Farook Sattar

• PhD, Lund University, Sweden
• University of Waterloo

Intelligent transportation systems (ITS) involve various emerging technologies and applications. This paper presents a comprehensive review of recent advances on data/information fusion and context-awareness referring to ITS. Data/Information fusion is necessary to fuse the data from different sensors and thereby extract relevant information on the...

Auscultation based diagnosis of pulmonary disorders relies on the presence of adventitious sounds. In this paper, we propose a new set of features based on temporal characteristics of filtered narrowband signal to classify respiratory sounds (RSs) into normal and continuous adventitious types. RS signals are first decomposed in the time-frequency d...

In this chapter, an effective blind source separation (BSS) algorithm is applied to solve the co-channel interference problem in wireless communication systems. Algorithms developed for this purpose must not only have the capability of working in the complex domain and improving output signal to interference plus noise ratio (SINR), but also have r...

Acoustic sensors have been in commercial use for more than 60 years [...]

In this paper, we study to improve acoustical methods to identify endangered whale calls with emphasis on the blue whale (Balaenoptera musculus) and fin whale (Balaenoptera physalus). A promising method using wavelet scattering transform and deep learning is proposed here to detect/classify the whale calls quite precisely in the increasingly noisy...

This paper focuses on livestock monitoring on a smart farm to improve animal well-being and production. The great potential for increased automation and technological innovation in agriculture could help livestock farmers to monitor the welfare of their animals for precision livestock farming. A new acoustical method exploiting contextual informati...

The occurrence of typical inter-click intervals (ICIs) is considered for the analysis of marine mammals signals. A new framework for passive acoustic monitoring is introduced based on ICI information retrieved from ocean acoustic data. The proposed scheme involves extraction of plausible click trains consist of echolocation clicks from various anno...

This paper focuses on an important issue of disease progression of COVID-19 (coronavirus disease 2019) through processing COVID-19 cough sounds by proposing a fully-automated method. The new method is based on time-domain exploiting only phase 1 data which is always available for any cough events. The proposed approach generates plausible click seq...

In this paper, a new method for detecting events in noisy hydrophone data is developed. The method takes an image processing approach to the 1D hydrophone data by first converting it into a logfrequency spectrogram image (cepstrum). This image is then filtered by reconstructing it based on mutual information (MI) criteria of the dominant orientatio...

Energy forecasting is a technique to predict future energy needs to achieve demand and supply equilibrium. This paper presents an overview on Big Data and machine learning technology in the context of energy forecasting. The overall objective of Big Data is to discover useful information and knowledge that might otherwise be overlooked or discounte...

This paper aims to review on body sensor networks (BSNs) for sports from performance monitoring point of view with some new thoughts. The focus of the paper is to show that wearable sensor is more efficient than cameras in measuring sport performance and thereby video data and video based systems can be replaced by wearable sensors. Here, the curre...

As the traffic problem increases, we are seeing a dramatic increase in the number of mentally stressed and angry drivers who are coming to our attention. Driver is the most important and valuable component of a vehicle. That is why, it is essential to monitor driver’s stress and anger levels for safety driving. Physiological parameters such as hear...

This paper presents an efficient deep learning framework for long-term monitoring of acoustic events from hydrophone big data. The large-scale noisy ONC (Ocean Networks Canada) data may contain rare acoustic events, which can be automatically recognized by utilizing a deep convolutional neural network. Few works have been reported in the area of de...

This paper proposes a novel method for anomaly and quality detection of marine mammal sounds using multitaper spectrogram and hydrophone big data. The proposed method is aimed to automatically detect anomaly, such as high-frequency vessel noise, doppler noise, in sperm whale (SPW) sound as well as the quality of the sound. A new signature function...

In this paper, we address the preparation of ecological datasets for data mining. We propose a new adaptive method for automatic dataset construction using Erblet transform, which can be seen as a non-uniform filter bank where the center frequency and the bandwidth of each filter match the ERB (Equivalent Rectangular Bandwidth) scale, followed by d...

Automatic respiratory sound (RS) analysis provides a possible solution for the minimization of inherent subjectivity caused by auscultation via stethoscope, and it allows a reproducible quantification of RS. As one of the crucial initial steps, reliable unsupervised respiratory phase detection plays an important role in automatic RS analysis. In th...

A new method for identification of fish vocalizations based on auditory analysis and support vector machine (SVM) classification is presented. In this method, high resolution features have been extracted from fish vocalization data using the amplitude modulation spectrogram (AMS) of the input signals to facilitate the identification of grunts and g...

