Intelligent Signal Research Articles

Monitoring, governmental data access and the invocation of Article 8 ECHR by legal persons

ABSTRACT The European Convention on Human Rights (ECHR) grants the rights enshrined in Article 8 to ‘everyone’. The European Court of Human Rights (ECtHR) has interpreted legal persons as falling within the scope of ‘everyone’ in various judgments, primarily concerning governmental searches and seizures. With the rise of digital technology, corporations now hold vast amounts of personal data, creating growing pressure to disclose such data to governmental surveillance agencies. This article examines ECtHR case law and applies theories of legal personality to assess the extent to which legal persons may invoke Article 8 in cases of governmental interferences affecting associated natural persons' rights. After outlining the legal framework, the article applies it to two use cases: governmental control of fishing vessels to identify regulatory infringements and signal intelligence practices affecting legal persons operating telecommunication infrastructure. These examples illustrate two types of governmental interference; methods impacting natural persons through data held by legal persons and interferences directed at the legal person itself. The article seeks to clarify the scope of rights under Article 8 for legal persons. The question of whether such interferences are proportionate is beyond the article's scope and remains a topic for future research.

The Warsaw coordination centre – international radio counterintelligence cooperation of the former European socialist countries

ABSTRACT This article looks back to the Cold War years in describing the international radio counterintelligence cooperation of the internal state security services of the formerly socialist European countries. The technical equipment for collecting and transmitting information from other countries was valorised in a context in which borders played a divisive role, especially in the first half of the Cold War period. Western secret service ‘agents’ in Eastern Bloc territory received information from their intelligence centres via secret radio devices, and often sent back information via encrypted radio transmissions. By the 1950s, the socialist countries had developed their own state security organizations and radio interception capabilities. From 1955 on, this was performed in cooperation with the other socialist countries under the coordination of the so-called Warsaw Coordination Centre. The aim of this article is to describe, based on Hungarian archival sources, the little-known inter-state security cooperation that existed from 1955 to the end of the Cold War, and to contribute to the historical understanding of the Eastern Bloc Signal Intelligence (SIGINT) activities.

Inertial Measurement Unit Self-Calibration by Quantization-Aware and Memory-Parsimonious Neural Networks

This paper introduces a methodology to compensate inertial Micro-Electro-Mechanical System (IMU-MEMS) time-varying calibration loss, induced by stress and aging. The approach relies on a periodic assessment of the sensor through specific stimuli, producing outputs which are compared with the response of a high-precision sensor, used as ground truth. At any re-calibration iteration, differences with respect to the ground truth are approximated by quantization-aware trained tiny neural networks, allowing calibration-loss compensations. Due to the unavailability of aging IMU-MEMS datasets, a synthetic dataset has been produced, taking into account aging effects with both linear and nonlinear calibration loss. Also, field-collected data in conditions of thermal stress have been used. A model relying on Dense and 1D Convolution layers was devised and compensated for an average of 1.97 g and a variance of 1.07 g2, with only 903 represented with 16 bit parameters. The proposed model can be executed on an intelligent signal processing inertial sensor in 126.4 ms. This work represents a step forward toward in-sensor machine learning computing through integrating the computing capabilities into the sensor package that hosts the accelerometer and gyroscope sensing elements.

Ultrafast Silicon/Graphene Optical Nonlinear Activator for Neuromorphic Computing

AbstractOptical neural networks (ONNs) have shown great promise in overcoming the speed and efficiency bottlenecks of artificial neural networks. However, the absence of high‐speed, energy‐efficient nonlinear activators significantly impedes the advancement of ONNs and their extension to ultrafast application scenarios like real‐time intelligent signal processing. In this work, a novel silicon/graphene ultrafast all‐optical nonlinear activator, leveraging the hybrid integration of silicon slot waveguides, plasmonic slot waveguides, and monolayer graphene is demonstrated. Exploiting the exceptional picosecond‐scale photogenerated carrier relaxation time of graphene, the response time of the activator is markedly reduced to ≈93.6 ps, establishing all‐optical activator as the fastest known in silicon photonics to knowledge. Moreover, the all‐optical nonlinear activator holds a low threshold power of 5.49 mW and a corresponding power consumption per activation of 0.51 pJ. Its feasibility and capability for use in ONNs, manifesting performance comparable with commonly used activation functions are experimentally confirmed. This breakthrough in speed and energy efficiency of all‐optical nonlinear activators opens the door to significant improvements in the performance and applicability of ONNs.

