Search

Quantum random number generators (QRNGs) produce random numbers based on the intrinsic probabilistic nature of quantum mechanics, making them true random number generators. In this paper, we design and fabricate an embedded QRNG that produces random numbers based on fluctuations of spontaneous emission and absorption in a light-emitting diode. To achieve a robust and reliable QRNG, we compare some usual post-processing methods and select the finite impulse response method for a real-time device. This device could pass the National Institute of Standards and Technology tests, the generation rate is 1 Mbit/s, and the randomness of the output data is invariant in time. [ABSTRACT FROM AUTHOR]

In this paper, a time-domain solution for Rayleigh wave scattering from a complex site of Alpine Valley is presented based on the scaled boundary finite element method (SBFEM). Both the near-field region and the far-field region are assumed to be homogeneous, isotropic linear elastomers, where the near-field region is discretized using the quadtree method and the far-field region is simulated with a displacement unit impulse response function. The input of Rayleigh waves is solved by the equivalent loading method. The scattering behavior of seismic waves in alpine valleys is comprehensively analyzed, and the results are of theoretical significance for site safety evaluation and seismic defenses. [ABSTRACT FROM AUTHOR]

Background: Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to bridge this gap by investigating the potential of SVD‐based deconvolution methods in 2D QA, particularly in addressing the variability of injection durations. Purpose: Building on the identified limitations in QA, the study aims to adapt SVD‐based deconvolution techniques from CTP to QA for IAs. This adaptation seeks to capitalize on the high temporal resolution of QA, despite its two‐dimensional nature, to enhance the consistency and accuracy of hemodynamic parameter assessment. The goal is to develop a method that can reliably assess hemodynamic conditions in IAs, independent of injection variables, for improved neurovascular diagnostics. Materials and methods: The study included three internal carotid aneurysm (ICA) cases. Virtual angiograms were generated using computational fluid dynamics (CFD) for three physiologically relevant inlet velocities to simulate contrast media injection durations. Time‐density curves (TDCs) were produced for both the inlet and aneurysm dome. Various SVD variants, including standard SVD (sSVD) with and without classical Tikhonov regularization, block‐circulant SVD (bSVD), and oscillation index SVD (oSVD), were applied to virtual angiograms. The method was applied on virtual angiograms to recover the aneurysmal dome impulse response function (IRF) and extract flow related parameters such as Peak Height PHIRF, Area Under the Curve AUCIRF, and Mean transit time MTT. Next, correlations between QA parameters, injection duration, and inlet velocity were assessed for unconvolved and deconvolved data for all SVD methods. Additionally, we performed an in vitro study, to complement our in silico investigation. We generated a 2D DSA using a flow circuit design for a patient‐specific internal carotid artery phantom. The DSA showcases factors like x‐ray artifacts, noise, and patient motion. We evaluated QA parameters for the in vitro phantoms using different SVD variants and established correlations between QA parameters, injection duration, and velocity for unconvolved and deconvolved data. Results: The different SVD algorithm variants showed strong correlations between flow and deconvolution‐adjusted QA parameters. Furthermore, we found that SVD can effectively reduce QA parameter variability across various injection durations, enhancing the potential of QA analysis parameters in neurovascular disease diagnosis and treatment. Conclusion: Implementing SVD‐based deconvolution techniques in QA analysis can enhance the precision and reliability of neurovascular diagnostics by effectively reducing the impact of injection duration on hemodynamic parameters. [ABSTRACT FROM AUTHOR]

Kramers–Kronig relations link the real and imaginary parts of the Fourier transform of a well-behaved causal transfer function describing a linear, time-invariant system. From the physical point of view, according to the Kramers–Kronig relations, absorption and dispersion become two sides of the same coin. Due to the simplicity of the assumptions underlying them, the relations are a cornerstone of physics. The rigorous mathematical proof was carried out by Titchmarsh in 1937 and just requires the transfer function to be square-integrable (L2), or equivalently that the impulse response of the system at hand has a finite energy. Titchmarsh's proof is definitely not easy, thus leading to crucial steps that are often overlooked by instructors and, occasionally, prompting some authors to attempt shaky shortcuts. Here, we share a rigorous mathematical proof that relies on the Laplace formalism and requires a slightly stronger assumption on the transfer function, namely it being Lebesgue-integrable (L1). While the result is not as general as Titchmarsh's proof, its enhanced simplicity makes a deeper knowledge of the mathematical aspects of the Kramers–Kronig relations more accessible to the audience of physicists. Editor's Note The Kramers–Kronig relations are fundamental to optics, linking dispersion to absorption. While incomplete proofs are found in many textbooks, the rigorous mathematical proof that requires the minimal assumption (that the transfer function is square-integrable) is quite challenging and, therefore, rarely taught. In this paper, the authors show that requiring a slightly stricter criterion for the transfer function (L1 = Lebesgue-integrable) allows the proof to be much more accessible. [ABSTRACT FROM AUTHOR]

This paper develops a novel Bayesian heterogeneous panel vector autoregressive model (B-HP-VAR) that quantifies the impact of geopolitical risk shocks on the tourism industry of 14 emerging market and developing economies (EMDE). We find that increasing geopolitical tensions have a persistent negative effect on tourism demand in most of these countries, as shown by our impulse response estimates. Furthermore, evidence from forecast error variance decomposition reveals that geopolitical risk shocks in many EMDE economies constitute the main driver of tourism demand. Analysis from historical decompositions demonstrates that geopolitical tensions have been particularly influential in driving tourism demand in Ukraine, Russia, Turkey, China, Indonesia, Thailand, Colombia, and Mexico. Our main findings are robust to several perturbations to the benchmark specification. Our results have several important implications for policymakers in their efforts to strengthen the ability of the tourism industry to absorb shocks from geopolitical tensions. [ABSTRACT FROM AUTHOR]

