Fast Fourier Research Articles

Development of a Real-Time Wearable Humming Detector Device

This study focuses on the development of a wearable real-time Humming Detector Device (HDD) aimed at enhancing the control of assistive devices through humming. As the need for portable user-friendly tools in assistive technology grows, the HDD offers a non-invasive solution to detect vocal cord vibrations. Vibrations, detected thanks to an accelerometer worn on the neck, are processed in real time using a Fast Fourier Transform (FFT) to identify specific humming frequencies, which are then translated into commands for controlling assistive devices via Bluetooth Low Energy (BLE) transmission. The device was tested with 13 healthy subjects to validate its potential and determine the optimal number of distinct commands that users can achieve. The HDD’s portability and precision make it a promising alternative to traditional voice recognition systems, particularly for individuals with speech impairments.

Aggregate: fast, accurate, and flexible approximation of compound probability distributions

Abstract Aggregate implements an efficient fast Fourier transform (FFT)-based algorithm to approximate compound probability distributions. Leveraging FFT-based methods offers advantages over recursion and simulation-based approaches, providing speed and accuracy to otherwise time-consuming calculations. Combining user-friendly features and an expressive domain-specific language called DecL, Aggregate enables practitioners and nonprogrammers to work with complex distributions effortlessly. The software verifies the accuracy of its FFT-based numerical approximations by comparing their first three moments to those calculated analytically from the specified frequency and severity. This moment-based validation, combined with carefully chosen default parameters, allows users without in-depth knowledge of the underlying algorithm to be confident in the results. Aggregate supports a wide range of frequency and severity distributions, policy limits and deductibles, and reinsurance structures and has applications in pricing, reserving, risk management, teaching, and research. It is written in Python.

Game on: a performance comparison of interpolation techniques applied to Shamir’s secret sharing

Abstract Public-key encryption is typically managed through a public key infrastructure. However, it relies on a central control point, the certification authority, which acts as a single point of failure. Recent technological advancements have led to the need for decentralized cryptographic protocols. This paper presents a comprehensive study on enhancing public-key encryption via threshold cryptography and multiparty computation to ensure robust security in decentralized systems. The focus lies in exploring various polynomial interpolation techniques within Shamir’s secret sharing scheme, particularly addressing the efficiency and practicality of Newton interpolation, fast Fourier transformation (FFT), and advanced versions of Lagrange’s method. Utilizing SageMath for a dedicated testing environment, the research investigates the swiftest interpolation methods for secret recovery, introducing new shares into the system, and evaluating the impact of optimizations on performance. The findings highlight FFT as the most effective interpolation method in speed and efficiency, albeit with limitations on the number of shares that can be processed. This paper critically evaluates these interpolation techniques against practical constraints and aims to answer pivotal research questions regarding the optimal approach for large-scale scenarios, challenging existing notions on the efficiency of Newton’s method and providing experimental evidence to support the superiority of FFT in specific contexts.

Optimization design of biomechanical parameters based on advanced mathematical modelling

In recent years the use of biomechanics in athletic training and performance has received a lot of attention, especially in university sports programs. Biomechanics is the study of the mechanical principles that control how biological things move or are constructed. It is critical for understanding the intricate relationships between physical performance, body mechanics, and injury prevention. The objective of this study is to establish how biomechanical variables can be designed and optimized in universities using mathematical modeling. In this study, a novel Emperor Penguin Search-driven Dynamic Feedforward Neural Network (EPSO-DFNN) is proposed to optimize the biomechanical parameters of athletes. Various biomechanical data are utilized from athletes participating in different sports. Biomechanical parameters include muscle activation patterns, joint angles, forces, and movement. The data was preprocessed using Z-score normalization from the obtained data. The Fast Fourier Transform (FFT) using features is extracted from preprocessed data. The proposed method is to identify the optimal configurations for athlete’s movements tailored to their sports and individual biomechanical profiles. The proposed method is the performance of various evaluation metrics such as F1-score (92.76%), precision (91.42%), accuracy (90.02%), and recall (89.69%). The result demonstrated that the proposed method effectively improved the performance in athletic capabilities compared to other traditional algorithms. This study demonstrates how mathematical modeling may be used to optimize biomechanical characteristics, providing insightful information that can be used to improve athletic performance and encourage safer behaviors in athletic settings.