Editorial Introduction for the Special Issue on Intelligent Signal Processing and Learning for Next Generation Multiple Access

Editorial Introduction for the Special Issue on Intelligent Signal Processing and Learning for Next Generation Multiple Access

Methods of protecting uav radio channels against unauthorized Access

The adoption and widespread use of unmanned aerial vehicles (UAVs) is constantly increasing in many areas. This is due to the ability of drones to record and relay real-time video and images along with the ability to fly and carry cargo. However, when using drones, a number of problems arise, because outsiders can gain access to information transmission channels to meet their own needs. Information attacks on UAVs lead to significant consequences, including commercial and non-commercial losses. The purpose of this work is to study UAVs and their vulnerability and protection during flight. To achieve the goal, the methods of searching scientific and technical literature, analysis, classification, systematization and generalization of available data on UAVs were used. On the basis of the found key industry publications, a comprehensive analysis of methods of protecting UAV radio channels against hacker attacks was carried out. The interception process is described as one of the options for UAV access threats, namely: signal interception, control interception, physical interception. The following methods of UAV radio channel protection are substantiated: signal encryption; use of spectral methods; authentication and authorization; use of physical barriers; creation of unique software for UAVs; Closing all radio communication channels on the UAV itself; returning it using a recorded route. The actual scientific and practical task, which consists in the theoretical and methodological substantiation of the approaches and principles of UAV radio channel protection in real time, has been solved. The variant of the mutual position in space of the UAV, the control panel (PU) and the radio electronic suppression station is presented, as well as the method of decentralized wireless interaction of these components. Prospects and advantages of using artificial intelligence are revealed.

Introducing a systematic and sustainable patient safety oversight function in a U.K. arms-length healthcare body

Background The healthcare landscape is experiencing a profound period of change with accelerating developments in medical research, new treatments, care delivery and the use of innovative technology to meet the challenges of delivering care. These changes offer opportunities but as with all innovation, require careful management to reduce the likelihood of harm and error. Objective The National Institute for Health and Care Excellence (NICE) has set out to introduce an adaptable, sustainable and systematic patient safety oversight group to ensure that patient safety is embedded at an organisation level supporting the healthcare system to overcome challenges and deliver high quality care, safely implementing the latest innovations in a timely manner. Method The patient safety oversight group was established through a scoping exercise, led by the senior responsible officer and a small project team. The article sets out the methods used, including extensive internal engagement through structured interviews with senior leadership teams, purposive sampling, data synthesis and reporting. An overview of the group is given and considerations for promoting patient safety information flows and optimising the use of safety signal intelligence are made. Conclusion Through reflection on changing needs within and surrounding the organisation, along with patient safety and complexity literature, NICE has developed an adaptable, systematic and sustainable model for managing patient safety. In addition to generating learning within NICE, the process highlights opportunities for improvement within the patient safety system. Result NICE has developed an adaptable, systematic and sustainable model for managing patient safety.

Acoustic modulation signal recognition based on endpoint detection

The modulation recognition technology for acoustic signals holds significant research importance in signal demodulation and communication signal reconnaissance, serving as a crucial component and key aspect. This paper investigates the modulation recognition technology for acoustic signals (< 20 kHz) from the perspectives of signal preprocessing and feature extraction. Firstly, it selects seven modulation signals 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and OFDM as recognition targets and systematically compares the effectiveness of four different endpoint detection algorithms in modulation signal recognition. To further enhance the performance of the short-time energy entropy ratio algorithm, this study introduces three different noise reduction algorithms for optimization. Finally, to accurately identify and distinguish between 2 and 4FSK signals, this study optimizes the related algorithms of the cyclic spectrum by using the kurtosis coefficient value Kur of the cyclic spectrum parameter matrix when the cyclic frequency α = 0 to differentiate between these two signals. The results show that at SNR of 4 dB, the proposed modulation recognition algorithm can effectively distinguish between these two signals, achieving a recognition accuracy of over 99%.

