IET Signal Processing最新文献

With the increase of large shopping malls, there are many large parking spaces in complex environments, which increases the difficulty of finding vehicles in such environments. To upgrade the consumer's experience, some car manufacturers have proposed detecting parking space numbers in parking spaces. The detection of parking space number in parking spaces in complex environments has problems such as the diversified background of parking space numbers, tilted direction of parking space numbers, and small parking space number scale. Since no scholar has proposed a high-performance method for such problems, a parking space number detection model based on the multi-branch convolutional attention is presented. Firstly, using ResNet50 as the backbone network, a multi-branch convolutional structure is proposed in the backbone network, which aims to process and fuse the feature map through three parallel branches, and enhance the network to represent ability information by convolutional attention, learn global features to selectively strengthen the features containing helpful information, and improve the ability of the model to detect the parking space number area. Secondly, a high-level feature enhancement unit is designed to adjust the features channel by channel, obtain more spatial correlation, and reduce the loss of information in the process of feature map generation. The data results of the model on the parking space number dataset CCAG show that the precision, recall, and F-measure are 84.8%, 84.6%, and 84.7%, respectively, which has certain advantages for parking space number detection.

Schizophrenia is a disease that affects approximately 1% of the population. Its early accurate diagnosis is of vital importance to apply adequate therapy as soon as possible. We present a Statistical Discriminant Diagnosing (SDD) system that discriminates between healthy controls and subjects and that supports diagnosis by a medical professional. The system works with {feature, electrode} EEG pairs which are selected based on the statistical significance of the p-values computed over the brain P3b wave. A bank of evoked potential pre-processed and filtered EEG signals is recorded during an auditory odd-ball (AOD) task and serves as input to the SDD system. These EEG signals comprise 20 features and 17 electrodes, both in time (t) and frequency (f) domain. The relevance of the Parieto-Temporal region is shown, allowing us to identify highly discriminant {feature, electrode} pairs in the detection of schizophrenia, resulting lower p-values in both Right and Left Hemispheres, as well as in Parieto-Temporal EEG signals. See for instance, the {PSE, P4} pair, with p-value = 0.00003 for (parametric) t Student and p-value = 0.00019 for (nonparametric) U Mann-Whitney tests, both under the 15 Hz cutoff frequency of a low pass EEG preprocessing filter. The relevance of this pair is in agreement with previously published related results. The proposed SDD system may provide the human expert (psychiatrist) with an objective complimentary information to help in the early diagnosis of schizophrenia.

In this paper, a new sparsity-optimised Farrow structure variable fractional delay (SFS-VFD) filter is proposed to address the aperture effect in wideband array. Our method is based on coefficient (anti-)symmetry and optimises the number and orders of its sub-filters, greatly reducing the non-zero coefficients. The established cost function is formulated as a parametric minimisation problem with multiple regularisation constraints, and solved by the modified three-block alternating direction multiplier method (MTB-ADMM), which is improved by introducing core variable correction items to ensure stable and fast convergence. Experimental results show that the SFS-VFD filter reduces the complexity of the system by decreasing the use of multipliers and adders while ensuring high delay accuracy. In wideband array, the SFS-VFD filter effectively corrects the aperture effect and achieves precise beam pointing.

With a focus on integrated sensing, communication, and computation (ISCC) systems, multiple sensor devices collect information of different objects and upload it to data processing servers for fusion. Appearance gaps in composite images caused by distinct capture conditions can degrade the visual quality and affect the accuracy of other image processing and analysis results. The authors propose a fused-image harmonisation method that aims to eliminate appearance gaps among different objects. First, the authors modify a lightweight image harmonisation backbone and combined it with a pretrained segmentation model, in which the extracted semantic features were fed to both the encoder and decoder. Then the authors implement a semantic-related background-to-foreground style transfer by leveraging spatial separation adaptive instance normalisation (SAIN). To better preserve the input semantic information, the authors design a simple and effective semantic-aware adaptive denormalisation (SADE) module. Experimental results demonstrate that the authors’ proposed method achieves competitive performance on the iHarmony4 dataset and benefits from the harmonisation of fused images with incompatible appearance gaps.

The range and azimuth information of a target can be obtained after coherent pulse accumulation of the traditional multiframe stepped-frequency (SF) synthesis wideband echo and spectrum analysis, and high-resolution two-dimensional imaging of the target can be achieved. However, the accumulation of a certain number of pulses requires a long beam dwell time, which cannot meet real-time imaging requirements for high-speed radar moving platforms. To solve the above problems, a scanning imaging mode is proposed by combining forward-looking imaging and scanning imaging, and a target echo signal model with the structure of scanning stepped-frequency is constructed. The SF pulses are grouped and transmitted according to the scanning order, and the echo pulses are sorted and reorganised. After the timing compensation and range Doppler coupling compensation are completed, the target is located and projected. The proposed imaging mode can achieve high-resolution scanning forward-looking imaging and can basically attain an azimuth resolution of approximately 0.1° within the forward-looking scanning range. This imaging mode has higher real-time performance and a larger target imaging range than the traditional methods. Moreover, the simulation results showed good performance via the scanning imaging method.

