, which may possess variable diagnostic performance depending on machine models. In our work, we have designed a robust system using wavelet decomposition filter banks for extraction of features from the ECG signal and then classifying the features. We believe this method will improve the accuracy of discriminating between shockable and non-shockable arrhythmia compared with existing conventional algorithms. We used a novel three channel orthogonal wavelet filter bank, which extracted features from ECG epochs of duration 2 s to distinguish between shockable and non-shockable arrhythmia. The fuzzy, Renyi and sample entropies are extracted from the various wavelet coefficients and fed to support vector machine (SVM) classifier for automated classification. We have obtained an accuracy of 98.9%, sensitivity and specificity of 99.08% and 97.11.9%, respectively, using 10-fold cross validation. The area under the receiver operating characteristic has been found to be 0.99 with F1-score of 0.994. The system developed is more accurate than the existing algorithms. Hence, the proposed system can be employed in automated defibrillators inside and outside hospitals for emergency revival of patients suffering from SCD. These automated defibrillators can also be implanted inside the human body for automatic detection of potentially fatal shockable arrhythmia and to deliver an appropriate electric shock to the heart.

Design of orthogonal graph filter bank with known eigenvalues of Laplacian matrix

In the conventional designs of orthogonal graph filter bank, both eigenvalues and eigenvectors of Laplacian matrix are assumed to be unknown such that graph filter bank can be efficiently implemented by using polynomial graph filters; that is, the output signal at each vertex is computed only from the signal at that vertex and its K -hop neighbourhoods. However, the disadvantage of these methods is that the designed orthogonal filter bank does not have the exact perfect reconstruction (PR). In this study, the authors first assume that the eigenvalues of Laplacian matrix are known but eigenvectors are still unknown. Then, the orthogonal graph filter bank is designed by solving the simultaneous equation or Lagrange multiplier method such that the PR condition at all eigenvalues can be achieved for a given graph structure. Although the proposed filter bank is a graph-structure-dependent design, it can be efficiently implemented by using polynomial graph filters. Finally, experimental results are shown to demonstrate the effectiveness of the proposed design method.

Fault feature extraction method based on EWT-SMF and MF-DFA for valve fault of reciprocating compressor

According to the nonlinearity and nonstationarity characteristics of reciprocating compressor vibration signal, a fault feature extraction method of reciprocating compressor based on the empirical wavelet transform (EWT) and state-adaptive morphological filtering (SMF) is proposed. Firstly, an adaptive empirical wavelet transform was used to divide the Fourier spectrum by constructing a scale-space curve, and an appropriate orthogonal wavelet filter bank was constructed to extract the AM-FM component with a tightly-supported Fourier spectrum. Then according to the impact characteristic of the reciprocating compressor vibration signal, the morphological structural elements were constructed with the characteristics of the signal to perform state-adaptive morphological filtering on the partitioned modal functions. Finally, the MF-DFA method of the modal function was quantitatively analyzed and the fault identification was performed. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve.

Co-existence of OFDM and FBMC for resilient photonic millimeter-wave 5G mobile fronthaul

This paper presents a novel co-existence of multiple Fifth-Generation (5G) services employing Orthogonal Frequency Division Multiplexing (OFDM) and Filter Bank Multicarrier (FBMC) waveforms in a resilient photonic Millimeter-wave (mmWave) mobile fronthaul architecture. The 5G services are delivered to the remote antenna using shared optical infrastructure and simultaneously upconverted to mmWave frequencies using only one optical local oscillator (LO) for heterodyne upconversion. Radio-over-fiber technology is employed for cloud-radio access network, in which the optical LO used for mmWave upconversion can be placed either at the remote radio head shared by both downlink and uplink signals or remotely delivered from the central office. In addition, the proposed architecture supports the integration of wavelength division multiplexing technology, which enables seamless scalability and management of the photonic infrastructure. As multiple lasers beat to generate the mmWave signals, laser phase noise becomes the dominant system impairment, degrading the detection performance. In order to mitigate the effect, frequency averaging technique is applied to enhance the channel estimation. The comprehensive analysis of different transmission scenarios using industry standard VPI Transmission Maker® simulation platform has demonstrated successful co-existence and transmission of OFDM and FBMC 5G services simultaneously upconverted to 28 GHz, 38 GHz and 60 GHz mmWave signals after 40 km fiber and 2 m wireless. Compared with FBMC, OFDM exhibits better resilience to phase noise with better Error Vector Magnitude (EVM) values at low received powers, but with the application of intra-symbol frequency-domain averaging, the EVM performance of FBMC is comparable to OFDM.