Interception of Bio-Mimicking Underwater Acoustic Communications Signals

Interception of Bio-Mimicking Underwater Acoustic Communications Signals

Synergizing intelligent signal processing with wavelength-division multiplexing for enhanced efficiency and speed in photonic network communications

Abstract The explosive growth of worldwide mobile data traffic seeks innovations in communication technology to cater to the mounting need for rapid connectivity, high-capacity connections. The mainstreaming of 5G technologies for communication is a dramatic step towards meeting the aforementioned goals, with the ability for reshaping IoT (Internet of things), D2D (device-to-Device) communications, and the smart grids. This work conveys an in-depth study of the fundamental innovations that underlie 5G, including full-duplex distribution, huge multiple-input-multiple-output, ultra-dense connections, the phenomenon of beamforming and millimeter-wave approaches. A special emphasis is focused on the integration of photonic technologies, or microwave photonics, which serves as a critical multidisciplinary study topic. Optical fibers, with their tremendous bandwidth and capacity, have been determined as the best medium for backhaul and fronthaul amenities, outpacing conventional copper cables to accommodate tiny cells and next-generation networks. The synergy between optical and wireless access technologies is analyzed with the emphasis on the central role of wavelength-division multiplexing (WDM) for improving network efficiency and speed. The investigation additionally explores the possibility of intelligent signal processing methods combined with WDM to optimize photonic network communications. The mingling of these technologies anticipates producing unrivaled levels of performance, rupturing the path for an additional intelligent, interconnected era.

Critical Analysis of Status of Counter-Terrorism Strategies in Manda, Lamu County, Kenya: A Multifaceted Examination of HUMINT and SIGINT

This paper presents a critical analysis of counter-terrorism strategies in Kenya, with a specific focus on the multifaceted examination of Human Intelligence (HUMINT) and Signals Intelligence (SIGINT). The study employed a sequential explanatory mixed-methods research design integrates both qualitative and quantitative approaches to provide a thorough understanding of Kenya&amp;apos;s efforts to combat terrorism. The target population of this study encompassed participants involved in counter-terrorism activities in Kenya. This included officers in security agencies such as the National Intelligence Service and the Kenya Defense Forces which formed our target population. A total of 93 officers participated. The respondents were drawn through purposive sampling. By addressing multiple levels of involvement, the paper provides a holistic view of counter-terrorism strategies and their impact. Quantitative data was collected through surveys administered to a representative sample of security personnel in the NIS and KDF. Qualitative data on the other hand was collected through interviews. Semi-structured interviews were done with key informants, such as heads of the National Intelligence Service and Kenya Defense Forces helped capture the nuanced perspectives on HUMINT and SIGINT. STATA and Statistical Package for Social Scences (SPSS) Software were used to analyze quantitative data from the survey. The statistical significance between the average expectations and average perceptions in both security agencies was analyzed using two-sample t-tests. Additionally, the significance of the gaps between the agencies was assessed with Hotelling’s T-squared test at a 5% significance level. Pearson’s correlation coefficients were also used to determine the strength and direction of the relationship between the independent variable (s) and the dependent variable. The study generally found that the indicators of the HUMINT strategy were effective in countering terrorism except for source penetration. Further, the study determined that all indicators of SIGINT strategy were ineffective in countering terrorism except for interception of communication and timely warnings.

Postmelting Encapsulation of Glass Microwires for Multipath Light Waveguiding within Phosphate Glasses.

Glass waveguides are the fundamental component of advanced photonic circuits and play a pivotal role in diverse applications, including quantum information processing, light generation, imaging, data storage, and sensing platforms. Up to date, the fabrication of glass waveguides relies mainly on demanding chemical processes or on the employment of expensive ultrafast laser equipment. In this work, we demonstrate an advanced, simple, low-temperature, postmelting encapsulation procedure for the development of glass waveguides. Specifically, silver iodide phosphate glass microwires (MWs) are drawn from splat-quenched glasses. These MWs are then incorporated in a controlled manner within transparent silver phosphate glass matrices. The judicious selection of glass compositions ensures that the refractive index of the host phosphate glass is lower than that of the embedded MWs. This facilitates the propagation of light inside the encapsulated higher refractive index MWs, leading to the facile development of waveguides. Importantly, we substantially enhance the light transmission within the MWs by leveraging the plasmon resonance effects due to the presence of silver nanoparticles spontaneously generated owing to the silver iodide phosphate glass composition. Employing this innovative approach, we have successfully engineered waveguide devices incorporating either one or two MWs. Remarkably, the dual MW devices are capable of transmitting light of different colors and in multipath direction, rendering the developed waveguides outstanding candidates for extending the functionalities of diverse photonic and optoelectronic circuits, as well as in intelligent signaling applications in smart glass technologies.

