Independent Component Analysis (ICA): Algorithms, Applications and Ambiguities

$155.00

Modern treatment of data requires powerful tools that allow the possible valuable contents of that data to be thoroughly understood and exploited. From the plethora of techniques proposed to achieve those objectives, the independent component analysis (ICA) has emerged as a flexible and efficient approach to model and characterize arbitrary data densities. Considering adequate data preprocessing, ICA can be implemented for any kind of data including imaging; biomedical signals; telecommunication data; and web data. In this framework, this book embraces a significant vision of ICA that presents innovative theoretical and practical approaches. ICA has been increasingly studied as a suitable method for many applications where available data describe complex geometries. Thus, this book aims to be an updated and advanced source of knowledge to solve real-world problems efficiently based on ICA. In contrast to classical time and frequency domain filtering, ICA has been proposed as a statistical filtering tool considering the observed data as mixtures of hidden non-Gaussian distributions called sources. Those sources extracted by ICA can be related with meaningful information about the origin of the data and for data detection/classification. Therefore, the successful of ICA has been widely demonstrated in challenging blind source separation (BSS), feature extraction, and pattern recognition tasks. The suitability of ICA for a given problem of data analysis can be posed from different perspectives considering the physical interpretation of the phenomenon under analysis: (i) Estimation of the probability density of multivariate data without physical meaning; (ii) learning of some bases (usually called activation functions), which are more or less connected to the actual behaviors that are implicit in the physical phenomenon; and (iii) to identify where sources are originated and how they mix before arriving to the sensors to provide a physical explanation of the linear mixture model. In any case, even though the complexity of the problem constrains a physical interpretation, ICA can be used as a general-purpose data mining technique. The chapters that compose this book are written by premier researchers that present enlightening discussions, convincing demonstrations, and guidelines for future directions of research. The contents of this book span biomedical signal processing, dynamic modeling, next generation wireless communication, and sound and ultrasound signal processing. It also includes comprehensive works based on the related ICA techniques known as bounded component analysis (BCA) and non-negative matrix factorization (NMF).Independent Component Analysis (ICA): Algorithms, Applications and Ambiguities and published by Nova. ISBNs for Independent Component Analysis (ICA): Algorithms, Applications and Ambiguities are 9781536139952, 1536139955 and the print ISBNs are 9781536139945, 1536139947.

Order Processing Time

All orders placed on our website are processed within 2-4 business days, from Monday to Friday, 8:00 AM – 6:00 PM Pacific Time (PT). Orders received after our daily cut-off time of 10:00 PM PT will be processed on the next business day. Please note that we do not process orders on weekends or public holidays.

Shipping Methods and Carriers

Zetlly partners exclusively with reputable shipping carriers to ensure timely delivery of your orders. We utilize:

FedEx
UPS
USPS

The choice of carrier is determined by factors such as destination, weight, and delivery timeframe to provide optimal service.

Shipping Rates and Fees

Free shipping is provided for all orders over $199.
Orders under $199 will incur a flat-rate shipping fee of $7.99.
All orders shipped within the United States will be subject to a sales tax charge of 5%.

Estimated Delivery Time

Once shipped, orders typically arrive within 6 to 10 business days. Our delivery times are from Monday to Friday, 8:00 AM – 6:00 PM Pacific Time (PT). Please allow additional time for deliveries to remote or rural locations.

Shipping Restrictions

Zetlly currently ships exclusively within the United States. At present, we do not offer international shipping or deliveries to P.O. boxes or APO/FPO addresses. Orders placed with addresses outside our designated delivery areas will be canceled, and refunds will be processed accordingly.

Tracking Your Order

Upon shipment, customers will receive a confirmation email containing tracking information. You can track your order directly through the provided tracking link or by visiting the carrier’s official website:

Please allow up to 48 hours for tracking information to update in the carrier’s system.

Eligibility for Returns and Exchanges

We accept returns and exchanges within 30 days from the date your order is delivered. Items must be unused, in the original condition, and accompanied by the original packaging and receipt or proof of purchase.

