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In this research, we aim to propose an image sharpening method to make it easy to identify concrete cracks from blurred images captured by a moving camera. This study is expected to help realize social infrastructure maintenance using a wide range of robotic technologies, and to solve the future labor shortage and shortage of engineers. In this paper, a method to estimate parameters of motion blur for Point Spread Function (PSF) is mainly discussed, where we assume that there are two main degradation factors caused by the camera, out-of-focus blur and motion blur. A major contribution of this paper is that the parameters can properly be estimated from a sub-image of the object under inspection if the sub-image contains uniform speckled texture. Here, the cepstrum of the sub-image is fully utilized. Then, a filter convoluted PSF which consists of convolution with PSF (motion blur) and PSF (out-of focus blur) can be utilized for deconvolution of the blurred image for sharpening with significant effect. PSF (out-of-focus blur) is a constant function unique to each camera and lens, and can be confirmed before or after shooting. PSF (motion blur), on the other hand, needs to be estimated on a case-by-case basis since the amount and direction of camera movement varies depending on the time of shooting. Previous research papers have sometimes encountered difficulties in estimating the parameters of motion blur because of the emphasis on generality. In this paper, the main object is made of concrete, and on the surface of it there are speckled textures. We hypothesized that we can narrow down the candidates of parameters of motion blur by using these speckled patterns. To verify this hypothesis, we conducted experiments to confirm and examine the following two points using a general-purpose camera used in actual bridge inspections: 1. Influence on the cepstrum when the isolated point-like texture unique to concrete structures is used as a feature point. 2. Selection method of multiple images to narrow down the candidate minima of the cepstrum. It is novel that the parameters of motion blur can be well estimated by using the unique speckled pattern on the surface of the object.

In this paper, traditional music time domain features and cepstrum domain features are extracted using the spectral center of mass, spectral energy, linear prediction cepstrum coefficients (LPCC) and Mel frequency cepstrum coefficients (MFCC). After that, the traditional music signal is normalized using a normalization algorithm based on the Short-Time Fourier Transform (STFT). Finally, the performance of music source separation is evaluated using NSDR. In this paper, the percentage of inheritance rate for the four parts of traditional vocal music, traditional instrumental music, and traditional drama music before digitization is less than or equal to 40%, 35.06%, and 31.25%, respectively. After digitization, their percentage of inheritance rate is greater than or equal to 86%, 93.51%, and 87.5%, respectively. The inheritance rate of the three kinds of traditional music after digitization increased in the interval of 56%. After digitization, the inheritance rate of three kinds of traditional music increased in the ranges of 56%-60%, 18.75%-31.25% and 56.25%-81.25%, respectively. This indicates that the inheritance rate of three types of traditional music increases dramatically after being processed by deep learning techniques. Obviously, the support of deep learning technology is indispensable to enhance the inheritance and innovation mechanisms of traditional music culture digitization.

The power cepstrum-based parameters for steady-state visually evoked potential (SSVEP) is proposed. To precisely represent the characteristics of frequency responses of a visually stimulated electroencephalography (EEG) signal, power cepstrum analysis is adopted to estimate the parameters in low-dimensional space. To represent the frequency responses of SSVEP, the log-magnitude spectrum of an EEG signal is estimated by fast Fourier transform. Subsequently, the discrete cosine transform is applied to linearly transform the log-magnitude spectrum into the cepstrum domain, and then generate a set of coefficients. Finally, a Bayesian decision model with a Gaussian mixture model is adopted to classify the responses of SSVEP. The experimental results demonstrated that the proposed approach was able to improve performance compared with previous approaches and was suitable for use in brain computer interface applications.

The increasing demand for clandestine communication in underwater acoustic environment reflects the remarkable growth of research in underwater acoustic communication and networking. Mariners are driven to transmit information covertly in the ocean keeping it hidden from unfriendly users and intruders. This research introduces a novel technique of covert underwater acoustic communication that mimics false killer whale whistles. The secret information is embedded using cepstrum transform to imitate Pseudorca crassidens whistles. This covert communication can be achieved even in the presence of eavesdroppers, who are unable to recognize the communication signal due to unique watermarking characteristics. The proposed model uses machine learning to assess imperceptibility and demonstrates exceptional robustness and improved capacity. To validate the model for secure communication and networks, underwater experiments were conducted, resulting in superior bit error rate and high watermark capacity with a perfect low probability of recognition constraint covert communication.

