Local Contrast Enhancement Using Intuitionistic Fuzzy Sets Optimized by Artificial Bee Colony Algorithm

The article presented the enhancement method of cells images. The first method used in the local contrast enhancement was Intuitionistic Fuzzy Sets (IFS). The proposed method is the IFS optimized by Artificial Bee Colony (ABC) algorithm. The ABC was used to optimize the membership function parameter of IFS. To measure the image quality, Image Enhancement Metric (IEM)was applied. The results of local contrast enhancement using both methods were compared with the results using histogram equalization method. The tests were conducted using two MDCK cell images. The results of local contrast enhancement using both methods were evaluated by observing the enhanced images and IEM values. The results show that the methods outperform the histogram equalization method. Furthermore, the method using IFSABC is better than the IFS method

Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

Sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set

v ABSTRACT This study focused on contrast enhancement of Flat Electroencephalography (fEEG) image during epileptic seizure. The main interest is in visualizing the path of brainstorm in the brain that occur during seizure. Selected techniques that are involved ranging from classical, ordinary fuzzy, and advanced fuzzy namely the intuitionistic fuzzy sets (IFS). Different techniques may result in different output of fEEG image. The methods in classical approach are Power Law Transformation, Histogram Equalization, and Image Size Dependent Normalization. The intensifier operator is implemented in the fuzzy contrast enhancement technique. For the IFS approach, the Window Based Enhancement Scheme (WBES) and its revised version (RWBES) are applied. The RWBES gives better results compared to the WBES whereby the vague boundary of the cluster centres are reduced resulting in a smaller area of the vague boundary. The vague boundary represents the strength of the electrical potential of the foci of seizure. Next, the quality of the output image is measured via the objective measure such as mean squared error (MSE), peak-signalto- noise-ratio (PSNR), universal image quality index (UIQI), and structural similarity index measure (SSIM). In IFS, the sum of membership and non-membership is not necessarily equal to one. Thus, there exists hesitancy in deciding the degree to which an element satisfies a particular property. Moreover, the sequence of enhanced fEEG images are demonstrated by varying the value of parameter, namely λ, that also influence the hesitation value π. In addition, the Sugeno type intuitionistic fuzzy generator which is used to compute the non-membership value v has been extended to the concept of fuzzy limit. Hence, by implementing the definition of fuzzy limit, different values of ∈ will be tested in obtaining the values of integer N that will determine the value of λ and hence the value of hesitation π. The relationship between membership, non-membership, and hesitation values are also demonstrated graphically

Sequence of image enhancemant of flat electroencephalography using intuitionistic fuzzy set

This study focused on contrast enhancement of Flat Electroencephalography (fEEG) image during epileptic seizure. The main interest is in visualizing the path of brainstorm in the brain that occur during seizure. Selected techniques that are involved ranging from classical, ordinary fuzzy, and advanced fuzzy namely the intuitionistic fuzzy sets (IFS). Different techniques may result in different output of fEEG image. The methods in classical approach are Power Law Transformation, Histogram Equalization, and Image Size Dependent Normalization. The intensifier operator is implemented in the fuzzy contrast enhancement technique. For the IFS approach, the Window Based Enhancement Scheme (WBES) and its revised version (RWBES) are applied. The RWBES gives better results compared to the WBES whereby the vague boundary of the cluster centres are reduced resulting in a smaller area of the vague boundary. The vague boundary represents the strength of the electrical potential of the foci of seizure. Next, the quality of the output image is measured via the objective measure such as mean squared error (MSE), peak-signalto- noise-ratio (PSNR), universal image quality index (UIQI), and structural similarity index measure (SSIM). In IFS, the sum of membership and non-membership is not necessarily equal to one. Thus, there exists hesitancy in deciding the degree to which an element satisfies a particular property. Moreover, the sequence of enhanced fEEG images are demonstrated by varying the value of parameter, namely �, that also influence the hesitation value π. In addition, the Sugeno type intuitionistic fuzzy generator which is used to compute the non-membership value � has been extended to the concept of fuzzy limit. Hence, by implementing the definition of fuzzy limit, different values of � will be tested in obtaining the values of integer N that will determine the value of � and hence the value of hesitation �. The relationship between membership, non-membership, and hesitation values are also demonstrated graphically

A review of deep learning-based detection methods for COVID-19

COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared.Scopu

An investigation into the geomorphology of the Hebron Fault, Namibia, using a satellite-derived, high-resolution digital elevation model (DEM)

