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In this study, we propose a method for recommending appropriate combinations of stocks based on the waveforms of stock price changes. Many Japanese prefer to maintain stability when managing their assets. Specialized knowledge is required to invest in stocks while reducing risk. Hence, stock recommendation methods with different characteristics are required. Stock price movements can be captured as waveforms. Dynamic time warping (DTW), cross–correlation functions, and fast Fourier transforms (FFTs) are used to compare the features of the waveforms. A combination of stocks with different waveforms avoids the risk of simultaneous crashes. In the current experiment, one–year stock waveforms are used to obtain the recommended stock. The combined stocks selected using the DTW, cross–correlation functions, and FFT are shown to be suitable. [ABSTRACT FROM AUTHOR]

This article proposes a compact compressor adder of Vedic multiplication for an area-efficient FFT architecture. A standard multi-radix-24,22,23 FFT with a single-path delay feedback structure is considered. Vedic multipliers employ the Urdhva Tiryakbhyam method, which reduces redundant steps and generates parallel partial products. The proposed 4–2 compressor adders have been introduced inside the vedic multiplier to minimize carry delay and speed up the multiplication process. The vedic multiplier designed on the compressor adder saves power and gate count. The devised FFT algorithm is implemented using 45nm CMOS technology. Simulation results show a gate reduction of 21.5% and power consumption of 18.5%. The throughput had increased to 1.86 GS/s at 186 MHz compared to existing FFT architectures. [ABSTRACT FROM AUTHOR]

Highlights A novel image‐driven deep learning approach embedded with model‐data‐knowledge information can directly predict the full field stress distribution and mechanical properties of composites solely based on initial scanning geometry images. The fiber spatial distribution and morphological features are identified by Scanning Electron Microscopy (SEM) initially. An effective random fiber generation algorithm is further utilized to generate images database comprising equivalent geometric models of various microstructures. Subsequently, the geometry model images database is analyzed by fast Fourier transform (FFT) to produce the database including the elastic modulus parameters and full field stress distributions of composites with distinct microstructures. Finally, an innovative image‐learning comprehensive paradigm uniting convolutional neural networks and convolutional autoencoders is elaborated systematically to learn the inherent laws of geometry images and field images. The results show that the proposed method have capacity to effectively and accurately predict the elastic modulus and full field stress distributions combined with error analysis even for stochastic boundary composites. The proposed methodology is a symbiosis of cutting‐edge image learning and non‐destructive testing techniques for composites, which can directly use the local non‐destructive or scanning images of composite structural components to quickly obtain field information and further predict damage evolution online. Proposing a novel deep learning approach combined with FFT directly to predict stress distribution of stochastic boundary composites solely based on micro‐images. Adopting equivalent geometric models dispersed by higher pixels mesh density considering extreme thin interface. Incorporate adaptive algorithms for streamlined training optimization. An innovative integration of deep learning and non‐destructive testing technology for the stress distribution and properties prediction online. [ABSTRACT FROM AUTHOR]

The affordability and accessibility of electrical data related to microgrids and active distribution networks (ADN) have significantly improved due to smart digital relays with advanced communication capabilities. These relays can communicate with each other and central protection systems. Due to data accessibility a machine learning (ML) based module is proposed. This module predicts both fault type and location within an IEEE 34 bus active distribution system featuring distributed generations. To design this module, current measurements are retrieved from five specified bus locations and further Fast Fourier Transform is applied for feature extraction. The feature creation method is much simpler without any complexity. These features are further used for ML module’s training. Practical ADN operating conditions are considered when curating the training dataset. Weighted K-nearest neighbors are used for fault type prediction and linear regression is applied for fault location estimation and they are proven to be superior to other ML approaches. The obtained results show high accuracy and reliability in predictions, making these modules suitable for ADN protection. They also exhibit robustness against input measurement noise, ensuring consistent protection in various operating scenarios. The ADN is simulated in SIMULINK 2020a, and ML modeling is done using MATLAB 2020a. [ABSTRACT FROM AUTHOR]

In the milling process of composite materials, the initial chatter frequency is not obvious and is easily swamped by the rest of the signals, making frequency monitoring difficult, so the study proposes a chatter frequency monitoring method based on frequency cancellation algorithms and wavelet packet decomposition. The results of the experiments shown that the frequency cancellation algorithm can successfully remove invalid signals, such as spindle rotation frequency and cutter tooth frequency, and only keep the necessary signals, at which point the chattering frequency may be observed at a frequency of roughly 1333 Hz. The influence of the frequency bands s5, s9, s10, s12, and s13 after de-frequency removal should be avoided because they all have a low energy share of roughly 23 %, 0.9 %, 5 %, 10 %, and 16 %, respectively, and are less sensitive to chatter. For milling edge depths of 0.5 mm, 2 mm, and 4 mm, the average chatter thresholds were around 3.27, 2.9, and 2.89, respectively. It was challenging to pinpoint the chatter of the system because the empirical modal decomposition observed an average chatter energy entropy of just 1.55 and found that its fluctuations at the milling edge depth junction were insignificant. On the other hand, the chattering could be plainly seen since the energy entropy experienced a substantial and dramatic fluctuation at the intersection of the milling edge depth when it was about 2.9, 2.6, and 2.5, respectively. The experimental findings demonstrated that the frequency cancellation technique and wavelet packet decomposition-based chattering frequency monitoring approach can precisely track the chattering state of the system. [ABSTRACT FROM AUTHOR]

We present a new term-structure model for commodity futures prices based on Trolle and Schwartz (2009), which we extend by incorporating seasonal stochastic volatility represented with two different sinusoidal expressions. We obtain a quasi-analytical representation of the characteristic function of the futures log-prices and closed-form expressions for standard European options' prices using the fast Fourier transform algorithm. We price plain vanilla options on the Henry Hub natural gas futures contracts, using our model and extant models. We obtain higher accuracy levels with our model than with the extant models. [ABSTRACT FROM AUTHOR]

