Designs of Digital Filters and Neural Networks using Firefly Algorithm

Firefly algorithm is an evolutionary algorithm that can be used to solve complex multi-parameter problems in less time. The algorithm was applied to design digital filters of different orders as well as to determine the parameters of complex neural network designs. Digital filters have several applications in the fields of control systems, aerospace, telecommunication, medical equipment and applications, digital appliances, audio recognition processes etc. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, processes information and can be simulated using a computer to perform certain specific tasks like clustering, classification, and pattern recognition etc. The results of the designs using Firefly algorithm was compared to the state of the art algorithms and found that the digital filter designs produce results close to the Parks McClellan method which shows the algorithm’s capability of handling complex problems. Also, for the neural network designs, Firefly algorithm was able to efficiently optimize a number of parameter values. The performance of the algorithm was tested by introducing various input noise levels to the training inputs of the neural network designs and it produced the desired output with negligible error in a time-efficient manner. Overall, Firefly algorithm was found to be competitive in solving the complex design optimization problems like other popular optimization algorithms such as Differential Evolution, Particle Swarm Optimization and Genetic Algorithm. It provides a number of adjustable parameters which can be tuned according to the specified problem so that it can be applied to a number of optimization problems and is capable of producing quality results in a reasonable amount of time

Efficient Adaptive Filter Algorithms Using Variable Tap-length Scheme

Today the usage of digital signal processors has increased, where adaptive filter algorithms are now routinely employed in mostly all contemporary devices such as mobile phones, camcorders, digital cameras, and medical monitoring equipment, to name few. The filter tap-length, or the number of taps, is a significant structural parameter of adaptive filters that can influences both the complexity and steady-state performance characteristics of the filter. Traditional implementation of adaptive filtering algorithms presume some fixed filter-length and focus on estimating variable filter\u27s tap-weights parameters according to some pre-determined cost function. Although this approach can be adequate in some applications, it is not the case in more complicated ones as it does not answer the question of filter size (tap-length). This problem can be more apparent when the application involves a change in impulse response, making it hard for the adaptive filter algorithm to achieve best potential performance. A cost-effective approach is to come up with variable tap-length filtering scheme that can search for the optimal length while the filter is adapting its coefficients. In direct form structure filtering, commonly known as a transversal adaptive filter, several schemes were used to estimate the optimum tap-length. Among existing algorithms, pseudo fractional tap-length (FT) algorithm, is of particular interest because of its fast convergence rate and small steady-state error. Lattice structured adaptive filters, on the other hand, have attracted attention recently due to a number of desirable properties. The aim of this research is to develop efficient adaptive filter algorithms that fill the gap where optimal filter structures were not proposed by incorporating the concept of pseudo fractional tap-length (FT) in adaptive filtering algorithms. The contribution of this research include the development of variable length adaptive filter scheme and hence optimal filter structure for the following applications: (1) lattice prediction; (2) Least-Mean-Squares (LMS) lattice system identification; (3) Recursive Least-Squares (RLS) lattice system identification; (4) Constant Modulus Algorithm (CMA) blind equalization. To demonstrate the capability of proposed algorithms, simulations examples are implemented in different experimental conditions, where the results showed noticeable improvement in the context of mean square Error (MSE), as well as in the context of convergence rate of the proposed algorithms with their counterparts adaptive filter algorithms. Simulation results have also proven that with affordable extra computational complexity, an optimization for both of the adaptive filter coefficients and the filter tap-length can be attained

Security-Aware RWA for Dynamic Traffic Using Path Protection In WDM Networks

Security and attack management have become the prime concern for the network operators due to high data transfer rates and vulnerabilities associated with transparency in WDM networks. In the recent years, there is a substantial increase in perception to develop suitable mechanisms for subduing the adverse effects of malicious attacks such as high power jamming and tapping attacks.In transparent optical networks (TONs) traffic is carried over the optical fibers in the form of signals called lightpaths, creating a virtual topology over the physical interconnections of an optical fiber. This allows an exchange of an enormous amount of data at a very high speed. A fault or an attack on the network can lead to data tampering and data loss. Unlike faults, malicious attacks may not be localized and we cannot handle them with the standard fault-tolerance mechanisms in WDM networks. The Routing and Wavelength Assignment (RWA) problem assigns appropriate routes and wavelengths to all associated lightpaths in the network. Most the researchers considered the static traffic model, where the network requests (i.e. lightpaths to be established) are known in advance and last over long durations. In this thesis, we are solving the security-aware problem for dynamic requests by using protection strategy known as dedicated path protection (DPP). In the dynamic model, lightpaths are generated on-demand, and RWA must be performed based on available resources that are not being used by ongoing lightpaths. We propose an Integer linear programming (ILP) formulation to maximize requests satisfaction and reducing the disruption in the network due to malicious attacks (In-band and out-band)