The Hebron fault scarp in southern Namibia is 45 km in length with an average height of 5.5 m and a maximum height of 8.9 m. Namibia is a Stable Continental Region (SCR) — a slowly deforming area within a continental plate. The country also has little recorded seismicity with the largest earthquake on the International Seismological Center (ISC) catalogue being MW 5.4. If the Hebron fault scarp was formed in a single event, this would represent a MW 7.3 earthquake. SCRs do occasionally experience large earthquakes, however, the recurrence intervals between these events is much larger than in rapidly deforming areas. Consequently, studying palaeo-earthquakes allows the record of seismicity to be extended and the characteristics of SCR events to be better understood. These studies may help refine the Mmax estimates required for seismic hazard assessment. Previous work on Hebron has been limited to field descriptions and theodolite survey scarp heights. Furthermore, there have been several interpretations of the fault mechanism and number of rupture events. This study produces a high-resolution Digital Elevation Model (DEM) via stereophotogrammetry using pan-sharpened Worldview-3 satellite imagery (0.31 m resolution). The DEM was used for several geomorphological analyses. These included measuring the scarp height at 160 locations along its length, measuring river channel displacements and identifying knickpoints along river profiles. Results indicate that the scarp formed from a normal, dip-slip fault that ruptured in a single event. This scenario would imply a high slip-to-length ratio. A comparison of other SCR fault scarps in the literature was made which shows that Hebrons’ slip-to-length ratio falls within the values found on other SCR faults. This study also discusses the implications of results for seismic hazard assessment in the region. Due a poor seismic record, probabilistic seismic hazard analysis (PSHA) will calculate a low seismic risk for Namibia. As large earthquakes can occur in SCRs, deterministic seismic hazard analysis (DSHA) can be used to inform policy makers of the worst case scenarios

Channel prediction in wireless communications

Knowledge of the channel over which signals are sent is of prime importance in modern wireless communications. Inaccurate or incomplete channel information leads to high error rates and wasted bandwidth and energy. Although active channel measurement is commonly used to gain channel knowledge, it can only accurately represent the channel at the time the measurement was taken, makes energy and bandwidth demands, and adds significant complexity to the radio system. Due to the highly time variant nature of wireless channels, active measurements become invalid almost as soon as they are taken, making alternative approaches to predicting future behaviour highly attractive. Such systems would allow maximum advantage to be taken of the limited bandwidth available and make significant power savings. This thesis investigates a number of complementary technologies, leading towards a channel prediction scheme suitable for mobile devices. As a first step towards channel prediction, anomaly detection is investigated within periodic wireless signals to establish when radical changes in the channel occur. In pre- vious experiments, long monotonic sequences had been observed to coincide with certain anomalies but not others when using Kullback-Leibler Divergence (KLD) analysis, possibly allowing the characterisation of anomaly types. An investigation is described to explain the origin of these features in a rigorous mathematical sense. A proof is given for the causes of the monotonic sequences, followed by a discussion of the types of signal anomaly which would underly such a feature and the value of this information. The second part describes a novel channel characterisation method which uses a class of Recurrent Neural Network (RNN) called an Echo State Network (ESN). Using this tool, a channel characterisation system can be constructed without an explicit statistical or mathematical model of the wireless environment, relying instead on observed data. This approach is much more convenient than existing models which require detailed information about the wireless system's parameters and also allows for new channel classifications to be added easily. It is able to achieve double the correct classification rate of a conventional statistical classifier, and is computationally simple to implement, making it ideal for inclusion on low-power mobile devices. Following their successful use in characterisation, ESNs are used in the final part in an investigation into channel prediction in a number of different scenarios. They were however found to be unable to produce useful predictions for all but the most trivial channel models. An alternative method is described for indoor environments using an approach inspired by ray tracing. It is simple and computationally lightweight to implement, again making it suitable for mobile devices. Simulation results show that it can outperform pilot-assisted methods by a significant margin, while not wasting bandwidth on channel measurement