How to Return or Exchange an Item

To initiate a return or exchange, please follow these steps:

Contact our customer support at contact@zetlly.com with your order number and reason for return or exchange.
Our team will respond within 24 hours to provide detailed instructions, including the specific Return Address for your shipment.
Package your item securely and include all original packaging and proof of purchase.

Return shipments should be sent to: Blanq LLC 1201 South Hope Street Apt 2413, Los Angeles, CA 90015, USA

Return Conditions

Items must be returned in their original condition, unworn, undamaged, and complete with all original packaging and documentation.
Items returned without prior authorization or not meeting the above conditions may not qualify for a refund or exchange.

Return Shipping Costs

Customers are responsible for return shipping costs unless the return is due to our error or a defective product. We recommend using a trackable shipping service to ensure your return reaches us safely.

Non-Returnable Items

The following items cannot be returned:

Digital products (e-books or downloadable content)
Personalized or customized items
Gift cards

Accepted Payment Methods

Zetlly accepts the following secure and widely trusted payment options:

PayPal: Easily pay through your PayPal account, benefiting from secure transactions and buyer protection.
Stripe: Pay securely using major credit and debit cards including Visa, MasterCard, American Express, and Discover via Stripe’s encrypted payment gateway.

Payment Security

At Zetlly, your security is our utmost priority. We utilize advanced encryption technologies and robust security protocols provided by PayPal and Stripe. All payment information entered on our site is encrypted using Secure Socket Layer (SSL) technology, ensuring your financial information remains private and secure throughout the transaction process.

Zetlly does not store any credit card or sensitive financial information directly on our servers, further enhancing the security and protection of your personal data.

Payment Process and Confirmation

Upon placing an order, your chosen payment method (PayPal or Stripe) will immediately process the transaction. You will receive an automated confirmation email shortly after your payment has been successfully completed, detailing your transaction and order summary.

Please retain this confirmation email for your records and reference in case of any inquiries or disputes.

Modern treatment of data requires powerful tools that allow the possible valuable contents of that data to be thoroughly understood and exploited.

From the plethora of techniques proposed to achieve those objectives, the independent component analysis (ICA) has emerged as a flexible and efficient approach to model and characterize arbitrary data densities. Considering adequate data preprocessing, ICA can be implemented for any kind of data including imaging; biomedical signals; telecommunication data; and web data. In this framework, this book embraces a significant vision of ICA that presents innovative theoretical and practical approaches. ICA has been increasingly studied as a suitable method for many applications where available data describe complex geometries. Thus, this book aims to be an updated and advanced source of knowledge to solve real-world problems efficiently based on ICA. In contrast to classical time and frequency domain filtering, ICA has been proposed as a statistical filtering tool considering the observed data as mixtures of hidden non-Gaussian distributions called sources. Those sources extracted by ICA can be related with meaningful information about the origin of the data and for data detection/classification. Therefore, the successful of ICA has been widely demonstrated in challenging blind source separation (BSS), feature extraction, and pattern recognition tasks. The suitability of ICA for a given problem of data analysis can be posed from different perspectives considering the physical interpretation of the phenomenon under analysis: (i) Estimation of the probability density of multivariate data without physical meaning; (ii) learning of some bases (usually called activation functions), which are more or less connected to the actual behaviors that are implicit in the physical phenomenon; and (iii) to identify where sources are originated and how they mix before arriving to the sensors to provide a physical explanation of the linear mixture model. In any case, even though the complexity of the problem constrains a physical interpretation, ICA can be used as a general-purpose data mining technique. The chapters that compose this book are written by premier researchers that present enlightening discussions, convincing demonstrations, and guidelines for future directions of research. The contents of this book span biomedical signal processing, dynamic modeling, next generation wireless communication, and sound and ultrasound signal processing. It also includes comprehensive works based on the related ICA techniques known as bounded component analysis (BCA) and non-negative matrix factorization (NMF).Independent Component Analysis (ICA): Algorithms, Applications and Ambiguities and published by Nova. ISBNs for Independent Component Analysis (ICA): Algorithms, Applications and Ambiguities are 9781536139952, 1536139955 and the print ISBNs are 9781536139945, 1536139947.