Design for Test and Hardware Security Utilizing Tester Authentication Techniques

Design-for-Test (DFT) techniques have been developed to improve testability of integrated circuits. Among the known DFT techniques, scan-based testing is considered an efficient solution for digital circuits. However, scan architecture can be exploited to launch a side channel attack. Scan chains can be used to access a cryptographic core inside a system-on-chip to extract critical information such as a private encryption key. For a scan enabled chip, if an attacker is given unlimited access to apply all sorts of inputs to the Circuit-Under-Test (CUT) and observe the outputs, the probability of gaining access to critical information increases. In this thesis, solutions are presented to improve hardware security and protect them against attacks using scan architecture. A solution based on tester authentication is presented in which, the CUT requests the tester to provide a secret code for authentication. The tester authentication circuit limits the access to the scan architecture to known testers. Moreover, in the proposed solution the number of attempts to apply test vectors and observe the results through the scan architecture is limited to make brute-force attacks practically impossible. A tester authentication utilizing a Phase Locked Loop (PLL) to encrypt the operating frequency of both DUT/Tester has also been presented. In this method, the access to the critical security circuits such as crypto-cores are not granted in the test mode. Instead, a built-in self-test method is used in the test mode to protect the circuit against scan-based attacks. Security for new generation of three-dimensional (3D) integrated circuits has been investigated through 3D simulations COMSOL Multiphysics environment. It is shown that the process of wafer thinning for 3D stacked IC integration reduces the leakage current which increases the chip security against side-channel attacks

Design and Development of a Testbed Prototype for Cognitive Radio Transmission over TV White Space

Considering the ever-increasing demand and the associated high costs of wireless electromagnetic spectrum, technologies that can facilitate efficient spectrum utilization are of utmost importance. Cognitive radio (CR), in conjunction with TV White Spaces (TVWS), can be a viable solution, where unlicensed or secondary users can opportunistically use the not-currently-in-use, aka idle, TV channels registered to licensed or primary users. This thesis focuses on the design and development of a testbed prototype for real-time testing of secondary user transmission in TVWS. Once an unused TV channel has been identified, our system uses that idle channel for transmitting and receiving signals. The testbed is built on Universal Software Radio Peripheral (USRP) device powered by GNU Radio Software, Software Defined Radio (SDR) receptor, and Spectrum Analyser. The developed prototype splits a given TVWS channel into multiple small sub-channels and performs channel characterization through end-to-end transmission and reception of information carrying signals. The channel characteristics are presented through Bit Transfer Rate (BTR) and frequency spectrum results. The prototype also facilitates provisions for applying error correction coding as a mean of undertaking comparative performance testing

Thermographic non-destructive evaluation for natural fiber-reinforced composite laminates

Natural fibers, including mineral and plant fibers, are increasingly used for polymer composite materials due to their low environmental impact. In this paper, thermographic non-destructive inspection techniques were used to evaluate and characterize basalt, jute/hemp and bagasse fibers composite panels. Different defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, homogeneous particleboards of sugarcane bagasse, a new plant fiber material, were studied. Pulsed phase thermography and principal component thermography were used as the post-processing methods. In addition, ultrasonic C-scan and continuous wave terahertz imaging were also carried out on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different methods was give

From Nuclear to Renewables: The role utility-led voluntary contribution, community renewable, and grid modernization initiatives can play in allowing for a transition to nuclear-free electricity production in Ontario

Ontario’s present electricity supply is one that relies heavily on nuclear generation to provide energy. Though it does not release greenhouse gases during operation, nuclear houses several other ecological risks. This paper looks at voluntary contribution initiatives, community renewable energy generation, and grid modernization, as three areas where initiatives and are being undertaken by utilities that will contribute to a greater portion of Ontario’s electricity demand being met by renewables as opposed to nuclear. Ultimately this paper seeks to determine if initiatives in any of these areas could ultimately lead to an electricity system transition in Ontario away from nuclear towards renewables. Grid modernization appears to have the highest potential to significantly increase the contribution of renewably-sourced electricity to Ontario’s supply. However, utilizing voluntary contribution strategies, and supporting the development of community renewable projects, while unlikely to eventually prompt a large electricity-system change, can meaningfully contribute to goal of increasing the supply of renewably-sourced electricity in Ontario

