Papers by Muhammad Kamran
Computers, Materials & Continua
The use of multimedia data sharing has drastically increased in the past few decades due to the r... more The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation (4G) and 5th generation (5G) etc. Researchers have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure way. Instead of the classical random walk, this paper proposes the quantum walk to achieve high image security. Classical random walk exhibits randomness due to the stochastic transitions between states, on the other hand, the quantum walk is more random and achieve randomness due to the superposition, and the interference of the wave functions. The proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient, entropy, histogram, time complexity, number of pixels change rate and unified average intensity etc. All experimental results validate the proposed scheme, and it is concluded that the proposed scheme is highly secured, lightweight and computationally efficient. In the proposed scheme, the values of the correlation coefficient, entropy, mean square error (MSE), number of pixels change rate (NPCR), unified average change intensity (UACI) and contrast are 0.0069, 7.9970, 40.39, 99.60%, 33.47 and 10.4542 respectively.
IEEE Access, 2021
Drowsiness during driving is a severe problem that must be addressed to improve road safety. Nume... more Drowsiness during driving is a severe problem that must be addressed to improve road safety. Numerous countermeasures have been proposed to resolve this issue like adaptive environmental settings (temperature, sound, and light). The objective of this study was to accurately predict the effects of exposure to different colors of light on human drowsiness by using functional near-infrared spectroscopy and other physical measurements (heart rate and eye closure). We targeted two regions of the brain (visual and pre-frontal cortices). Twenty-three healthy subjects were investigated to evaluate all variables related to the awakening state, and twenty-one healthy subjects were also examined in the drowsy state evaluation. Eventually, the ten most suitable subjects were exposed to red, green, and blue lights under drowsy conditions, according to the experimental paradigm. Dim light was maintained in the experimental premises before and after colored light exposure to limit the results to those produced only in response to the desired stimuli. Eye closure, heart rate, and changes in oxy and deoxy hemoglobin concentrations were measured to characterize the condition (awake/drowsy) of the subject. A support vector machine classifier was used to identify the classification accuracy of awake and drowsy states. In conclusion, exposure to blue light triggered the activation of oxy hemoglobin in targeted brain regions; however, deoxy hemoglobin was not significantly affected by exposure to any of the colored lights. Noticeably, our study revealed that blue light exposure is more effective at reducing drowsiness than exposure to red and green lights. INDEX TERMS Functional near-infrared spectroscopy, colored light exposure, drowsiness, sleep deprivation, heart rate, and eye closure.
Applied Sciences, 2021
Photoacoustic imaging (PAI) is an emerging nondestructive testing technique to evaluate ever-grow... more Photoacoustic imaging (PAI) is an emerging nondestructive testing technique to evaluate ever-growing steel products and structures for safety and reliability. In this study, we have analyzed steel material with inbuilt cracks using computer-aided numerical simulations, imitating the PAI methodology. Cracks are introduced in a steel cylinder along three axes at different locations, and then a finite element method simulation in Abaqus software is performed to generate an acoustic wave and read it back at sensing locations after passing through the crack. The data are observed, analyzed, and modeled using the composite sine wave data fitting modeling technique. Afterwards, the Nelder–Mead simplex method is used to optimize the parameters of the model. It is concluded that with the change in the crack location, there is a change in the model parameters such as amplitude and frequencies. Results for cracks at seven different locations along each of the three axes are added, and listed i...
Complexity, 2020
The mathematical modeling of malaria disease has a crucial role in understanding the insights of ... more The mathematical modeling of malaria disease has a crucial role in understanding the insights of the transmission dynamics and corresponding appropriate prevention strategies. In this study, a novel nonlinear mathematical model for malaria disease has been proposed. To prevent the disease, we divided the infected population into two groups, unaware and aware infected individuals. The growth rate of awareness programs impacting the population is assumed to be proportional to the unaware infected individuals. It is further assumed that, due to the effect of awareness campaign, the aware infected individuals avoid contact with mosquitoes. The positivity and the boundedness of solutions have been derived through the completing differential process. Local and global stability analysis of disease-free equilibrium has been investigated via basic reproductive number R0, if R0 < 1, the system is stable otherwise unstable. The existence of the unique endemic equilibrium has been also deter...