This paper presents a novel framework for monitoring fish sounds based on acoustic analysis of noisy big ocean data. The proposed method involves multiresolution acoustic features (MRAF) extraction and RPCA (robust principal component analysis) based feature selection for monitoring of natural fish sounds produced in situ by the plainfin midshipman...

This paper presents a new approach to automatically segmenting speech signals in noisy environments. Segmentation of speech signals is formulated as an optimization problem and the boundaries of the speech segments are detected using a genetic algorithm (GA). The number of segments present in a signal is initially estimated from the reconstructed s...

This article addresses the problem of single-channel speech enhancement in the presence of nonstationary noise. A novel-modified NMF-based filter bank approach is proposed for speech enhancement. The method consists of filter bank analysis of the noisy input signal followed by extraction of speech signal based on a modified NMF (MNMF) by learning a...

This article deals with the challenging problem of segmenting narrowly spaced cardiac events (S1 and S2) in noisy phonocardiogram (PCG) signals by using a novel application of NMF based on time-scale approach. A novel energy-based method is proposed for the segmentation of noisy PCG signals in order to detect car- diac events, which could be closel...

In this paper, we address the problem of multi-class classification of hydrophone data for acoustic events using low-dimensional features. A new iterative multiclass classification scheme is proposed based on the combination of adaptive MFCC feature set and an improved HMM-GMM classifier. The adaptive window length for MFCC is important since for a...

In this paper, we present a new framework of multiple classifiers fusion to classify acoustic events in ONC (Ocean Network Canada) hydrophone data. The outputs of three different classifiers are fused based on aggregation of a generated decision matrix. An ensemble class label is thereby obtained for the classification of acoustic events into multi...

In this paper, a new statistical method for detecting bilabial closure gestures is proposed based on articulatory data. This can be surprisingly challenging, since mere proximity of the lips does not imply their involvement in a directed phonological goal. This segment-based bilabial closure detection scheme uses principal differential analysis (PD...

In this paper, an optic disk (OD) localization method is proposed for the retinal images based on a novel patch filtering approach. The patch filtering has been performed sequentially based on clustering in two stages. In the first stage, the patches are selected exploiting an ’isotropic’ measure based on the ratio of maximum and minimum eigenvalue...

This paper addresses the problem of heart sound (HS) extraction in different types of single-channel respiratory sound (RS) signals by proposing a multiscale mean shift localization approach. First, the incoming respiratory signal (RS) are identified into linear/nonlinear portions by using third-order cumulant. Second, the identified linear and non...

In this paper, we address the problem of separating N unknown non-stationary signals using as many observed mixtures. Using short-term Fourier Transform (STFT) of the mixtures along with a classification approach based on affinity propagation (AP) clustering provide an efficient technique for separating non-stationary signals. The proposed method i...

n this paper, a new context-aware method for detecting events in noisy hydrophone data is proposed. The method transforms first the 1D hydrophone data into a 2D relevance map. A dynamic context-aware relevance features set is then proposed extracted from the normalized relevancy map. Feature classification is finally performed using a least-squares...

The proposed VANET IR-CAS is a context aware system that utilizes information retrieval (IR) techniques, such as indexing, document scoring and document similarity, to enhance context aware information dissemination in VANET. It uses a hybrid context model; spatial model for service filtering, ontology model for context reasoning and knowledge shar...

This paper proposes a system level study of an adaptive communication gateway for Intelligent Vehicles (IV) in Intelligent Transportation System (ITS). This study starts with the analysis of the different communications standards that are expected to link the IV to mobile devices inside the vehicle, other IVs in the ITS network, the road side unite...

In Vehicular Ad-hoc Networks (VANETs), one of the challenging issues is to find an accurate localization information. In this paper, we have addressed this problem by introducing a novel approach based on the idea of cooperative localization. Our proposed scheme incorporates different techniques of localization along with data fusion as well as veh...

Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image proc...

Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image proc...

Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image proc...

Dental radiographs are essential in oral diagnostic procedures. This paper presents a new method for segmentation and object detection of dental radiograph images based on phase congruency. This phase congruency based approach provides local image structure and is invariant to image scaling, rotation, translation, variable lightning conditions, as...

Vehicular Ad-hoc Networks (VANETs) have attracted attention in the support of safe driving, intelligent navigation, and emergency and entertainment applications. VANET can be viewed as an intelligent component of the Transportation Systems as vehicles communicate with each other as well as with roadside base stations located at critical points of t...

