A comparative analysis of local and global adaptive threshold estimation techniques for energy detection in cognitive radio

In this paper, we compare local and global adaptive threshold estimation techniques for energy detection in Cognitive Radio (CR). By this comparison, a sum-up synopsis is provided regarding the effective performance range and the operating conditions under which both classes best apply in CR. Representative methods from both classes were implemented and trained using synthesized signals to fine tune each algorithm’s parameter values. Further tests were conducted using real-life signals acquired via a spectrum survey exercise and results were analyzed using the probability of detection and the probability of false alarm computed for each algorithm. It is observed that while local based methods may be adept at maintaining a low constant probability of false alarm, they however suffer a grossly low probability of detection over a wide variety of CR spectra. Consequently, we concluded that global adaptive threshold estimation techniques are more suitable for signal detection in CR than their local adaptive thresholding counterparts.Research data for this article is available at https://data.mendeley.com/datasets/nyvcpv4s8k/1http://www.elsevier.com/locate/phycom2019-08-01hj2018Electrical, Electronic and Computer Engineerin

Statistical Properties and Applications of Empirical Mode Decomposition

Signal analysis is key to extracting information buried in noise. The decomposition of signal is a data analysis tool for determining the underlying physical components of a processed data set. However, conventional signal decomposition approaches such as wavelet analysis, Wagner-Ville, and various short-time Fourier spectrograms are inadequate to process real world signals. Moreover, most of the given techniques require \emph{a prior} knowledge of the processed signal, to select the proper decomposition basis, which makes them improper for a wide range of practical applications. Empirical Mode Decomposition (EMD) is a non-parametric and adaptive basis driver that is capable of breaking-down non-linear, non-stationary signals into an intrinsic and finite components called Intrinsic Mode Functions (IMF). In addition, EMD approximates a dyadic filter that isolates high frequency components, e.g. noise, in higher index IMFs. Despite of being widely used in different applications, EMD is an ad hoc solution. The adaptive performance of EMD comes at the expense of formulating a theoretical base. Therefore, numerical analysis is usually adopted in literature to interpret the behavior. This dissertation involves investigating statistical properties of EMD and utilizing the outcome to enhance the performance of signal de-noising and spectrum sensing systems. The novel contributions can be broadly summarized in three categories: a statistical analysis of the probability distributions of the IMFs and a suggestion of Generalized Gaussian distribution (GGD) as a best fit distribution; a de-noising scheme based on a null-hypothesis of IMFs utilizing the unique filter behavior of EMD; and a novel noise estimation approach that is used to shift semi-blind spectrum sensing techniques into fully-blind ones based on the first IMF. These contributions are justified statistically and analytically and include comparison with other state of art techniques

Adaptive threshold techniques for cognitive radio‐based low power wide area network

Some low power wide area network (LPWAN) developers such as Sigfox, Weightless, and Nwave, have recently commenced the integration of cognitive radio (CR) techniques in their respective LPWAN technologies, generally termed CR‐LPWAN systems. Their objective is to overcome specific limitations associated with LPWANs such as spectra congestion and interference, which in turn will improve the performance of many Internet of Things (IoT)‐based applications. However, in order to be effective under dynamic sensing conditions, CR‐LPWAN systems are typically required to adopt adaptive threshold techniques (ATTs) in order to improve their sensing performance. Consequently, in this article, we have investigated some of these notable ATTs to determine their suitability for CR‐LPWAN systems. To accomplish this goal, first, we describe a network architecture and physical layer model suitable for the effective integration of CR in LPWAN. Then, some specific ATTs were investigated following this model based on an experimental setup constructed using the B200 Universal Software Radio Peripheral kit. Several tests were conducted, and our findings suggest that no single ATT was able to perform best under all sensing conditions. Thus, CR‐LPWAN developers may be required to select a suitable ATT only based on the specific condition(s) for which the IoT application is designed. Nevertheless, some ATTs such as the forward consecutive mean excision algorithm, the histogram partitioning algorithm and the nonparametric amplitude quantization method achieved noteworthy performances under a broad range of tested conditions. Our findings will be beneficial to developers who may be interested in deploying effective ATTs for CR‐LPWAN systems.http://wileyonlinelibrary.com/journal/ett2021-04-01hj2020Electrical, Electronic and Computer Engineerin

A cuckoo search optimization-based forward consecutive mean excision model for threshold adaptation in cognitive radio

