The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.
Most psychologists believe that facial behavior through depression differs from facial behavior in the absence of depression, so facial behavior can be utilized as a dependable indicator for spotting depression. Visual depression diagnosis system (VDD) establishes dependents on expressions of the face that are expense-effective and movable. At this work, the VDD system is designed according to the Facial Action Coding System (FACS) to extract features of the face. The key concept of the Facial Action Coding System (FACS) to explain the whole face behavior utilizing Action Units (AUs), every AU is linked to the motion of unique or maybe further face muscles. Six AUs have utilized as depression features; those action units are AUs 4, 5, 6, 7, 10, and 12. The datasets that employed to evaluate the performance of the proposed system are gathered for 125 participants (30 males, 95 females); many of them are among 17-60 years of age. At the final step of the current system, four kinds of classification techniques were applied separately; those classifiers algorithms are KNN, SVM, PCA, and LDA. The outcomes of the simulation indicate that the best outcomes are achieved utilizing the KNN and LDA classifiers, where the success rate is 85%. New classification methods in the VDD system are the key contributions of this research, gather real databases that can utilize to compute the performance of every other VDD system based on face emotions, and choose appropriate features of the face.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.