Sensors, 2020
Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–co... more Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of t...
IEEE Access, 2019
Drowsiness/sleepiness is a serious issue that needs to be addressed for improvement in the safety... more Drowsiness/sleepiness is a serious issue that needs to be addressed for improvement in the safety of road driving. Past statistical data on road accidents has shown enormous increases in car crashes due to drowsy/sleepy feelings. This study comprehensively summarizes all aspects of the drowsy state and its effects during car driving: its symptoms, causes, preventive actions, car accident statistics, sleep stages, and the behavioral, physiological and neural activation changes occurring during wakefulness and in the drowsy state. It considers drivers' behavioral data and corresponding methodologies for its analysis, the biomedical signals of the human body (including neuronal signals in the forms of electrical and hemodynamic responses), and their use for drowsiness detection. All of the existing methodologies, their uses and pros and cons, are comprehensively summarized. A detailed survey of the data published by neuro-imaging methodology-, physiological signal-and behavioral methodology-based studies in addition to studies using electro-mechanical installed sensors are statistically and theoretically summarized. Additionally, the neuronal activity occurring during the drowsy and awake states are analyzed, and the important contributions of fNIRS, fMRI and EEG in this context are discussed in detail. Differing existing drowsinessdetection systems installed in popular car brands also are reviewed. Finally, the remaining challenges and future suggestions for drowsiness-detection systems are summarized as well.
Complexity, 2018
It is a fact that contamination of EEG by ocular artifacts reduces the classification accuracy of... more It is a fact that contamination of EEG by ocular artifacts reduces the classification accuracy of a brain-computer interface (BCI) and diagnosis of brain diseases in clinical research. Therefore, for BCI and clinical applications, it is very important to remove/reduce these artifacts before EEG signal analysis. Although, EOG-based methods are simple and fast for removing artifacts but their performance, meanwhile, is highly affected by the bidirectional contamination process. Some studies emphasized that the solution to this problem is low-pass filtering EOG signals before using them in artifact removal algorithm but there is still no evidence on the optimal low-pass frequency limits of EOG signals. In this study, we investigated the optimal EOG signal filtering limits using state-of-the-art artifact removal techniques with fifteen artificially contaminated EEG and EOG datasets. In this comprehensive analysis, unfiltered and twelve different low-pass filtering of EOG signals were us...
Energies, 2018
Reliable and accurate state of charge (SOC) monitoring is the most crucial part in the design of ... more Reliable and accurate state of charge (SOC) monitoring is the most crucial part in the design of an electric vehicle (EV) battery management system (BMS). The lithium ion battery (LIB) is a highly complex electrochemical system, which performance changes with age. Therefore, measuring the SOC of a battery is a very complex and tedious process. This paper presents an online data-driven battery model identification method, where the battery parameters are updated using the Lagrange multiplier method. A battery model with unknown battery parameters was formulated in such a way that the terminal voltage at an instant time step is a linear combination of the voltages and load current. A cost function was defined to determine the optimal values of the unknown parameters with different data points measured experimentally. The constraints were added in the modified cost function using Lagrange multiplier method and the optimal value of update vector was determined using the gradient approac...
Frontiers in neuroinformatics, 2018
Functional near-infrared spectroscopy (fNIRS) has evolved as a neuro-imaging modality over the co... more Functional near-infrared spectroscopy (fNIRS) has evolved as a neuro-imaging modality over the course of the past two decades. The removal of superfluous information accompanying the optical signal, however, remains a challenge. A comprehensive analysis of each step is necessary to ensure the extraction of actual information from measured fNIRS waveforms. A slight change in shape could alter the features required for fNIRS-BCI applications. In the present study, the effect of the differential path-length factor (DPF) values on the characteristics of the hemodynamic response function (HRF) was investigated. Results were compiled for both simulated data sets and healthy human subjects over a range of DPF values from three to eight. Different sets of activation durations and stimuli were used to generate the simulated signals for further analysis. These signals were split into optical densities under a constrained environment utilizing known values of DPF. Later, different values of DP...
Frontiers in human neuroscience, 2016
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality that measur... more Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality that measures the concentration changes of oxy-hemoglobin (HbO) and de-oxy hemoglobin (HbR) at the same time. It is an emerging cortical imaging modality with a good temporal resolution that is acceptable for brain-computer interface applications. Researchers have developed several methods in last two decades to extract the neuronal activation related waveform from the observed fNIRS time series. But still there is no standard method for analysis of fNIRS data. This article presents a brief review of existing methodologies to model and analyze the activation signal. The purpose of this review article is to give a general overview of variety of existing methodologies to extract useful information from measured fNIRS data including pre-processing steps, effects of differential path length factor (DPF), variations and attributes of hemodynamic response function (HRF), extraction of evoked response, re...
Frontiers in Human Neuroscience, 2016
Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-tem... more Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG.
Sensors, 2016
Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes t... more Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.
Frontiers in Behavioral Neuroscience, 2015
Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique... more Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650-950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > t critical and p-value < 0.05).
IEEE Transactions on Knowledge and Data Engineering, 2015
Advancement in information technology is playing an increasing role in the use of information sys... more Advancement in information technology is playing an increasing role in the use of information systems comprising relational databases. These databases are used effectively in collaborative environments for information extraction; consequently, they are vulnerable to security threats concerning ownership rights and data tampering. Watermarking is advocated to enforce ownership rights over shared relational data and for providing a means for tackling data tampering. When ownership rights are enforced using watermarking, the underlying data undergoes certain modifications; as a result of which, the data quality gets compromised. Reversible watermarking is employed to ensure data quality along-with data recovery. However, such techniques are usually not robust against malicious attacks and do not provide any mechanism to selectively watermark a particular attribute by taking into account its role in knowledge discovery. Therefore, reversible watermarking is required that ensures; (i) watermark encoding and decoding by accounting for the role of all the features in knowledge discovery; and, (ii) original data recovery in the presence of active malicious attacks. In this paper, a robust and semi-blind reversible watermarking (RRW) technique for numerical relational data has been proposed that addresses the above objectives. Experimental studies prove the effectiveness of RRW against malicious attacks and show that the proposed technique outperforms existing ones.
The large datasets are being mined to extract hidden knowledge and patterns that assist decision ... more The large datasets are being mined to extract hidden knowledge and patterns that assist decision makers in making effective, efficient and timely decisions in an ever increasing competitive world. This type of "knowledge-driven" data mining activity is not possible without sharing the "datasets" between their owners and data mining experts (or corporations); as a consequence, protecting ownership (by embedding watermark) on the datasets is becoming relevant. The most important challenge in watermarking (to be mined) datasets is: how to preserve knowledge in features or attributes?. Usually, an owner needs to manually define "Usability constraints" for each type of dataset to preserve the contained knowledge. The major contribution of this paper is a novel formal model that facilitates a data owner to define usability constraints-to preserve the knowledge contained in the dataset-in an automated fashion. The model aims at preserving "classification potential" of each feature and other major characteristics of datasets that play an important role during the mining process of data; as a result, learning statistics and decision making rules also remain intact. We have implemented our model and integrated it with a new watermark embedding algorithm to prove that the inserted watermark not only preserves the knowledge contained in a dataset but also significantly enhances watermark security compared with existing techniques. We have tested our model on 25 different data-mining datasets to show its efficacy, effectiveness and the ability to adapt and generalize.
