Intelligent Signal Research Articles

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.

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.

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.

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.

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.

The Ukraine war and the shift in Russian intelligence priorities

ABSTRACT The war in Ukraine has transformed Russian intelligence activities. It has drawn the bulk of Russian intelligence collection resources, both inside Ukraine and further afield, to focus on war-related, often low-level operational/tactical targets. Even strategic collection is related to the war, especially directed toward bolstering Russia’s global reputation. However, the war has also led to the dismantling of a large portion of Russia’s intelligence apparatus, including both human and signals intelligence, especially in Europe, just when it is needed the most. It has prompted greater scrutiny and international counterintelligence cooperation against Russian intelligence activities than has been seen since the 1980s. Russia’s own actions have drawn those reactions. Nevertheless, Russian intelligence services are resilient and persistent. They learn from mistakes and adapt to changing circumstances.

Localization-antilocalization of soundwaves in a disordered phononic crystal with mirror symmetry

Anderson localization in a disordered potential leads to exponential decay of an incoming wave. However, if a 1D disordered potential possesses mirror symmetry, V(-x) = V(x), then the eigenstates are either even or odd functions of x. As such, a wave localized on one side of such a symmetric disordered potential should elicit a corresponding antilocalized peak at the symmetric position on the other side. The distribution of pressure in a symmetric disordered potential is similar to the wave function profile in a symmetrical double-well potential. This similarity opens a way to demonstrate quantum tunneling using acoustic waves. This effect has an important application to secure communications, as the transmitted signal is reduced by disorder to the level of noise, thus excluding the possibility of signal interception, then enhanced by the symmetric part of the potential at the receiver. This is a secure method of information transmission without the need for encryption and decryption. We developed a methodology for identifying the frequency spectrum consisting of narrow doublets that correspond to eigenstates of different parities. A 2 × 30 phononic crystal with orientational disorder was fabricated for experimental observation of antilocalization. [This work is supported by the NSF under EFRI Grant No. 1741677.]

Штучний інтелект як передвісник істотних змін в освіті

The rapid development of programs based on the principles of machine learning (ML) and artificial intelligence (AI) signals significant changes in the components of education, namely in the provider, the tool of transmission, and the recipient of knowledge. Historical data analysis regarding the key functions of education serves as the basis for identifying fundamental innovations introduced through AI and ML. The impact of writing, printing, and the Internet has significantly altered the tool for knowledge transmission, influencing the volume of information and the number of knowledge recipients. The implementation of AI and ML transforms not only the tool but also the provider of knowledge itself, which can become impersonal thanks to the corresponding computer programs. With the historically justified increase in the volume of knowledge possessed by humanity, there is a transformation observed in education systems. This is especially true for democratic societies, where the emphasis is increasingly shifting from providing a large amount of knowledge to developing critical thinking. It has been researched that programs based on AI and ML, applying linguistic models, are capable of effectively systematizing knowledge. This lays the foundation for personalizing the entire education process for a specific knowledge recipient, without burdening the provider. However, there have been cases when such imitation misleads scientists, who perceive it as attempts at communication between programs that have an equivalence to the human level. The conclusions drawn indicate a significant transformation of the education system caused by AI and ML-based programs. However, intelligent programs are unable to evolve into independent knowledge recipients due to their inability to consciously attribute meaning to information, transforming it into knowledge.

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

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

Air–ground integrated artificial intelligence of things with cognition-enhanced interference management

Integrated air–ground network enhances AIoT performance by improving spectral efficiency, achieving high-speed, stable network connectivity, and enabling sensing, learning, and decision-making. However, unmanned aerial vehicles (UAVs) can lead to local spectrum congestion and competition. To address this issue, intelligent signal processing techniques are employed to enhance AIoT system performance and stability through intelligent multi-channel sensing and communication. A novel communication framework inspired by brain cognition for UAV communication in heterogeneous environments is introduced. This framework iteratively determines the importance of signals, effectively eliminating unimportant signals with interference characteristics, and reducing their transmission power. Simulation results demonstrate the superiority of this method in terms of communication performance.