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With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB. [ABSTRACT FROM AUTHOR]

In the context of the Internet of Things, there is a growing demand for real-time monitoring of water distribution systems (WDS). Among the various leak detection methods, acoustic leak detection is considered to be a suitable method. However, existing methods are not very effective in environments with high daytime ambient noise. To address this issue, this paper conducted on-site data collection experiments and designed a monitoring system that combines traditional nighttime monitoring with daytime monitoring, combining water company pipeline inspections and repair work. A large number of daytime audio samples were collected. In this paper, the logarithmic spectrogram (log spectrogram) was used to represent the features of the leak signal. By comparing the features of the signal during day and night, noisy and quiet environments, and leak and normal signals, we identified the interfering frames that required noise reduction, and applied frame-level noise reduction processing to the signal. Based on this, a log PS-ResNet18 model was developed to identify leaks, and its performance was compared with other classification models [including traditional nighttime detection methods, random forests, XGBoost, and convolutional neural network (CNN)]. The results showed that the log PS-ResNet18 model had the best performance, with an all-day accuracy rate of 99.4% and a daytime accuracy rate of 99.3%. In addition, by conducting ablation experiments to explore the role and contribution of the log PS-ResNet18 and noise reduction methods in the model, the results showed that the log spectrogram and noise reduction methods increased the all-day accuracy rate by 18.8% and 23.2%, respectively, and by 24.7% when used together. In another practical application, the log PS-ResNet18 model achieved an all-day detection accuracy rate of 99.6%. This study demonstrated the applicability of the log spectrogram and CNN combination in daytime leak detection, overcoming research limitations in the field. This research presents the log PS-ResNet18 framework, which combines deep learning models and denoised logarithmic spectrograms to improve leak detection in water supply pipelines under daytime environmental noise. The research focuses on field data collection and analysis of cast iron pipes with different diameters in Hangzhou (HZ). The model was tested on cast iron pipes in Lishui (LS) and proved to be effective. The proposed method is highly versatile and can be applied to different regions and pipe materials after sufficient sample collection and model training validation. The research recommends a comprehensive leak monitoring solution that involves initial intelligent detection using front-end noise meters and secondary identification of suspicious audio signals using the log PS-ResNet18 model in the cloud. This enables water utility operators to respond quickly to pipeline leaks, leading to more efficient water resource conservation and improved water supply service quality. [ABSTRACT FROM AUTHOR]

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been successfully applied in many fields. The gradual application of the latest architectures of deep learning in the field of time series forecasting (TSF), such as Transformers, has shown excellent performance and results compared to traditional statistical methods. These applications are widely present in academia and in our daily lives, covering many areas including forecasting electricity consumption in power systems, meteorological rainfall, traffic flow, quantitative trading, risk control in finance, sales operations and price predictions for commercial companies, and pandemic prediction in the medical field. Deep learning-based TSF tasks stand out as one of the most valuable AI scenarios for research, playing an important role in explaining complex real-world phenomena. However, deep learning models still face challenges: they need to deal with the challenge of large-scale data in the information age, achieve longer forecasting ranges, reduce excessively high computational complexity, etc. Therefore, novel methods and more effective solutions are essential. In this paper, we review the latest developments in deep learning for TSF. We begin by introducing the recent development trends in the field of TSF and then propose a new taxonomy from the perspective of deep neural network models, comprehensively covering articles published over the past five years. We also organize commonly used experimental evaluation metrics and datasets. Finally, we point out current issues with the existing solutions and suggest promising future directions in the field of deep learning combined with TSF. This paper is the most comprehensive review related to TSF in recent years and will provide a detailed index for researchers in this field and those who are just starting out. [ABSTRACT FROM AUTHOR]