This study empirically investigates the dynamic effects of weather shock on within‐country income inequality. Using panel data of 17 Asia‐Pacific Economic Cooperation (APEC) member economies, we estimate impulse responses via the local projection method. Moreover, temperature and precipitation shocks, defined as deviations of temperature and precipitation from their historical norms, are exploited to measure country‐specific weather shocks. The empirical results reveal the following. First, temperature and precipitation shocks deteriorate income inequality measured by the Gini index; these effects are long‐lasting. Moreover, asymmetric effects exist: heat waves and droughts more significantly increase income inequality than cold waves and floods. Lastly, current redistribution policies do not seem to effectively mitigate those adverse effects. [ABSTRACT FROM AUTHOR]

We develop a framework for measuring under- and overreaction in expectation formation. The basic insight is that under- and overreaction to new information is identified (up to sign) by the impulse response function of forecast errors. Our measurement procedure yields estimates of under- and overreaction to different shocks at various horizons. In an application to inflation expectations, we find that forecasters underreact to aggregate shocks but overreact to idiosyncratic shocks. We illustrate how our approach can be used to (i) quantify the importance of different biases, (ii) estimate theoretical models, and (iii) shed light on existing empirical approaches and puzzles. [ABSTRACT FROM AUTHOR]

Mobile technology is currently trending toward supporting multiple communication standards on a single device. This means that some reconfigurable techniques must be the foundation of their design. The two essential requirements of channel filters are minimized complexity and reconfigurability. In this research, a novel extension of Frequency Response Masking (FRM) was investigated by employing Time Division Multiplexing (TDM)-based single Multiply and Accumulate (MAC) architecture using the principle of resource sharing to realize multiple sharp filter responses from a single prototype constant group delay low pass filter. This paper uses a single multiply and add units regardless of the quantity of channels and taps. The suggested reconfigurable filter was synthesized on technology based on 0.18-µm CMOS and put into practice. Further trials were carried out on Virtex-II 2v3000ff1152-4 FPGA device. The outcomes revealed that the suggested channel filter, which was synthesized using FPGA, provides 21.36% of the area curtail and 14.88% of power scaling down on average and put into practice using ASIC provides 5.18% of the area reduction and 9.08% of power scaling down on average. [ABSTRACT FROM AUTHOR]

This paper uses a state-dependent local projection model to empirically test the dynamic risk performance of cryptocurrency assets under geopolitical risk events, and to examine whether they have safe-haven properties in the face of major global external shocks. We demonstrate that the volatility of the cryptocurrency market exhibits a non-linear relationship with geopolitical risk. They are uncorrelated in normal times, but the risk of cryptocurrency market rises significantly under extreme geopolitical risk events. The cumulative impulse response pattern of volatility in the cryptocurrency market is similar to that of volatility in speculative assets such as stocks and bonds, but negatively correlated with that of volatility in safe-haven assets such as gold and the U.S. dollar. Our findings suggest that the volatility of cryptocurrencies should not be underestimated when investors consider hedging strategies under external shocks. [ABSTRACT FROM AUTHOR]

This research investigates the relationship between clean energy stock and oil market returns utilizing Granger predictability in distribution and quantile impulse response analysis. We find that clean energy stock returns Granger predict oil price returns during "normal times" based on the distribution's center, but not vice versa. During bullish market episodes, there is bidirectional Granger predictability between the returns of clean energy stocks and oil market returns. Nonetheless, we find that clean energy stock returns Granger predict oil returns in bearish markets without any evidence of the contrary. This indicates that oil returns cannot be used to hedge the downside risk associated with renewable energy company purchases. Quantile impulse responses for the relationship between clean energy stocks and the crude oil market reveal bidirectional and significant responses, where a negative shock during an extremely down market reveals a negative response in the other market and a positive shock during an extremely up market reveals a significant positive response. This shows that neither market can be utilized to offset risks in the other market. [ABSTRACT FROM AUTHOR]

This paper contributes to the literature by analysing shock propagation mechanisms between world uncertainty, exchange rates and country-specific commodity terms of trade. Using monthly data from 2008 to 2020 for eight commodity currencies' exchange rates, we analyse the impulse responses based on local projections. The study results show that a shock from the exchange rates which are net transmitters of shocks through the direct United States (US) dollar effect causes world uncertainty to rise and subsequently fall due to the indirect commodity terms of trade effect. Also, in response to world uncertainty shock, the exchange rates fall in the risk-on period and subsequently overshoot during the risk-off period when investors seek the safe haven of the dollar. The world uncertainty shock on the exchange rate is predominantly experienced in the economies that largely trade commodities with the US, particularly Russia and Canada. The study finds evidence that the dollar is a prime cause of world uncertainty. Hence, for policy implications, the study discusses that policymakers, investors, or traders pay attention to the dollar which is the main invoicing currency in the international market. [ABSTRACT FROM AUTHOR]

This paper focuses on financial asset return spillovers and economic policy uncertainty spillovers in three continents (Europe, America, and Asia) in the last few decades. We examine three financial asset markets (stock, bond, and foreign exchange). Spillovers are measured using the Diebold–Yilmaz spillover index. In the first part, we measure the size of spillovers and find a significant increase in spillovers during the global financial crisis, the European sovereign crisis, and the recent pandemic. In the second part, we test for the effect of uncertainty spillovers on financial asset return spillovers. Using rolling impulse response functions, we obtain the following results: First, the responses of financial markets spillovers to uncertainty spillovers are time-varying and are mostly positive. Second, the highest responses in financial market return spillovers to uncertainty spillovers occur in America and the smallest responses in financial market return spillovers occur in Europe. Third, among the three financial markets, the highest responses apply to the foreign exchange market. Finally, the largest responses during the pandemic apply in Europe. [ABSTRACT FROM AUTHOR]