Detection of shockable ventricular arrhythmia using optimal orthogonal wavelet filters

Sudden cardiac death (SCD) is caused by lethal arrhythmia. Ventricular fibrillation (VF) and ventricular tachycardia (VT) are amenable to defibrillation or electrical shock therapy (“shockable” arrhythmia) that can abolish the VF/VT and restore normal electrical and mechanical heart function. The challenge is to differentiate between shockable and non-shockable arrhythmia during the emergency response to SCD. When it comes to saving the life, accurate electrocardiogram (ECG) diagnosis and fast delivery of appropriate treatment is imperative. Automated systems to differentiate shockable from non-shockable arrhythmia have been developed to overcome the difficulty, and possible errors due to the manual inspection. In the present work, we have devised an efficient, effective and robust automated system to detect shockable and non-shockable arrhythmia using an optimal wavelet-based features extracted from ECG epochs of 2 s durations. We employed optimal two-channel frequency selective orthogonal wavelet filter bank to diagnose shockable ventricular arrhythmia. The optimization was carried out by minimizing the stop band ripple energy of the wavelet filter. The optimal orthogonal wavelet filter has been designed using a semi-definite programming (SDP) formulation without the use of any parameterization. The SDP solution gave us the desired optimal orthogonal wavelet filter bank with minimum stop band energy and the desired degree of regularity for the given length of filter. Fuzzy entropy and Renyi entropy features were extracted from the 2-s ECG epochs. These extracted features were then fed into the classifiers for discrimination of shockable arrhythmia rhythms and non-shockable arrhythmia rhythms. The best results were obtained from support vector machine. Accuracy of 97.8%, sensitivity of 93.42%, and specificity of 98.35% were obtained using a tenfold cross validation scheme. The developed automated system is accurate and robust; therefore, it can be integrated in automated external defibrillators that can be deployed for hospitals as well as out-of-hospital emergency resuscitation of SCD.

Adaptive fault detection of the bearing in wind turbine generators using parameterless empirical wavelet transform and margin factor

Wind turbines revolve in difficult operating conditions due to stochastic loads and produce massive vibration signals, which cause obstacles in detecting potential fault information. To overcome this, an adaptive fault detection approach is presented in this paper on the basis of parameterless empirical wavelet transform (PEWT) and the margin factor. PEWT can decompose the vibration signal into a series of empirical modes (EMs) through splitting its Fourier spectrum, using the scale space method and adaptively constructing an orthogonal wavelet filter bank. The margin factor is utilized as a key metric for automatically selecting the EM which is sensitive to the potential faults. The method presented in this paper will improve the efficiency and accuracy of fault information for the condition monitoring of wind turbines.

FBMC vs. OFDM: 5G mobile communication system

The study presents the designing of an orthogonal frequency division multiplexing (OFDM) and filter bank multi carrier (FBMC) modulation techniques. The proposed method consists of OFDM and FBMC prototype filters for transmitting and receiving the data symbols. Both of them are multicarrier technologies used to increase the performance of wireless communication system. Transmission waveform, bit error rate (BER), peak average power ratio (PAPR), capacity and spectrum sensing measurements were simulated and analysed.

FBMC Vs OFDM: 5G Mobile Communication System

The study presents the designing of an orthogonal frequency division multiplexing (OFDM) and filter bank multi carrier (FBMC) modulation techniques. The proposed method consists of OFDM and FBMC prototype filters for transmitting and receiving the data symbols. Both of them are multicarrier technologies used to increase the performance of wireless communication system. Transmission waveform, bit error rate (BER), peak average power ratio (PAPR), capacity and spectrum sensing measurements were simulated and analysed.