Multisegment Overlap–Save Method for Coherent Dedispersion

Dispersion occurs due to the interstellar medium, which functions as a prism and causes different time delays in radio waves of varying frequencies. The coherent dedispersion technique is often used in pulsar and fast radio burst observations to mitigate this phenomenon. The widely recognized Overlap–Save approach enables this dedispersion algorithm to efficiently perform long linear convolution. However, with the present implementation, the necessary filter length for dedispersion can reach 100 million points or more. The corresponding fast Fourier transform (FFT) points should be larger than this value and a GPU cluster can be used to tackle this demanding process. This study presents the Multisegment Overlap–Save Method (MS-OSM) to effectively address this problem. Our algorithm divides signal bands into separate short segments based on frequency component delays during dedispersion. By using the short segments shuffling technique with the Overlap-Save structure, MS-OSM can greatly reduce the FFT and inverse FFT points required down to fewer than 65,536 points. To evaluate the performance of MS-OSM, a synthetic pulsar signal is created and verified using standard software tools like DSPSR and PRESTO. The results show that MS-OSM maintains the same resolution while reducing execution time and resource usage. The validation of MS-OSM is taken by processing real pulsar observation data.

Abstract Su503: Relationships Between Quantitative ECG Measures and Defibrillation Success in Early Ventricular Fibrillation

Introduction: Rapid termination of ventricular fibrillation (VF) has long been recognized as the cornerstone of effective cardiac arrest resuscitation. In recent years, amplitude spectral area (AMSA) has been shown to be highly associated with defibrillation success, and AMSA has been experimentally used to personalize defibrillation timing and strategy. However, questions remain about whether the interpretation of AMSA holds uniformly in varied conditions, such as ultra-early or prolonged cardiac arrest or after anti-arrhythmic administration. Hypothesis: AMSA is greater after 20 seconds of untreated VF compared to 50 seconds of untreated VF; a higher AMSA predicts greater first-defibrillation success. Methods: VF was induced in adult swine (n = 3) with a bipolar pacing catheter placed in the right ventricle. Animals were randomized to 20- or 50-second-long episodes of VF (n = 123), followed by standard 150 J biphasic defibrillation. In MATLAB, AMSA was calculated as the summed product of frequency and square root of power after Fast Fourier Transform for the four second period of VF immediately prior to defibrillation. Results were analyzed by a two-sided Chi-squared test or ANOVA with Tukey’s HSD in R; a p-value < 0.05 was considered significant. Results: First shock success was lower in the 20-second-VF group compared to the 50 second group (31/78 (39.7%) vs 31/45 (68.8%), p = 0.003). The AMSA at 20 seconds was significantly higher for successful compared to unsuccessful first defibrillation (8.20 vs. 7.31 mV-Hz, p = 0.034). The AMSA at 50 seconds was not statistically different between successful and unsuccessful defibrillations (6.32 vs. 5.67 mV-Hz, p = 0.470). Evaluating the AMSA after a duration of VF irrespective of shock success, AMSA was higher at 20 seconds compared to 50 seconds (7.66 vs 6.12 mV-Hz, p < 0.001). Conclusion(s): AMSA decreases from 20 to 50 seconds of untreated VF, and higher AMSA at a given time point is associated with greater first defibrillation success. However, first defibrillation success was higher after 50 seconds of untreated VF compared to 20 seconds, raising questions about special cases, such as edge effects, that may influence AMSA interpretation.

