Fast Fourier Transform Technique Research Articles

Aeromagnetic Data Analysis for Sustainable Structural Mapping of the Missiakat Al Jukh Area in the Central Eastern Desert: Enhancing Resource Exploration with Minimal Environmental Impact

This study integrates aeromagnetic data with geological information to develop a consistent interpretation of both shallow and deep structural frameworks at various depths in the Missiakat Al Jukh area, located in the Central Eastern Desert, Egypt. The research begins by processing reduced-to-the-north magnetic pole (RTP) anomalies, using Fast Fourier Transformation (FFT) techniques to distinguish between local residual structures and broader regional features. This multi-scale approach enables a more detailed understanding of the geological complexity in the region, revealing its subsurface structures. Advanced geophysical methods such as upward continuation, Euler deconvolution, source parameter imaging (SPI), and global particle swarm optimization (GPSO) were applied to further refine the determination of structural depths, offering critical insights into the distribution and orientation of geological features at varying depths. The study reveals dominant structural orientations aligned in the NNW-SSE, ENE-WSW, north–south, and east–west directions, reflecting the region’s complex tectonic history. This research is of great importance in terms of sustainability. By delivering detailed subsurface maps and providing more accurate depth estimates of basement rocks (between 0.6 and 1.3 km), it contributes to sustainable resource exploration in the region. A better understanding of the geological structure helps minimize the environmental impact of exploration by reducing unnecessary drilling and concentrating efforts on areas with higher potential. Additionally, the use of non-invasive geophysical techniques supports the transition toward more environmentally conscious exploration practices. The integration of these advanced methods promotes a more sustainable approach to mineral and resource extraction, which is crucial for balancing economic growth with environmental preservation in geologically sensitive areas. Ultimately, this work provides a thorough geological interpretation that not only aids future exploration efforts but also aligns with the global push for sustainable and eco-friendly resource management.

Oxidation behavior of β-Nb formed in Zr-1Nb under neutron irradiation in PWR conditions

This work focuses on neutron irradiated Zr-1Nb alloy, using High Resolution Transmission Electron Microscopy (HRTEM) to investigate the oxidation behavior of β-Nb at different distances from the Oxide /Metal (O/M) interface within the oxide film. Results show that β-Nb was initially oxidized to T-NbO2 at 0 nm at O/M interface, then into a complex morphology of T-NbO2, M-Nb2O5, and O-Nb2O5 within 600nm. Finally, it was completely oxidized to M-Nb2O5 within 800nm. β-Nb in this study did not exhibit amorphous morphology within observed distances. In addition, Inverse Fast Fourier Transformation (IFFT) and Weak Beam Dark Field (WBDF) techniques are employed to characterize the dislocation density and distribution in the oxide film, results indicate that the distribution of dislocations generated by neutron irradiation in the oxide film is relatively uniform and neutron irradiation is not the primary reason affecting the oxidation behavior of β-Nb.

Towards diagnostics of aerospace structural defects using a novel physics-based post-processing scheme employing lock-in thermography

During its service life, an aircraft experiences various loadings that affect its structural performance. Thin sheet riveted joints are ubiquitous in aircraft and require thorough inspections to arrest any defects that may cause catastrophic failure. Subsequently once flown off, loose and/or cracked rivets may get stuck in critical areas such as flight controls or get ingested into the engine and become a source of Internal Object Damage (IoD). While several inspection schemes are employed in the aviation industry for rivets-related defect detection, most of them are subjective and skill-dependent. Towards the aim of developing an efficient rivet defects diagnostic technique, in this research work, a novel physics-based post-processing scheme employing lock-in thermography (LIT) is proposed for the detection of such defects. For this purpose, a hierarchical experimental thrust has been adopted, where in the beginning the scheme was developed on the lab experiments which was further extended/refined to detect defects on actual aircraft. Two different post-processing techniques have been used for the interpretation of LIT, namely the Fast Fourier Transform (FFT) technique and an indigenously developed temporal temperature gradient-based approach to capture defects whereby localized thermal emissivity variance at the defect location affects the local thermal conductivity and the surface temperature profile. The study considers two common defect i.e., looseness and sub-surface cracks in rivets. The results obtained from the novel post-processing technique provide empirical understanding, better quantification, and clearer comprehension of the two types of rivets-related defects by making it more objective as compared to the FFT technique. The proposed novel methodology in this work exhibits higher fidelity than conventional FFT by accurately identifying the rivet defects thereby providing a promising alternative to in-vogue visual inspections.