A new approach to identify obstructive sleep apnea using an optimal orthogonal wavelet filter bank with ECG signals

Abstract Obstructive sleep apnea (OSA) is a severe sleep ailment. It manifests when a person's breathing is interrupted while sleeping due to an inadequate supply of oxygen to the brain and physical body. The detection of OSA at an early stage can provide better mitigation from severe health impairments. An accurate method to detect OSA can improve the quality of life significantly. The computer-aided diagnosis (CAD) system of OSA developed using single-channel, single-signal physiological signals can efficiently cut costs, and can be used even at home. Therefore, a simple and portable OSA-CAD system using single-channel electrocardiogram (ECG) is introduced in this paper. This study proposes the automated identification of ECG signal affected from OSA employing an optimal two-band filter bank (FB) technique. While designing the filter bank, our central objective is to minimize the spectral localization, subjected to exact regeneration and regularity criteria. The identification of OSA using ECG signals is duly based on the newly designed FB. Using the proposed FB, ECG signals were split into wavelet frequency-bands (WFBs). The fuzzy-entropy (FUEN) and the log of signal-energy (LOEN) of WFBs have been employed as the distinguishing features. The 35-fold cross-validation technique was exercised using various classifiers, namely K nearest neighbor (KNN), decision tree (DT), linear discriminant, logistic regression, and support vector machine (SVM) to separate into normal and OSA affected subjects. The proposed model has attained respective highest average accuracy (AVAC), average sensitivity (AVSE), average specificity (AVSP) and F1-score of 90.87%, 92.43% 88.33% and 92.61%. Our proposed model outperformed the existing systems developed using the same database and was found to be more efficient, robust, and easy to use.

Automated heartbeat classification and detection of arrhythmia using optimal orthogonal wavelet filters

Abstract Electrocardiogram (ECG) is commonly used to analyze heartbeats. Arrhythmia is a disturbance of the heartbeat that can potentially be lethal. Arrhythmia can be detected by identifying an individual abnormal heartbeat, which can occur in isolation or sequentially. Heartbeats can be classified into five types: non-ectopic (N), supra ventricular ectopic (S), ventricular ectopic (V), fusion (F) and unknown beats (Q). Automated classification of the incidence and pattern of abnormal heartbeats enables arrhythmia detection, triage and life-threatening arrhythmia. Therefore, it is vital to develop a tool for the classification of these classes. ECG signals are contaminated by noise rendering difficulty and challenging to discriminate between different types of heartbeats. In this work, we used noisy as well as denoised (clean) ECG signals to classify heartbeats. Using stop-band energy (SBE) minimized dyadic orthogonal filter bank, wavelet decomposition of the ECG signals was performed. After this, fuzzy entropy, Renyi entropy and fractal dimension features were extracted for easier and accurate classification into the five classes. These features were then fed to classifiers, achieving maximum accuracy (MAAC) of 98%, maximum sensitivity (MASE) of 85.33% and maximum specificity (MASP) of 98.22% for noisy data, and MAAC of 98.1%, MASE of 85.63% and MASP of 98.27% for clean data, with KNN classifier. Our developed system can be used in intensive care units to assist the clinicians to aid in their diagnosis.

Analysis of knee-joint vibroarthographic signals using bandwidth-duration localized three-channel filter bank

A vibrarthographic (VAG) signal is the recorded vibration signal produced by human knee-joints during movements of the leg. These signals are useful in quantifying lubrication and roughness of articular cartilage layers of the knee-joint. The early detection and treatment of an abnormal knee-joint will help in providing a better quality of life for the suffering patients. The VAG signals are nonlinear and non-stationary. Hence, in the proposed study, we have employed an optimal bandwidth-duration localized three-band (OBDLTB) orthogonal wavelet filter banks (OWFB) to automatically identify the health of knee-joints using VAG signals. We have used the database created at the University of Calgary containing a total of 89 VAG signals (51 normal and 38 pathological). The log-energy (LOEN) features are obtained from various sub-bands using OBDLTB-OWFB. The extracted LOEN features are separated using supervised machine learning algorithms into normal and abnormal VAG signals. Our automated system obtained an average classification accuracy of 89.89% and F1-score of 87.67% with 10-fold cross-validation. The performance of the model indicates that the proposed technique is able to identify the abnormal knee-joint reliably and thus this technique can aid the orthopedic surgeons in the early diagnosis of knee-joint abnormalities.