Hybridization of Cockcroft-Walton and Dickson Voltage Multipliers for Maximum Output Through Effective Frequency Dedication in Harvesting Radio Frequency Energy

Objective: This study investigates solutions to the challenges of limited RF energy harvesting by designing a hybridized voltage multiplier system aimed at optimizing output across a wide frequency range. Theoretical Framework: The research centers on the principles and comparative efficiencies of the Cockcroft-Walton and Dickson voltage multipliers, known for their applications in RF energy harvesting. These multipliers’ performance was analyzed theoretically to guide a hybrid design that could adaptively respond to input frequency variations. Method: Voltage multipliers were designed and simulated in Multisim, with further analysis in MATLAB. Both the Cockcroft-Walton and Dickson voltage multipliers were subjected to a constant input of 1V across frequencies from 50 Hz to 5 GHz to assess their respective efficiencies. Subsequently, a hybrid voltage multiplier system was developed, combining an 8-stage Cockcroft-Walton and an 8-stage Dickson multiplier. A fast Fourier transform (FFT) frequency-selective algorithm, implemented in MATLAB, dynamically directed input voltages to the optimal multiplier based on frequency. Results and Discussion: Results showed that the Dickson multiplier excelled in the lower frequency range (50 Hz to 1 MHz), achieving a maximum output of 14.763V at 5 kHz and 10 kHz. In contrast, the Cockcroft-Walton multiplier was more effective in the higher frequency range (1 MHz to 5 GHz), reaching a peak output of 6.671V at 5 GHz. The hybrid system demonstrated efficient, frequency-dependent voltage multiplication and aligned well with anticipated performance metrics, suggesting an improvement in RF energy harvesting across the tested frequency range. Research Implications: This work contributes to the field of RF energy harvesting by introducing a frequency-adaptive system that enhances voltage output through targeted frequency routing. The results underscore the potential for hybrid designs to overcome limitations associated with individual voltage multipliers, presenting a versatile approach to harvesting RF energy effectively across broad frequency spectra. Originality/Value: By implementing a hybrid approach with a frequency-selective algorithm, this study offers an innovative solution for frequency-dependent RF energy harvesting. The findings provide a foundation for future research into adaptable energy harvesting systems that optimize voltage output across diverse frequencies, with practical implications for RF-powered devices and wireless energy transfer applications.

Low hardware-complexity IQ skew tolerant finite field and time domain hybrid adaptive equalization for 400G/800G-ZR coherent transmission

In 400G/800G-ZR transmission systems afflicted by substantial inter-symbol interference (ISI) stemming from chromatic dispersion (CD), frequency-domain adaptive equalizers (FD-AEQs) are preferred to time-domain AEQs (TD-AEQs) as the former have relatively lower computation load. However, the reliance of FD-AEQs on computationally expensive fast Fourier transform (FFT)/inverse fast Fourier transform (IFFT) operations poses a major obstacle in the development of low-power 400G/800G-ZR digital coherent transceivers. Furthermore, to mitigate the significant performance degradation due to receiver in-phase and quadrature (IQ) skew, the employment of twice the number of filters becomes imperative to enable individual adjustments of the I and Q tributaries, nearly doubling the number of necessary FFT/IFFT operations and computation load. This prohibitively high computation load poses a formidable obstacle to the realization of low-power and cost-effective transceivers. To address this issue, we introduce a novel low hardware-complexity IQ skew tolerant finite field (FF) and TD hybrid AEQ for 400G/800G-ZR coherent transmission. By optimizing the AEQ design and the Fermat number transform (FNT) based linear convolution scheme, the proposed hybrid AEQ can achieve a remarkable reduction in hardware complexity by more than 70%, compared with the existing IQ skew tolerant FD-AEQ, while preserving the same receiver sensitivity. Both numerical simulations and experiments demonstrate that the performance of the low complexity hybrid AEQ is the same as the FD-AEQ with much higher complexity. The proposed comprehensive and hardware efficient hybrid AEQ scheme is very appealing for 400G/800G-ZR coherent receivers which need to compensate for large ISI and IQ skew under stringent power constraints.

