Signal Recognition Research Articles

Recognition and classification of noise signals by dolphins under conditions of noise interference and spatial uncertainty of their simultaneous presentation

The ability of dolphin`s auditory system to recognize and classify noise signals according to certain invariant features under the influence of noise interference and spatial uncertainty of their simultaneous presentation was studied. The bottlenose dolphins trained to differentiate such signals had to solve this problem under conditions simulating real marine ones, when the perception of a useful signal occurs against the background of similar signals and noise interference. First, the noise signals were sequentially presented to the animal against the background of white masking noise. Further the dolphin had to identify a signal of a positive class from several simultaneously sounding sound sources. The efficiency of the animal was evaluated at several given levels of noise interference. In this case, the actual noise interference was both white noise and simultaneously sounding negative signals. It`s shown that the efficiency and noise immunity of dolphin`s auditory system depend on the degree of alternativeness of the spatial uncertainty of the simultaneous presentation of signals.

Recognition of Underwater Acoustic Radar Signals Based on Multiresolution and Dense Convolutional Neural Network

Recognition of Underwater Acoustic Radar Signals Based on Multiresolution and Dense Convolutional Neural Network

Adversarial attacks in signature verification: a deep learning approach

Handwritten signature recognition in forensic science is crucial for identity and document authentication. While serving as a legal representation of a person’s agreement or consent to the contents of a document, handwritten signatures de termine the authenticity of a document, identify forgeries, pinpoint the suspects and support other pieces of evidence like ink or document analysis. This work focuses on developing and evaluating a handwritten signature verification sys tem using a convolutional neural network (CNN) and emphasising the model’s efficacy using hand-crafted adversarial attacks. Initially, handwritten signatures have been collected from sixteen volunteers, each contributing ten samples, fol lowed by image normalization and augmentation to boost synthetic data samples and overcome the data scarcity. The proposed model achieved a testing accu racy of 91.35% using an 80:20 train-test split. Additionally, using the five-fold cross-validation, the model achieved a robust validation accuracy of nearly 98%. Finally, the introduction of manually constructed adversarial assaults on the sig nature images undermines the model’s accuracy, bringing the accuracy down to nearly 80%. This highlights the need to consider adversarial resilience while designing deep learning models for classification tasks. Exposing the model to real look-alike fake samples is critical while testing its robustness and refining the model using trial and error methods.

Advanced Algorithms for Offline Signature Recognition and Enhanced Forgery Detection Mechanisms

In contemporary society, signatures hold significant importance in various critical documents such as bank cheques, passports, and driver's licenses. Unfortunately, they can be counterfeited through various means, giving rise to issues like fraudulent identifications, identity theft, and cyber-attacks. To mitigate this problem, our project is centered around the development of a system that discerns the authenticity of a signature, distinguishing between genuine and forged signatures within a dataset. We have employed Convolutional Neural Networks (CNN) and deep learning for this purpose. This choice is driven by the understanding that signatures evolve over time due to a range of behavioral factors like age, mental state, and physical well-being. Consequently, our system is designed to adapt and learn from diverse training datasets, enhancing its accuracy in detection. While online and offline signature verification methods exist, our project primarily focuses on the latter, specifically targeting the detectionof forged offline signatures.

Research and Analysis of Deep Learning in Environmental Perception for Autonomous Driving

Autonomous driving technology, as a revolutionary development in the transportation field, relies on efficient and reliable environmental perception systems. Deep learning plays a key role in the environmental perception of autonomous driving, with its capabilities widely applied in areas such as signal and sign recognition, semantic segmentation, instance segmentation, and SLAM (Simultaneous Localization and Mapping). This study conducts research and analysis on the aforementioned key technologies. It concludes that while deep learning technology has shown excellent performance in the perceptual systems of autonomous driving, it still faces challenges that require further research and resolution, such as generalization performance, eal-time requirements, and safety issues.

Structural Basis for Chemerin Recognition and Signaling Through Its Receptors

Chemerin is a chemotactic adipokine that participates in a multitude of physiological processes, including adipogenesis, leukocyte chemotaxis, and neuroinflammation. Chemerin exerts biological functions through binding to one or more of its G protein-coupled receptors (GPCRs), namely chemokine-like receptor 1 (CMKLR1), G protein-coupled receptor 1 (GPR1), and CC-motif receptor-like 2 (CCRL2). Of these receptors, CMKLR1 and GPR1 have been confirmed as signaling receptors of chemerin, whereas CCRL2 serves as a chemerin-binding protein without transmembrane signaling. High-resolution structures of two chemerin receptors are now available thanks to recent advancements in structure biology. This review focuses on the structural perspectives of the chemerin receptors with an emphasis on the structure–activity correlation, including key components of the two receptors for ligand recognition and conformational changes induced by chemerin and its derivative peptides for G protein activation. There are also comparisons between the two chemerin receptors and selected GPCRs with peptide ligands for better appreciation of the shared and distinct features of the chemerin receptors in ligand recognition and transmembrane signaling, and in the evolution of this subclass of GPCRs.

Clinical characteristics of idiopathic inflammatory myopathies related to anti-SRP: a single center experience

Idiopathic inflammatory myopathy (IIM) with autoantibodies recognizing the signal recognition particle (SRP) is characterized by prominent proximal weakness, infrequent extramuscular involvement, dramatically elevated creatine kinase levels, and myofiber necrosis with few inflammatory cell infiltrates in muscle tissue. To enhance understanding of the clinically used diagnosis and treatment of this disease, this study presents a single-center experience and analysis of a Chinese cohort with anti-SRP IIM. The most recent European Neuromuscular Center criteria were used to include Anti-SRP IIM patients from September 2016 to November 2019. Prior to treatment initiation, all sera were collected for the detection of anti-SRP autoantibodies and other myositis-related autoantibodies. Muscle strength, concurrent autoimmune conditions, comorbidities like interstitial lung disease (ILD), treatment outcomes, and follow-up results were also documented. Univariate logistic regression was employed to determine factors affecting prognosis. Among 271 patients with IIM, we identified 23 (8.5%) patients with anti-SRP IIM. Lower limb muscle weakness was frequently more severe. Interstitial lung disease (ILD) was observed in 50% of anti-SRP IIM patients. The presence of ILD may serve as a predictor of a poor prognosis, as revealed by univariate logistic regression analysis (odds ratio, 3.8, 95% CI: 1.0-6.8, p = 0.05).This Chinese Anti-SRP IIM cohort from Hunan province seems to have higher incidences of ILD, and associated ILD may be a risk factor for a poor prognosis. To fully understand the specific role of the anti-SRP autoantibody in this unique subset with ILD, further research is necessary.

Exploring deceptions: cognitive strategies and dynamics in espionage

Abstract The paper examines deceptions employed during the Cold War with the purpose of exploring their underlying conceptions, mechanisms, and applications. It focuses on the interactions between the deceiver and the deceived, analyzing both sides’ perspectives. The examination centers on declassified guides, recently published as The Official C.I.A. Manual of Trickery and Deception, which contain detailed instructions concerning how spies should carry out their activities of deception. Three kinds of procedures were selected for analysis: behavior in public for men and women, group infiltration, and the use of secret recognition signals. To provide a practical visualization, the 2012 movie Argo, based on a real-life operation using the same principles, is complementarily analyzed. Merleau-Ponty’s approach to perception and sensation is considered along with Husserl’s concepts of sedimentation and lifeworld, while narrative semiotics and its framework guide the treatment of textuality and the approach to persuasion. The article conceives of sedimentation and contemplation of the lifeworld as mandatory components of a kind of deception that is primarily acted, reflecting upon how the spies’ interference affects the environment. The results indicate a common structure for deceptions that builds on what Greimas calls a “fiduciary contract.”

Graph Neural Network-Based Speech Emotion Recognition: A Fusion of Skip Graph Convolutional Networks and Graph Attention Networks

In speech emotion recognition (SER), our research addresses the critical challenges of capturing and evaluating node information and their complex interrelationships within speech data. We introduce Skip Graph Convolutional and Graph Attention Network (SkipGCNGAT), an innovative model that combines the strengths of skip graph convolutional networks (SkipGCNs) and graph attention networks (GATs) to address these challenges. SkipGCN incorporates skip connections, enhancing the flow of information across the network and mitigating issues such as vanishing gradients, while also facilitating deeper representation learning. Meanwhile, the GAT in the model assigns dynamic attention weights to neighboring nodes, allowing SkipGCNGAT to focus on both the most relevant local and global interactions within the speech data. This enables the model to capture subtle and complex dependencies between speech segments, thus facilitating a more accurate interpretation of emotional content. It overcomes the limitations of previous single-layer graph models, which were unable to effectively represent these intricate relationships across time and in different speech contexts. Additionally, by introducing a pre-pooling SkipGCN combination technique, we further enhance the ability of the model to integrate multi-layer information before pooling, improving its capacity to capture both spatial and temporal features in speech. Furthermore, we rigorously evaluated SkipGCNGAT on the IEMOCAP and MSP-IMPROV datasets, two benchmark datasets in SER. The results demonstrated that SkipGCNGAT consistently achieved state-of-the-art performance. These findings highlight the effectiveness of the proposed model in accurately recognizing emotions in speech, offering valuable insights and a solid foundation for future research on capturing complex relationships within speech signals for emotion recognition.

SOAMC: A Semi-Supervised Open-Set Recognition Algorithm for Automatic Modulation Classification

Traditional automatic modulation classification methods operate under the closed-set assumption, which proves to be impractical in real-world scenarios due to the diverse nature of wireless technologies and the dynamic characteristics of wireless propagation environments. Open-set environments introduce substantial technical challenges, particularly in terms of detection effectiveness and computational complexity. To address the limitations of modulation classification and recognition in open-set scenarios, this paper proposes a semi-supervised open-set recognition approach, termed SOAMC (Semi-Supervised Open-Set Automatic Modulation Classification). The primary objective of SOAMC is to accurately classify unknown modulation types, even when only a limited subset of samples is manually labeled. The proposed method consists of three key stages: (1) A signal recognition pre-training model is constructed using data augmentation and adaptive techniques to enhance robustness. (2) Feature extraction and embedding are performed via a specialized extraction network. (3) Label propagation is executed using a graph convolutional neural network (GCN) to efficiently annotate the unlabeled signal samples. Experimental results demonstrate that SOAMC significantly improves classification accuracy, particularly in challenging scenarios with limited amounts of labeled data and high signal similarity. These findings are critical for the practical identification of complex and diverse modulation signals in real-world wireless communication systems.

Speckle-decoded temperature-insensitive strain identification in a multimode optical fiber.

Fiber-optic sensing systems are significant tools for measuring various physical or biochemical parameters. However, temperature cross-sensitivity prevents accurate recognition of the target input signal when optical sensors are applied in practical scenarios. Herein, leveraging a deep learning algorithm, a speckle-decoded temperature-insensitive strain sensor is proposed and experimentally demonstrated. Scattering patterns are utilized to estimate the axial strain since the external force could change the coherent superposition of the amplitudes of propagating modes. The experimental results show that the recognition accuracy of the sensing system based on a classification model can reach 99.28% within a wide strain range of 0-0.3 N in the presence of temperature cross talk. In addition, the strain prediction demonstrates an average root-mean-square error of 1.02 N%. Such an intelligent speckle sensing strategy has the potential to broaden the applications of fiber-optic sensors in various engineering applications.

Digital signal detection and recognition in the communication field combining DAEN and CNN

In communication, the detection and recognition of digital signals have always faced problems such as low adaptability and high misclassification rate. To address these issues, this study innovatively fused the deep auto-encoder network with convolutional neural networks to construct a novel digital signal detection and recognition model. Firstly, this study utilized the powerful feature extraction capabilities of the deep auto-encoder network to extract key feature information from massive amounts of data. Then these features were combined with convolutional neural networks to construct a detection and recognition model. These results confirmed that the constructed digital signal detection and recognition model had values of 93.75% and 94.18% in data detection recognition rate and average classification accuracy, respectively. Meanwhile, this model also performed well in terms of data processing accuracy and recall. In the comparison of data processing, the accuracy and recall rates were 93.59% and 94.67%, respectively, and the performance of data detection and recognition was better than that of the comparison methods. This indicates that the constructed digital signal detection and recognition model can significantly improve the reliability and robustness of signal detection and recognition. This paper brings new breakthroughs to the development of digital signal detection and recognition technology in communication.

Implications of the Lipidic Ecosystem for the Membrane Binding of ApoE Signal Peptide: Importance of Sphingomyelin.

The unidirectional movement of nascent secretory proteins in the cell is primarily assisted by the signal recognition particles (SRP). However, this does not completely justify the importance of the signal peptide (SP) which gets eliminated after the protein translocation. We have earlier demonstrated that a negatively charged lipid such as POPG plays an important role in the higher binding affinity and cholesterol-discriminating ability of the apolipoprotein E (ApoE) SP. In this present work, we aimed to understand the role of sphingomyelin, an important constituent of ER, on the membrane binding of ApoE SP. Our results demonstrate that sphingomyelin promotes membrane binding but cannot discriminate cholesterol. However, sphingomyelin shows a synergistic effect with POPG toward the membrane binding of the ApoE SP. We have further shown that the membrane domains do not have any impact on the binding of ApoE SP. Based on our results we propose that the lipid composition of the endoplasmic reticulum (ER) where ApoE translocates, enhances the binding of the ApoE signal peptide to the ER membrane.

Thought-Controlled Computer Applications: A Brain–Computer Interface System for Severe Disability Support

This study introduces an integrated computational environment that leverages Brain–Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human–Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system’s Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.

Analysis of Machine Learning-Based NLOS Signal Identification Algorithm for UWB Indoor Localization Using CIR Waveform Features

Abstract. Ultra-wideband (UWB) technology stands out among numerous indoor positioning techniques due to its high operating frequency, low interception capability, resistance to multipath effects, and strong penetration. The UWB uses the time-of-arrival (TOA) to estimate the distance between the transmitter and receiver anchors in centimeter accuracy. However, in complex indoor positioning environments, obstacles such as walls, glass windows, metal plates, and wooden doors may block and reflect signals, inevitably causing non-line-of-sight (NLOS) errors that significantly affect positioning accuracy. The NLOS signal has lower signal energy due to the reflections. Thus, the channel impulse responses (CIR) from NLOS and LOS are different. To address the NLOS signal identification issue in UWB positioning, we utilize UWB CIR data collected from various positioning scenarios as the data source. CIR waveform input features are provided for the NLOS signal recognition model, and four machine learning models—Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), K-Nearest Neighbors (KNN), and XGBoost—are trained and optimized for NLOS signal recognition. The aim of the study is to analyze the performance of different machine learning algorithms for NLOS signal recognition in UWB indoor localization using these features. Experimental results indicate that machine learning-based NLOS signal recognition algorithms can achieve an accuracy of approximately 77.46%, precision of 80.46%, and an F1 score of 0.81. Among the four models, the XGBoost model demonstrates generally better recognition performance.

Abstract IA01: Dumpster-diving into the cancer degradome

Abstract Recently, we uncovered a pivotal role for altered proteasome complex composition in shaping tumor immunogenicity in non-small cell lung cancer (NSCLC). Our research demonstrated that the upregulation of a regulatory cap of the proteasome, PSME4, leads to a reduction in antigenic diversity through alterations in proteasomal cleavage patterns, consequently impeding T-cell-mediated anti-tumor immunity. These findings underscore the importance of probing the tumor degradome and its implications for tumor-immune interactions. Yet, numerous questions remain unanswered: Do specific substrates exhibit preferential degradation in PSME4-capped proteasomes? What constitutes their recognition signal? And fundamentally, how do alterations in the degradation landscape influence tumor inflammation? In my talk, I will delve into our latest advancements in addressing these questions, offering insights into the intricate interplay between proteasome-mediated degradation, tumor inflammation, and STAT signaling. Citation Format: Yifat Merbl. Dumpster-diving into the cancer degradome [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2024 Oct 18-21; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2024;12(10 Suppl):Abstract nr IA01.

Sequential structure probing of cotranscriptional RNA folding intermediates.

Cotranscriptional RNA folding pathways typically involve the sequential formation of folding intermediates. Existing methods for cotranscriptional RNA structure probing map the structure of nascent RNA in the context of a terminally arrested transcription elongation complex. Consequently, the rearrangement of RNA structures as nucleotides are added to the transcript can be inferred but is not assessed directly. To address this limitation, we have developed linked- m ultipoint T ranscription E longation C omplex RNA structure prob ing (TECprobe-LM), which assesses the cotranscriptional rearrangement of RNA structures by sequentially positioning E. coli RNAP at two or more points within a DNA template so that nascent RNA can be chemically probed. We validated TECprobe-LM by measuring known folding events that occur within the E. coli signal recognition particle RNA, Clostridium beijerinckii pfl ZTP riboswitch, and Bacillus cereus crcB fluoride riboswitch folding pathways. Our findings establish TECprobe-LM as a strategy for detecting cotranscriptional RNA folding events directly using chemical probing.

The Research of Intra-Pulse Modulated Signal Recognition of Radar Emitter under Few-Shot Learning Condition Based on Multimodal Fusion

Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a 5−way5−shot setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments.

Enabling High Strength and Toughness Polyurethane through Disordered-Hydrogen Bonds for Printable, Recyclable, Ultra-Fast Responsive Capacitive Sensors.

The rapid advancement of smart, flexible electronic devices has paralleled a surge in electronic waste (e-waste), exacerbating massive resource depletion and serious environmental pollution. Recyclable materials are extensively investigated to address these challenges. Herein, this study designs a unique polyurethane (SPPUs) with ultra-high strength up to 60MPa and toughness of 360MJ m-3. This synthetic SPPUs can be fully recycled at room temperature by using green solvents of ethanol. Accordingly, the resultant SPPU-Ni composites, created by mixing the ethanol-dissolved SPPUs solution with nickel (Ni) powder, effectively combine the flexibility and recyclability of SPPUs with the electrical conductivity of the nickel filler. Notably, this work develops the printable capacitive sensors (PCBS) through transcribing the paste of SPPUs-Ni slurry onto PET film and paper using screen-printing technology. The devised PCBS have fast response time ≈50ms, high resolution, and multiple signal recognition capabilities. Remarkably, SPPUs and Ni powder can be fully recycled by only dissolving the waste PCBS in ethanol. This work offers a sustainable solution to the growing e-waste problem in recyclable flexible electronics.

Dual-Feature Fusion Learning: An Acoustic Signal Recognition Method for Marine Mammals

Marine mammal acoustic signal recognition is a key technology for species conservation and ecological environment monitoring. Aiming at the complex and changing marine environment, and because the traditional recognition method based on a single feature input has the problems of poor environmental adaptability and low recognition accuracy, this paper proposes a dual-feature fusion learning method. First, dual-domain feature extraction is performed on marine mammal acoustic signals to overcome the limitations of single feature input methods by interacting feature information between the time-frequency domain and the Delay-Doppler domain. Second, this paper constructs a dual-feature fusion learning target recognition model, which improves the generalization ability and robustness of mammal acoustic signal recognition in complex marine environments. Finally, the feasibility and effectiveness of the dual-feature fusion learning target recognition model are verified in this study by using the acoustic datasets of three marine mammals, namely, the Fraser’s Dolphin, the Spinner Dolphin, and the Long-Finned Pilot Whale. The dual-feature fusion learning target recognition model improved the accuracy of the training set by 3% to 6% and 20% to 23%, and the accuracy of the test set by 1% to 3% and 25% to 38%, respectively, compared to the model that used the time-frequency domain features and the Delay-Doppler domain features alone for recognition.