As one of the main components of the Earth orientation parameters, short-term prediction of the geodetic polar motion series is crucial in the field of deep-space exploration, high-precision positioning, and timing services, which require high real-time performance. Additionally, its middle- and long-term prediction is equally important in climate forecasting and geodynamics research. In this study, we propose the combined BiLSTM+ARIMA model, which is based on bidirectional long- and short-term memory (BiLSTM) and autoregression integrated moving average (ARIMA). First, ensemble empirical mode decomposition (EEMD) is performed as a filter to decompose the polar motion time series to obtain low- and high-frequency signals. The EOP14 C04 time series provided by International Earth Rotation and Reference Systems Service and decomposed by EEMD includes low-frequency signals like the long-term trend, decadal oscillation, Chandler wobble, and prograde annual wobble, along with shorter-period high-frequency signals. Second, low- and high-frequency signals are predicted using BiLSTM and ARIMA models, respectively. Finally, the low- and high-frequency signal forecast components are reconstructed to obtain geodetic polar motion predictions. In middle- and long-term polar motion prediction, the results show that the proposed model can improve the prediction accuracy by up to 42% and 17%, respectively. This demonstrated that the BiLSTM+ARIMA model can effectively improve the accuracy of polar motion prediction. [ABSTRACT FROM AUTHOR]

In this article, the foreign equity price and the foreign exchange rate are supposed to follow the regime-switching multi-scale jump-diffusion processes. In addition, the correlations of the two processes are not only manifested in the diffusion parts but also in the jump components. First, the Esscher transform is used to identify an equivalent martingale measure since the financial market is incomplete. Then, some pricing problems of foreign equity options are studied and the analytic solutions of pricing formulae are derived by using Fourier transform method. Numerical results are given by the fast Fourier transform algorithm and analysis are also presented. Moreover, the numerical result shows that the economic states and model's parameters have significant effects on the values of these different kinds of foreign equity options. Finally, empirical studies are provided to illustrate the practical implementation of the proposed pricing model. [ABSTRACT FROM AUTHOR]

Unsteady characteristics of pinhole-ejected ultrahigh-pressure hydrogen jets in the near field were investigated by performing particle image velocimetry and fast Fourier transform–inverse fast Fourier transform (IFFT) analysis of time-series shadowgraph images. Hydrogen stored at a maximum pressure of 99 MPa was ejected through pinholes of varying diameters. The fluctuating components caused by turbulence were removed to identify the barrel shock within the high-pressure hydrogen jets. The relationship between the Mach disk height and pressure ratios obtained through time-averaging aligned with the Mach disk height measured in experiments involving non-hydrogen gases under low ambient pressure. The obtained results exhibited similarities to the time-averaged velocity observed in the far field, suggesting the feasibility of utilizing Schlieren image velocimetry to analyze jet velocity characteristics in the near field. By applying IFFT analysis, the spatiotemporal information of these components was reintegrated into the field, enabling separate identification and demonstration of the turbulent components comprising the high-pressure hydrogen jet. [Display omitted] [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) with wearable inertial sensors is a burgeoning field, propelled by advances in sensor technology. Deep learning methods for HAR have notably enhanced recognition accuracy in recent years. Nonetheless, the complexity of previous models often impedes their use in real-life scenarios, particularly in online applications. Addressing this gap, we introduce SenseMLP, a novel approach employing a multi-layer perceptron (MLP) neural network architecture. SenseMLP features three parallel MLP branches that independently process and integrate features across the time, channel, and frequency dimensions. This structure not only simplifies the model but also significantly reduces the number of required parameters compared to previous deep learning HAR frameworks. We conducted comprehensive evaluations of SenseMLP against benchmark HAR datasets, including PAMAP2, OPPORTUNITY, USC-HAD, and SKODA. Our findings demonstrate that SenseMLP not only achieves state-of-the-art performance in terms of accuracy but also boasts fewer parameters and lower floating-point operations per second. For further research and application in the field, the source code of SenseMLP is available at [ABSTRACT FROM AUTHOR]

In IoT applications, prediction models have fundamental challenges such as real-time processing, producing results with considerable/without delay, and taking action against pattern drift. While existing models can excel when data statistics remain relatively stable, real-time systems may encounter difficulties, particularly when confronted with dynamic shifts in data behavior. Analyzing data streams generated by different IoT applications and detecting complex pattern on the fly has become an open area of research. Complex event processing with adaptivity is a must to get desired features in such models. To address this issue, a comprehensive model for prediction has been proposed in this paper. It consists of two phases: (1) the basic model is constructed using historical data, (2) a fast Fourier transform-based adaptive support vector regression (FFT-ASVR) approach is proposed to predict events embedded in IoT data streams. FFT-ASVR predicts abnormal events by experiencing a change in data streams with real-time model updation. The performance of FFT-ASVR with a similar existing method SVM-RBF is presented using real-time traffic data of Madrid city. The proposed approach has significant improvement in terms of mean absolute percentage error (MAPE) for prediction, is adaptive in nature, and is also capable of handling the issue of pattern drift. [ABSTRACT FROM AUTHOR]

The article contains a study of methods for solving integral equations in the context of acoustic problems. The methodology considered is applied to describe acoustic wave propagation and scattering. Efficient discretization methods are used together with iterative methods to solve the operator equations, including an apparatus for fast multiplication of the resulting post-discretization Toeplitz matrices by a vector using the fast Fourier transform. The theoretical analysis of the proposed numerical algorithm demonstrates its efficiency in terms of the required number of arithmetic operations and the memory footprint of the computing system. The presented numerical simulation demonstrates the possibility of solving the problem of acoustic wave propagation in transparent media using the proposed methods. A visualization of the obtained solutions for a practical problem with a high level of discretization of the solution volume domain is also presented. [ABSTRACT FROM AUTHOR]

This paper presents a study of 2D roughness profiles on a flat surface generated on a steel workpiece by ball nose end milling with linear equidistant tool paths (pick-intervals). The exploration of the milled surface with a surface roughness tester (on the pick and feed directions) produces 2D roughness profiles that usually have periodic evolutions. These evolutions can be considered as time-dependent signals, which can be described as a sum of sinusoidal components (the wavelength of each component is considered as a period). In order to obtain a good approximate description of these sinusoidal components, two suitable signal processing techniques are used in this work: the first technique provides a direct mathematical (analytical) description and is based on computer-aided curve (signal) fitting (more accurate); the second technique (synthetic, less accurate, providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform. This study can be seen as a way to better understand the interaction between the tool and the workpiece or to achieve a mathematical characterisation of the machined surface microgeometry in terms of roughness (e.g., its description as a collection of closely spaced 2D roughness profiles) and to characterise the workpiece material in terms of machinability by cutting. [ABSTRACT FROM AUTHOR]

The discrete Fourier transform (DFT) is the most commonly used signal processing method in modern digital sensor design for signal study and analysis. It is often implemented in hardware, such as a field programmable gate array (FPGA), using the fast Fourier transform (FFT) algorithm. The frequency resolution (i.e., frequency bin size) is determined by the number of time samples used in the DFT, when the digital sensor's bandwidth is fixed. One can vary the sensitivity of a radio frequency receiver by changing the number of time samples used in the DFT. As the number of samples increases, the frequency bin width decreases, and the digital receiver sensitivity increases. In some applications, it is useful to compute an ensemble of FFT lengths; e.g., 2 P − j for j = 0 ,   1 ,   2 ,   ... ,   J , where j is defined as the spectrum level with frequency resolution   2 j · Δ f . Here Δ f is the frequency resolution at j = 0 . However, calculating all of these spectra one by one using the conventional FFT method would be prohibitively time-consuming, even on a modern FPGA. This is especially true for large values of P ; e.g., P ≥ 20 . The goal of this communication is to introduce a new method that can produce multi-resolution spectrum lines corresponding to sample lengths   2 P − j for all J + 1 levels, concurrently, while one long 2 P -length FFT is being calculated. That is, the lower resolution spectra are generated naturally as by-products during the computation of the 2 P -length FFT, so there is no need to perform additional calculations in order to obtain them. [ABSTRACT FROM AUTHOR]

We experimented analyzing motor vibration with aid of Raspberry Pi when, at that time, the engine vibration was abnormal. The Pi signal is transmitted to a relay by the motor supply disconnection. The control unit, nevertheless, monitors and sends the data to the storage system in good form with proper temperature. A FO-PID controller is utilized to analyze the effects of IM due to harmonic current, vibration, and noise. The induction motor's response to harmonic and current fluctuations is stabilized by a FO-PID controller. The findings can be displayed on the mobile. The tests were carried out in a static state of vibration condition, and fast Fourier transformation is used to analyze the measured vibration data signals. The results of this model were based on the convolutional neural network (CNN), which considerably monitors early diagnostics of the vibration. With a maximum delay of around 1 s, the controller can forward cloud vibration data. Using the CNN model train to analyze the performance of the classification accuracy, the stored data are collected. This article offers a novel way of building tools for measuring vibration in real time based on the schematic architecture provided by the Python mode. [ABSTRACT FROM AUTHOR]

Forward modelling of potential field data is an important part of optimization algorithms used to invert large datasets such as those involving rugged terrain or borehole data. Two‐dimensional fast Fourier transform modelling with a prism or a dipole is one of the most efficient methods compared to the forward modelling in the space domain. However, the exact solution of a prismatic source is limited to the case of a half‐space with the computation of data on a horizontal datum above the topography. Starting from the three‐dimensional Fourier forward modelling analytical formulation for a prism, an integration according to the wavenumber w is accomplished which allowed to find a two‐dimensional Fourier exact analytical formulation outside, at the interfaces of, and inside a prism for all potential field components. This new formulation requires the calculation of only four integrals. The gravity and magnetic fields are computed with this two‐dimensional fast Fourier transform formulation in the entire domain and compared with the analytical space domain and the three‐dimensional fast Fourier transform formulations. From the three‐dimensional calculated field, each component can be interpolated with the tri‐linear interpolation method along a borehole or on a drape surface simulating an airborne survey. Based on experiments demonstrated in this work, the two‐dimensional formulation in the Fourier domain gave accurate results with greater speed of execution in comparison to modelling in the space domain. The forward modelling method is tested on real gravity data from the north of Alberta (Canada). [ABSTRACT FROM AUTHOR]

In this paper, we consider a general notion of convolution. Let D be a finite domain and let D n be the set of n-length vectors (tuples) of D . Let f : D × D → D be a function and let ⊕ f be a coordinate-wise application of f. The f -Convolution of two functions g , h : D n → { - M , ... , M } is (g ⊛ f h) (v) : = ∑ v g , v h ∈ D n s.t. v = v g ⊕ f v h g (v g) · h (v h) for every v ∈ D n . This problem generalizes many fundamental convolutions such as Subset Convolution, XOR Product, Covering Product or Packing Product, etc. For arbitrary function f and domain D we can compute f -Convolution via brute-force enumeration in O ~ (| D | 2 n · polylog (M)) time. Our main result is an improvement over this naive algorithm. We show that f -Convolution can be computed exactly in O ~ ((c · | D | 2 ) n · polylog (M)) for constant c : = 3 / 4 when D has even cardinality. Our main observation is that a cyclic partition of a function f : D × D → D can be used to speed up the computation of f -Convolution, and we show that an appropriate cyclic partition exists for every f. Furthermore, we demonstrate that a single entry of the f -Convolution can be computed more efficiently. In this variant, we are given two functions g , h : D n → { - M , ... , M } alongside with a vector v ∈ D n and the task of the f -Query problem is to compute integer (g ⊛ f h) (v) . This is a generalization of the well-known Orthogonal Vectors problem. We show that f -Query can be computed in O ~ (| D | ω 2 n · polylog (M)) time, where ω ∈ [ 2 , 2.372) is the exponent of currently fastest matrix multiplication algorithm. [ABSTRACT FROM AUTHOR]

• A time domain evaluation technique to validate the band structure is proposed. • The application of the localized radial basis function collocation method is extended. • The direct method is further applied to enhance the performance of the method. In this paper, the localized radial basis function collocation method is applied to investigate the in-plane elastic wave propagation of 2D phononic crystals. The direct method with local support domain in one line is used to enhance the stability of the localized radial basis function collocation method when dealing with the derivative calculations in the boundary and interface conditions. The Houbolt method is employed to discretize the time, and then the Fast Fourier Transform is adopted to obtain the band structure according to the wave vectors in the first Brillouin Zone. Different acoustic impedance ratios, filling rates, and lattice forms are studied. The numerical results can be used to validate the band structures obtained directly from the frequency domain. [ABSTRACT FROM AUTHOR]