In non-cooperative scenarios, the spreading sequences or waveforms of the direct sequence spread spectrum (DSSS) signals is unknown to the receiver. This paper focuses on addressing the problem of blind estimation of the spreading waveform under multipath channels. In the scenario of direct signal path transmission, the spreading sequences can be directly obtained based on the estimated spreading waveforms. However, in the presence of multipath channels, the spreading waveform becomes the convolution of the spreading sequence and channel response, thus deconvolution should also be performed after estimating the spreading waveforms. In order to perform blind despreading and deconvolution of asynchronous multiuser DSSS signals under multipath channels, the authors propose to exploit the finite symbol characteristics of information and spreading sequences and then the iterative least square with projection method is adopted. Besides, the Cramer-Rao bound of spreading waveforms is derived in such a circumstance as a performance benchmark. The effectiveness of the proposed method is verified via simulation experiments.

It is difficult for a receiver to intercept the signals from a radar system that can emit low probability of intercept (LPI) polyphase coded signals. The traditional Wigner Hough transform (WHT) algorithm requires a large amount of computation and takes a long time to estimate the parameters of the LPI radar polyphase coded signals. To address this problem, an iterative angle search (IAS) algorithm, which when used in combination with the WHT algorithm significantly reduces the computational cost is proposed. When the signal-to-noise ratio is in the range of −4 to 20 dB, the carrier frequency, number of subcodes, and number of cycles of the carrier frequency per subcode of five polyphase coded signals, namely, Frank, P1, P2, P3, and P4, are accurately estimated in simulation experiments. Based on the selected IAS algorithm parameters, the estimation accuracy of the proposed method is the same as that of the traditional WHT algorithm. However, the operation time is only 5.14% of that of the traditional method. The IAS algorithm has certain application prospects. Experiments indicate that the proposed algorithm provides excellent performance and can rapidly and accurately estimate the parameters of LPI polyphase codes.

Human speech signals may contain specific information regarding a speaker's characteristics, and these signals can be very useful in applications involving interactive voice response (IVR) and automatic speech recognition (ASR). For IVR and ASR applications, speaker classification into different ages and gender groups can be applied in human–machine interaction or computer-based interaction systems for customised advertisement, translation (text generation), machine dialog systems, or self-service applications. Hence, an IVR-based system dictates that ASR should function through users' voices (specific voice-frequency bands) to identify customers' age and gender and interact with a host system. In the present study, we intended to combine a pitch detection (PD)-based extractor and a voice classifier for gender identification. The Yet Another Algorithm for Pitch Tracking (YAAPT)-based PD method was designed to extract the voice fundamental frequency (F₀) from non-stationary speaker's voice signals, allowing us to achieve gender identification, by distinguishing differences in F₀ between adult females and males, and classify voices into adult and children groups. Then, in vowel voice signal classification, a one-dimensional (1D) convolutional neural network (CNN), consisted of a multi-round 1D kernel convolutional layer, a 1D pooling process, and a vowel classifier that could preliminary divide feature patterns into three level ranges of F₀, including adult and children groups. Consequently, a classifier was used in the classification layer to identify the speakers' gender. The proposed PD-based extractor and voice classifier could reduce complexity and improve classification efficiency. Acoustic datasets were selected from the Hillenbrand database for experimental tests on 12 vowels classifications, and K-fold cross-validations were performed. The experimental results demonstrated that our approach is a very promising method to quantify the proposed classifier's performance in terms of recall (%), precision (%), accuracy (%), and F1 score.

It is anticipated that sixth-generation (6G) systems would present new security challenges while offering improved features and new directions for security in vehicular communication, which may result in the emergence of a new breed of adaptive and context-aware security protocol. Physical layer security solutions can compete for low-complexity, low-delay, low-footprint, adaptable, extensible, and context-aware security schemes by leveraging the physical layer and introducing security controls. A novel physical layer security scheme that employs the concept of radio frequency fingerprinting (RF-FP) for location estimation is proposed, wherein the RF-FP values are collected at different points with in the cell. Then, based on the estimated location, the nearest possible road-side unit for sending the information signal is located. After this, the effects on secrecy capacity (SC) and secrecy outage probability (SOP) in the presence of multiple eavesdropper per unit time are analysed. It has been shown via simulations that the proposed RF-FP scheme increases SC by up to 25% for the same signal-to-noise ratio (SNR) values as those of the benchmarks, while the SOP tends to decrease by up to 30% as compared to the benchmark scheme for the same SNR value. Thus, the proposed RF-FP-based location estimation provides much better results as compared to the existing physical layer security schemes.

This article deals with peak to average power (PAPR) reduction in a single and multi-user orthogonal chirp division multiplexing (OCDM) context. Two methods for PAPR reduction based on the selection of the frequency variation (up or down) of the chirps are first presented in a single user system. The first technique consists in considering two OCDM signals generated with up and down chirps, respectively, and selecting the one offering lowest PAPR. The second PAPR reduction method is based on usual clipping, and in that case the chirp selection aims to reduce the clipping noise. An adapted receiver is presented, based on the maximum likelihood estimation of the frequency variation (up or down) of the chirp. Then, a general procedure for multi-user OCDM transmission is introduced, where a sub-band of the available bandwidth is dedicated to each user, whose frequency of the chirps varies within this sub-band. Next, the PAPR reduction techniques are generalised to this multi-user OCDM system. Moreover, a performance analysis of the first PAPR reduction method is developed, and it is shown through simulations that theoretical and numerical results match for both Nyquist rate and oversampled signals. It is also shown that the chirp selection reduces the clipping noise, and improves the bit error rate performance compared with clipping only.