A Novel Filter Bank and Fourier Transform Convolutional Neural Network for SSVEP Classification

Abstract. Classifying Steady-State Visual Evoked Potentials (SSVEPs) is crucial for enhancing the performance of Brain-Computer Interface (BCI) systems. This study introduces a new method for SSVEP classification within BCI frameworks, called the Filter Bank and Fourier Transform Convolutional Neural Network (FBFCNN). The FBFCNN model effectively extracts key harmonic features by first segmenting SSVEP signals into multiple sub-bands through a filter bank. Subsequently, the Fast Fourier Transform (FFT) is applied to convert these signals from the time domain into the frequency domain. The generated spectrum's real and imaginary components are then fed into a convolutional neural network (CNN). This method improves SSVEP feature extraction and increases classification accuracy by fusing the advantages of CNNs with filter banks. Experimental results, derived from publicly available SSVEP datasets, show that the FBFCNN model surpasses traditional techniques in both accuracy and Information Transfer Rate (ITR). Offering a reliable and efficient solution for real-time brain signal decoding, the FBFCNN model represents a significant advancement in SSVEP-based BCI systems.

SuperDARN Radar Wind Observations of Eastward-Propagating Planetary Waves

An array of SuperDARN meteor radars at northern high latitudes was used to investigate the sources and characteristics of eastward-propagating planetary waves (EPWs) at 95 km, with a focus on wintertime. The nine radars provided the daily mean meridional winds and their anomalies over 180 degrees of longitude, and these anomalies were separated into eastward and westward waves using a fast Fourier transform (FFT) method to extract the planetary wave components of zonal wavenumbers 1 and 2. Years when a sudden stratospheric warming event with an elevated stratopause (ES-SSW) occurred during the winter were contrasted with years without such events and composited through superposed epoch analysis. The results show that EPWs are a ubiquitous—and unexpected—feature of meridional wind variability near 95 km. Present even in non-ES-SSW years, they display a regular annual cycle peaking in January or February, depending on the zonal wavenumber. In years when an ES-SSW occurred, the EPWs were highly variable but enhanced before and after the onset.

Improved Polar Current Shell Algorithm for Ocean Current Retrieval from X-Band Radar Data

This paper presents an improved algorithm for retrieving ocean surface currents from X-band marine radar images. The original polar current shell (PCS) method begins with a 3D fast Fourier transform (FFT) of the radar image sequence, followed by the extraction of the dispersion shell from the 3D image spectrum, which is then transformed into a PCS using polar coordinates. Building on this foundation, the improved approach is to analyze all data points corresponding to different wavenumber magnitudes in the PCS domain rather than analyzing each specific wavenumber magnitude separately. In addition, kernel density estimation (KDE) to identify high-density directions, interquartile range filtering to remove outliers, and symmetry-based filtering to further reduce noise by comparing data from opposite directions are also utilized for further improvement. Finally, a single curve fitting is applied to the filtered data rather than conducting multiple curve fittings as in the original method. The algorithm is validated using simulated data and real radar data from both the Decca radar, established in 2008, and the Koden radar, established in 2017. For the 2008 Decca radar data, the improved PCS method reduced the root-mean-square deviation (RMSD) for speed estimation by 0.06 m/s and for direction estimation by 3.8° while improving the correlation coefficients (CCs) for current speed by 0.06 and direction by 0.07 compared to the original PCS method. For the 2017 Koden radar data, the improved PCS method reduced the RMSD for speed by 0.02 m/s and for direction by 4.6°, with CCs being improved for current speed by 0.03 and direction by 0.05 compared to the original PCS method.

Federated learning with differential privacy via fast Fourier transform for tighter-efficient combining.

Spurred by the simultaneous need for data privacy protection and data sharing, federated learning (FL) has been proposed. However, it still poses a risk of privacy leakage in it. This paper, an improved Differential Privacy (DP) algorithm to protect the federated learning model. Additionally, the Fast Fourier Transform (FFT) is used in the computation of the privacy budget [Formula: see text], to minimize the impact of limited arithmetic resources and numerous users on the effectiveness of training model. Moreover, instead of direct analyses of the privacy budget ε through various methods, Privacy Loss Distribution (PLD) and privacy curves are adopted, while the number of artificial assignments hyperparameters is reduced, and the grid parameters delineated for FFT use are improved. The improved algorithm tightens parameter bounds and minimizes human factors' influence with minimal efficiency impact. It decreases the errors caused by truncation and discreteness of PLDs while expanding the discreteness interval to reduce the calculation workload. Furthermore, an improved activation function using a temper sigmoid with only one parameter [Formula: see text], smooths the accuracy curve and mitigates drastically fluctuating scenarios during model training. Finally, simulation results on real datasets show that our improved DP algorithm, which accounts for long trailing, facilitates a better balance between privacy and utility in federated learning models.

Implementasi Fast Fourier Transform dalam Penyelesaian Persamaan Difusi Panas Satu Dimensi

The Fast Fourier Transform (FFT) method for solving the 1-D heat diffusion equation offers an efficient approach for resolving partial differential equations (PDEs) with various time steps . FFT is used to transform the 1-D heat diffusion equation into the frequency domain and back to the time domain through inverse FFT. Using mathematical modeling with initial and Dirichlet boundary conditions, the numerical solutions produced by FFT are compared with analytical solutions. The accuracy of the method is validated using MAE and MSE calculated in Matlab. At several time intervals , the obtained MAE and MSE values indicate a good agreement between the numerical and analytical solutions, with very small errors. Numerical stability analysis confirms the reliability of the FFT method across various The variation in time step has a significant impact on the accuracy and stability of the solution. Smaller time steps improve accuracy and stability but require longer computation times. The optimal time step selected in this study is Increasing the number of discretization points also enhances accuracy but implies an increase in computational load and memory usage. The FFT method demonstrates good numerical consistency with increasing

Processing of Idiopathic Pulmonary Fibrosis images based on spatial interpolation using DFT, FFT, DCT and LPF

The work focuses on the study of existing issues with some medical images obtained from patients with Idiopathic Pulmonary Fibrosis (IPF) who have survived this COVID-19 pandemic. This study analyzes potential causes of incorrect medical diagnoses with this disease. In this regard, we employ numerical algorithms such as DFT (Discrete Fourier Transform), FFT (Fast Fourier Transform), DCT (Discrete Cosine Transform), and LPF (Low-pass filter). The main objective of this work is to demonstrate that with these numerical algorithms based on Continuous and Discrete Fourier Theory, it is possible to filter existing noise in IPF radiography images. Furthermore, it is possible to compress, enhance, and improve their resolution so that better decisions can be made in a medical protocol. In this way, it contributes to the understanding of the true images that would be used for an optimal medical diagnosis.

Evaluation of Dose Calculation Algorithms Accuracy for ISOgray Treatment Planning System in Motorized Wedged Treatment Fields

Abstract Background: Different dose calculation methods vary in accuracy and speed. While most methods sacrifice precision for efficiency Monte Carlo (MC) simulation offers high accuracy but slower calculation. ISOgray treatment planning system (TPS) uses Clarkson, collapsed cone convolution (CCC), and fast Fourier transform (FFT) algorithms for dose distribution. This study’s primary goal is to evaluate the dose calculation accuracy for ISOgray TPS algorithms in the presence of a wedge. Methods: This study evaluates the dose calculation algorithms using the ISOgray TPS in the context of radiation therapy. The authors compare ISOgray TPS algorithms on an Elekta Compact LINAC through MC simulations. The study compares MC simulations for open and wedge fields with ISOgray algorithms by using gamma index analysis for validation. Results: The percentage depth dose results for all open and wedge fields showed a more than 98% pass rate for points. However, there were differences in the dose profile gamma index results. Open fields passed the gamma index analysis in the in-plane direction, but not all points passed in the cross-plane direction. Wedge fields passed in the cross-plane direction, but not all in the in-plane direction, except for the Clarkson algorithms. Conclusion: In all investigated algorithms, error increases in the penumbra areas, outside the field, and at cross-plane of open fields and in-plane direction of wedged fields. By increasing the wedge angle, the discrepancy between the TPS algorithms and MC simulations becomes more pronounced. This discrepancy is attributed to the increased presence of scattered photons and the variation in the delivered dose within the wedge field, consequently impacts the beam quality. While the CCC and FFT algorithms had better accuracy, the Clarkson algorithm, particularly at larger effective wedge angles, exhibited greater effectiveness than the two mentioned algorithms.