This study examines the possible utilization of machine learning and decision-making in the woodworking sector. This refers to the recognition of certain sounds produced during tool idling. The physical and geometric properties of the circular saw blade result in different noises being generated during idling. It was assumed that the respective circular saw blades can be recognized by these noises. The noises of three different circular saw blades were examined while idling at the same speed. In order to obtain useful data for the deep learning process, the coarse signals were subjected to frequency analysis. A total of 240 noise samples were taken for each circular saw blade and later subjected to signal processing. Frequency-power spectra were created using a custom program in Matlab Campus Edition software, such as for the spectrograms. A short Fourier transform was used to create the average spectral density plot using self-made software. The input data for the deep learning network was created in Matlab using a custom program. The GoogleNet deep learning network was used as a data classifier. After training the network, an accuracy of 97.5% was achieved in recognizing circular saw blades. [ABSTRACT FROM AUTHOR]

Dataflow architecture is an alternative to traditional von Neumann computing architecture. However, known variants of dataflow architecture have a range of serious problems with no effective solutions up to the present day. This paper represents Hybrid Recurrent Signal Processor's (HRSP) hardware verification results. It describes HRSP's register transfer level model implementing its architectural specification and hardware prototype on the HAN Pilot Platform development board with Intel Arria10 field-programmable gate array. HRSP consists of a von Neumann master processor on a control layer and a recurrent dataflow unit on an operational layer. Dataflow unit includes four computing cores. HRSP's hardware model combines either software or hardware implementation of the control processor and the hardware model of the operational layer. Testing the HRSP's hardware prototype on the development board using an isolated word recognizer (IWR) as a typical data processing application has proven that the hardware model is bit-exact with both HRSP's imitation model and the original IWR C++ model. The HRSP's hardware prototype's achieved performance ensures IWR's operation in real-time mode on the development board. It is slightly better than the performance of the TMSC55x (Texas Instruments) digital signal processor. Verification of the HRSP's hardware implementation on synthetic tests showed that its average performance is 5% higher than the performance of the DSP TMSC55x digital signal processor. The results of the proposed optimization of hardware support for Fast Fourier Transform (FFT) in HRSP prove that such an optimization speeds up the FFT calculation, significantly reduces the capsule size, reduces the required hardware resources and simplifies FFT scaling. [ABSTRACT FROM AUTHOR]

In the field of water depth inversion using imagery, the commonly used methods are based on water reflectance and wave extraction. Among these methods, the Optical Bathymetry Method (OBM) is significantly influenced by bottom sediment and climate, while the wave method requires a specific study area. This study introduces a method combining the FFT and spatial profile measurement to invert the wavelength of the wave bathymetry method (WBM), which enhances accuracy and reduces workload. The method was applied to remote sensing images of Sanya Bay in China, obtained from the Worldview satellite. The average error of the inverted depth results after applying the wavelength inversion technique was 15.9%, demonstrating consistency with the depth measurements obtained through the OBM in clear water of the bay. The WBM has notable advantages over the OBM, as it is unaffected by water quality. In addition, the influence of wave period on the accuracy of water depth retrieval was theoretically evaluated, revealing that a larger wave period leads to a better depth measurement. The depth measurement from two images with different wave periods aligned with the theoretical analysis. These results showcase the applicability and potential of the WBM for accurately estimating water depth in various coastal environments. [ABSTRACT FROM AUTHOR]

The Total Harmonic Distortion (THD) value is an important metric used to evaluate the performance of various devices and systems in electrical systems. A low THD value is expected for optimal performance. In this study, the THD value of the output voltage of a cascaded H-bridge multi-level inverter (CHB-MLI) was optimized by using a newly developed Dwarf Mongoose Optimization (DMO) algorithm. The performance of the adapted DMO algorithm has been tested alongside the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms on a three-phase, nine-level cascaded H-bridge multi-level inverter (CHB-MLI). Convergence, consistency, and speed tests have been conducted for these algorithms. After detailed validation through MATLAB simulations, the results were analyzed, and it was concluded that the DMO algorithm outperformed the mentioned algorithms in the modulation index range of 0.1 to 1.0. In conclusion, it has been demonstrated that the DMO algorithm can effectively optimize the THD value of the output voltage of CHB-MLI. [ABSTRACT FROM AUTHOR]

This paper reports on a comparative experimental study of the current waveforms accompanying the propagation of creeping discharges over different liquid/pressboard interfaces under both negative and positive lightning impulse voltages; the investigated insulating liquids are mineral oil (MO), a synthetic ester (SE), a natural ester (NE) and an extra virgin olive oil (OO). Two different arrangements are used; in the first one, the point electrode was positioned in a perpendicular direction to the pressboard, and in the second one, the pressboard was positioned parallel to the axis of the point electrode. Due to the similarity of the recorded creeping discharge current shapes, it is hard to differentiate between the currents measured for each type of interface with time domain approaches. To circumvent this difficulty and identify the specific feature(s) associated to each type of interface, two techniques are used to analyze the signal of the discharge currents, namely discrete wavelet and fast Fourier-transform methods. It is shown that: (1) for the first arrangement, there is a particular spectral signature for each type of liquid/pressboard interface, and (2) for the second arrangement, the obtained results are in accordance to those obtained for the first arrangement only in some cases. [ABSTRACT FROM AUTHOR]

A new harmonic analysis technique using the Littlewood–Paley dyadic decomposition is developed for constructing low-regularity integrators for the one-dimensional cubic nonlinear Schrödinger equation in a bounded domain under Neumann boundary condition, when the frequency analysis based on the Fourier series cannot be used. In particular, a low-regularity integrator is constructively designed through the consistency analysis by the Littlewood–Paley decomposition of the solution, in order to have almost first-order convergence (up to a logarithmic factor) in the $L^{2}$ norm for $H^{1}$ initial data. A spectral method in space, using fast Fourier transforms with $\mathcal{O}(N\ln N)$ operations at every time level, is constructed without requiring any Courant-Friedrichs-Lewy (CFL) condition, where $N$ is the degrees of freedom in the spatial discretization. The proposed fully discrete method is proved to have an $L^{2}$ -norm error bound of $\mathcal{O}(\tau [\ln (1/\tau)]^{2}+ N^{-1})$ for $H^{1}$ initial data, where $\tau $ is the time-step size. [ABSTRACT FROM AUTHOR]