This paper examines the heterogeneous effects of monetary policy on consumer prices in the United Kingdom. We estimate a proxy Structural Vector Autoregressive (SVAR) model, using extended high‐frequency monetary surprises from Cesa‐Bianchi et al. (European Economic Review, 123, 2020, 103375) to instrument shifts in UK monetary policy. We then analyze the impulse responses for various components of the UK Consumer Price Index. Our findings reveal that while monetary policy tightening leads to a persistent decline in aggregate consumer prices, the impact on disaggregated components is highly heterogeneous. Notably, we observe that energy price changes offset movements in food, beverage, and tobacco prices, resulting in identical responses of core and headline consumer CPIH inflation measures. The contrasting effects across different CPI components highlight the importance of examining disaggregated data when assessing the transmission of monetary policy to consumer prices. [ABSTRACT FROM AUTHOR]

The viscosity of a medium affects the amplitude attenuation and velocity dispersion of seismic waves. Therefore, it is necessary to consider these factors during migration. First, to eliminate the viscous effect of a medium, we combine the Futterman model with the integral equation of the De Wolf approximation to construct a compensation operator of the De Wolf approximation for a visco‐acoustic medium. Next, we use the visco‐acoustic screen approximation method to realize the continuation operator then establish a prestack depth migration algorithm. Finally, an error analysis, impulse response test and model test are performed. The results show that three different generalized visco‐acoustic screen methods (phase screen method, generalized screen method and extended local Born Fourier method) can satisfactorily compensate for the attenuation of deep interface amplitude. Among these methods, the visco‐acoustic extended local Born Fourier method has the highest accuracy and the best compensation effect. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) outbreak has caused tremendous turmoil worldwide and is yet to be contained. Therefore, mitigating the number of people infected by COVID-19 remains a fundamental policy goal for several countries. This study aims to analyse the dynamic relationship between mobility and the rate of change in the number of new infections (NNIs) in Japan. Another goal is to evaluate the effects of various policies, such as mobility control and vaccination, as well as the impact of climate factors on the NNIs. The analysis reveals a strong positive relationship between the growth rate of the NNIs and mobility. Our results also indicate that the government-imposed measure of regulating mobility shows a weaker effect on preventing the spread of infection, as the emergency declaration is repeatedly invoked. By contrast, the recent increase in the infection rate seems to reduce the spread of infection by inducing voluntary restraint, and this phenomenon has become even stronger in the recent period. Regarding the effect of vaccination, the results demonstrate little effect on attenuating the spread of infection. However, it has significantly weakened the mobility – spread relationship, suggesting that it may help in the implementation of economic revitalization policies. [ABSTRACT FROM AUTHOR]

ABSTRACT This study analyses and compares the determinants of inflation expectations among households and professional forecasters in India. To this end, we utilize quarterly survey data from the Reserve Bank of India (RBI), specifically the Inflation Expectations Survey of Households (IESH) and the Professional Forecasters’ Survey (SPF). The analysis employs a non‐recursive structural vector autoregressive (SVAR) model to explore the relationships between variables. The impulse response function and variance decomposition analysis shed light on the response of inflation expectations to macroeconomic shocks. Our findings indicate that apart from conventional macroeconomic factors like realized inflation and monetary policy, exogenous factors such as oil prices significantly shape inflation expectations. Comparing IESH and SPF, we found out that household expectations are more sensitive to shocks related to food prices, reflecting the influence of food expenses on household decision‐making. Conversely, professional forecasters’ expectations are more influenced by core inflation. Finally, we observed that inflation expectations adjust more quickly than realized inflation in response to macroeconomic shocks, suggesting a leading role. This study is novel with the contribution to the existing literature as it provides a comparative analysis of determinants of inflation expectations between households and professional forecasters in India, while also considering inflation targeting as a significant factor. [ABSTRACT FROM AUTHOR]

Introduction: Despite the relative harmony of the Western states on essential security issues, the July 2024 parliamentary elections in the United Kingdom (the UK), ending with the overwhelming victory of the Labour Party, who has returned to power after 14 years, may have a significant impact on the relationship this country has with the European Union (the EU). Since Brexit, the diminished trade has contributed to a decline in the economy of the United Kingdom and it is one of the countries most significantly impacted by the "increasing cost of living” crisis. Methods: Using an Impulse Response Function (IRF), this paper will assess the impact of a further decline of EU-UK relations as well as the policy implications of some proposals supported by significant fractions of the Conservative Party, namely tax cuts. Results and discussion: The paper concludes that a further decline in trade relations between the EU and the UK will lead to a further decline in the economy of the UK that has few feasible alternatives in terms of trading partners. [ABSTRACT FROM AUTHOR]

In the field of neuroscience, a significant challenge lies in extracting essential features from biological signals like Electroencephalography (EEG). Utilized as a non-invasive method, EEG records brain activities through metal electrodes on the scalp. The analysis of EEG data finds applications in various domains, including concealed information tests, aimed at detecting deception. This paper introduces the Spatial Spiking Neural Network, a supervised approach for classifying EEG data collected during concealed information tests. Temporal EEG data undergoes filtration using a Finite Impulse Response (FIR) filter, while Common Spatial Pattern (CSP) is employed to extract spatial components. Binary classification is achieved through an integrate-and-fire neuron model, where the frequency of spike generation determines the classification. Spiking Neural Networks (SNNs) offers advantages in terms of temporal precision, event-driven processing, and low power consumption. Their spike-based communication allows for efficient handling of sparse data and recognition of temporal patterns, contributing to robustness and energy efficiency. The proposed model is applied separately to each subject's EEG data, and the results are compared with traditional classification algorithms. The proposed approach attains a peak accuracy of 90.15%, showcasing superior performance compared to alternative methods. [ABSTRACT FROM AUTHOR]