The combined role of hematocrit and shear flow on the morphology of platelet aggregates by Fast Fourier Transform and 4D confocal microscopy

The combined role of hematocrit and shear flow on the morphology of platelet aggregates by Fast Fourier Transform and 4D confocal microscopy

Characterization of micro-wire electrical discharge machining surface texture by empirical mode decomposition

Surface roughness is a critical factor for the operational efficacy and lifespan of the manufactured components, often serving as a key metric for evaluating manufacturing processes and determining acceptance of a component. Traditional surface roughness measurements are highly sensitive to the fixed standard cut-off length, influencing the scale at which surface texture features are analyzed. Thus, developing techniques with more reliable filtering of surface features is essential for accurate surface metrology. This research proposes a novel method that combines Empirical Mode Decomposition (EMD) and Fast Fourier Transform (FFT) for surface roughness characterization. We applied our method to Nitinol shape memory alloy (equal atomic proportions of nickel and titanium) surfaces processed using micro-Wire Electrical Discharge Machining (μ-WEDM). Our results demonstrate that the EMD-FFT method significantly outperforms traditional filtering, especially for surfaces without distinct patterns like μ-WEDM. This improvement is particularly valuable for materials like Nitinol, which is widely used in critical applications where precise surface roughness control is essential. By accurately assessing the effects of μ-WEDM parameters on surface roughness, the proposed method reveals that for a constant pulse on time and a constant discharge current, the variation of pulse off time is more important than servo voltage. Thus, this method can contribute to optimizing manufacturing processes and producing higher-quality components with improved performance and durability.

Effects of Interfacial Imperfections on Nanoscale Adhesive Contact for Layered Medium

Depending on processing technologies and working conditions, imperfect bonding at the layer-substrate interface may occur, resulting in diverse mechanical responses compared to a perfectly bonded layer-substrate system. This study focuses on an imperfect interface under force-like conditions and incorporates it into a nanoscale adhesive contact model to explore the influences of interfacial imperfection on the adhesive contact behaviors of the layered medium. The adhesive contact model is formulated based on the Lennard-Jones (LJ) potential and the Hammaker summation method. The adhesive contact problem is addressed by solving the nonlinear surface gap equations between the contact bodies. The deformation within the gap equations, accounting for the influence of imperfections, is computed using the fast Fourier transform (FFT) algorithm. This study explores the influence of three stress jumping coefficients t1,t2 and t3, which quantitatively characterize the interfacial imperfection, and their coeffects with material parameters, including imperfection depth (layer thickness), adhesion work, and elastic modulus, on the adhesive contact behaviors of the layered medium. The findings underscore that the normal stress jumping coefficient t3 exerts the most significant impact, wherein a higher t3 value corresponds to a smaller adhesive force and a larger absolute contact approach, while tangential stress jumping coefficients t1 and t2 exhibit negligible influence. Decreasing t3 values correspond to varying interaction force-contact approach responses and contribute to alleviating contact stability in cases with large Tabor parameters. Interfacial imperfections manifest their influence by modifying the pressure-displacement response, with noticeable effects only within a specific imperfection depth range h¯<40. While the introduction of interfacial imperfections does not alter the fundamental impact of material parameters—such as imperfection depth, adhesion work ratios, and elastic modulus ratios—on adhesive force and contact approach, it does modify the magnitude of these effects. Furthermore, imperfections alter stress distribution, increasing maximal von Mises stress and causing stress concentration within the layer and at the interface. In summary, force-like imperfections reduce surface displacement, resulting in a smaller region of positive pressure and ultimately contributing to a larger adhesive force. However, this effect is accompanied by increased stress concentration at the imperfect interface. This heightened stress level poses a potential risk to the system's reliability.