We propose a unified transform-based method, which we call the extended double spiral (EDS) method, for pricing arithmetic Asian options under general two-dimensional (2D) models that nest regime-switching Lévy models, stochastic volatility (SV) models with Lévy jumps, and time-changed Lévy models. We first construct a new single backward induction in the state space that relaxes the restriction of the independent increments of the log-asset price. Second, we build an exact and explicit double backward induction in the Fourier space based on this single backward induction, a combination of the 1D Fourier transform method and a key function characterizing the 2D model, and the double spiral method. Third, we develop a unified EDS algorithm to recursively implement this double backward induction via the fast Fourier transform (FFT), various quadrature rules, asymmetric truncation boundaries, and so on. Extensive numerical results across a broad class of 2D models, monitoring frequencies, option moneyness, and model parameters demonstrate that our method is remarkably accurate, efficient, robust, simple to implement, and widely applicable. [ABSTRACT FROM AUTHOR]

Numerical simulations, based on the theories of computational fluid dynamics and combustion, are currently a powerful tool in the field of fire engineering. Field models allow us to analyze key aspects such as the integrity of buildings and safety. However, it is fundamental to define strategies that allow engineers to obtain a balance between the precision of the results and the computational cost. One of the most relevant sub-models is the turbulence model. This paper presents the research carried out in the field of two-dimensional computational simulations of turbulent dilatable flows to evaluate the behavior of diffusion flames, hot gases, and smoke produced in accidental fires. Several computational simulations have been performed using direct numerical simulations and large eddy simulation turbulence models in the two-dimensional field, analyzing the ability of the models to correctly characterize the transport of hot gases and the behavior of the thermal plumes if the grid resolution is adequate to the physics of the problem. Additionally, several three-dimensional models have been developed to contrast and validate the results obtained in the two-dimensional simulations. In order to validate the capacity to develop a qualitative analysis of two-dimensional models in fire engineering, an evaluation criterion is presented based on the frequency spectral analysis to study the capacity of each type of turbulence model to accurately capture the vorticity of these dilatable flows. [ABSTRACT FROM AUTHOR]

Although deep learning makes great success in increasing the accuracy for long sequence time-series forecasting, its complex neural network structure, which comprises many different types of layers and each layer containing hundreds and thousands of neurons, challenges the computing and memory capability of embedded platforms. This paper proposes a lightweight and efficient neural network called TTFNet, which forecasts long time series using three types of features (i.e., the Trend, Temporal attention, and Frequency attention) extracted from raw time series. In TTFNet, we perform a pooling operation on the historical data in a recent time window to extract a general trend, use a multi-layer perceptron to discover the temporal correlation between data as temporal attention, and apply the fast Fourier transforms on data to obtain frequency information as frequency attention. Each feature is separately extracted from its neural network branch with an output result, and we weight the three results to generate the final prediction while optimal weights are learnt. Also, the three prediction results can run in parallel since they are independent from one another. The experimental results show that the proposed method reduces the memory overhead and runtime by 62% and 81% of the five counterpart methods on average while achieving comparable performance. [ABSTRACT FROM AUTHOR]

We present a numerical scheme for obtaining guaranteed (reliable) and arbitrarily close two sided bounds to effective (homogenized) parameters of the linear elasticity problem. For the upper bounds, we use standard finite element (FE) discretization of the so‐called primal problem with preconditioning based on the fast discrete Fourier transformation (FFT). For the lower bounds, we use the dual formulation and some smoother FE approximation spaces. Moreover, instead of solving the discretized dual problem, we can only compute an L2‐orthogonal projection of an auxiliary field built from the primal solution. The projection can be computed easily by FFT and provides a lower bound of almost the same quality as that obtained as the exact solution of the discretized dual problem. In addition, a simple low‐dimensional optimization improves the projected solution. Numerical examples are presented to support the theoretical developments. [ABSTRACT FROM AUTHOR]

The basic information survey on homesteads requires understanding the shape of homesteads, and the shape of the homesteads based on the spatial location can reflect information such as their outline and regularity, but the current shape classification of rural homesteads at the parcel scale lacks analytical methods. In this study, we endeavor to explore a classification model suitable for characterizing homestead shapes at the parcel scale by assessing the impact of various research methods. Additionally, we aim to uncover the evolutionary patterns in homestead shapes. The study focuses on Yangdun Village, located in Deqing County, Zhejiang Province, as the research area. The data utilized comprise Google Earth satellite imagery and a vector layer representing homesteads at the parcel scale. To classify the shapes of homesteads and compare classification accuracy, we employ a combination of methods, including the fast Fourier transform (FFT), Hu invariant moments (HIM), the Boyce and Clark shape index (BCSI), and the AlexNet model. Our findings reveal the following: (1) The random forest method, when coupled with FFT, demonstrates the highest effectiveness in identifying the shape categories of homesteads, achieving an average accuracy rate of 88.6%. (2) Combining multiple methods does not enhance recognition accuracy; for instance, the accuracy of the FFT + HIM combination was 88.4%. (3) The Boyce and Clark shape index (BCSI) proves unsuitable for classifying homestead shapes, yielding an average accuracy rate of only 58%. Furthermore, there is no precise numerical correlation between the homestead category and the shape index. (4) It is noteworthy that over half of the homesteads in Yangdun Village exhibit rectangular-like shapes. Following the "homesteads reform", square-like homesteads have experienced significant vacating, resulting in a mixed arrangement of homesteads overall. The research findings can serve as a methodological reference for the investigation of rural homestead shapes. Proficiency in homestead shape classification holds significant importance in the realms of information investigation, regular management, and layout optimization of rural land. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel approach using dominant frequency analysis to diagnose inner race faults in rolling ball bearings of three-phase induction motor. The main objective of the proposed scheme is to identify damaged bearings by analyzing their characteristic frequency components within a specific time interval in segmented signal. This work has been carried out on vibration signal data provided by Bearing Center Case Reserve Western University (CWRU), USA. In this study, IR007, IR014 and IR021 bearing defects are analyzed by Frequency Statistical Analysis in MATLAB. Mean and standard deviation of dominant frequencies are computed from the recorded vibration signals after dividing the signal into multiple segments of equal length. It is observed that both frequency mean, and standard deviation have been found to be highly sensitive with variations of motor speed and connected load. Therefore, motor speed is also studied to calculate the statistical parameters. The test results suggest that the proposed scheme provides comprehensive information about fault analysis through vibration data and could potentially aid researchers in fault analysis using the CRWU datasets. Overall, this paper presents a promising approach to diagnose the faults in the inner race of rolling ball bearings using frequency domain analysis and statistical parameters. [ABSTRACT FROM AUTHOR]