Room impulse responses (RIRs) between static loudspeaker and microphone locations can be estimated using a number of well-established measurement and inference procedures. While these procedures assume a time-invariant acoustic system, time variations need to be considered for the case of spatially dynamic scenarios where loudspeakers and microphones are subject to movement. If the RIR is modeled using image sources, then movement implies that the distance to each image source varies over time, making the estimation of the spatially dynamic RIR particularly challenging. In this paper, we propose a procedure to estimate the early part of the spatially dynamic RIR between a stationary source and a microphone moving on a linear trajectory at constant velocity. The procedure is built upon a state-space model, where the state to be estimated represents the early RIR, the observation corresponds to a microphone recording in a spatially dynamic scenario, and time-varying distances to the image sources are incorporated into the state transition matrix obtained from static RIRs at the start and end points of the trajectory. The performance of the proposed approach is evaluated against state-of-the-art RIR interpolation and state-space estimation methods using simulations, demonstrating the potential of the proposed statespace model. [ABSTRACT FROM AUTHOR]

Human–robot collaboration will play an important role in the fourth industrial revolution in applications related to hostile environments, mining, industry, forestry, education, natural disaster and defense. Effective collaboration requires robots to understand human intentions and tasks, which involves advanced user profiling. Voice-based communication, rich in complex information, is key to this. Beamforming, a technology that enhances speech signals, can help robots extract semantic, emotional, or health-related information from speech. This paper describes the implementation of a system that provides substantially improved signal-to-noise ratio (SNR) and speech recognition accuracy to a moving robotic platform for use in human–robot interaction (HRI) applications in static and dynamic contexts. This study focuses on training deep learning-based beamformers using acoustic model-based multi-style training with measured room impulse responses (RIRs). The results show that this approach outperforms training with simulated RIRs or matched measured RIRs, especially in dynamic conditions involving robot motion. The findings suggest that training with a broad range of measured RIRs is sufficient for effective HRI in various environments, making additional data recording or augmentation unnecessary. This research demonstrates that deep learning-based beamforming can significantly improve HRI performance, particularly in challenging acoustic environments, surpassing traditional beamforming methods. [ABSTRACT FROM AUTHOR]

Photonic integrated circuits (PICs) offer ultra‐broad optical bandwidths that enable unprecedented data throughputs for signal processing applications. Dynamic reconfigurability enables compensation of fabrication flaws and fluctuating external environments, tuning for adaptive equalization and training of optical neural networks. The initial step in PIC reconfiguration entails measuring its dynamic performance, often described by its frequency response. While measuring the amplitude response is straightforward, e.g. using a tunable laser and optical power meter, measuring the phase response presents challenges due to various factors, including phase variations in test connections and instrumentation limitations. To address these challenges, a universal and robust characterization technique is proposed, which uses an on‐chip reference path coupled to the signal processing core (SPC), with a delay gap larger than the total delay across the signal processing paths. A Fourier transform of the chip's power response reveals the SPC's impulse response. The method is more robust against low reference‐path power and imprecise delays. Experiments using a finite‐impulse‐response (FIR) structure demonstrate rapid SPC training, overcoming thermal crosstalk and device imperfections. This approach offers a promising solution for PIC characterization, facilitating expedited physical parameter training for advanced applications in communications and optical neural networks. [ABSTRACT FROM AUTHOR]

The coupling between fluid-solid interaction (FSI) and structural response is a crucial factor in understanding the resistance of sandwich structures to underwater blasts. In this study, we present a theoretical model that predicts the dynamic response of multilayer foam core sandwich beams subjected to underwater impulses. We carried out a time-scale intercoupling analysis by considering the compressible core in both incident impulse and structural response. In the incident impulse coupling phase, the one-dimensional fluid-structure interaction in terms of cavitation evolution is conducted to obtain the incident pressure profile. A four inter-stages response model is proposed for further analyzing the structural response coupling phase and its coupling with core strength. Explicit finite element calculations are performed to verify the theoretical results in terms of the velocity profile, transverse deflection, and core compression. The results suggest that the interaction between the four stages of the dynamic response is significantly influenced by the impulsive intensity and core strength, and the sandwich beam does not undergo all the four stages. The equivalent core strength used in the theoretical analysis is confirmed accurate to predict the impact resistance of the corresponding graded core sandwich beam, which is inferior to the sandwich beam with uniform cores, despite having the same areal mass. [ABSTRACT FROM AUTHOR]

In wireless communication systems, the information is transferred as digital symbols that get affected by noise interference while transmitting through the wireless channel. In today's world, the increased demand for rapid data transmission rates leads to more inter-symbol interference in the received signal. To diminish the effects of inter-symbol interference, adaptive channel equalization is utilized in digital communication. In this paper, the inter-symbol interference effect in the finite impulse response channel is reduced in terms of an adaptive equalizer method. The weights/coefficients of the equalizer are optimized through a tunicate swarm algorithm. Channel equalization involves optimizing the channel coefficients to mitigate the impact of inter-symbol interference. This equalization process can be viewed as an iterative optimization task, aimed at lessening the mean square error among the transmitted signal and the equalizer's output. The implementation of the proposed method is executed using MATLAB software. We assess its effectiveness through a comparative analysis, considering metrics such as mean square error, learning rate, bit error rate, convergence rate, and computational time. Furthermore, we conduct a comparative evaluation with other optimization approaches, including the Bat Algorithm, Slime Mould Algorithm, and Harris Hawks Optimization Algorithm. This comparison demonstrates the superiority of the proposed algorithm over traditional optimization techniques. [ABSTRACT FROM AUTHOR]