It is desired to apply deep learning models (DLMs) to assist physicians in distinguishing abnormal/normal lung sounds as quickly as possible. The performance of DLMs depends on feature-related and model-related parameters heavily. In this paper, the relationship between performance and feature-related parameters of a DLM, i.e., convolutional neural network (CNN) is analyzed through experiments. ICBHI 2017 is selected as the lung sounds dataset. The sensitivity analysis of classification performance of the DLM on three parameters, i.e., the length of lung sounds frame, overlap percentage (OP) of successive frames and feature type, is performed. An augmented and balanced dataset is acquired by the way of white noise addition, time stretching and pitch shifting. The spectrogram and mel frequency cepstrum coefficients of lung sounds are used as features to the CNN, respectively. The results of training and test show that there exists significant difference on performance among various parameter combinations. The parameter OP is performance sensitive. The higher OP, the better performance. It is concluded that for fixed sampling frequency 8 kHz, frame size 128, OP 75% and spectrogram feature is optimum under which the performance is relatively better and no extra computation or storage resources are required.

This paper presents the first implementation of a spiking neural network (SNN) for the extraction of cepstral coefficients in structural health monitoring (SHM) applications and demonstrates the possibilities of neuromorphic computing in this field. In this regard, we show that spiking neural networks can be effectively used to extract cepstral coefficients as features of vibration signals of structures in their operational conditions. We demonstrate that the neural cepstral coefficients extracted by the network can be successfully used for anomaly detection. To address the power efficiency of sensor nodes, related to both processing and transmission, affecting the applicability of the proposed approach, we implement the algorithm on specialised neuromorphic hardware (Intel ® Loihi architecture) and benchmark the results using numerical and experimental data of degradation in the form of stiffness change of a single degree of freedom system excited by Gaussian white noise. The work is expected to open a new direction of SHM applications towards non-Von Neumann computing through a neuromorphic approach.

The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector’s hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively.

The diesel engine has been a significant component of large-scale mechanical systems for the intelligent manufacturing industry. Because of its complex structure and poor working environment, it has trouble effectively acquiring the representative fault features. Further, fault diagnosis of the diesel engine faces great challenges. This paper presents a new fault diagnosis method for the detection of diesel engine faults under multiple operation conditions instead of conventional methods confined to a single condition. First, an adaptive correlation threshold process is designed as a preprocessing unit to enhance data quality by weakening non-impact region characteristics. Next, a feature extraction method for sound signals based on the Mel frequency cepstrum (MFC) is improved and introduced into the machinery fault diagnosis. Then, the combination of the improved feature and vibrational mode decomposition (VMD) is proposed to incorporate VMD into an effective adaptive decomposition of non-stationary signals to combine it with an excellent feature representation of the vibration signal. Finally, the vector quantization algorithm is adopted to reduce the feature dimensions and generate codebook model bases, which trains the K-Nearest Neighbor classifiers. Five comparative methods were carried out, and the experimental results show that the proposed method offers a good effect of the common valve clearance fault of diesel engines under different conditions.

In response to the lagging speech recognition capabilities in supplier services, this study integrates speech recognition, speech synthesis, and semantic understanding technologies to improve speech recognition capabilities in different environments. The Mel Cepstral acoustic feature algorithm and deep convolutional neural network model are used to construct speech feature extraction algorithm models, speech recognition acoustic models, and speech training modules, which enhance speech training, processing, recognition, and application capabilities. Integrating the designed model into the company’s business platform has improved the semantic understanding ability in the power grid field and greatly enhanced the standardized management of business data. Through experiments, the speech recognition error rate of the designed solution by our research institute has been reduced to below 1%, greatly improving the speech recognition capability of the supplier center and thereby enhancing the service level of the supplier center.

In this study, an experimental system was built to acquire vibration and acoustic emission (AE) signals from faulted bearings methodology based on cepstrum pre-whitening (CPW), tested for vibration signals, and was applied for both types of signals to compare and enhance results on machining condition monitoring. The methodology was applied to 9 vibration and 9 AE signals from the experimental system database. For the 18 analyzed signals, in 5 the identification of fault components was easily made, in 12 the fault identification was possible, and in 1 the identification was not completed. The comparison between vibration and AE from 9 tests of experimental system results in 6, vibration has a better result than AE, specifically in the inner race and rolling element faults, for the remaining 3 tests that correspond to outer race fault, AE has a better result.