In this paper, the internal structure and dielectric properties of unsaturated polyester resin-based neat and single-walled carbon nanotube reinforced composites were comprehensively evaluated with the fractal analysis using the Fast Fourier Transform (FFT). The greyscale images, bitmap (BMP) images and 3D tomographic images were obtained by converting the scanning electron microscope images of the materials. It was observed that the distributions of components in the resin for both materials are irregular and their surfaces exhibit anisotropic behaviors. The surface coating rate (SCR) and fractal dimensionality (FD) of the materials were also calculated using the power spectrum. It has been observed that the fractal dimensionality of the composites can be changed by the doping process and the fractalization of the nanotube doped sample increases compared to the pure material due to nanotube agglomeration, spatial distribution and the orientation. The increase in fractalization as a result of this agglomeration and orientation in carbon nanotubes explains the high dielectric constant values observed at low frequencies by increasing the number and size of carbon nanotubes clusters that act as micro capacitors in certain regions of the matrix. It has been reported that the calculations for the surface coverage ratios for both samples also support these results. [ABSTRACT FROM AUTHOR]

The Tapi two-strings lute is typically made from Pulai (Alstonia spp.), a light hardwood in the Apocynaceae family. This study determines the tuning retrieval of the Tapi two strings lute instrument. This work investigated the string notes using a microphone for recording the Fast Fourier Transform (FFT) of the signal to determine the fundamental and the partial frequency of Tapi two strings lute instrument. The Tapi was tuned to the E major key with F#, C#, G#, and D# note. The time frequency analysis (TFA) showed that each open string has its own timbre characteristics. The open string 1 showed only 3 important partials, whereas open string 2 showed 6 important partials. Frets 2, 4, 6, and 8 showed more partials than frets 1, 3, 5, and 7. Both fret 9 and 10 showed a similar number of partials. Frets 11, 12, 13, 14, 15, and 16 showed less partials and were evenly distributed, whereas fret 17 clearly showed only 4 partials. [ABSTRACT FROM AUTHOR]

This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach. [ABSTRACT FROM AUTHOR]

This paper details an efficient procedure to extract and analyze the pipe system characteristics from the pressure and stress waves generated during the first half-period of the water hammer with fluid–structure interaction (FSI). To this end, a new transient signal is generated using the first half-period of pressure heads through verified mathematical and numerical computations in the time domain. Then, the system's resonant frequencies are extracted from the new transient signal by employing a fast Fourier transform (FFT) algorithm. The extraction procedure is verified using the analytical results from the literature. The analytical relations of the dominant resonant frequencies corresponding to the pressure and stress waves in the new transient signal are also derived to analyze the frequency data captured using the proposed approach. The effects of the stress wave (structural) frequencies are closely investigated, considering several hypothetical case studies. Both major FSI mechanisms during water hammer in a reservoir–pipe–valve​ system, namely Poisson and junction, are taken into account. The results demonstrate that the presented approach can manifest the hydraulic and structural properties of the pipeline system. The main stress wave frequencies result in a considerable increase in the amplitude of the extracted resonant frequencies, whereas a reduction effect is revealed due to the reflected stress wave frequencies. This research is of crucial importance for system identification in pipelines. [ABSTRACT FROM AUTHOR]

A system, the synthesis convolver, is described that can process the output of an analysis filter bank and: • Reconstruct the analysis filter bank input signal, • Implement continuous convolution on the reconstructed signal, and • Resample the reconstructed signal to different sample rates. The synthesis convolver combines the capabilities of a synthesis filter bank and a continuous convolver. The synthesis convolver is based on earlier work but improves upon it, adding convolution and resampling. As well as reconstructing filterbank data, convolution allows dechirping of pulsar signals and resampling allows synthesized data to conform to the VLBI VDIF standard. The spectral overlap-add approach described, compared to earlier work, reduces errors and is more robust to channel gain errors. The system uses windows with smoothed or apodized edges, with the classical Tukey window being used previously. Here the Tukey window is generalized leading to a class of apodized windows. This class of windows is explored and one is found that is close to optimal in all conditions and can reduce errors by up to 40 dB compared to an equivalent Tukey window. Achievable aliasing errors are lower than those of a standard polyphase synthesis filter bank. The synthesis convolver provides a high quality and versatile replacement for polyphase synthesis filter banks. [ABSTRACT FROM AUTHOR]

Based on the splitting form of the Green's function, a hybrid fast algorithm is proposed for efficient analysis of multiscale problems. In this algorithm, the Green's function is a priori split into two parts: a spectrally band-limited part and a spatially localized part. Then, the fast Fourier transforms (FFT) utilizing the global Cartesian grid and the matrix compression method aided by an adaptive octree grouping are implemented for these two parts, respectively. Compared with the traditional methods which only employ the FFT for acceleration, the proposed hybrid fast algorithm is capable of maintaining low memory consumption in multiscale problems without compromising time cost. Moreover, the proposed algorithm does not need cumbersome geometric treatment to implement the hybridization, and can be established in a concise and straightforward manner. Several numerical examples discretized with multiscale meshes are provided to demonstrate the computational performance of proposed hybrid fast algorithm. [ABSTRACT FROM AUTHOR]

In this paper we compare three different orthogonal systems in L 2 (R) which can be used in the construction of a spectral method for solving the semi-classically scaled time dependent Schrödinger equation on the real line, specifically, stretched Fourier functions, Hermite functions and Malmquist–Takenaka functions. All three have banded skew-Hermitian differentiation matrices, which greatly simplifies their implementation in a spectral method, while ensuring that the numerical solution is unitary—this is essential in order to respect the Born interpretation in quantum mechanics and, as a byproduct, ensures numerical stability with respect to the L 2 (R) norm. We derive asymptotic approximations of the coefficients for a wave packet in each of these bases, which are extremely accurate in the high frequency regime. We show that the Malmquist–Takenaka basis is superior, in a practical sense, to the more commonly used Hermite functions and stretched Fourier expansions for approximating wave packets. [ABSTRACT FROM AUTHOR]

Based on a brief review of forward algorithms for the computation of topographic gravitational and magnetic effects, including spatial, spectral and hybrid-domain algorithms working in either Cartesian or spherical coordinate systems, we introduce a new algorithm, namely the CP-FFT algorithm, for fast computation of terrain-induced gravitational and magnetic effects on arbitrary undulating surfaces. The CP-FFT algorithm, working in the hybrid spatial-spectral domain, is based on a combination of CANDECOMP/PARAFAC (CP) tensor decomposition of gravitational integral kernels and 2D Fast Fourier Transform (FFT) evaluation of discrete convolutions. By replacing the binomial expansion in classical FFT-based terrain correction algorithms using CP decomposition, convergence of the outer-zone computation can be achieved with significantly reduced inner-zone radius. Additionally, a Gaussian quadrature mass line model is introduced to accelerate the computation of the inner zone effect. We validate our algorithm by computing the gravitational potential, the gravitational vector, the gravity gradient tensor, and magnetic fields caused by densely-sampled topographic and bathymetric digital elevation models of selected mountainous areas around the globe. Both constant and variable density/magnetization models, with computation surfaces on, above and below the topography are considered. Comparisons between our new method and space-domain rigorous solutions show that with modeling errors well below existing instrumentation error levels, the calculation speed is accelerated thousands of times in all numerical tests. We release a set of open-source code written in MATLAB language to meet the needs of geodesists and geophysicists in related fields to carry out more efficiently topographic modeling in Cartesian coordinates under planar approximation. [ABSTRACT FROM AUTHOR]

Condition monitoring of induction machines (IMs) with the aim of increasing the machine's lifetime, improving the efficiency and reducing the maintenance cost is necessary and inevitable. Among different types of methods presented for mechanical and electrical fault tracing in induction machines, stator current signature analysis has attracted great attention in recent decades. This popularity is mainly due to the non-invasive nature of this technique. A non-recursive method named variational mode decomposition (VMD) is used for the decomposition of any signal into several intrinsic mode functions (IMFs). This technique can be employed for detection of faulty components in a current signature. However, mode mixing of extracted IMFs makes the mechanical and electrical fault detection of IMs complicated, especially in the case where fault indices emerge close to the supply frequency. To achieve this, we rectify the signal of stator current prior to applying VMD. The main advantage of the presented approach is allowing the fault indices to be properly demodulated from the main frequency to avoid mode mixing phenomenon. The method shows that the dominant frequencies of the current signal can be isolated in each IMFs, appropriately. The proposed strategy is validated to detect the rotor asymmetric fault (RAF) in a wound rotor induction machine (WRIM), in both transient and steady-state conditions. [ABSTRACT FROM AUTHOR]

Numerical solution of the time‐dependent Schrödinger equation with a position‐dependent effective mass is challenging to compute due to the presence of the non‐constant effective mass. To tackle the problem we present operator splitting‐based numerical methods. The wavefunction will be propagated either by the Krylov subspace method‐based exponential integration or by an asymptotic Green's function‐based time propagator. For the former, the wavefunction is given by a matrix exponential whose associated matrix–vector product can be approximated by the Krylov subspace method; and for the latter, the wavefunction is propagated by an integral with retarded Green's function that is approximated asymptotically. The methods have complexity O(NlogN)$$ O\left(N\log N\right) $$ per step with appropriate algebraic manipulations and fast Fourier transform, where N$$ N $$ is the number of spatial points. Numerical experiments are presented to demonstrate the accuracy, efficiency, and stability of the methods. [ABSTRACT FROM AUTHOR]

Infrasound signal classification is vital in geological hazard monitoring systems. The traditional classification approach extracts the features and classifies the infrasound events. However, due to the manual feature extraction, its classification performance is not satisfactory. To deal with this problem, this paper presents a classification model based on variational mode decomposition (VMD) and convolutional neural network (CNN). Firstly, the infrasound signal is processed by VMD to eliminate the noise. Then fast Fourier transform (FFT) is applied to convert the reconstructed signal into a frequency domain image. Finally, a CNN model is established to automatically extract the features and classify the infrasound signals. The experimental results show that the classification accuracy of the proposed classification model is higher than the other model by nearly 5%. Therefore, the proposed approach has excellent robustness under noisy environments and huge potential in geophysical monitoring. [ABSTRACT FROM AUTHOR]

Vibration is a significant factor causing structural damage to nearby structures. This study, conducted in Athi River-Nairobi Metropolitan, Kenya in accordance with ISO-14837, focuses on the importance of structural health monitoring and structural audits for existing structures. The instrumentation and data acquisition system used in this study comprised triaxial ADXL-345 and MPU 6050 accelerometers, Arduino UNO R3, and I2C protocol communication for data logging. Field measurements were conducted on moving trains, revealing a maximum peak particle velocity of 50.77 mm/s at the rail vibration source, and a minimum of 1.049 mm/s at a distance of 16 m from the rail. According to BS7385–2 (1993), ground-borne vibration becomes damaging at a peak particle velocity of 50 mm/s at 4 Hz, while the Standards Association of Australia (ASCA) prescribes a limiting value of 25 mm/s. The Swiss Association for Standardization recommends a limiting value of 8 mm/sec within the frequency range of 10–60 Hz. The measured vibration values were consistent with established standards for peak particle velocity values for damage and demonstrate the effectiveness of the proposed methodology in assessing and managing ground-borne vibrations. This research emphasizes the importance of early vibration detection through digital technology to mitigate structural damage and as a precondition prior to development approvals. [ABSTRACT FROM AUTHOR]

In this article, we consider the band structure calculations of three-dimensional (3D) anisotropic photonic crystals (PhCs), especially those with nonorthogonal Bravais lattices. By directly working with the intrinsic coordinate system with {al}³l=1 as the basis and the covariant formulation of Maxwell's equations, the approach proposed here treats nonorthogonal and orthogonal Bravais lattices in an equal and unified manner and trivially incorporates the Bloch condition in the discretization. Yee's scheme does not require the coordinate system or mesh to be orthogonal, and assisted by linear interpolation, this scheme can handle anisotropic media, by which the frequency domain Maxwell's equations are discretized into a standard eigenvalue problem (SEP). With an alternative understanding of null-space deflation techniques, this SEP is transformed into several equivalent null-space free SEPs, whose smallest few positive eigenvalues and associated eigenvectors are calculated by an iterative eigensolver of the inner-outer type. Comparison with other methods for band structure calculations of 3D anisotropic and isotropic PhCs is made to demonstrate the effectiveness and strength of our present approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient exponential integrator finite element method for solving a class of semilinear parabolic equations in rectangular domains. The proposed method first performs the spatial discretization of the model equation using the finite element approximation with continuous multilinear rectangular basis functions, and then takes the explicit exponential Runge-Kutta approach for time integration of the resulting semidiscrete system to produce a fully discrete numerical solution. Under certain regularity assumptions, error estimates measured in H¹-norm are successfully derived for the proposed schemes with one and two Runge-Kutta stages. More remarkably, the mass and coefficient matrices of the proposed method can be simultaneously diagonalized with an orthogonal matrix, which provides a fast solution process based on tensor product spectral decomposition and the fast Fourier transform. Various numerical experiments in two and three dimensions are also carried out to validate the theoretical results and demonstrate the excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