Identification of Power Quality Disturbances in Electrical Distribution System using Fast Fourier Transforms and Super Learner Ensembles

Currently, the use of non-linear loads and equipment, as well as renewable energy sources injected into the power system, tends to increase. As a result, the waveform of the electrical signal changes, and distortion occurs in the distribution system, which affects the quality and reliability of the electrical system. Importantly, sometimes this leads to malfunctions in protection equipment. This paper presents the algorithm for power quality disturbance (PQD) identification in electrical distribution systems, which involves three main steps: (1) Generating simulated waveforms using a signal processing approach; (2) extracting features using the Fast Fourier Transforms (FFT) technique; and (3) identifying the type of PQD using Super Learner Ensembles (SLE), which employs cross-validation to assess the performance of multiple machine learning models. Subsequently, the model’s efficiency is verified and tested using data from electronic energy meters installed in the distribution system of the Provincial Electricity Authority (PEA). The accuracy resulting from synthetic and experimental data sets is 99.90% and 99.69%, respectively. The results indicate that the model performs well in identifying power quality disturbances and achieves high accuracy.

A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images

Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.

An Exploration of the Phonon Frequency Spectrum and Born–von Karman Periodic Boundary Conditions in 1D and 2D Lattice Systems Using a Computational Approach

Periodic boundary conditions (PBCs) are a pivotal concept in the treatment of ideal lattices of infinite extent as a finite lattice. Most undergraduate texts that delve into the analytical treatment of lattice dynamics solve the problem in the reciprocal space with an implicit assumption of PBCs. While the traditional approach centered in the reciprocal or [Formula: see text]-space is elegant for simple one-dimensional lattice structures, the method becomes mathematically cumbersome for more complex lattices with real-world applications, such as a honeycomb lattice involving a [Formula: see text] secular determinant. In this work, we have explored the solution of lattice dynamics in direct space numerically, by constructing unit cells through an explicit implementation of Born–von Karman PBCs and solving the lattice dynamical equations in the displacement–time domain using the fourth-order Runge–Kutta algorithm. The Fast Fourier Transform technique is then used to obtain the phonon frequency spectrum corresponding to the computed instantaneous displacements. The fidelity of the model is validated by the agreement of the computational results obtained for the phonon spectrum at high symmetry points in the Brillouin zone with the analytical values for the given problem. Initially, the dynamics and the phonon spectrum are explored for monatomic and diatomic linear lattices which serve to act as introductory cases for the approach before investigating the 2D monatomic square and honeycomb lattices using the nearest and next-nearest neighbor approximations. A short-range force constant model employing the central forces and angular forces is used for the monatomic square lattice to account for the interatomic interactions. Our computational approach is expected to be physically more intuitive for a student for visualizing the periodicity of a given lattice and understanding its related dynamics. The approach demonstrates the usage of Born–von Karman PBCs in constructing a unit cell that effectively captures the dynamics of a lattice system, complementing the traditional secular determinant formalism. The work also illustrates an instance of how the computer programming can be used as a tool to empower the students to explore abstract concepts in physics. A honing of programming and computational skills of students enables them to interact with dynamic models in physics and gain deeper insight into the real-world applications of physics principles. Target group of this work is the students and educators at undergraduate level who seek a thorough and didactic comprehension of lattice dynamics.

Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

SummaryThe channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm.