The vibrational response of an elastic panel to incident acoustic waves is determined by the direction-of-arrival (DOA) of the waves relative to the spatial structure of the panel's bending modes. By monitoring the relative modal excitations of a panel immersed in a sound field, the DOA of the source may be inferred. In reverberant environments, early acoustic reflections and the late diffuse acoustic field may obscure the DOA of incoming sound waves. Panel microphones may be especially susceptible to the effects of reverberation due to their large surface areas and long-decaying impulse responses. An investigation into the effect of reverberation on the accuracy of DOA estimation with panel microphones was made by recording wake-word utterances in eight spaces with reverberation times (RT60s) ranging from 0.27 to 3.00 s. The responses were used to train neural networks to estimate the DOA. Within ±5°, DOA estimation reliability was measured at 95.00% in the least reverberant space, decreasing to 78.33% in the most reverberant space, suggesting an inverse relationship between RT60 and DOA accuracy. Experimental results suggest that a system for estimating DOA with panel microphones can generalize to new acoustic environments by cross-training the system with data from multiple spaces with different RT60s. [ABSTRACT FROM AUTHOR]

In two experiments, spatial release from masking (SRM) was measured using an ambisonic reproduction system for a range of different ambisonic orders. The first experiment used ambisonic panning while the second experiment used impulse responses recorded from a sixth-order ambisonic microphone. Both experiments found a progressive increase in SRM with increasing ambisonic order for speech presented against a single speech-shaped-noise interferer. SRM increased progressively up to at least fourth order and continued to asymptotically improve at higher-ambisonic orders toward the level achieved with point sources. The second experiment found that this effect was robust for different acoustic environments. [ABSTRACT FROM AUTHOR]

The Particle Time of Flight (PTOF) detector is a chemical vapor deposition diamond-based detector used to measure bang times in low-yield (≲ 1015 neutrons) experiments at the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL). Historically, the impulse response for PTOF diamond detectors has been obtained from x-ray timing shots on the NIF and shots on the MegaRay pulsed electron accelerator at LLNL. The impulse response may alternatively be obtained using single particle interactions with the detector, at substantially less cost and higher frequency compared to NIF timing shots, which typically occur months apart. Here, the response of a PTOF detector setup is characterized by statistically averaging a large number of single particle waveforms. A high fidelity instrument response function can be constructed in this way. This is confirmed by comparison of the single particle counting-constructed response to the impulse response function measured for the same detector at LLNL's MegaRay facility. [ABSTRACT FROM AUTHOR]

An acoustic frequency-selective Y-shaped waveguide splitter based on in-line Helmholtz resonators is demonstrated analytically via a modified transfer matrix method, computationally through finite element simulations using COMSOL, and experimentally by an impulse response measurement. This demultiplexer arrangement consists of a single input waveguide that splits into two output waveguides in a Y-configuration. Each output arm has a single tuned Helmholtz resonator embedded in-line along the length of the waveguide. The Helmholtz resonators in each arm consist of a single cavity with two necks—one directed toward the input and output sides of the guide. The phenomenon of extraordinary acoustic transmission results in near perfect transmission of sound along each output arm in a narrow frequency interval at the Helmholtz resonant frequency. The Helmholtz resonance and hence the demultiplexed frequencies are determined by the physical dimensions of the resonator. Using a single Helmholtz resonator in each output arm means that the system is more compact compared to other proposed schemes using either side-loaded Helmholtz resonators or stubs. The experimental results are in good agreement with the computational predictions. [ABSTRACT FROM AUTHOR]

Purpose: Competitively multipolar international system demands bilateral and multilateral partnerships. Joint innovation signals close partnerships. Regional proximity of Central and Eastern Europe (CEE) to the European Union (EU) defines its research and development objectives. These objectives are additionally subjected to the USA's geopolitical strategy in this geographical area. Hence, CEE's limited resources require limited resources make international innovation cooperation. This paper aims to analyse whether and how CEE countries make international innovation cooperation decisions. Design/methodology/approach: This paper used Organisation for Economic Co-operation and Development (OECD) database of total patent applications filed to the patent cooperation treaty (PCT) with co-applicants from abroad, where co-patents with at least one foreign inventor present a measure of international innovation partnership. A vector autoregression analysis and impulse response function were used to analyse international innovation partnership choices of eight CEE OECD countries for the period 1990–2018. Findings: Innovation with the EU is of collaborative nature, commonly displaying complementary properties with the rest of the examined innovation partners, while co-patenting with the Russia and China act as substitutes or complements. Co-patenting with Russia is the most versatile, displaying both properties of collaboration and competition. Some countries exhibit complementarity in co-patenting activities with multiple partners. The significance levels of these relationships vary, indicating varying degrees of impact. Overall, these findings highlight the complex dynamics of co-patenting activities and the influence of different partners on countries' collaborative innovation strategies. Research limitations/implications: In addition to significant relationships, insignificant relationships as well as those that could bring about greater synergy are flagged in the paper. Those relationships portray possible direction into which national funds could be channelled to incite cooperation between different sectors and countries, especially as innovation partnerships are not always successful and require a long time period to materialise. Originality/value: By examining bilateral innovation partnerships, this study provides an insight into the strategic political and economic spheres of influence in the CEE region. [ABSTRACT FROM AUTHOR]

An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter's parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system's input and the IIR filter's output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value. [ABSTRACT FROM AUTHOR]

The causal shift-invariant convolution is studied from the point of view of inversion. Abel's algorithm, used in the tautochrone problem, is considered and Sonin's existence condition is deduced. To generate pairs of functions verifying Sonin's condition, the class of Mittag-Leffler type functions is used. In particular, functions that are impulse responses of ARMA(N,N) systems serve as a basis. The possible use of Abel's procedure as a support for introducing generalized fractional derivatives is evaluated. [ABSTRACT FROM AUTHOR]