The voltage sag signal is one of the most basic Power Quality (PQ) disturbance events. It is defined as a 0.1-0.9 p.u. per-unit decrease in the normal voltage value. This PQ disturbance signal can cause damage to sensitive devices in the system. Therefore, a detailed analysis of the voltage sag signal is necessary. Harmonic analysis and Total Harmonic Distortion (THD) values are used to determine and analyze this signal in detail. In this study, harmonic magnitudes and THD values of the voltage sag signal with different noise values were investigated. Fast Fourier Transform (FFT) was used to calculate harmonic magnitudes and THD values. It is observed that the harmonic distributions and total harmonic distortion values of the signal at different time intervals change with the variation in noise. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) has been used to determine whether patients have latent illnesses and as an assessment indicator of the autonomic nervous system. Studies have also employed HRV for lie detection, which is commonly performed in the law system. However, several studies have concluded that due to environmental and procedure-related problems, lie detectors may yield inconsistent results. This study used smart recognition technology and proposed a systemized method of lie detection. To verify the reliability of the noncontact lie detection approach, empirical lie detection studies were conducted and the relevant literature was searched for item selection techniques. This study employed a noncontact HRV lie detection system. By experimenting on 15 participants split into four groups, this study verified the reliability of using the physiological characteristic of HRV for lie detection. [ABSTRACT FROM AUTHOR]

Is Kuhn's notion of exemplar applicable to ongoing biomedical sciences? Many philosophers may be skeptical because Kuhn's cases are mostly from physics and chemistry. However, how do philosophers test the above (non-)applicability directly? We will use examples to illustrate a scientometric approach to the integrated history and philosophy of science (SciHPS) and argue that SciHPS can provide an empirical basis to empirically test and revise a philosophical concept questioned for its applicability to biomedical sciences. This paper will build on Yan, K., M. L. Tsai, and T. R. Huang. [2021. "Improving the Quality of Case-Based Research in the Philosophy of Contemporary Sciences." Synthese 198 (10): 9591–9610. ] heart-rate variability (HRV) case study to examine the biomedical changes within the HRV community from 1970 to 2022. We will investigate how a task force standardises and entrenches methodological standardisations, and argue that some of the task force's methodological standardisations are tool-afforded by an algorithm. These tool-afforded aspects further explain why an HRV method is robustly dominant in the HRV community despite other HRV scholars having developed alternatives to compete with the dominant one. We will then show how to use SciHPS to empirically test and revise the Kuhnian concept of exemplar into a concept of citation-exemplar that better captures the above tool-afforded aspects of standardisations. [ABSTRACT FROM AUTHOR]

When images are customized to identify changes that have occurred using techniques such as spectral signature, which can be used to extract features, they can be of great value. In this paper, it was proposed to use the spectral signature to extract information from satellite images and then classify them into four categories. Here it is based on a set of data from the Kaggle satellite imagery website that represents different categories such as clouds, deserts, water, and green areas. After preprocessing these images, the data is transformed into a spectral signature using the Fast Fourier Transform (FFT) algorithm. Then the data of each image is reduced by selecting the top 20 features and transforming them from a two-dimensional matrix to a one-dimensional vector matrix using the Vector Quantization (VQ) algorithm. The data is divided into training and testing. Then it is fed into 23 layers of deep neural networks (DNN) that classify satellite images. The result is 2,145,020 parameters, and the evaluation of performance measures was accuracy = 100%, loopback = 100%, and the result F1 = 100 %. [ABSTRACT FROM AUTHOR]

Biometric images are an important means of personal identity verification and identification and are related to personal privacy and property security. To address the problems of poor security and low image reconstruction quality in the encryption and transmission of multibiometric images, a multibiometric images encryption method based on Fast Fourier Transform (FFT) and hyperchaotic system is proposed. First, the FFT is used to transform the multibiometric images from spatial to frequency domain. Then, the initial values of the hyperchaotic Lorenz system are generated using a one-dimensional chaotic logistic system to generate the key stream. Combined with the recoding rules of matrix reconstruction and scrambling without repetition of using multimatrix, the multiple matrices of amplitude and phase in the transform domain are reconstructed to be multiple RGB three-channel color images by using the inverse fast Fourier transform. Then, we combine the two diffusion methods of additive mode and Galois domain diffusion on each color channel to perform confusion and diffusion. Finally, the multiple grayscale images are encrypted to become multiple color images. The experimental results demonstrate that the method can effectively defend against various attacks. In addition, it solves the problem of low reconstruction accuracy that exists in the field of multiple images security. [ABSTRACT FROM AUTHOR]

This paper presents an automated Kannada subtitle generator from Kannada video which is implemented to assist people with auditory problems for watching videos. Henceforth the subtitle generation has become an important task for supporting such special people and it integrates an audio extraction and a speech recognition module. Three phases of the proposed technique were implemented, such as extracting audio from video, Recognition of Speech and Generation of Subtitle. An adaptive speech recognition module is implemented AMFCC for feature extraction which was an alternative to the most commonly used FFT. Hankel transform which was similar to FFT, but includes no elementary particles such as FFT. In addition to it, in the decoder acoustic module, such as Adaptive Hidden Markov Model using the Baum-Welch algorithm is utilized instead of a Viterbi algorithm to reduce the computational time and memory usage. The text file from the speech recognition module is rendered to synchronize the missing offset with the video using parallel processing by defining the start time, the end time, the delay time. Best outcomes are demonstrated by the experimental results of the proposed technique with 98.4% of accuracy compare with existing techniques. The proposed technique which gives 3.8% better accuracy performance compare with existing technique i.e. MFCC, DNN and CNN. [ABSTRACT FROM AUTHOR]