A Comparison of Hilbert and Fast Fourier Transform Techniques for Reciprocating Motors

Human existence has been significantly impacted by the growth of information and technology, particularly in relation to the requirement for effective mobility. But as a car ages, symptoms that affect its performance and safety start to show. These symptoms are a serious worry. Interference with car parts, such coils, is one issue that can have a major effect on driving safety and performance. Vibration analysis techniques have been successful in addressing this problem by detecting damage to different car parts. With the intention of comprehending piston motor vibration characteristics at different rotational speeds. The piston motor was used to collect vibration data at three distinct speeds: 900 rpm, 1500 rpm, and 3500 rpm. Both the Hilbert approach, which offers profound insight into frequency energy, and an FFT analyzer, which produces clear frequency spectrum information, were used in the analysis. The findings demonstrated that vibration characteristics changed in response to variations in reciprocating motor speed. On the other hand, the average frequency energy tends to remain constant across a range of speeds, suggesting that the features of reciprocating motors may be complicated and not very affected by variations in speed. According to this study, different perspectives on the vibration characteristics of piston motors operating at different speeds can be gained by utilizing vibration analysis techniques like FFT and Hilbert. Improved comprehension of this phenomenon could have a significant impact on maintenance initiatives and performance analysis of vehicles. To fully understand the complexity of vibration characteristics in reciprocating motors at various speed conditions, more research will be needed

Cerebral palsy-affected individuals' brain-computer interface for wheelchair movement in an indoor environment using mental tasks

The technique of measuring brain signals or activities by placing electrodes on the scalp of human beings is called Electroencephalogram (EEG). Brain-computer interface (BCI) is a technique to capture brain signals and translate them into control signals to run external devices. With the combination of these two techniques, we can create BCI using brain signals. Methods: In this study, the author considered conducting two types of methods offline and online both in the indoor environment using the Fast Fourier Transform (FFT) technique with Feed Forward Neural Network trained with Bat optimization algorithm (FFNNBOA). The study was carried out on two different age groups between 30 to 45 years and 46 to 60 years with four different tasks. Based on the execution of the four different tasks concerning two different age groups, the accuracy obtained during classification is 94.35 % and 93.76 % for offline and online modes. Results: The results it is observed that the classification accuracy for the age group belonging 46 to 60 is comparably higher than that of the conventional classification model. The offline and online tests were conducted for both age groups persons and obtained the recognizing accuracy of 95 %, 93.25 %, and 93.75 %, 91.75 % for the two modes. This study confirms that the performances of the subjects belonging to age groups 30 to 45 are higher than the age groups belonging to 46 to 60 in terms of classification, offline, and online mode. Finally, this study also identified that subject S4 from the 30 to 45 age group showed 100 % accuracy in both offline and online signal acquisition.

On the use of machine learning and data-transformation methods to predict hydration kinetics and strength of alkali-activated mine tailings-based binders

The escalating production of mine tailings (MT), a byproduct of the mining industry, constitutes significant environmental and health hazards, thereby requiring a cost-effective and sustainable solution for its disposal or reuse. This study proposes the use of MT as the primary ingredient (≥70%mass) in binders for construction applications, thereby ensuring their efficient upcycling as well as drastic reduction of environmental impacts associated with the use of ordinary Portland cement (OPC). The early-age hydration kinetics and compressive strength of MT-based binders are evaluated with an emphasis on elucidating the influence of alkali activation parameters and the amount of slag or cement that are used as minor constituents. This study reveals correlations between cumulative heat release and compressive strengths at different ages; these correlations can be leveraged to estimate the compressive strength based on hydration kinetics. Furthermore, this study presents a random forest (RF) model—in conjunction with fast Fourier and direct cosine transformation techniques to overcome the limitations associated with limited volume and diversity of the database—to enable high-fidelity predictions of time-dependent hydration kinetics and compressive strength of MT-based binders in relation to mixture design. Overall, this study demonstrates a sustainable approach to upcycle mine tailings as the primary component in low-carbon construction binders; and presents both analytical and machine learning-based approaches for accurate a priori predictions of hydration kinetics and compressive strength of these binders.