Integrating Machine Learning and Feature Extraction for Islanding Detection in Grid-Connected Photovoltaic Systems: A Hybrid Intelligent Approacha

Introduction: In recent years, the integration of renewable energy sources, particularly Photovoltaic (PV) systems, into the grid has garnered considerable attention. However, the distributed nature of these grid-integrated PV systems has introduced challenges concerning grid faults and maintenance. Method: This paper aims to present a pioneering approach to augment the monitoring of gridintegrated PV systems by integrating intelligent methods, specifically machine learning and feature extraction techniques. The primary focus of this approach is on islanding detection, which involves promptly identifying grid faults or maintenance challenges and initiating the grid's transition to an isolated mode of operation. To accomplish this, an intelligent signaling method is employed, capitalizing on the capabilities of Distributed Generation (DG) networks. By recording critical signals such as voltage, current, and frequency at common coupling points, fault conditions can be accurately detected. Signals obtained from the grid are subjected to wavelet transformation to extract pertinent information that characterizes fault conditions. These extracted features are then utilized as inputs to machine learning methods, facilitating the proposal of intelligent islanding scenarios. To assess the efficacy of the proposed approach, simulations are conducted on a grid-connected PV system. The recorded signals are meticulously analyzed, and the extracted features are employed to train machine learning models. The performance of these models is evaluated based on their ability to detect fault conditions and initiate appropriate islanding scenarios accurately. Result: The results obtained demonstrate the immense potential of the proposed approach in bolstering the monitoring of grid-integrated PV systems. Conclusion: By synergizing machine learning techniques with feature extraction and intelligent signaling methods with the KNN-Confusion Matrix, all predicted labels match the true labels, resulting in a 100% accuracy. The detection of grid faults and maintenance challenges can be substantially improved, thereby fostering more efficient and dependable operation of these systems.

Emerging technology and the cult of the offensive

ABSTRACT Military organizations are generally defined by a “cult of the offensive.” For cyberspace, this gives rise to an empirical puzzle, at least in the United States. What accounts for the early restraint and skepticism about cyber offense publicly expressed by many US military cyber leaders? In this article, I explore the US military’s approach to cyberspace as a theory-developing case study. I examine more than a decade of public discourse among senior military leaders about when and how the military should employ cyber force. I argue that, in the formative years of US military cyber organization, the military’s predisposition to the offense was challenged by other cultural and institutional influences, especially from the signals intelligence and strategic nuclear communities. Moreover, this influence shaped the public discourse even after US military cyber organizations gained greater autonomy and turned toward more objectively offensive strategies. This suggests implications for theory and policy.

Predicting Arterial Stiffness From Single-Channel Photoplethysmography Signal: A Feature Interaction-Based Approach.

Arterial stiffness (AS) serves as a crucial indicator of arterial elasticity and function, typically requiring expensive equipment for detection. Given the strong correlation between AS and various photoplethysmography (PPG) features, PPG emerges as a convenient method for assessing AS. However, the limitations of independent PPG features hinder detection accuracy. This study introduces a feature selection method leveraging the interactive relationships between features to enhance the accuracy of predicting AS from a single-channel PPG signal. Initially, an adaptive signal interception method was employed to capture high-quality signal fragments from PPG sequences. 58 PPG features, deemed to have potential contributions to AS estimation, were extracted and analyzed. Subsequently, the interaction factor (IF) was introduced to redefine the interaction and redundancy between features. A feature selection algorithm (IFFS) based on the IF was then proposed, resulting in a combination of interactive features. Finally, the Xgboost model is utilized to estimate AS from the selected features set. The proposed approach is evaluated on datasets of 268 male and 124 female subjects, respectively. The results of AS estimation indicate that IFFS yields interacting features from numerous sources, rejects redundant ones, and enhances the association. The interaction features combined with the Xgboost model resulted in an MAE of 122.42 and 142.12 cm/sec, an SDE of 88.16 and 102.56 cm/sec, and a PCC of 0.88 and 0.85 for the male and female groups, respectively. The findings of this study suggest that the stated method improves the accuracy of predicting AS from single-channel PPG, which can be used as a non-invasive and cost-effective screening tool for atherosclerosis.