IEEE Transactions on Information Forensics and Security, 2013
The large datasets are being mined to extract hidden knowledge and patterns that assist decision ... more The large datasets are being mined to extract hidden knowledge and patterns that assist decision makers in making effective, efficient and timely decisions in an ever increasing competitive world. This type of "knowledge-driven" data mining activity is not possible without sharing the "datasets" between their owners and data mining experts (or corporations); as a consequence, protecting ownership (by embedding watermark) on the datasets is becoming relevant. The most important challenge in watermarking (to be mined) datasets is: how to preserve knowledge in features or attributes?. Usually, an owner needs to manually define "Usability constraints" for each type of dataset to preserve the contained knowledge. The major contribution of this paper is a novel formal model that facilitates a data owner to define usability constraints-to preserve the knowledge contained in the dataset-in an automated fashion. The model aims at preserving "classification potential" of each feature and other major characteristics of datasets that play an important role during the mining process of data; as a result, learning statistics and decision making rules also remain intact. We have implemented our model and integrated it with a new watermark embedding algorithm to prove that the inserted watermark not only preserves the knowledge contained in a dataset but also significantly enhances watermark security compared with existing techniques. We have tested our model on 25 different data-mining datasets to show its efficacy, effectiveness and the ability to adapt and generalize.
World Journal of Laparoscopic Surgery with DVD, 2013
Objective: To determine the feasibility and efficacy of the laparoscopic myomectomy for large fib... more Objective: To determine the feasibility and efficacy of the laparoscopic myomectomy for large fibroids (>10 cm) with use of synchronous uterine artery embolization. Design: A prospective observational case series of laparoscopic myomectomy performed for single and multiple uterine fibroids. Setting: A tertiary referral center for gynecological oncology and minimal access surgery. Population: A 15 premenopausal women with large fibroids who wished to conserve their uterus from March 2005 to August 2011. Materials and methods: Laparoscopic myomectomy was performed using harmonic scalpel with synchronized preoperative uterine artery embolization (UAE) following multidisciplinary team discussion. Tissue retrieval was performed by morcellation. Main outcome measures: Early discharge and reduced intraoperative blood loss. Results: Of all 15 cases, mean number of fibroids removed was 2 (range: 1-4) and mean mass of tissue excised was 450 gm (range: 320-1540). The mean diameter of the fibroids removed was 13 cm (range: 10-20 cm). Mean estimated blood loss was 156 ml (range: 25-1,000 ml) and the mean operating time was 113 minutes (17-200 minutes). All cases were successfully completed laparoscopically with no conversions to open surgery. One patient who had a 13 cm broad ligament fibroid required a 2 unit blood transfusion intraoperatively and another patient developed a single port-site infection 2 weeks following the procedure. Histology confirmed benign leiomyomas in all cases. Conclusion: Laparoscopic myomectomy with use of synchronized UAE using harmonic scalpel is feasible and efficient procedure for single or multiple large fibroids within an experienced multidisciplinary setting.
Annals of thoracic and cardiovascular surgery : official journal of the Association of Thoracic and Cardiovascular Surgeons of Asia, 2008
It is not uncommon for aspirin therapy to be withheld before coronary artery bypass grafting (CAB... more It is not uncommon for aspirin therapy to be withheld before coronary artery bypass grafting (CABG) because it is thought to increase the risk of postoperative bleeding. Many studies have shown that continued aspirin therapy reduces postoperative myocardial infarction and increases survival. The purpose of this study is to analyze the effect of preoperative aspirin on postoperative bleeding in patients undergoing CABG. Patients (n=30) undergoing CABG were divided into two groups, group 1 (n=15) who received aspirin till the day of surgery, and group 2 in whom aspirin was stopped 5 days before surgery. Postoperative bleeding up to 76 h (approximately 3 days) was noted in both groups. Preoperative, intraoperative, and postoperative variables were equal in both groups. Postoperative bleeding in the 2nd hour was significantly lower in group 1 compared to group 2 (p=0.004). Bleeding 28-76 h postoperatively was also significantly lower in the first group (p=0.043). Our study suggests that...
A Novel Image Compression Architecture with proficient Layered scenario
Sixth International Conference on Intelligent Systems Design and Applications, 2006
The architecture proposed in this paper performs all inevitable operations effectively to compres... more The architecture proposed in this paper performs all inevitable operations effectively to compress the data for image processing. The given behavioral architecture is not only verified algorithmically but also gave clear snapshot of the facts hindering in the efficient operation of data ...
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Papers by Muhammad Kamran