This scientific paper explores the use of correlation coefficients of vibration signals and machine learning algorithms for structural damage assessment in beams under moving loads. The paper discusses the challenges of maintaining structural integrity and the importance of automated, nondestructive monitoring techniques. Preprocessing techniques, such as the random decrement technique (RDT), are highlighted for improving data analysis. Machine learning algorithms are identified as valuable tools for structural damage assessment. The paper concludes by emphasizing the potential of machine learning in safeguarding critical infrastructures. The text also discusses trigger points and the vibration response of a slender beam under a moving load. An artificial neural network (ANN) is proposed as a computational model for identifying non-linear features. Experimental testing on a simulated bridge girder using accelerometers collected data to identify and locate damage in the beam. The ANN achieved high accuracy in detecting damage appearance and location, but further research is needed to improve accuracy in real-world situations. [Extracted from the article]

This study examines the expected impact of capital control tools on financial stability. We demonstrate that capital controls lower the probability of a banking crisis (positive effect) and simultaneously reduce economic growth (negative effect). We apply a dynamic panel logit model with fixed effects on a sample of countries composed of emerging and advanced economies for the period 2000–2020. Moreover, we use an impulse response function analysis to assess the net effect of capital controls. The results show that while regulation of capital flows directly stabilizes the economy, it also indirectly destabilizes it by impeding economic growth. Furthermore, we found that the positive impact of lowering the probability of a crisis dominates the adverse effect on economic growth. These impacts are different across the subgroups of emerging market economies and advanced countries. However, in both subgroups the effect of capital controls is independent of capital controls tightening. These results have significant implications for policymakers, who must adjust the capital flows regulatory policy according to economic growth and financial stability objectives. [ABSTRACT FROM AUTHOR]

Designing high-performance integrated circuits that balance area, speed, and power is increasingly challenging. This study optimises hardware implementation of FIR filters using an innovative Amalgam Multiplier, minimising resource use without compromising performance. The key challenge in designing signal processing hardware is the multiplier design, which significantly affects efficiency. This study explores integrated circuit optimisation, addressing the delicate balance between area utilisation, speed, and power consumption. Focusing on hardware implementation of Finite Impulse Response (FIR) filters in signal processing, the article starts with a conventional FIR filter design using a standard array multiplier which consumed 1.790 W, with 8 s real-time completion. Then, Linear Phase FIR (LP-FIR) filter is designed using Braun and Dadda Multiplier. LP-FIR filter with Braun multiplier consumed 1.436 W power and 26 s of completion time whereas LP-FIR with Dadda multiplier consumed 1.363 W power and 23 s of completion time. Finally, the implementation of novel LP-FIR enhanced by Amalgam multiplier consumed 0.302 W power and 10.29 s of completion time, marking a significant advancement in integrated circuit design. [ABSTRACT FROM AUTHOR]

The Hilbert transformation for generating the analytic signal or signal envelope is widely used in modern communication receivers, in radar and sonar systems. It introduces a 90 ∘ phase shift of the input signal. Since the impulse response of the ideal Hilbert transformer is non-causal, it must be approximated by an FIR or IIR filter. This paper shows results of novel algorithms for designing broadband digital IIR Hilbert transformers and its implementation. The designs employ Galerkin or collocation techniques. The transfer function of the Hilbert transformer is a rational polynomial of low order and exhibits approximately linear phase. The filters match the 90 ∘ phase shift requirement of Hilbert transformers almost perfectly and exhibit approximately constant group delay in the passband. The achieved image rejection ratio is typically larger than 50 dB. The quantization of the filter coefficients is realized by a Canonical Signed Digit (CSD) representation, reducing the hardware resources compared with two's complement. The resulting filters are multiplier-less, which is crucial for high-speed signal processing and low power consumption. The design techniques and the CSD representation are realized in a MATLAB toolbox. The filters were moreover implemented in VHDL and SystemC. Additionally, a MATLAB tool for automatically generating a VHDL package containing the filter parameters has been implemented. [ABSTRACT FROM AUTHOR]

An analytical solution for the diffusive wave model with a concentrated lateral inflow for a finite channel is presented. Flow depth and flow discharge are taken as the upstream boundary, whereas the downstream end is subjected to a stage–discharge relationship. The focus is on analyzing the impact of the lateral inflow concentrated at different locations on the flow discharge and flow depth. Unit-step responses for all the impulse responses for the lateral inflow are also presented and compared with the corresponding results of a semi-infinite channel where the downstream boundary is ignored. For higher Peclet numbers, the semi-infinite channel responses are found to be a satisfactory substitute for the finite channel responses to predict flow discharge, when a flow discharge is imposed at the upstream boundary, and a steady uniform condition is applied at the downstream boundary. However, for lower values of Peclet number, the same is not replicated. Furthermore, for the flow depth responses, the semi-infinite channel results are not appropriate approximations for finite length channel responses since the flow depth is observed to be sensitive to the backwater effect induced by the downstream boundary. [ABSTRACT FROM AUTHOR]

