Recently, business protocol discovery has taken more attention in the field of web services. This activity permits a better description of the web service by giving information about its dynamics. The latter is not supported by theWSDL language which concerns only the static part. The problem is that the only information available to construct the dynamic part is the set of log files saving the runtime interaction of the web service with its clients. In this paper, a new approach based on the Discrete Wavelet Transformation (DWT) is proposed to discover the business protocol of web services. The DWT allows reducing the problem space while preserving essential information. It also overcomes the problem of noise in the log files. The proposed approach has been validated using artificially-generated log files.

Acquiring labels in anomaly detection tasks is expensive and challenging. Therefore, as an effective way to improve efficiency, pretraining is widely used in anomaly detection models， which enriches the model's representation capabilities, thereby enhancing both performance and efficiency in anomaly detection. In most pretraining methods, the decoder is typically randomly initialized. Drawing inspiration from the diffusion model, this paper proposed to use denoising as a task to pretrain the decoder in anomaly detection, which is trained to reconstruct the original noise-free input. Denoising requires the model to learn the structure, patterns, and related features of the data, particularly when training samples are limited. This paper explored two approaches on anomaly detection: simultaneous denoising pretraining for encoder and decoder, denoising pretraining for only decoder. Experimental results demonstrate the effectiveness of this method on improving model’s performance. Particularly, when the number of samples is limited, the improvement is more pronounced.

Performance of standard Direction of Arrival (DOA) estimation techniques degraded under real-time signal conditions. The classical algorithms are Multiple Signal Classification (MUSIC), and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT). There are many signal conditions hamper on its performance, such as closely spaced and coherent signals caused due to the multipath propagations of signals results in a decrease of the signal to noise ratio (SNR) of the received signal. In this paper, a novel DOA estimation technique named CW-PCA MUSIC is proposed using Principal Component Analysis (PCA) to threshold the nearby correlated wavelet coefficients of Dual-Tree Complex Wavelet transform (DTCWT) for denoising the signals before applying to MUSIC algorithm. The proposed technique improves the detection performance under closely spaced, and coherent signals with relatively low SNR conditions. Also, this method requires fewer snapshots, and less antenna array elements compared with standard MUSIC and wavelet-based DOA estimation algorithms.

The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.

The effective utilisation of monitoring data of the coal mine is the core of realising intelligent mine. The complex and challenging underground environment, coupled with unstable sensors, can result in “dirty” data in monitoring information. A reliable data cleaning method is necessary to figure out how to extract high-quality information from large monitoring data sets while minimising data redundancy. Based on this, a cleaning method for sensor monitoring data based on stacked denoising autoencoders (SDAE) is proposed. The sample data of the ventilation system under normal conditions are trained by the SDAE algorithm and the upper limit of reconstruction errors is obtained by Kernel density estimation (KDE). The Apriori algorithm is used to study the correlation between monitoring data time series. By comparing reconstruction errors and error duration of test data with the upper limit of reconstruction error and tolerance time, cooperating with the correlation rule, the “dirty” data is resolved. The method is tested in the Dongshan coal mine. The experimental results show that the proposed method can not only identify the dirty data but retain the faulty information. The research provides effective basic data for fault diagnosis and disaster warning.

In situ time series measurements of ocean ambient noise, have been made in deep waters of the Arabian Sea, using an autonomous passive acoustic monitoring system deployed as part of the Ocean Moored buoy network in the Northern Indian Ocean (OMNI) buoy mooring operated by the National Institute of Ocean Technology (NIOT), in Chennai during November 2018 to November 2019. The analysis of ambient noise records during the spring (April–June) showed the presence of dolphin whistles but contaminated by unwanted impulsive shackle noise. The frequency contours of the dolphin whistles occur in narrow band in the range 4–16 kHz. However, the unwanted impulsive shackle noise occurs in broad band with the noise level higher by ∼20 dB over the dolphin signals, and it reduces the quality of dolphin whistles. A wavelet based threshold denoising technique followed by a subtraction method is implemented. Reduction of unwanted shackle noise is effectively done and different dolphin whistle types are identified. This wavelet denoising approach is demonstrated for extraction of dolphin whistles in the presence of challenging impulsive shackle noise. Furthermore, this study should be useful for identifying other cetacean species when the signal of interest is interrupted by unwanted mechanical noise.

With improved technological successions, wireless communication applications have been incessantly evolving. Owing to the challenges posed by the multipath wireless channel, radio design prototypes have become elemental in all wireless systems before deployment. Further, different signal processing requirements of the applications, demand a highly versatile and reconfigurable radio such as Software Defined Radio (SDR) as a crucial device in the design phase. In this paper, two such SDR modules are used to develop an Orthogonal Frequency Division Multiplexing (OFDM) wireless link, the technology triumphant ever since 4G. In particular, a non-coherent end-to-end OFDM wireless link is developed in the Ultra High Frequency (UHF) band at a carrier frequency of 470 MHz. The transmitter includes Barker sequences as frame headers and pilot symbols for channel estimation. At the receiver, pulse alignment using Max energy method, frame synchronization using sliding correlator approach and carrier offset correction using Moose algorithm are incorporated. In addition, wireless channel is estimated using Least Square (LS) based pilot aided channel estimation approach with denoising threshold and link performance is analyzed using average Bit Error Rate (BER), in different pilot symbol scenarios. In a typical laboratory environment, the results of BER versus receiver gain show that with 4 pilot symbols out of 128 carriers, at a gain of 20 dB, BER is 0.160922, which is reduced to 0.136884 with 16 pilot symbols. The developed link helps OFDM researchers to mitigate different challenges posed by the wireless environment and thereby strengthen OFDM technology.

Due to the characteristics of color vegetation canopy images which have multiple details and Gaussion noise interference, the adaptive mean filtering (AMF) algorithm is used to perform the denoising experiments on noised images in RGB and YUV color space. Based on the single color characteristics of color vegetation canopy images, a simplified AMF algorithm is proposed in this paper to shorten the overall running time of the denoising algorithm by simplifying the adaptive denoising processing of the component V, which contains less image details. Experimental results show that this method can effectively reduce the running time of the algorithm while maintaining a good denoising effect.