An empirical model for the tropical Indian region using in-situ and space-borne observations taking into account of tropical oscillations

An empirical model of temperature is developed using long-term observations of radiosonde (52 years), M-100 Rocket (17 years), and Sounding of the Atmosphere by Broadband Emission Radiometry (SABER) (22 years) data. The dominant oscillations such as Annual Oscillation (AO), Semi-Annual Oscillation (SAO), Terr-Annual oscillation (TAO), and Quasi-Biennial Oscillation (QBO) variability has been incorporated into the temperature to obtain the empirical atmosphere. The monthly mean of temperature obtained from radiosonde from 0 to 25 km, M-100 rocket from 26 to 80 km and SABER from 26 to 109 km has been subjected to the Fast Fourier Transform technique to retrieve the amplitudes and phases of AO, SAO, TAO, and QBO. The amplitudes and phases obtained are put in an empirical formula along with the temperature obtained from the observations to retrieve the empirical temperature. The empirical model is developed for two epochs (1971–1990, and 2002–2023). The amplitude is similar in both epochs for the TAO, and it is high in the first compared to the second epoch for SAO, AO, and QBO. The peak in the amplitude (3–4 K) is obtained between 80 and 90 km for TAO, and between 70 and 80 km (2–3 K) for SAO. The amplitude is less than 1 K for AO in both the epochs. The peaks in the QBO are observed between 20 and 30 km (2–3 K), another between 60 and 70 km (3–4 K) in the first epoch and above 80 km (2–3 K). Downward phase propagation is dominant for all the oscillations observed below 50 km, and upward propagation above. The empirical temperature obtained compares well with the observations below 20 km (RMSE <1 K) and shows differences above it (RMSE ∼ 4–6 K). This is due to the fact, that the amplitude of the oscillation is more in the stratosphere and mesosphere which results in perturbation of the background temperature.

Analysis of material incision in milling machine against workpiece by vibration method

The Frais Machine is a piece of equipment used in manufacturing. To reach the specified production capacity, the machining process' time requirements must be more effectively managed. The goal of this study is to use the Fast Fourier Transform (FFT) technique to ascertain how changes in workpiece and spindle speed affect vibration. The milling machine utilized in this investigation is the Lushan Model ZX32, and the measuring device is a vibration analyzer coupled to an accelerometer sensor. Sengon Wood, PVC, and Aluminum 5052 are the workpiece varieties used. At spindle speeds of 95 and 150 rpm, the study's findings on aluminum material produced Y axis personal frequencies of 118 Hz with an amplitude of 15.12 mm/s2 and 144 Hz with an amplitude of 9.657 mm/s2. While PVC produced personal frequencies on the Y axis of 71 Hz with amplitudes of 12.11 mm/s2 and 40 Hz with amplitudes of 7.025 mm/s2 at spindle speeds of 95 and 150 rpm. Personal frequencies at 118 Hz with an amplitude of 10.9 mm/s2 and 74 Hz with an amplitude of 7.97 mm/s2 in the X axis are seen in wood materials with spindle speeds of 95 and 150 rpm. The findings demonstrate that differences in spindle rotation and workpiece have an impact on vibration; smaller vibrations are produced at higher rotation rates. Additionally, differences in the workpiece can impact tool life

An efficient method for simulating fluctuating wind speed fields in two-spatial dimensions based on a frequency-dependent acceptance-rejection scheme