The United States Dollar (USD) replacement of the sterling as the dominant currency is not only the result of the “invisible hand”, but also the “visible hand”. This study analyzes the effect of the monetary policy in the United States (U.S.) on the international share of the USD from 1914 to 1945 using the Bayesian technique, to estimate the time-varying parameter vector autoregressive (TVP-VAR) model. The study posits two main findings. First, the time-point impulse response shows that the increase in the U.S. interest rate results in an increase in the international share of the USD, implying that this increase has an expansion effect on the USD, and the effect has no time-varying characteristics. Second, the equal time interval impulse response shows that the effect of the monetary policy on the share of the USD is greater in the short-term. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology that combines fractional-order control theory with robust control under a parametric uncertainty approach to enhance the performance of linear time-invariant uncertain systems with integer or fractional order, referred to as Fractional-Order Robust Control (FORC). In contrast to traditional approaches, the proposed methodology introduces a novel formulation of inequalities-based design, thus expanding the potential for discovering improved solutions through linear programming optimization. As a result, fractional order controllers are designed to guarantee desired transient and steady-state performance in a closed-loop system. To enable the digital implementation of the designed controller, an impulse response invariant discretization of fractional-order differentiators (IRID-FOD) is employed to approximate the fractional-order controllers to an integer-order transfer function. Additionally, Hankel's reduction order method is applied, thus making it suitable for hardware deployment. Experimental tests carried out in a thermal system and the assessment results, based on time-domain responses and robustness analysis supported by performance indices and set value analysis in a thermal system test-bed, demonstrate the improved and robust performance of the proposed FORC methodology compared to classical robust control under parametric uncertainty. • New method for fractional controllers using Interval poles, improving performance. • Sets region to sweep frequency based on settling time and stability region of α. • Reduces conservativeness, allowing stability changes for flexible performance. • Ensures target with chosen fractional-order controller based on desired actions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel optimization approach to designing wideband infinite impulse response (IIR) digital fractional order differentiators (DFODs) with improved accuracy at low frequency bands. In the new method, the objective function is formulated as an optimization problem with two tuning parameters to control the error distribution over frequencies. The gradient based optimizer (GBO) is effectively employed on the proposed objective function. A wide range of design examples are presented to illustrate the effectiveness of the proposed approach. The proposed approximations are compared to those of recent literature in terms magnitude, phase, and group delay errors. The result reveal that our method can attain approximations have a favorable low frequency performance (with about 60% of relative magnitude error reduction) and maintain a comparable accuracy at most of the Nyquist band to those of the existing ones. Thus, our approximations can be attractive for low frequency applications. [ABSTRACT FROM AUTHOR]

In this paper, a novel maximum likelihood sequence estimation (MLSE) optimum receiver design is proposed to reduce the complexity of the conventional optimum receiver. We first propose a multipath channel classification method according to the zero distribution of the channel impulse response; that is, the multipath channel is divided into a minimum phase channel and a non-minimum phase channel. Then, an MLSE optimum receiver design with channel identification based on zero distribution is also proposed. In this design, the distribution of zero can be obtained directly by calculating the Z transform of the channel. Thus, the complexity is reduced. The number of the multiplication reduced is N (2 N + 1) . In addition, 2 N + 1 memory units storing the autocorrelation sequence are reduced. The simulation results indicate that this channel classification method effectively represents the characteristics of the channel. Moreover, the MLSE optimum receiver proposed performs similarly and is less complex than the conventional one. The performance is greatly improved compared with the minimum mean square error (MMSE) equalizer. [ABSTRACT FROM AUTHOR]

This work examines the direct impact of solar events on the financial sector of the United States, while also investigating their indirect effects on other sectors of the US economy. The study introduces a cutting-edge methodology based on the Global Vector Autoregressive (GVAR) model, utilizing a comprehensive dataset, to estimate indirect global impulse response functions. By putting forward this approach, the study highlights the significant contribution of solar events on financial and insurance activities, establishing a clear connection to the broader US economy. Moreover, it quantifies and demonstrates the direct and indirect influence of solar events on the US economy, paving the way for future in-depth investigations in this field. [ABSTRACT FROM AUTHOR]

We intend to show the directional volatility spillovers between the European short term interbank lending rates (3‐month Euro Interbank Offered Rate [EURIBOR] and Euro Short‐Term Rate [ESTR]) and the Hungarian Budapest Interbank Offered Rate (BUBOR) and Euro‐Hungarian Forint exchange rate. To determine the extent to which the variables affect each other's volatilities we build a five‐variable vector autoregression (VAR) and determine the spillover table like in Diebold‐Yilmaz's 2012 work. This methodology is preferred to a simple impulse response function (IRF) because we manage to avoid the problem of non‐orthogonal innovations via the generalized forecast error variance decomposition framework. The issue of variable ordering, therefore, does not arise. We focus on three episodes of increased volatility in Hungarian and European short‐term interest rates: Q3–Q4 of 2019, Q1 of 2020 and Q3 of 2022. These episodes correspond to volatility spikes in EU markets that to some extent had a measurable spillover effect on Hungarian interbank rates. We find that on average, across the entire sample of 957 observations, about 6.3% of the volatility forecast error variance in all five European and Hungarian variables comes from spillovers. The total and directional spillovers over the sample are extremely low. We conclude that the European Central Bank's surprise policy decisions have a marginal impact on Hungarian interbank rates. We also find that BUBOR is primarily a net receiver of spillovers from the MAX short‐term government bond benchmark rather than the EURIBOR—this disproved our initial considerations. [ABSTRACT FROM AUTHOR]

This study investigates the effect of financial development on inequality in Bangladesh. We run a vector autoregressive (VAR) model for the economy of Bangladesh from 1980 to 2020. Using the impulse response functions (IRFs), we find that the disposable income and market income Gini coefficients respond positively to private credit. It suggests that financial development contributes to raising inequality in Bangladesh. We also find that growth has an adverse impact on inequality whereas income inequality has a positive effect on economic growth. However, economic growth has a greater and more pronounced negative effect on inequality than inequality itself, even though these two variables exhibit opposite interaction responses. It indicates that growth lowers inequality in Bangladesh. The forecast error variance decomposition function shows that private credit can explain the variations of disposable income Gini by 8.65 percent; on the other hand, disposable income Gini can explain the variation of private credit by 8.90 percent. [ABSTRACT FROM AUTHOR]