Vehicular Ad-hoc Network (VANET) has become an active area of research due to its major role to improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. This paper thus addresses some of the attributes and challenging issues related to Vehicular Ad-hoc Networks (VANETs). A lot of VANET...

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds. Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused in multi-scale analysis. This paper proposes a new si...

Computerized patient monitoring provides valuable information on clinical disorders in medical practice, and it triggers the need to simplify the extent of resources required to describe large set of complex biomedical signals. In this paper, we present a new signal quantification method based on block-wise similarity measurement between the neighb...

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds (ASs). Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused on the analysis of the evolution of symptom-rel...

EXchangeable Image File format (EXIF) is a metadata header containing shot-related camera settings such as aperture, exposure time, ISO speed etc. These settings can affect the photo content in many ways. In this paper, we investigate the underlying EXIF-Image correlation and propose a novel model, which correlates image statistical noise features...

In this paper, we propose an efficient method for long-term monitoring of a wide variety of marine mammals and human related activities using hydrophone data. The proposed method uses a combination of a two-stage denoising process followed by a new event detection function that estimates temporal predictability. The detection function utilizes long...

In this paper, we consider the problem of underdetermined blind source separation for anechoic speech recordings. Existing two-stage methods which first estimate mixing matrix and then separate sources are suitable only for instantaneous mixtures and do not cater for anechoic speech recordings. Other time-frequency (TF) methods based on binary mask...

In this paper a new method for classifying events in noisy hydrophone data is developed. The method takes an image processing approach to the 1D hydrophone data by first converting it into a log-frequency spectrogram image (cepstrum). This image is then filtered by reconstructing it based on mutual information (MI) criteria of the dominant orientat...

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system. Auscultation based diagnosis of pulmonary disorders relies on the presence of adventitious sounds. This paper proposes a new method for auto­ matic RS classification based on instantaneous frequency (IF) anal­ ysis with the aim to...

In this paper, a new image based method for detecting and extracting events in noisy hydrophone data sequence is developed. The method relies on dominant orientation and its robust reconstruction based on mutual information (MI) measure. This new reconstructed dominant orientation map of the spectrogram image can provide key segments corresponding...

Two fragile image watermarking methods are proposed for image authentication. The first method is based on time-frequency analysis and the second one is based on time-scale analysis. For the first method, the watermark is chosen as an arbitrary nonstationary signal with a particular signature in the time-frequency plane. Experimental results show t...

Blind Source Separation (BSS) algorithms have been used in previous literature to mitigate, among other problems, co-channel interference in wireless communication systems. Algorithms developed for this purpose must not only have the capability of working in the complex domain but also be able to take into account non-stationarity of sources and se...

In this paper, we attempt to extend single channel source separation techniques to the separation of respiratory sound (RS) and heart sounds (HS). This single channel recording is analyzed and shown to be a convolutive mixture model. After analyzing the reasons for failure of commonly used blind source separation algorithms, we evaluate the efficac...

In this paper, we consider the problem of heart sounds (HS) removal from respiratory sounds (RS), and a novel semi-blind single-channel source extraction algorithm is proposed. The proposed method is able to extract the underlying pure RS from the HS corrupted noisy input signals by incorporating the filter banks and template-based matching using F...

In this paper, we propose a high capacity data hiding method in binary document images towards semi-fragile authentication. Achieving high capacity in binary images with strict imperceptibility criterion is found to be a difficult task. In this method, noise type pixels are selected for pixel-wise data embedding using a secret key. The data hiding...

This paper proposes a robust and fully automated respiratory phase segmentation method using single channel tracheal breath sounds (TBS) recordings of different types. The estimated number of respiratory segments in a TBS signal is firstly obtained based on noise estimation and nonlinear mapping. Respiratory phase boundaries are then located throug...

We propose a new method for tamper localization and restoration using noise pixels in binary document images. For such images, it is difficult to find a sufficient number of low-distortion pixels in individual blocks with blind detection property. Also, a perceptual watermark cannot be embedded in white regions of the document image, making such re...

In this communication, identification of nonlinear portions in tracheal sound (TS) using third-order cumulant has been performed. The tracked nonlinearity has been then analyzed in time-frequency (TF) domain by applying a novel nonlinear analysis method based on optimally weighted Wigner-Ville distributions of the weighted subband signals from a fi...

