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This study proposes a spectral-amplitude-coding optical code-division multiple-access (SAC-OCDMA) framework for accessing the vibration frequency of a test object by using a Michelson interferometer vibration sensor (MIVS). Each sensor node possesses an individual signature codeword, and liquid crystal spatial light modulator (LC-SLM) encoders/decoders (codecs) are adopted to provide excellent orthogonal properties in the frequency domain. The proposed LC-SLM-based OCDMA system mitigates multiple access interference among all sensor nodes. When optical beams strike and are reflected by the object, the sensing interferometer becomes sensitive to external physical parameters such as temperature, strain, and vibration. The MIVS includes a Michelson interferometer placed at a specific distance from the test object on a designed vibration platform. A balanced photodetector (BPD) was used to convert the light output of the LC-SLM decoders into electrical signals, and a digitizing oscilloscope was used to Fourier transform the BPD electrical signal output, thereby yielding the vibration frequency of the test object. The results showed that the proposed sensor network with an interferometer can be used as a distributed highly sensitive sensor to obtain mechanical values. This study provides a new optical sensor network for current vibration frequency measurements.

The study proposes hybrid analog/digital transmission format scheme which integrated optical code-division multiple-access (OCDMA) and polarization multiplexing technique in free space optics (FSO) transmission. Orthogonal frequency division multiplexing (OFDM) transmits as the analog format and (on-off keying) OOK transmits as digital format in the study, respectively. In the proposed hybrid OCDMA system, it has high-speed transmission, signal security and low cost…etc. The multiple access interference (MAI) can be efficiently eliminated by using the balanced detection scheme at the receive end.

This paper develops an effective and efficient scheme to integrate Gaussian mixture model (GMM), support vector machine (SVM), and dynamic time wrapping (DTW) for automatic speaker recognition. GMM and SVM are two popular classifiers for speaker recognition applications. DTW is a fast and simple template matching method, and it is frequently seen in applications of speech recognition. In this work, DTW does not play a role to perform speech recognition, and it will be employed to be a verifier for verification of valid speakers. The proposed combination scheme of GMM, SVM and DTW, called SVMGMM-DTW, for speaker recognition in this study is a two-phase verification process task including GMM-SVM verification of the first phase and DTW verification of the second phase. By providing a double check to verify the identity of a speaker, it will be difficult for imposters to try to pass the security protection; therefore, the safety degree of speaker recognition systems will be largely increased. A series of experiments designed on door access control applications demonstrated that the superiority of the developed SVMGMM-DTW on speaker recognition accuracy.

The main work of this paper is to identify the musical genres of the singer by performing the optical detection of lip motion. Recently, optical music recognition has attracted much attention. Optical music recognition in this study is a type of automatic techniques in information engineering, which can be used to determine the musical style of the singer. This paper proposes a method for optical music recognition where acoustic formant analysis of both vocal fold vibration and lip motion are employed with interpolated Gaussian mixture model (GMM) estimation to perform musical genre classification of the singer. The developed approach for such classification application is called GMM-Formant. Since humming and voiced speech sounds cause periodic vibrations of the vocal folds and then the corresponding motion of the lip, the proposed GMM-Formant firstly operates to acquire the required formant information. Formant information is important acoustic feature data for recognition classification. The proposed GMM-Formant method then uses linear interpolation for combining GMM likelihood estimates and formant evaluation results appropriately. GMM-Formant will effectively adjust the estimated formant feature evaluation outcomes by referring to certain degree of the likelihood score derived from GMM calculations. The superiority and effectiveness of presented GMM-Formant are demonstrated by a series of experiments on musical genre classification of the singer.

The main work of this paper is to identify the musical genres of the singer by performing the optical detection of lip motion. Recently, optical music recognition has attracted much attention. Optical music recognition in this study is a type of automatic techniques in information engineering, which can be used to determine the musical style of the singer. This paper proposes a method for optical music recognition where acoustic formant analysis of both vocal fold vibration and lip motion are employed with interpolated Gaussian mixture model (GMM) estimation to perform musical genre classification of the singer. The developed approach for such classification application is called GMM-Formant. Since humming and voiced speech sounds cause periodic vibrations of the vocal folds and then the corresponding motion of the lip, the proposed GMM-Formant firstly operates to acquire the required formant information. Formant information is important acoustic feature data for recognition classification. The proposed GMM-Formant method then uses linear interpolation for combining GMM likelihood estimates and formant evaluation results appropriately. GMM-Formant will effectively adjust the estimated formant feature evaluation outcomes by referring to certain degree of the likelihood score derived from GMM calculations. The superiority and effectiveness of presented GMM-Formant are demonstrated by a series of experiments on musical genre classification of the singer.