In this paper, a new measure for uncertainty that affects the economy is proposed, constructed, and applied to an emerging economy, Türkiye. We have constructed an index of economic country-specific uncertainty (ECSU) that is in line with the methodology used in constructing economic policy uncertainty indexes. As the economic uncertainty is of the Knightian type, the essence of measuring it lies in counting the frequency of joint appearances of words related to economics and uncertainty in Turkish-language newspapers. The uncertainty index constructed using local language sources- Turkish performs significantly better in measuring country-specific uncertainty in Türkiye. However, some indexes use English language sources to measure uncertainty in Türkiye- did not make them country-specific. The ECSU was tested by evaluating the dynamic real effects of the uncertainty. This evaluation was performed by the analysis of impulse responses from uncertainty to some economic variables in a vector autoregressive model describing the economy of Türkiye. We find that an unexpected increase in uncertainty in the Turkish-language press is related to decreases in industrial production, employment, and trade. If the uncertainty measure is based on the articles from the English-language press only, no such relationship can be confirmed. We also find that an increase in uncertainty leads to increase in inflation and stock and oil prices. [ABSTRACT FROM AUTHOR]

Purpose: Foreign direct investment in the real estate (FDIRE) sector is required to bridge the gap between investment needed and domestic funds. Further, foreign direct investment is gaining importance because other sources of raising finance such as External Commercial Borrowing and foreign currency convertible bonds have been banned in the Indian real estate sector. Therefore, the objective of the study is to explore the determinants attracting foreign direct investment in real estate and to assess the impact of those variables on foreign direct investments in real estate. Design/methodology/approach: Johansen cointegration test, vector error correction model along with variance decomposition and impulse response function are employed to understand the nexus of the relationship between various macroeconomic variables and foreign direct investment in real estate. Findings: The results indicate that infrastructure, GDP and tourism act as drivers of foreign direct investment in real estate. However, interest rates act as a barrier. Originality/value: This article aimed at exploring factors attracting FDIRE along with estimating the impact of identified variables on FDI in real estate. Unlike other studies, this study considers FDI in real estate instead of foreign real estate investments. [ABSTRACT FROM AUTHOR]

This paper presents an innovative general structure for a single-loop Sigma-Delta (Σ Δ) modulator that combines low-distortion and noise-coupled techniques to achieve high-resolution for low-power applications. The low-distortion technique adjusts the signal transfer function of the proposed structure to unity, while the noise-coupled technique enhances the order of quantization noise-shaping at the modulator output. These techniques were incorporated into the design to increase the modulator order without requiring additional operational amplifiers during its circuit implementation, resulting in a low-power modulator compared to similar structures. To reduce the number of required amplifiers, a second-order infinite impulse response (IIR) filter was used in the modulator loop filter, in place of two integrators. To assess the proposed structure's performance, a third-order modulator sample was implemented and simulated for speech recognition applications, specifically, digital hearing aids, using 180 nm complementary metal oxide semiconductor (CMOS) technology. The results indicate that for a third structure with a sampling rate of 64, an input sine signal of −6 dBFS and a sampling frequency of 2.56 MHz, the signal to noise plus distortion (SNDR) was 81.9 dB, and the dynamic range (DR) was 88 dB for a 20 KHz bandwidth. The modulator's power consumption was 126.9 μ W. Circuit and system-level simulations demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose an innovative method for determining the fill level of containers, such as trash cans, addressing a critical aspect of waste management. The method combines spatial impulse response analysis with machine learning (ML) techniques, offering a unique and effective approach for sound-based classification that can be extended to various domains beyond waste management. By employing a buzzer-generated sine sweep signal, we create a distinctive signature specific to the fill level of the waste container. This signature, once accurately decoded, is then interpreted by a specially developed ensemble learning algorithm. Our approach achieves a classification accuracy of over 90% when implemented locally on a development board, optimizing operational efficiencies and eliminating the need to delegate complex classification tasks to external entities. Using low-cost and energy-efficient hardware components, our method offers a cost-effective approach that contributes to sustainable and efficient waste management practices, providing a reliable and locally deployable solution. [ABSTRACT FROM AUTHOR]

The present study investigates the price behaviour, structural breaks, price cointegration and price transmission in major onion markets of India in light of the COVID-19 pandemic. The findings of this study revealed that seasonal price indices were highest in November for southern region markets. A major structural break in the selected markets was noted in August or September 2019, showing that the structural break did not align with the COVID-19 pandemic lockdown period as anticipated. Further, the study found 11 cointegration equations at 1% significance level by trace test, indicating long-term cointegration in onion markets despite their geographical segmentation. Some markets exhibited bi-directional or uni-directional influences of prices on other selected markets, with Delhi showing the highest number of bi-directional influences. Additionally, the impulse response function revealed that the Delhi price shock had a positive effect up to seven periods, which later became a follower. [ABSTRACT FROM AUTHOR]

This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering–learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring. [ABSTRACT FROM AUTHOR]

In technology and industrial production, many applications require wide-bandwidth current measurements. In this paper, a signal fusion scheme for a current sensor comprising tunneling magnetoresistance and a current transformer is proposed, achieving a flat frequency response in the DC to MHz range. The measurement principles in different cases of the scheme are introduced, and the total transfer function of the entire scheme is derived by analyzing each section separately. Furthermore, the feasibility and selected parameters of the scheme are verified through a systematic simulation utilizing the MATLAB software. Based on the proposed scheme, a group of principal prototypes are built to experimentally evaluate the bandwidth, amplitude and phase flatness, accuracy, sensitivity, and impulse response. The relative amplitude variation in the passband of the fusion sensor is less than 4%, and the estimated bandwidth of the fusion sensor is close to 17 MHz. The accuracy is better than 0.6%, even when measuring the current at 1 MHz, and the relative standard deviation is 5% when measuring the impulse signal. The sensors developed using this scheme, with a low financial cost, have advantages in many wide-bandwidth current measuring scenarios. [ABSTRACT FROM AUTHOR]