This paper addresses blind source separation (BSS) problem of multiple speech signals in low signal-to-interference-noise ratio (SINR) environment. We consider an over-determined case so that we can form multiple sub-arrays (of which there are as many sensors as speech signals), and propose a novel hybrid scheme to obtain high fidelity speech signa...

This paper introduces a novel method to identify inspiratory and expiratory phases from single channel tracheal breath sound (TBS) of different types, by proposing a new anno-tating index name as "mixing index" (MI). An alignment scheme based on phase shift difference information has been firstly introduced to align the consecutive respiratory phas...

This paper addresses the problem of non-invasive respiratory rate (RR) monitoring using single channel tracheal sound (TS) recordings. We have recently developed a robust res-piratory phase segmentation method based on genetic algo-rithm (GA) which works well only for preprocessed clean TS. Therefore, an enhanced respiratory phase monitoring method...

This chapter discusses on forensic tracking through digital watermarking for secure multimedia distribution. The existing watermarking schemes are elaborated and their assumptions as well as limitations for tracking are discussed. Especially, an Independent Component Analysis (ICA) based watermarking scheme is presented, which overcomes the problem...

Pulmonary auscultation has been the key method to detect and evaluate respiratory dysfunctions for many years. However, auscultation with a stethoscope is a subjective process that depends on the individual’s own hearing, experience, and ability to differentiate between different sounds (Sovijarvi et al, 2000). Therefore, the computerized method fo...

A new method to discriminate between speech and music related to the automatic transcription of broadcast news is presented. In the proposed method, a time series regularity, sample entropy (SampEn), is mainly used as an efficient feature to discriminate speech and music of broadcast audio stream. SampEn is a variant of the approximate entropy (ApE...

This chapter focuses on the issue of transaction tracking in multimedia distribution applications through digital watermarking terminology. The existing watermarking schemes are summarized and their assumptions as well as the limitations for tracking are analyzed. In particular, an Independent Component Analysis (ICA)-based watermarking scheme is p...

This paper introduces a novel semi-blind single-channel source extraction algorithm to solve the problem of heart sounds (HS) cancellation from single channel respiratory sounds (RS) recordings. Underlying RS are extracted from those HS contaminated segments in the recorded signal by the proposed algorithm which incorporates shelving filter, filter...

This paper addresses the problem of heart sounds (HS) localization from single channel respiratory sounds (RS) recordings by applying wavelet-based localization scheme. After a wavelet-based multiscale decomposition of the noisy signal, HS contaminated segments are localized in the noisy RS signal based on the cumulative sums of likelihood ratios c...

This paper proposes a new algorithm for a directional aid with hearing defenders. Users of existing hearing defenders experience distorted information, or in worst cases, directional information may not be perceived at all. The users of these hearing defenders may therefore be exposed to serious safety risks. The proposed algorithm improves the...

In this paper, we propose a robust and automatic wheeze detection method using sample entropy (SampEn) histograms of the filtered narrow band respiratory sound signals. The sound signals are segmented first into their respective inspiration/expiration phases. Time-frequency distribution of each segment is then obtained using Gabor spectrogram. Afte...

This paper presents efficient FPGA hardware architecture for the implementation of a digital video processing algorithm for improving picture quality when displayed on devices such as LCD and PDP panels. The algorithm performs dynamic range compression on the photographic quality input video and produces the output suitable for displaying on the pa...

In this paper, we address the problem of discriminating normal breath and adventitious, continuous (e.g. wheeze) sounds based on a new adaptive function. The proposed function is based on fast Gabor time-frequency distribution and autoregressive (AR) averaging. Using a time-frequency (TF) representation of the input signal together with a gain proc...

In this paper, a new approach to automatically segment noisy respiratory sound signals is proposed. Segmentation is formulated as an optimization problem and the boundaries of the signal segments are detected using a genetic algorithm (GA). As the estimated number of segments present in a segmenting signal is initially obtained, a multi-population...

This paper proposes a robust segmentation method for differentiating consecutive inspiratory/expiratory episodes of different types of tracheal breath sounds. This has been done by applying minimal Walsh basis functions to transform the original input respiratory sound signals. Decision module is then applied to differentiate transformed signal int...

This chapter focuses on the issue of transaction tracking in multimedia distribution applications through digital watermarking terminology. The existing watermarking schemes are summarized and their assumptions as well as the limitations for tracking are analyzed. In particular, an Independent Component Analysis (ICA)-based watermarking scheme is p...