With the rapid development of the mobile communication and the Internet, the previous web anomaly detection and identificationmodels were built relying on security experts' empirical knowledge and attack features. Although this approach can achieve higher detection performance, it requires huge human labor and resources to maintain the feature library. In contrast, semantic feature engineering can dynamically discover new semantic features and optimize feature selection by automatically analyzing the semantic information contained in the data itself, thus reducing dependence on prior knowledge. However, current semantic features still have the problem of semantic expression singularity, as they are extracted from a single semantic mode such as word segmentation, character segmentation, or arbitrary semantic feature extraction. This paper extracts features of web requests from dual semantic granularity, and proposes a semantic feature fusion method to solve the above problems. The method first preprocesses web requests, and extracts word-level and character-level semantic features of URLs via convolutional neural network (CNN), respectively. By constructing three loss functions to reduce losses between features, labels and categories. Experiments on the HTTP CSIC 2010, Malicious URLs and HttpParams datasets verify the proposedmethod. Results show that compared withmachine learning, deep learningmethods and BERT model, the proposed method has better detection performance. And it achieved the best detection rate of 99.16% in the dataset HttpParams. [ABSTRACT FROM AUTHOR]

This paper presents the latest advances in machine learning techniques and highlights deep learning (DL) methods in recent studies. This technology has recently received great attention as it can solve complex problems. This paper focuses on covering one of the deep learning algorithms (deep neural network) and learning about its types such as convolutional neural network (CNN), Recurrent Neural Networks (RNN), etc. We have discussed recent changes, such as advanced DL technologies. Next, we continue analyzing and discussing the challenges and possible solutions of machine learning, such as big data and object detection, studying more papers in deep learning, and knowing the main experiments and future directions. In addition, this review also identifies some successful deep learning applications in recent years. Moreover, in this paper, one of the deep learning methods, convolutional neural networks, is applied to detect objects in images by using the You Only Look One model and comparing it with RetinaNet using pre-trained models. The results found that CNN (using YOLOv3) is a more accurate model than RetinaNet. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The combination of computer algorithms to diagnose clinical images has attracted more and more attention. This research aims to improve the efficiency of gastric cancer (GC) diagnosis, so deep learning (DL) algorithms are tentatively used to assist doctors in the diagnosis of gastric cancer. In the experiment, the collected 3591 gastroscopic images were divided into network training set and experimental verification test set. The lesion samples in the image are all marked by many endoscopists with many years of clinical experience. In order to improve the experimental effect, 5261 endoscopic images were obtained by expanding the training set. Then the obtained training set is input into the convolutional neural network (CNN) for training, and finally get the algorithm model DLU‐Net. By inputting 598 test set samples into the CNN constructed in this paper, five results such as advanced GC, early GC, precancerous lesions, normal and benign lesions can be identified and output, with a total accuracy of 94.1%. It can be concluded that the DL algorithm model constructed in this paper can effectively identify the staging characteristics of cancer as well as gastroscopic images, greatly improve efficiency, and effectively assist physicians in the diagnosis of GC under gastroscopy. [ABSTRACT FROM AUTHOR]

Utilizing multi-modal data, as opposed to only hyperspectral image (HSI), enhances target identification accuracy in remote sensing. Transformers are applied to multi-modal data classification for their long-range dependency but often overlook intrinsic image structure by directly flattening image blocks into vectors. Moreover, as the encoder deepens, unprofitable information negatively impacts classification performance. Therefore, this paper proposes a learnable transformer with an adaptive gating mechanism (AGMLT). Firstly, a spectral–spatial adaptive gating mechanism (SSAGM) is designed to comprehensively extract the local information from images. It mainly contains point depthwise attention (PDWA) and asymmetric depthwise attention (ADWA). The former is for extracting spectral information of HSI, and the latter is for extracting spatial information of HSI and elevation information of LiDAR-derived rasterized digital surface models (LiDAR-DSM). By omitting linear layers, local continuity is maintained. Then, the layer Scale and learnable transition matrix are introduced to the original transformer encoder and self-attention to form the learnable transformer (L-Former). It improves data dynamics and prevents performance degradation as the encoder deepens. Subsequently, learnable cross-attention (LC-Attention) with the learnable transfer matrix is designed to augment the fusion of multi-modal data by enriching feature information. Finally, poly loss, known for its adaptability with multi-modal data, is employed in training the model. Experiments in the paper are conducted on four famous multi-modal datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and Houston2013 (HU). The results show that AGMLT achieves optimal performance over some existing models. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches. [ABSTRACT FROM AUTHOR]

The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town's collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies. [ABSTRACT FROM AUTHOR]

