Signals Intelligence

This volume is a result of an activity to evaluate U.S. signals intelligence practices. This study is among several of the administration’s responses to heightened public concern about U.S. intelligence agency surveillance programs that followed Edward Snowden’s disclosure of numerous internal National Security Agency (NSA) documents beginning in mid-2013.

The book covers the most recent developments in machine learning, signal analysis, and their applications. It covers the topics of machine intelligence such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and electromyogram (EMG) as well as other signals such as speech signals, communication signals, vibration signals, image, and video. Further, it analyzes normal and abnormal categories of real-world signals, for example normal and epileptic EEG signals using numerous classification techniques.