This chapter focuses on the issue of transaction tracking in multimedia distribution applications through digital watermarking terminology. The existing watermarking schemes are summarized and their assumptions as well as the limitations for tracking are analyzed. In particular, an Independent Component Analysis (ICA)-based watermarking scheme is p...

In this paper, we propose a new secure authentication method in binary document images using erasable watermarks. For localization, a sufficient number of low-distortion pixels may not be available in a block and to embed the authentication signature with blind detection constraint poses a challenging problem. Also, a perceptual watermark cannot be...

This paper presents two novel corner detection methods for gray level images based on log-Gabor wavelet transform. The input image is decomposed at multiscales and along multi-orientations. In the first algorithm, the magnitude along the direction that is orthogonal to the gradient orientation represents the "cornerness" measurement. Using this det...

We suggest a method for automatic identification of respiratory sounds, for example, identifying wheeze from normal breath sounds. Here we apply higher order moments over time and frequency planes. The method is based on the use of efficient fast Gabor spectrogram followed by our recursively measured instantaneous kurtosis and the sample entropy. T...

This paper presents a scheme that matches interest point features detected on two images taken from different points of view. To accomplish this objective, we jointly consider the corner detection and matching problems. Firstly, a new multi-scale Plessey corner detector (MPCD) is used to detect the interest points. Secondly, the geometric constrain...

A new corner detection method for contour images is proposed based on dyadic wavelet transform (WT) at local natural scales. The points corresponding to wavelet transform modulus maxima (WTMM) at different scales are taken as corner candidates. For each candidate, the scale at which the maximum value of the normalized WTMM exists is defined as its...

This paper presents a novel corner detection method for gray level images based on log-Gabor wavelet transform (WT). The input image is decomposed at multiscales and along multi-orientations. The magnitudes of the decomposition are formulated into the second moment matrix. The smaller eigenvalue of the second moment matrix is used as the "cornernes...

The speech segmentation problem can be formulated as estimating the locations and durations of speech and non-speech components of the measured speech data. In this paper, a new time-scale transform based segmentation method and one of its important application in speech processing, are presented. The proposed scheme is tested on a number of record...

This paper presents a new method to detect speech/nonspeech components of a given noisy signal. Employing the combination of binary Walsh basis functions and an analysis-synthesis scheme, the original noisy speech signal is modified first. From the modified signals, the speech components are distinguished from the nonspeech components by using a si...

We propose a secure watermarking scheme that integrates watermarking with cryptography for addressing some important issues in copyright protection. We address three copyright protection issues--buyer-seller identification, copyright infringement, and ownership verification. By buyer-seller identification, we mean that a successful watermark extrac...

For copyright protection, the robustness of a watermarking scheme against various attacks is an essential requirement. Many proposed robust watermarking schemes may achieve good robustness but sacrifice the good quality of the water- markedimage. This paper, therefore,proposesa transparent robust watermarking scheme, which embeds the watermark (or...

In this paper, we address the problem of separating unknown multi-component signals from their instantaneous mixtures. Using linear time-frequency (TF) representation of the mixtures along with vectors classification scheme provide us a simple and efficient technique to separate multicomponent signals. The proposed algorithm can handle monocomponen...

In this paper, we address the problem of separating N unknown sources using as many observed mixtures. The sources considered here are assumed to be of a non-stationary nature, i.e., their spectral contents are assumed to be time-varying. Using linear time-frequency (TF) representations of the mixtures along with a classification procedure based on...

Enhanced aacPlus audio codec is a combination of MPEG-4 Advanced Audio Coding (AAC), Spectral Band Replication (SBR) and Parametric Stereo (PS). To deal with transient signal, SBR and AAC employ separate transient detectors, although both detectors basically perform detection on the same signal. This paper presents an idea of a low-complexity trans...

This paper proposes a wavelet-domain multiresolution fragile watermarking scheme using an improved quantization-index-modulation (QIM) embedding technique. A secure embedding zone is exploited in our proposed scheme to reduce the false detection rate of Kundur's scheme. The frequency modulated (FM) complex chirp signal is employed as watermark. Bot...

In this paper, a signal-adaptive, stereo-to-mono downmixing scheme associated with MPEG-4 parametric stereo (PS) encoding is presented. The proposed scheme minimizes signal cancellation and coloration due to inter-channel phase misalignment. By using the inter-channel phase difference information which is calculated as a PS spatial parameter, the p...