The goal of automatic image captioning in computer vision and natural language processing is to provide precise and insightful image captions. The paper provides a novel approach for automatic image captioning, which combines an attention mechanism with a deep learning model based on gated recurrent units (GRUs). Using the attention mechanism, which dynamically weighs the image's attributes, the model may focus on relevant portions of the image during the caption production process. This makes it easier to match related words in the generated captions with the features of the image. The output captions accurately depict word relationships and mimic the sequential structure of natural language through the application of recurrent neural networks of the GRU variety. With a large collection of images and captions, the network is taught to generate rational and contextually relevant descriptions for various image types. By assessing the proposed model with widely-used metrics such as BLEU, METEOR, ROUGE, and CIDEr, we demonstrate its ability to generate high-quality captions. The findings show that the approach outperforms baseline techniques, highlighting the advantages of combining GRU with an attention mechanism for image captioning. The method produces captions that are accurate and convey a deeper understanding of the visual content in the photos, making it highly applicable in real-world applications such as image interpretation, accessibility and content suggestion. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (FPGA), and implements a one-dimensional (1D) convolutional neural network (CNN) and long short-term memory (LSTM) neural network (NN) model for radar emitter signal recognition, directly targeting the intermediate frequency (IF) data of radar emitter signal for classification and recognition. The implementation of the 1D-CNN-LSTM neural network on FPGA is realized by multiplexing the same systolic array to accomplish the parallel acceleration of 1D convolution and matrix vector multiplication operations. We implemented our network on Xilinx XCKU040 to evaluate the effectiveness of our proposed solution. Our experiments show that the system can achieve 7.34 giga operations per second (GOPS) data throughput with only 5.022 W power consumption when the radar emitter signal recognition rate is 96.53%, which greatly improves the energy efficiency ratio and real-time performance of the radar emitter recognition system. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoTs) technology is being seriously impeded by insecure networks and data. An effective intrusion detection model is essential for safeguarding the network and data security of IoTs. In this paper, a hybrid parallel intrusion detection model based on deep learning (DL) called HPIDM features a three-layer parallel neural network structure. Combining stacked Long short-term memory (LSTM) neural networks with convolutional neural network (CNN) and SK Net self-attentive mechanism in the model allows HPIDM to learn temporal and spatial features of traffic data effectively. HPIDM fuses the acquired temporal and spatial feature data and then feeds it into the CosMargin classifier for classification detection to reduce the impact of data imbalance on the performance of the Intrusion Detection System (IDS). Finally, HPIDM was experimentally compared with classical intrusion detection models and the two comparative models designed in this paper, and the experimental results show that HPIDM achieves 99.87% accuracy on the ISCX-IDS 2012 dataset and 99.94% accuracy on the CICIDS 2017 dataset. In addition, it outperforms other comparable models in terms of recall, precision, false alarm rate (FAR), and F1 score, showing its feasibility and superiority. [ABSTRACT FROM AUTHOR]

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from −20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic air-to-ground (A2G) channel model selection method based on machine learning (ML) using digital surface model (DSM) terrain data. In order to verify whether a communication network for a new non-terrestrial user service such as Urban Air Mobility (UAM) satisfies the required performance, it is necessary to perform a simulation reflecting the characteristics of the corresponding terrain environments as accurately as possible. For this simulation, A2G channel models corresponding to various terrain environments and a method of automatically classifying the terrain type of the simulation area must be provided. Many A2G channel models based on actual measurement results exist, but the practical automatic topography classification method still needs to be developed. This paper proposes the first practical automatic topography classification method using a two-step neural network-based classifier utilizing various geographic feature data as input. Since there is no open topography dataset to evaluate the accuracy of the proposed method, we built a new dataset for five topography classes that reflect the characteristics of Korea's topography, which is also a contribution of our study. The simulation results using the new data set show that the proposed ML-based method could increase the selection accuracy compared to the technique for direct classification by humans or the existing cross-correlation-based classification method. Since the proposed method utilizes the DSM data, open to the public, it can easily reflect the different terrain characteristics of each country. Therefore, the proposed method can be effectively used in the realistic performance evaluation of new non-terrestrial communication networks utilizing vast airspace such as UAM or 6G mobile communications. [ABSTRACT FROM AUTHOR]