This paper mainly proposes a line segment detection method based on pseudo peak suppression and local Hough transform, which has good noise resistance and can solve the problems of short line segment missing detection, false detection, and oversegmentation. In addition, in response to the phenomenon of uneven development in nuclear emulsion tomographic images, this paper proposes an image preprocessing process that uses the “Difference of Gaussian” method to reduce noise and then uses the standard deviation of the gray value of each pixel to bundle and unify the gray value of each pixel, which can robustly obtain the linear features in these images. The tests on the actual dataset of nuclear emulsion tomographic images and the public YorkUrban dataset show that the proposed method can effectively improve the accuracy of convolutional neural network or vision in transformer-based event classification for alpha-decay events in nuclear emulsion. In particular, the line segment detection method in the proposed method achieves optimal results in both accuracy and processing speed, which also has strong generalization ability in high quality natural images.

In many screen to camera communication (S2C) systems, the barcode preprocessing method is a significant prerequisite because barcodes may be deformed due to various environmental factors. However, previous studies have focused on barcode detection under static conditions; to date, few studies have been carried out on dynamic conditions (for example, the barcode video stream or the transmitter and receiver are moving). Therefore, we present a detection and tracking method for dynamic barcodes based on a Siamese network. The backbone of the CNN in the Siamese network is improved by SE-ResNet. The detection accuracy achieved 89.5%, which stands out from other classical detection networks. The EAO reaches 0.384, which is better than previous tracking methods. It is also superior to other methods in terms of accuracy and robustness. The SE-ResNet in this paper improved the EAO by 1.3% compared with ResNet in SiamMask. Also, our method is not only applicable to static barcodes but also allows real-time tracking and segmentation of barcodes captured in dynamic situations.

It is shown that simple preprocessing on the reference signals in multichannel feedforward ANC system can improve the convergence performance of the adaptive ANC algorithm. A fast and efficient blind preprocessing algorithm in frequency domain is proposed to reduce the computational complexity even that the reference sensors are located far from the noise sources. The permutation problem at different frequency bin is also addressed and solved by an independent vector analysis algorithm. The basic principle and performance comparison are given to verify our conclusion.

In order to reduce the convergence time in an iterative procedure, some gradient based preliminary processes are employed to eliminate outliers. The adaptive variable block size is also introduced to balance the accuracy and computational complexity. Moreover, the use of Canberra distance instead of Euclidean distance illustrates higher performance in measuring motion similarity.

Direction of arrival estimation of coherent multipath waves by using superresolution technique often requires decorrelation preprocessings. Spatial smoothing preprocessings are the most popular schemes as the techniques. In mobile environment, position change of the target/transmitter often brings us decorrelation effect. In addition, multiple signals transmitted by an antenna array, such as a MIMO transmitter, can also cause the same effect. These effects can be categorized as the spatial smoothing preprocessing at the transmitter. In this paper, we analyze the spatial smoothing effect at the transmitter in the presence of multipath coherent waves. Theoretical and simulation results show that the spatial smoothing at the transmitter has a good feature in comparison with the conventional SSP at the receiving array. We also show that better decorrelation performance can be obtained when the SSPs at the transmitter and receiving array are applied simultaneously.

The subspace algorithm can be utilized for the blind detection of space-time block codes (STBC) without knowledge of channel state information (CSI) both at the transmitter and receiver. However, its performance degrades when the channels are correlated. In this letter, we analyze the impact of channel correlation from the orthogonality loss between the transmit signal subspace (TSS) and the statistical noise subspace (SNS). Based on the decoding property of the subspace algorithm, we propose a revised detection in favor of the channel correlation matrix (CCM) only known to the receiver. Then, a joint transmit-receive preprocessing scheme is derived to obtain a further performance improvement when the CCM is available both at the transmitter and receiver. Analysis and simulation results indicate that the proposed methods can significantly improve the blind detection performance of STBC over the correlated channels.

In various applications of signals transmission and processing, there is always a possibility of loss of samples. The iterative algorithm of Papoulis-Gerchberg (PG) is famous for solving the band-limited lost samples recovery problem. Two problems are known in this domain: (1) a band-limited signal practically is difficult to be obtained and (2) the convergence rate is too slow. By inserting a subtraction by a polynomial in the PG algorithm, using boundary-matched concept, a significant increase in performance and speed of its convergence has been achieved. In this paper, we propose an efficient approach to restore lost samples by adding a preprocess which meets the periodic boundary conditions of Fast Fourier transform in the iteration method. The accuracy of lost samples reconstruction by using the PG algorithm can be greatly improved with the aid of mapping the input data sequence of satisfying the boundary conditions. Further, we also developed another approach that force the signal to meet a new critical boundary conditions in Fourier domain that make the parameters of the preprocessing can be easily obtained. The simulation indicates that the mean square error (MSE) of the recovery and the convergence rate with the preprocess concept is better and faster than the one without preprocess concept. Our both proposed approaches can also be applied to other cases of signal restoration, which allow Cadzow's iterative processing method, with more convenience and flexibility.

The 2-terminal shortest path problem is to find a shortest path between two specified vertices in a given graph G. In this paper, we consider this problem in the following situation: G is given before the two vertices are specified so that some preprocessing is allowed to reduce the response time. We present a method for calculating lower bounds of the length of the shortest path for any pair of vertices. Experimental results show that the A* algorithm with our method performs much better than previous methods.

In this paper, a preprocessing approach to improving the quality of the music on a mobile phone using Enhanced variable rate codec (EVRC) is presented. Our approach works well on music signals considerably reducing the number of time-clipped frames.

An optimization of the smoothing preprocessing for the correlated signal parameter estimation was considered. Although the smoothing factor (the number of subarrays) is a free parameter in the smoothing preprocessing, a useful strategy to determine it has not yet been established. In this paper, we investigated thoroughly about the smoothing factor and also proposed a new scheme to optimize it. The proposed method, using the smoothed equivalent diversity profile (SED profile), is able to evaluate the effect of smoothing preprocessing without any a priori information. Therefore, this method is applicable in the real multipath parameter estimation.

Current computing systems are too slow for information processing because of the huge number of procedural steps required. A decrease in the number of calculation steps is essential for real-time information processing. We have developed two kinds of novel architectures for automatic elimination of redundant calculation steps. The first architecture employs the new digit-serial algorithm which eliminates redundant lower digit calculations according to the most-significant-digit-first (MSD-first) digit-serial calculation scheme. Basic components based on this architecture, which employ the redundant number system to limit carry propagation, have been developed. The MSD-first sequential vector quantization processor (VQP) is 3.7 times faster than ordinary digital systems as the result of eliminating redundant lower-bit calculation. The second architecture realizes a decrease in the number of complex calculation steps by excluding useless data before executing the complex calculations according to the characterized value of the data. About 90% of Manhattan-distance (MD) calculations in VQP are excluded by estimating the MD from the average distance.

A superresolution technique is considered for use in antenna gain measurements. A modification of the MUSIC algorithm is employed to resolve incident signals separately in the time domain. The modification involves preprocessing the received data using a spatial scheme prior to applying the MUSIC algorithm. Interference rejection in the antenna measurements using the fast Fourier transform (FFT) based techniques have been realized by a recently developed vector network analyzer, and its availability has been reported in the literature. However, response resolution in the time domain of these conventional techniques is limited by the antenna bandwidth. The MUSIC algorithm has the advantage of being able to eliminate unwanted responses when performing antenna measurements in situations where the antenna band-width is too narrow to support FFT based techniques. In this paper, experimental results of antenna gain measurements in a multipath environment show the accuracy and resolving power of this technique.