The forward consecutive mean excision (FCME) algorithm is one of the most effective adaptive threshold estimation algorithms presently deployed for threshold adaptation in cognitive radio (CR) systems. However, its effectiveness is often limited by the manual parameter tuning process and by the lack of prior knowledge pertaining to the actual noise distribution considered during the parameter modeling process of the algorithm. In this paper, we propose a new model that can automatically and accurately tune the parameters of the FCME algorithm based on a novel integration with the cuckoo search optimization (CSO) algorithm. Our model uses the between-class variance function of the Otsu’s algorithm as the objective function in the CSO algorithm in order to auto-tune the parameters of the FCME algorithm. We compared and selected the CSO algorithm based on its relatively better timing and accuracy performance compared to some other notable metaheuristics such as the particle swarm optimization, artificial bee colony (ABC), genetic algorithm, and the differential evolution (DE) algorithms. Following close performance values, our findings suggest that both the DE and ABC algorithms can be adopted as favorable substitutes for the CSO algorithm in our model. Further simulation results show that our model achieves reasonably lower probability of false alarm and higher probability of detection as compared to the baseline FCME algorithm under different noise-only and signal-plus-noise conditions. In addition, we compared our model with some other known autonomous methods with results demonstrating improved performance. Thus, based on our new model, users are relieved from the cumbersome process involved in manually tuning the parameters of the FCME algorithm; instead, this can be done accurately and automatically for the user by our model. Essentially, our model presents a fully blind signal detection system for use in CR and a generic platform deployable to convert other parameterized adaptive threshold algorithms into fully autonomous algorithms.http://link.springer.com/journal/5002020-11-03hj2020Electrical, Electronic and Computer Engineerin

Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations

Deep Neural Network (DNN) models are challenging to interpret because of their highly complex and non-linear nature. This lack of interpretability (1) inhibits adoption within safety critical applications, (2) makes it challenging to debug existing models, and (3) prevents us from extracting valuable knowledge. Explainable AI (XAI) research aims to increase the transparency of DNN model behaviour to improve interpretability. Feature importance explanations are the most popular interpretability approaches. They show the importance of each input feature (e.g., pixel, patch, word vector) to the model’s prediction. However, we hypothesise that feature importance explanations have two main shortcomings concerning their inability to describe the complexity of a DNN behaviour with sufficient (1) fidelity and (2) richness. Fidelity and richness are essential because different tasks, users, and data types require specific levels of trust and understanding. The goal of this thesis is to showcase the shortcomings of feature importance explanations and to develop explanation techniques that describe the DNN behaviour with greater richness. We design an adversarial explanation attack to highlight the infidelity and inadequacy of feature importance explanations. Our attack modifies the parameters of a pre-trained model. It uses fairness as a proxy measure for the fidelity of an explanation method to demonstrate that the apparent importance of a feature does not reveal anything reliable about the fairness of a model. Hence, regulators or auditors should not rely on feature importance explanations to measure or enforce standards of fairness. As one solution, we formulate five different levels of the semantic richness of explanations to evaluate explanations and propose two function decomposition frameworks (DGINN and CME) to extract explanations from DNNs at a semantically higher level than feature importance explanations. Concept-based approaches provide explanations in terms of atomic human-understandable units (e.g., wheel or door) rather than individual raw features (e.g., pixels or characters). Our function decomposition frameworks can extract specific class representations from 5% of the network parameters and concept representations with an average-per-concept F1 score of 86%. Finally, the CME framework makes it possible to compare concept-based explanations, contributing to the scientific rigour of evaluating interpretability methods.The author would like to appreciate the generous sponsorship of the Engineering and Physical Sciences Research Council (EPSRC), The Department of Computer Science and Technology at the University of Cambridge, and Tenyks, Inc

GSI Scientific Report 2016

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Exploring the Effect of Climate Change on Biological Systems

The present and potential future effect of global warming on the ecosystem has brought climate change to the forefront of scientific inquiry and discussion. For our investigation, we selected two organisms, one from cyanobacteria and one from a cereal plant to determine how climate change may impact these biological systems. The study involved understanding the physiological and adaptive responses at both the genetic and protein function levels to counteract environmental stresses. An increase in atmospheric carbon dioxide is a key factor in global climate change and can lead to alterations in ocean chemistry. Cyanobacteria are important, ancient and ubiquitous organisms that can aid in the study of the biological response to increasing carbon dioxide. Climate predictions estimate that by the year 2100 atmospheric carbon dioxide will exceed 700 ppm. In our first study, we looked at the transcriptional effect of high pCO2 on the cyanobacteria, Trichodesmium erythraeum. Total RNA sequencing was used to quantify changes in gene expression in T. erythraeum grown under present day and projected pCO2 concentrations for the year 2100. Two bioinformatics methods were used to analyze the transcriptional data. The results from this study indicate that a substantial number of genes are affected by high pCO2. However, increased pCO2 does not completely alter any one specific metabolic pathway. As the climate shifts throughout the world, it becomes essential for crops to withstand weather changes. In our second study, we investigated the function of the temperature induced lipocalin (Tatil) from Triticum aestivum, which is proposed to help plants survive adverse conditions. This protein is part of a functionally diverse and divergent superfamily of proteins called the lipocalins; they share a common three-dimensional structure, which consists of an antiparallel β-barrel and a C-terminal α-helix. Lipocalins are found in various organisms with a wide range of functions such as pheromone activity, lipid transport and coloration. Recently, proteins from wheat and Arabidopsis were identified as lipocalins through the elucidation of three structurally conserved regions. The study is particularly timely, as recent studies within the scientific community have shown that at higher temperatures wheat yields will decrease and production will decline by 6% for each 1°C increase. We analyzed the nature of conservation in a large group of sequentially divergent and functionally diverse lipocalins and identified seventeen highly conserved positions as well as built models of the native three-dimensional state of the wheat lipocalin. Based on these computational studies, the wild-type protein and three variants were chosen for a cellular localization study involving site-directed mutagenesis, a gene gun and a confocal microscope. The results provide support for the hypothesis that the L5 loop is involved in the association of the protein with the plasma membrane. We also developed an expression and purification system to produce the wild-type wheat lipocalin protein. Gel filtration chromatography eluted two different sized proteins. Based on the elution volume, one is believed to be the wheat lipocalin trimer while the other one is the monomer. Circular dichroism and fluorescence spectroscopy show that the biological characteristics of the two proteins are different. In the study, Tatil maintains its structure up to approximately 50°C (122°F). In summary, we provide experimental data to better understand mechanistically how microorganisms and plants adapt to environmental change. In cyanobacteria, we show that T. erythraeum adapts to pCO2 increases by up- or down-regulating its genes. In plants, we provide insight into the way in which Tatil interacts with the plant cell membrane as part of its putative function to facilitate robustness in response to temperature increases. The study of Tatil is vital as this protein is believed to help plants tolerate oxidative stress and extreme conditions which broadens our understanding of plant sustainability in different environments

Stochastic Modeling and Optimal Control for Colloidal Organization, Navigation, and Machines

Colloidal suspensions consisting of particles undergoing Brownian motion are ubiquitous in scientific research and emerging technologies. Longstanding challenges in strategic control of complex colloidal systems are to investigate the principle of optimal control, overcome the curse of dimensionality, design efficient algorithms, and develop generalizable control strategies. In the first part of this dissertation, we present methods and results from three case studies to illustrate how these challenges are addressed from the perspectives of modeling and optimal control. Single-agent optimal navigation in complex mazes. We investigate the optimal navigation principle of a self-propelled colloidal particle in complex mazes. We construct approximate Markov chain model and use the Markov decision process framework to obtain the general principle of optimal navigation. Multiple-agent cooperation and coordination for colloidal machines. Using self-propelled Janus motors as the model system, we illustrate a new paradigm for cargo capture and transport based on multiple-agent feedback control. The control algorithm can coordinate multiple motors to cooperate on forming a reconfigurable machine for cargo capture and transport. Low-dimensional modeling and ensemble control. Optimal control in a high dimensional self-assembly processes with limited actuations presents a challenge in both modelling and controller design. We use colloidal crystallization in an electric field as a model system to illustrate the methodologies of low-dimensional modeling and control for self-assembly processes. We use a nonlinear machine learning algorithm to characterize the dimensionality and parametrize the low-dimension manifold on which the system evolves. A low-dimensional Smoluchowski model is constructed and calibrated to illustrate the dynamic pathways of the assembly process. The resulting model is further leveraged to perform optimal control of the assembly process. In the second part of dissertation, we report three additional relevant research projects on colloidal interaction, dynamics, and control. The first project extends ensemble control from finite-size systems to infinite-size systems using feedback control in sedimentation. The second project develops a computational method to model depletion interactions between general geometric objects The third project develops modified Stokesian dynamics methods to investigate the colloidal rod motion near a planar wall with hydrodynamic interactions