Eight Principles for Intelligence Sharing: A Holistic and Strategic Approach

This paper reviews the strategic use of warning intelligence to pre-emptively address threats in complex geopolitical scenarios through rapid intelligence sharing. Specifically, the paper reviews the question How, based on research and experience, can a set of principles be applied by states to enhance situational awareness and tackle threat actors through a holistic and collaborative approach to intelligence sharing? The paper examines historical and contemporary alliances like the Five Eyes and reviews a Signals Intelligence Alliance as a case in point, highlighting the importance of collaborative approaches to enhance situational awareness and tackle threat actors. The study, grounded in the philosophical paradigm of interpretivism, adheres to the principles for transparent science when researchers use tools such as large-language-models as grammar editors or research assistants. The paper also acknowledges limitations such as the generalisability of the SIGINT model and the need for continuous adaptation of intelligence sharing practices. The results discuss policy, process, and people challenges to intelligence sharing. The paper concludes that successful intelligence sharing should follow eight general principles. Future research directions include exploring the impact of emerging technologies, human aspects of intelligence sharing, and context-based intelligence sharing alliances.

Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review.

Prevention of suicide is a global health priority. Approximately 800,000 individuals die by suicide yearly, and for every suicide death, there are another 20 estimated suicide attempts. Large language models (LLMs) hold the potential to enhance scalable, accessible, and affordable digital services for suicide prevention and self-harm interventions. However, their use also raises clinical and ethical questions that require careful consideration. This scoping review aims to identify emergent trends in LLM applications in the field of suicide prevention and self-harm research. In addition, it summarizes key clinical and ethical considerations relevant to this nascent area of research. Searches were conducted in 4 databases (PsycINFO, Embase, PubMed, and IEEE Xplore) in February 2024. Eligible studies described the application of LLMs for suicide or self-harm prevention, detection, or management. English-language peer-reviewed articles and conference proceedings were included, without date restrictions. Narrative synthesis was used to synthesize study characteristics, objectives, models, data sources, proposed clinical applications, and ethical considerations. This review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) standards. Of the 533 studies identified, 36 (6.8%) met the inclusion criteria. An additional 7 studies were identified through citation chaining, resulting in 43 studies for review. The studies showed a bifurcation of publication fields, with varying publication norms between computer science and mental health. While most of the studies (33/43, 77%) focused on identifying suicide risk, newer applications leveraging generative functions (eg, support, education, and training) are emerging. Social media was the most common source of LLM training data. Bidirectional Encoder Representations from Transformers (BERT) was the predominant model used, although generative pretrained transformers (GPTs) featured prominently in generative applications. Clinical LLM applications were reported in 60% (26/43) of the studies, often for suicide risk detection or as clinical assistance tools. Ethical considerations were reported in 33% (14/43) of the studies, with privacy, confidentiality, and consent strongly represented. This evolving research area, bridging computer science and mental health, demands a multidisciplinary approach. While open access models and datasets will likely shape the field of suicide prevention, documenting their limitations and potential biases is crucial. High-quality training data are essential for refining these models and mitigating unwanted biases. Policies that address ethical concerns-particularly those related to privacy and security when using social media data-are imperative. Limitations include high variability across disciplines in how LLMs and study methodology are reported. The emergence of generative artificial intelligence signals a shift in approach, particularly in applications related to care, support, and education, such as improved crisis care and gatekeeper training methods, clinician copilot models, and improved educational practices. Ongoing human oversight-through human-in-the-loop testing or expert external validation-is essential for responsible development and use. OSF Registries osf.io/nckq7; https://osf.io/nckq7.

Research on Intelligent Signal Timing Optimization of Signalized Intersection Based on Deep Reinforcement Learning Using Floating Car Data

Research on Intelligent Signal Timing Optimization of Signalized Intersection Based on Deep Reinforcement Learning Using Floating Car Data

Traffic Flow Optimization for Metropolitan Cities

This research paper presents the design and implementation of an adaptive traffic signal timer system that utilizes real-time traffic density calculation and an intelligent signal switching algorithm to optimize traffic flow at intersections. The proposed system leverages computer vision techniques like object detection to count and classify vehicles, and then dynamically adjusts the green signal durations based on the detected traffic density. A simulation module is also developed to visualize the system's performance and compare it to a static traffic signal implementation. The results demonstrate the effectiveness of the adaptive approach in reducing vehicle waiting times and improving overall intersection throughput

Capturing wartime history to forge postwar intelligence liaison: Telford Taylor’s battle with the US Navy over the future of Anglo-American signals intelligence

ABSTRACT Telford Taylor was not a career military intelligence officer, but his short career in military intelligence during World War Two had an outsized impact on the development of American interservice as well as Anglo-American signals intelligence. Near the end of the war, Taylor put forth a visionary proposal to harness wartime lessons learned against Germany and Japan in order to prepare for postwar collaboration against likely rivals, particularly the Soviets. While his British interlocutors were amenable to such a project, the US Navy was not.