A Comprehensive Method to Analyze Single Cell Vibrations

All living cells vibrate depending on metabolism. It has been hypothesized that vibrations are unique for a given phenotype and thereby suitable to diagnose cancer type, stage, and pre-assess the effectiveness of pharmaceutical treatments in real time. However, cells exhibit highly variable vibrational signals, can be subject to environmental noise, and may be challenging to differentiate, having so far limited the phenomenon’s applicability. Here, we combined the sensitive method of force-spectroscopy using optical tweezers (OT) with comprehensive statistical analysis. After data acquisition, the signal was decomposed into its spectral components via Fast Fourier Transform (FFT). Peaks were parameterized and subjected to Principal Component Analysis (PCA), to perform an unbiased multivariate statistical evaluation. This method, which we term Cell Vibrational Profiling (CVP), systematically assesses cellular vibrations. To validate CVP technique, we conducted experiments on five U251 glioblastoma (GBM) cells, using 8-10 μm polystyrene beads as a control for comparison. We collected raw data using OT, segmenting into 150+ five-second intervals. Each segment was converted into power spectra (PS) representing a frequency resolution of 10,000 Hz for both cells and controls. U251 GBM cells exhibited significant vibrations at 402.6, 1254.6, 1909.0, 2169.4, and 3462.8 Hz (p<0.0001). This method was further verified with PCA modelling, which revealed that in cell-cell comparisons using the selected frequencies, overlap frequently occurred, and clustering was difficult to discern. In contrast, comparison between cell-bead models showed that clustering was easily distinguishable. Our paper establishes CVP as an unbiased, comprehensive technique to analyze cell vibrations. This technique effectively differentiates between cell types and evaluates cellular responses to therapeutic interventions. Notably, CVP is a versatile, cell-agnostic technique requiring minimal sample preparation and no labelling or external interference. By enabling definitive phenotypic assessments, CVP holds promise as a diagnostic tool and could significantly enhance the evaluation of pharmaceutical treatments.

Adaptive tunicate swarm optimization with partial transmit sequence for phase optimization in MIMO-OFDM

&lt;span&gt;Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are widely utilized in wireless systems and maximum data rate communications. The MIMO-OFDM technology increases the efficiency of spectrum utilization. The peak-to-average-power ratio (PAPR) minimization in MIMO-OFDM is a complex task in wireless communications systems. In this research, an adaptive tunicate swarm optimization with partial transmit sequence (ATSO-PTS) algorithm is proposed for a reduction of PAPR in MIMO-OFDM. The nonsquare-matrix-based differential space time coding (N-DSTC) scheme is used for the encoding and decoding process of MIMO-OFDM. The N-DSTC encoding and decoding are linear error-correcting codes that are utilized for message transmission over noisy channels. The pre-specified quadrate phase shift keying (QPSK) symbol is deployed for the modulation and demodulation scheme. On the receiver side, the serial to parallel (S/P) conversion, and fast Fourier transform (FFT) are accomplished, alongside the received data bits being demodulated to obtain the output bits. The proposed ATSO-PTS method achieves better results according to performance metrices PAPR, bit error rate (BER) and signal-to-noise-ratio (SNR), with values of about 2.9, 0.01 and 0.025, respectively. This ensures superior results when compared to the existing methods of twin symbol hybrid optimization applied to partial transmit sequence (TSHO-PTS), selective level mapping and PTS (SLM-PTS), and particle swarm and grey wolf (PS-GW) with PTS, respectively.&lt;/span&gt;