Interval type-2 Atanassov-intuitionistic fuzzy logic for uncertainty modelling

This thesis investigates a new paradigm for uncertainty modelling by employing a new class of type-2 fuzzy logic system that utilises fuzzy sets with membership and non-membership functions that are intervals. Fuzzy logic systems, employing type-1 fuzzy sets, that mark a shift from computing with numbers towards computing with words have made remarkable impacts in the field of artificial intelligence. Fuzzy logic systems of type-2, a generalisation of type-1 fuzzy logic systems that utilise type-2 fuzzy sets, have created tremendous advances in uncertainty modelling. The key feature of the type-2 fuzzy logic systems, with particular reference to interval type-2 fuzzy logic systems, is that the membership functions of interval type-2 fuzzy sets are themselves fuzzy. These give interval type-2 fuzzy logic systems an advantage over their type-1 counterparts which have precise membership functions. Whilst the interval type-2 fuzzy logic systems are effective in modelling uncertainty, they are not able to adequately handle an indeterminate/neutral characteristic of a set, because interval type-2 fuzzy sets are only specified by membership functions with an implicit assertion that the non-membership functions are complements of the membership functions (lower or upper). In a real life scenario, it is not necessarily the case that the non-membership function of a set is complementary to the membership function. There may be some degree of hesitation arising from ignorance or a complete lack of interest concerning a particular phenomenon. Atanassov intuitionistic fuzzy set, another generalisation of the classical fuzzy set, captures this thought process by simultaneously defining a fuzzy set with membership and non-membership functions such that the sum of both membership and non-membership functions is less than or equal to 1. In this thesis, the advantages of both worlds (interval type-2 fuzzy set and Atanassov intuitionistic fuzzy set) are explored and a new and enhanced class of interval type-2 fuzzy set namely, interval type-2 Atanassov intuitionistic fuzzy set, that enables hesitation, is introduced. The corresponding fuzzy logic system namely, interval type-2 Atanassov intuitionistic fuzzy logic system is rigorously and systematically formulated. In order to assess this thesis investigates a new paradigm for uncertainty modelling by employing a new class of type-2 fuzzy logic system that utilises fuzzy sets with membership and non-membership functions that are intervals. Fuzzy logic systems, employing type-1 fuzzy sets, that mark shift from computing with numbers towards computing with words have made remarkable impacts in the field of artificial intelligence. Fuzzy logic systems of type-2, a generalisation of type-1 fuzzy logic systems that utilise type-2 fuzzy sets, have created tremendous advances in uncertainty modelling. The key feature of the type-2 fuzzy logic systems, with particular reference to interval type-2 fuzzy logic systems, is that the membership functions of interval type-2 fuzzy sets are themselves fuzzy. These give interval type-2 fuzzy logic systems an advantage over their type-1 counterparts which have precise membership functions. Whilst the interval type-2 fuzzy logic systems are effective in modelling uncertainty, they are not able to adequately handle an indeterminate/neutral characteristic of a set, because interval type-2 fuzzy sets are only specified by membership functions with an implicit assertion that the non-membership functions are complements of the membership functions (lower or upper). In a real life scenario, it is not necessarily the case that the non-membership function of a set is complementary to the membership function. There may be some degree of hesitation arising from ignorance or a complete lack of interest concerning a particular phenomenon. Atanassov intuitionistic fuzzy set, another generalisation of the classical fuzzy set, captures this thought process by simultaneously defining a fuzzy set with membership and non-membership functions such that the sum of both membership and non-membership functions is less than or equal to 1. In this thesis, the advantages of both worlds (interval type-2 fuzzy set and Atanassov intuitionistic fuzzy set) are explored and a new and enhanced class of interval type-2 fuzz set namely, interval type-2 Atanassov intuitionistic fuzzy set, that enables hesitation, is introduced. The corresponding fuzzy logic system namely, interval type-2 Atanassov intuitionistic fuzzy logic system is rigorously and systematically formulated. In order to assess the viability and efficacy of the developed framework, the possibilities of the optimisation of the parameters of this class of fuzzy systems are rigorously examined. First, the parameters of the developed model are optimised using one of the most popular fuzzy logic optimisation algorithms such as gradient descent (first-order derivative) algorithm and evaluated on publicly available benchmark datasets from diverse domains and characteristics. It is shown that the new interval type-2 Atanassov intuitionistic fuzzy logic system is able to handle uncertainty well through the minimisation of the error of the system compared with other approaches on the same problem instances and performance criteria. Secondly, the parameters of the proposed framework are optimised using a decoupledextended Kalman filter (second-order derivative) algorithm in order to address the shortcomings of the first-order gradient descent method. It is shown statistically that the performance of this new framework with fuzzy membership and non-membership functions is significantly better than the classical interval type-2 fuzzy logic systems which have only the fuzzy membership functions, and its type-1 counterpart which are specified by single membership and non-membership functions. The model is also assessed using a hybrid learning of decoupled extended Kalman filter and gradient descent methods. The proposed framework with hybrid learning algorithm is evaluated by comparing it with existing approaches reported in the literature on the same problem instances and performance metrics. The simulation results have demonstrated the potential benefits of using the proposed framework in uncertainty modelling. In the overall, the fusion of these two concepts (interval type-2 fuzzy logic system and Atanassov intuitionistic fuzzy logic system) provides a synergistic capability in dealing with imprecise and vague information