CIR Parametric Rules Precocity For Ranging Error Mitigation In IR-UWB

The cutting-edge technology to support high ranging accuracy within the indoor environment is Impulse Radio Ultra Wide Band (IR-UWB) standard. Besides accuracy, IR-UWB’s low-complex architecture and low power consumption align well with mobile devices. A prime challenge in indoor IR-UWB based localization is to achieve a position accuracy under non-line-of-sight (NLOS) and multipath propagation (MPP) conditions. Another challenge is to achieve acceptable accuracy in the conditions mentioned above without any significant increase in latency and computational burden. This dissertation proposes a solution for addressing the accuracy and reliability problem of indoor localization system satisfying acceptable delay or computational complexity overhead. The proposed methodology is based on rules for identification of line-of-sight (LOS) and NLOS and the range error bias estimation and correction due to NLOS and MPP conditions. The proposed methodology provides accuracy for two major application domains, namely, wireless sensor networks (WSNs) and indoor tracking and navigation (ITN). This dissertation offers two different solutions for the localization problem. The first solution is a rules-based classification of LOS / NLOS and geometric-based range correction for WSN. In the first solution, the Boolean logic based classification is designed for identification of LOS/NLOS. The logic is based on channel impulse response (CIR) parameters. The second solution is based on fuzzy logic. The fuzzy based solution is appealing well for the stringent precision requirements in ITN. In this solution, the parametric Boolean logic from the first solution is converted and expanded into rules. These rules are implemented into a fuzzy logic based mechanism for designing a fuzzy inference system. The system estimates the ranging errors and correcting unmitigated ranges. The expanded rules and designed methodology are based on theoretical analysis and empirical observations of the parameters. The rules accommodate the parameters uncertainties for estimating the ranging error through the relationship between the input parameters uncertainties and ranging error using fuzzy inference mechanism. The proposed solutions are evaluated using real-world measurements in different indoor environments. The performance of the proposed solutions is also evaluated in terms of true classification rate, residual ranging errors’ cumulative distributions and probability density distributions, as well as outage probabilities. Evaluation results show that the true classification rate is more than 95%. Moreover, using the proposed fuzzy logic based solution, the residual errors convergence of 90% is attained for error threshold of 10 cm, and the reliability of the localization system is also more than 90% for error threshold of 15 cm

Enhanced infrared image processing for impacted carbon/glass fiber-reinforced composite evaluation

In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided

Efficient Blind Source Separation Algorithms with Applications in Speech and Biomedical Signal Processing

Blind source separation/extraction (BSS/BSE) is a powerful signal processing method and has been applied extensively in many fields such as biomedical sciences and speech signal processing, to extract a set of unknown input sources from a set of observations. Different algorithms of BSS were proposed in the literature, that need more investigations, related to the extraction approach, computational complexity, convergence speed, type of domain (time or frequency), mixture properties, and extraction performances. This work presents a three new BSS/BSE algorithms based on computing new transformation matrices used to extract the unknown signals. Type of signals considered in this dissertation are speech, Gaussian, and ECG signals. The first algorithm, named as the BSE-parallel linear predictor filter (BSE-PLP), computes a transformation matrix from the the covariance matrix of the whitened data. Then, use the matrix as an input to linear predictor filters whose coefficients being the unknown sources. The algorithm has very fast convergence in two iterations. Simulation results, using speech, Gaussian, and ECG signals, show that the model is capable of extracting the unknown source signals and removing noise when the input signal to noise ratio is varied from -20 dB to 80 dB. The second algorithm, named as the BSE-idempotent transformation matrix (BSE-ITM), computes its transformation matrix in iterative form, with less computational complexity. The proposed method is tested using speech, Gaussian, and ECG signals. Simulation results show that the proposed algorithm significantly separate the source signals with better performance measures as compared with other approaches used in the dissertation. The third algorithm, named null space idempotent transformation matrix (NSITM) has been designed using the principle of null space of the ITM, to separate the unknown sources. Simulation results show that the method is successfully separating speech, Gaussian, and ECG signals from their mixture. The algorithm has been used also to estimate average FECG heart rate. Results indicated considerable improvement in estimating the peaks over other algorithms used in this work