11 research outputs found
Control activo de ruido impulsivo basado en la correntropía del error con ancho de kernel variable
Active control is a methodology based on the waves destructive interference that has proven to be effective for attenuating noise in the low frequency audible spectral range. However, the case of impulsive type noise sources, as harmful as frequent in industrial environments, represents a challenge to the convergence of the control algorithm that is still a matter of study. Outliers in the measured signals cause overcorrections in adaptive adjustment of filter weights which can produce instability. This paper presents the results of applying a new robust methodology to attenuate impulsive noise in a single-channel system. The proposed algorithm based on the maximum correntropy criterion with recursively adjusted kernel size, does not require prior statistical information on noise. The convergence properties and the effectiveness of the control indices are verified by simulation in different conditions of noise environments. Impulsive noise is represented by the nongaussian model proposed in the bibliography.Workshop: WPSSTR - Procesamientos de Señales Sistemas de Tiempo RealRed de Universidades con Carreras en Informátic
Mathematics and Digital Signal Processing
Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems
Developing reliable anomaly detection system for critical hosts: a proactive defense paradigm
Current host-based anomaly detection systems have limited accuracy and incur
high processing costs. This is due to the need for processing massive audit data
of the critical host(s) while detecting complex zero-day attacks which can leave
minor, stealthy and dispersed artefacts. In this research study, this observation
is validated using existing datasets and state-of-the-art algorithms related to the
construction of the features of a host's audit data, such as the popular semantic-based
extraction and decision engines, including Support Vector Machines, Extreme
Learning Machines and Hidden Markov Models. There is a challenging
trade-off between achieving accuracy with a minimum processing cost and processing
massive amounts of audit data that can include complex attacks. Also,
there is a lack of a realistic experimental dataset that reflects the normal and
abnormal activities of current real-world computers.
This thesis investigates the development of new methodologies for host-based
anomaly detection systems with the specific aims of improving accuracy at a minimum
processing cost while considering challenges such as complex attacks which,
in some cases, can only be visible via a quantified computing resource, for example,
the execution times of programs, the processing of massive amounts of audit data,
the unavailability of a realistic experimental dataset and the automatic minimization
of the false positive rate while dealing with the dynamics of normal activities.
This study provides three original and significant contributions to this field of
research which represent a marked advance in its body of knowledge.
The first major contribution is the generation and release of a realistic intrusion
detection systems dataset as well as the development of a metric based on fuzzy
qualitative modeling for embedding the possible quality of realism in a dataset's
design process and assessing this quality in existing or future datasets.
The second key contribution is constructing and evaluating the hidden host
features to identify the trivial differences between the normal and abnormal artefacts
of hosts' activities at a minimum processing cost. Linux-centric features include
the frequencies and ranges, frequency-domain representations and Gaussian
interpretations of system call identifiers with execution times while, for Windows,
a count of the distinct core Dynamic Linked Library calls is identified as a hidden
host feature.
The final key contribution is the development of two new anomaly-based statistical
decision engines for capitalizing on the potential of some of the suggested
hidden features and reliably detecting anomalies. The first engine, which has
a forensic module, is based on stochastic theories including Hierarchical hidden
Markov models and the second is modeled using Gaussian Mixture Modeling and
Correntropy. The results demonstrate that the proposed host features and engines
are competent for meeting the identified challenges
Wavelet Theory
The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior
Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences
Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc