Intelligent Signal Research Articles

Optimized Position Estimation in Mobile Multipath Environments Using Machine Learning

<h3>Abstract</h3> The positioning accuracy of global navigation satellite system receivers is frequently degraded in urban areas due to reflected signals. A moving receiver faces additional challenges because it needs to adjust to changes in the statuses of the signals received, including line-of-sight (LOS), multipath, non-LOS, or invisible. This paper proposes two new algorithms that can be used to enhance the accuracy of a moving receiver. The first algorithm is called Optimized Position Estimation (OPE). The OPE algorithm estimates the most likely paths and identifies the one with the optimal weight. The second algorithm is called Intelligent Signal Status Estimation (ISE). The ISE algorithm utilizes a self-organizing map machine-learning algorithm to estimate the probability of a change in signal status. The algorithms are tested using global positioning system C/A signals, which have over 50 changes in their statuses. The results obtained using these algorithms reveal that the accuracy is enhanced by as much as 96.3% (i.e., a 27-fold improvement) when compared to results using a conventional navigation algorithm.

Multimodality-aided Multicarrier Waveform Recognition in low SNR Regimes based on Denoised Cyclic Autocorrelation Transformation

Wireless signal recognition driven by artificial intelligence (AI) plays a pivotal role in 6G ultra-reliable wireless communications, facilitating spectrum surveillance to impede illegal radio interference. As a promising wireless technique, multicarrier waveform recognition (MWR) has been explored to enhance the reliability of wireless data transmission. However, existing works fail to achieve reliable recognition accuracy under strong noise. It remains a daunting task for MWR in signal-to-noise ratio (SNR) regimes. To deal with this issue, we propose a denoised cyclic autocorrelation-based multimodality fusion network (DCA-MFNet). Specifically, we first leverage the cyclic autocorrelation transformation to convert intercepted signals into cyclic autocorrelation (CA) features in cyclic frequency domains, which have the robust property of being insensitive to low SNR. Next, the singular value decomposition (SVD) method is employed to weaken the strong noise effects on useful CA peak values. Based on the denoised CA matrix (DCAM), the projection accumulation strategy is proposed to generate the time delay accumulation vector (TDV) and cyclic frequency accumulation vector (CFV), which can enlarge the discrimination among multicarrier signals. Finally, we fed the multimodality features of DCAM, TDV, and CFV into the developed DCA-MFNet to perform hierarchical learning, feature aggregation training, and multicarrier type prediction. Experimental results demonstrate that the proposed DCA-MFNet obtains better recognition performance than existing algorithms. Moreover, DCA-MFNet can effectively identify six multicarrier signals with a recognition accuracy of 100% at a low SNR of even -2 dB.

Controversial Victory: The “Tanker War” Against Japan, 1942–1944

Abstract In the immediate aftermath of the Second World War, the Army Air Corps (soon to be Air Force) provoked controversy by criticizing the Navy’s “tanker war” against Japanese oil, stating that Japan’s collapse might have occurred months if not years sooner had a coordinated submarine campaign against tankers been conducted. For years, submariners pointed to a host of problems as to why enemy oil tankers were not targeted effectively until later in the war, including prewar doctrine, faulty torpedoes and lack of intelligence. The reality – classified until the early 1980s – was that intelligence on the Japanese was so sparse that only through a highly classified signal intelligence effort was enough information derived to target enemy oil. This article will examine the developing and maturing link between signal intelligence and submarines using primary sources declassified in the 1980s. The research will demonstrate that there was no single answer to targeting enemy oil tankers, but only through intense analysis and coordination among multiple Services and agencies were the submarines able to launch their devasting offensive against Japanese oil tankers in late 1943.

Beauty in the Eyes of Machine: A Novel Intelligent Signal Processing-Based Approach to Explain the Brain Cognition and Perception of Beauty Using Uncertainty-Based Machine Voting

The most mysterious question humans have ever attempted to answer for centuries is, “What is beauty, and how does the brain decide what beauty is?”. The main problem is that beauty is subjective, and the concept changes across cultures and generations; thus, subjective observation is necessary to derive a general conclusion. In this research, we propose a novel approach utilizing deep learning and image processing to investigate how humans perceive beauty and make decisions in a quantifiable manner. We propose a novel approach using uncertainty-based ensemble voting to determine the specific features that the brain most likely depends on to make beauty-related decisions. Furthermore, we propose a novel approach to prove the relation between golden ratio and facial beauty. The results show that beauty is more correlated with the right side of the face and specifically with the right eye. Our study and findings push boundaries between different scientific fields in addition to enabling numerous industrial applications in variant fields such as medicine and plastic surgery, cosmetics, social applications, personalized treatment, and entertainment.

Robust Displacement Sensing by Direct-Current Triboelectric Nanogenerator Via Intelligent Waveform Recognition.

A triboelectric nanogenerator (TENG) facilitates the advancement of self-powered displacement sensors, which are important for many autonomous intelligent microsystems. However, the amplitude-based displacement sensing of conventional TENG-based sensors still suffers significantly from varying charge densities in harsh environments. Benefiting from the combination of intelligent signal processing algorithms and direct-current TENG sensors, this study proposes an environmentally robust character-based displacement sensing method that eliminates the influences of varying charge density in principle. The experimental results show that under drastically changing air humidity and other harsh environments, the sensing of threshold and maximum displacement has far superior consistency and stability than that of traditional amplitude-based TENG sensors, providing a novel route to realize reliable self-powered displacement sensing in environment-variable applications.

Energy-Efficient UAV-Mounted RIS Assisted Mobile Edge Computing

Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) have been recently applied in the field of mobile edge computing (MEC) to improve the data exchange environment by proactively changing the wireless channels through maneuverable location deployment and intelligent signals reflection, respectively. Nevertheless, they may suffer from inherent limitations, e.g., UAV’s low endurance and RIS’s finite coverage, which limit their wider applications. UAV-mounted RIS (U-RIS), as a promising integrated approach, can combine the advantages of UAV and RIS to break the limit. Inspired by this, we consider a novel U-RIS assisted MEC system, where a U-RIS is deployed to assist the communication between the ground users and an MEC server. The joint UAV trajectory, RIS passive beamforming and MEC resource allocation design is developed to maximize the energy efficiency (EE) of the system. To tackle the intractable non-convex problem, we divide it into two subproblems and solve them iteratively based on successive convex approximation (SCA) and the Dinkelbach method. Finally we obtain a high-performance suboptimal solution. Simulation results show that the proposed algorithm significantly improves the energy efficiency of the MEC system.

Collaborative autonomous system based wireless security in signal processing using deep learning techniques

Intelligent signal processing offers a variety of methods for communication system design and operation, including fuzzy logic, neural networks and evolutionary computation. This study proposes a novel technique for designing a mobile sensor network algorithm based on wireless security to provide a secure as well as energy-efficient communication system that allows sensor swarms to move while maintaining optimal sensor node spacing. Here signal feature extraction has been done using cuckoo Meta search algorithm (Cu_MeSA) and classification using Fuzzy based Boltzmann convolution machine neural network with (Fuz_Bol_ConVMNN) architecture. The experimental results shows that the output signal of feature extraction and classification with comparative parametric analysis in terms of packet arrival rate, network lifespan, energy optimization, probability of attack detection, throughput.

Proceed with caution: A reply to The perilous prerogative

AbstractThis article provides a reply to Leah West's article The Perilous Prerogative. In this reply, I argue against West's contention that the Canadian military no longer has prerogative authority to conduct foreign signals intelligence. I demonstrate that the Communication Security Establishment Act has not entirely displaced the prerogative for foreign signals intelligence in a military context. Next, I examine West's argument that the Canadian military requires a statutory framework to conduct human intelligence. I conclude that West makes a compelling case, but that Parliament should proceed with caution before granting the military an authority to collect intelligence on Canadian foreign fighters.

Skin-friendly PVA/PDA/Tyr-PEAm composite hydrogel with long-term antibacterial and self-recovery ability for wearable strain/pressure sensor

Flexible and self-healing conductive hydrogel, as a typical electronic skin with the function of transmitting sensory signals, has attracted remarkable attentions in the biomaterials field. However, the practical application of electronic skin has been severely limited owing to the potential bacterial infection threats. Here, in order to improve the antibacterial properties of electronic skin, a star-shaped polyetheramide (Tyr-PEAm) derived from L-tyrosine was synthesized and cross-linked into the hydrogel network. A compliant and self-healing conductive hydrogel (defined as PVA/PDA/Tyr-PEAm) was assembled from polyvinyl alcohol (PVA), polydopamine (PDA) and Tyr-PEAm. The hydrogel achieved a comprehensive performance of long-term antibacterial activity (the inhibition zones remained unchanged over 7 days), low hemolysis activity (hemolysis rate of 0.042 %), good conductivity and rapid self-healing ability (healing within 1 s). Therefore, the hydrogel could be used not only as a detection device to check normal skin, but also as the wound skin detection in real time. Furthermore, the good conductivity and rapid self-healing ability contributed to the hydrogel to detect various macro and micro movements of the human body, as well as to recycle as a new sensor. The PVA/PDA/Tyr-PEAm hydrogel that integrated intelligent signals and resisted bacterial infections exhibited broad application prospects in biomedical materials.

The perilous prerogative: An argument for legislating defence intelligence in Canada

AbstractThe National Security and Intelligence Committee of Parliamentarians (NSICOP) has twice reviewed the intelligence activities of CAF/DND and recommended the government provide explicit legislative authority for those activities. In “Defence intelligence and the Crown prerogative in Canada,” Philippe Lagassé rebukes NSICOP's argument and defends the continued reliance on the Crown prerogative. This article proposes that the critiques raised by Lagassé minimize the importance of the legal and operational concerns raised by NSICOP and do not reflect the legal history of Canadian intelligence activities. It also introduces the argument that the DND/CAF may no longer rely on the Crown prerogative for signals intelligence.

A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges

Fifth generation (5G) network and beyond envision massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with the spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL)-based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect – background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues – necessary to elucidate this topic to the reader in an encyclopedic manner.

Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection

Weak fault detection of rolling bearing presents difficult, because the periodic transient signature produced via localized incipient damage is easily submerged by various interference components and background noise. Hybrid intelligent fusion method is a breakthrough strategy for revealing feature frequency of rolling bearing fault by comprehensively using a variety of intelligent signal processing technologies, possessing the advantage of each technology. Considering the rolling bearing often construct a transmission device combination with gear and shaft, its vibration signal is often vulnerable to other multi-morphology components, such as harmonic modulation, noise. Thus, how to identify the fault frequency in repetitive transients is crucial to accurately identify rolling bearing fault detection. To address this issue, a novel hybrid intelligent method is proposed to effectively apply on periodic transients extraction, enhancement and rolling bearing fault diagnosis. The innovation of this method is to solve three problems, namely, the separation of multi-morphology components, noise reduction without periodic transients distortion, weak fault frequency enhancement. The proposed method is tested and validated on simulated signal, rolling bearing fault signal from accelerated rolling bearing degradation rig. In addition, comparisons with other classical rolling bearing fault detection methods have been conducted to highlight the superiority of the proposed methodology.

Search for Extraterrestrial Intelligence with the ngVLA

The next generation Very Large Array (ngVLA) will be the premiere centimeter-wave radio array in the Northern Hemisphere by the mid 2030s and thus has the potential to be one of the most effective instruments for the search for extraterrestrial intelligence (SETI). We show that, as of now, the ngVLA will be the only facility capable of detecting an extraterrestrial intelligence (ETI) signal generated by an Arecibo-like transmitter further than 300 pc. We present the optimal antenna array configurations and study the proposed frequency band coverage of the ngVLA and its implications to SETI. We argue for the ability to form of the order of 64 commensal high spectral resolution beams, as the large number of line of sights is critical to provide a competitive survey speed when compared to other modern surveys with telescopes such as MeerKAT and the future SKA. We advocate an Ethernet-based telescope architecture design for the ngVLA, which will provide a high degree of flexibility in SETI data analysis and will benefit the wider astronomy community through commensal science and open-source code, maximizing the potential scientific output of the ngVLA.

Chiral smart bionic skin film with changeable structural colors and tunable luminescence by polymer-assisted supramolecular assembly of the photonic crystals

The biological skin system of advanced organisms can obtain various environmental stimuli to change skin colors by adjusting the micro-nano structure of the skin surface or by electroluminescence and photoluminescence. The assembly of biominetic skin materials with both changeable structural colors and tunable luminescence is a significant challenge in the fields of advanced materials and light science. This work describes a novel chiral smart bionic skin film with changeable structural colors, tunable luminescence by the polymer-assisted supramolecular assembly of the photonic crystals. A smart nanodot based on the molecule with four-armed mimosa-like structure was assembled on the cellulose nanocrystals (CNC) photonic crystal. The chiral bionic skin film obtained had multi-channel fluorescence-emission, sensitive structural color stimulus responses to the light and humidity and the tunable chiral helical nematic domains. It demonstrates a series of the supramolecular interactions that trigger multiple intelligent signals, including the adjustable chiral pitch, photo-induced electron transfer (PET), intramolecular fluorescence resonance energy transfer (FRET), π-π stacking and the changes of the hydrogen bonds. The photonic crystal skin materials with sensitive multi-signals intelligent responses as smart bionic skin have potential applications for the tissue engineering, 3D printing materials, bionic artificial skin materials, and optical devices.

Application of Intelligent Signal Reflection in the Communication Model of an Electronic Controller

This paper analyses the automatic control effect of an electronic controller, combined with artificial intelligence technology in order to improve the automatic control of an electronic controller. In order to improve the automatic control effect of the electronic controller, a novel centralized intelligent reflective surface-assisted millimeter-wave computational imaging scheme based on pixel block division and block sparse signal recovery is proposed in this paper. Meanwhile, this paper proposes a fast block-sparse Bayesian learning algorithm. It combines the GAMP algorithm with machine learning, so it can achieve similar performance with much lower computational complexity than the traditional block sparse Bayesian learning algorithm. The simulation clustering study shows that the electronic controller automation system based on artificial intelligence technology proposed in this paper can effectively improve the control effect of the electronic controller.

The Cross-Postgraduate Training Mode Driven by Intelligent Signal Processing Technology: Taking the Major of Finance as an Example

At present, various economic and social problems have become more complex, requiring a multidisciplinary perspective to seek solutions, and thus more complex and innovative talents are needed. Deepening the reform of postgraduate interdisciplinary education, improving the quality of postgraduate interdisciplinary education, and cultivating high-level technical talents with a multidisciplinary background, innovation ability and comprehensive quality have also become the core issues to be solved urgently in postgraduate education. This paper takes the interdisciplinary training mode of postgraduates in finance as the research object and uses interdisciplinary education theory, systems science theory, and higher education theory to explore the components and main characteristics of the postgraduate interdisciplinary training mode. On this basis, this paper investigates the current situation of interdisciplinary training of postgraduates in finance in Chinese universities and then analyzes and discusses the problems existing in the current mode of interdisciplinary cultivation of postgraduates in finance in my country. Finally, based on the above research results, relevant suggestions for improving the interdisciplinary training of postgraduates in finance in my country are put forward. The research results show that postgraduate interdisciplinary training is a new form of talent training that readjusts the source and composition of each training link after introducing the concept of interdisciplinary education. The components of the postgraduate interdisciplinary training model include interdisciplinary support elements and postgraduate training elements. The former provides interdisciplinary external support for postgraduate training, and the latter constitutes the main content of postgraduate interdisciplinary training. The research results can be used to guide the interdisciplinary educational practice of other types of postgraduates and provide certain theoretical and empirical references for cultivating more high-level compound talents urgently needed by society.

Assessment of Complex Signal Parameters Effect on the Energy Concealment Degree

The article deals with the problem of obtaining a qualitative assessment of the energy concealment degree of radio signals. Energy concealment is regarded as the most significant form of signal concealment, since a designer of radio transmitting devices has the most influence and the greatest freedom in defining these properties. The purpose of the article is to systematize the available knowledge about the methods of assessment of energy concealment in order to conduct a mutual comparison of the characteristics of various signals. The authors describe and analyze a number of methods for assessment of energy concealment of signals based on the number of necessary measurements of the signal level range of the signal reconnaissance, as well as probabilistic assessment of signal disclosure. The results of the analysis showed that described methods are not suitable for obtaining a comparative assessment. Based on the conclusions made in the study energy concealment assessment methods, the author's method for assessment the degree of energy concealment of signals in terms of transmitted symbol energy is proposed. The article clarifies the application of the method to assess the parameters of chirp signals, narrowband and wideband FM, BPSK, QPSK, as well as quadrature signals. A comparative assessment of the chirp, FM, BPSK, QPSK and QAM-16 signal parameters for the decimeter VHF band is obtained, taking into account the transmission speed and power. Assessment of energy concealment of signals were obtained at typical information transfer rates. On the basis of the results, some regularities were described that make it possible to evaluate the effect of changing individual parameters of a radio transmitting device on final energy concealment.

Capability of a Ground-Based Passive Surveillance System to Detect and Track Spaceborne SAR in LEO Orbits

Monitoring the Earth’s surface from the low Earth orbit (LEO) by synthetic aperture radar (SAR) provides meaningful information about a number of human activities such as the ecosystem, agriculture, and of course security matters. During its operation in LEO orbits, spaceborne SAR, at altitudes between 200 km and 2000 km, inherently emit electromagnetic signals toward the Earth’s surface. It has been experimentally verified that it is possible to detect and intercept spaceborne SAR signals at a distance of hundreds of kilometers by a single electronic support measure (ESM) non-comms sensor, placed on the Earth’s surface. The work described in this document is a follow-up to the paper and describes the principle of a new non-cooperative passive tracking method of spaceborne SAR carriers, tracked by four ground-based passive ESM non-comms sensors. These sensors used the time difference of arrival (TDOA) method in order to determine the current spaceborne SAR position in LEO orbits. Simulations were performed to assess the impact of ESM sensor configurations on the SAR’s signal interception and positioning error. The simulation results showed that the proposed method is sufficient to detect a spaceborne SAR position in LEO.

전자기스펙트럼 작전과 신호정보 발전방향

A plan to establish a strategic command in the Republic of Korea has been announced recently. Commensurate strategic intelligence must be achieved to efficiently operate strategic assets. Signal intelligence (SIGINT) that collects invisible electromagnetic spectrum and produces strategic intelligence, and electromagnetic spectrum operations (EMSO) that conducts military operations based on the provided strategic intelligence are closely related to utilizing the electromagnetic spectrum. From this perspective, establishing the relationship between EMSO and SIGINT and developing a SIGINT strategy in an EMSO environment are currently critical issues when a strategic command establishment is planned, as SIGINT is a strategic tool. In this paper, we propose a SIGINT strategy in an EMSO environment to secure the initiative of the electromagnetic spectrum in modern and future warfare.

Modulation Recognition of Communication Signals Based on Multimodal Feature Fusion.

Modulation recognition is the indispensable part of signal interception analysis, which has always been the research hotspot in the field of radio communication. With the increasing complexity of the electromagnetic spectrum environment, interference in signal propagation becomes more and more serious. This paper proposes a modulation recognition scheme based on multimodal feature fusion, which attempts to improve the performance of modulation recognition under different channels. Firstly, different time- and frequency-domain features are extracted as the network input in the signal preprocessing stage. The residual shrinkage building unit with channel-wise thresholds (RSBU-CW) was used to construct deep convolutional neural networks to extract spatial features, which interact with time features extracted by LSTM in pairs to increase the diversity of the features. Finally, the PNN model was adapted to make the features extracted from the network cross-fused to enhance the complementarity between features. The simulation results indicated that the proposed scheme has better recognition performance than the existing feature fusion schemes, and it can also achieve good recognition performance in multipath fading channels. The test results of the public dataset, RadioML2018.01A, showed that recognition accuracy exceeds 95% when the signal-to-noise ratio (SNR) reaches 8dB.