The simulation of fluctuating wind speed fields holds paramount importance in the wind-resistant design of significant structures such as high-rise buildings, long-span bridges, and wind turbines. To address the computational challenges posed by the original spectral representation method (SRM), a novel approach has emerged. This approach, rooted in the frequency-wavenumber spectrum (FWS), offers a solution to the complexities associated with the original SRM. An enhanced non-uniform discrete technique based on the acceptance-rejection (A-R) criterion was introduced to improve the efficiency of the FWS-based SRM. Nonetheless, this technique currently faces certain constrains, including its demand for a substantial allocation of random access memory (RAM) and its inability to be seamlessly integrated with the fast Fourier transform (FFT) algorithm. Given this scenario, a notably more effective strategy for selecting representative points emerges -the frequency-dependent acceptance-rejection (A-R) scheme. This innovative scheme holds the advantage of diminishing RAM utilization, transforming wind field modeling from a supercomputers-exclusive task to one feasible on personal computers. Moreover, its seamless integration with FFT technology bolsters simulation efficiency. The provided numerical simulation instances of two-spatial dimensional fluctuating wind speed fields underscore the proficiency of this proposed frequency-dependent A-R scheme. The outcomes demonstrated the combined A-R and FFT technique’s efficiency and precision, in simulating two-spatial dimensions fluctuating wind speed fields.

Design and Development of Experimental Test Rig for Fault Diagnosis of Ball Bearing Using Fuzzy Logic Concept

Rolling element bearings are frequently employed in industry. Many machine-related issues are linked to bearing failures. To minimize downtime and preserve product quality in a highly automated production, an online detection system is required. Condition-based monitoring for deep groove ball bearings is becoming more common. All rotating machinery uses these bearings extensively to accommodate both static and dynamic loads. Techniques for condition-based monitoring can be utilized to diagnose bearing defects to prevent this failure. So, it is important to study these faults present in the machines. The techniques for fault detection will be described in this paper. We are discussing Fast Fourier Transform (FFT) technique. In FFT, we obtain frequency-relationship graphs, and based on peak frequencies, we predict faults. A detailed analysis using the FFT Methodology is done to find out the possible faults, and finally validate with MATLAB software. For the aim of bearing diagnostics, the system performs vibration analysis. More advanced diagnostic systems use fuzzy logic and classification methods to identify the state of the machinery. These techniques enable the creation of more automatic and trustworthy diagnostic systems.

Predictive maintenance of planetary gearboxes using FFT and machine learning technique

Planetary gearboxes are widely used in manufacturing processes, and non-destructive assessment is becoming increasingly important for monitoring their state. We outlined a fine-tuned random decision tree (FT-RDT) in this study for classifying and fault-finding the gearbox via signals generated by vibrations. This approach concentrates on the identification of worn gears, consequently distinct classes—healthy gears, ringed gears containing damaged tooth faces, and planetary gears featuring damaged tooth faces—were established. Each of the categories consists of 150 specimens, divided into two separate sets of 50 specimens for the testing data and 100 specimens for the data used for training. The Fast Fourier Transform (FFT) was used to convert the temporal signals to frequencies. The next step was to gather 24 statistical characteristics from frequency data. The retrieved characteristic was fed into the fault classification procedure (FT-RDT). Combining these methods yields rates of classification accuracy across train and test data of 92.75% and 91.50%, demonstrating the excellent reliability and capability of the problem identification solution that is created.

Experimental Comparative Investigations to Evaluate Cavitation Conditions within a Centrifugal Pump Based on Vibration and Acoustic Analyses Techniques

Cavitation is an essential problem that occurs in all kinds of pumps. This cavitation contributes highly towards the deterioration in the performance of the pump. In industrial applications, it is very vital to detect and decrease the effect of the cavitation in pumps. Using different techniques to analysis and diagnose cavitation leads to increase in the reliability of cavitation detection. The use of various techniques such as vibration and acoustic analyses can provide a more robust detection of cavitation within the pump. In this work therefore, focus is put on detecting and diagnosing the cavitation phenomenon within a centrifugal pump using vibration and acoustic techniques. The results obtained from vibration and acoustic signals in time and frequency domains were analysed in order to achieve better understanding regarding detection of cavitation within a pump. The effect of different operating conditions related to the cavitation was investigated in this work using different statistical features in time domain analysis (TDA). Moreover, Fast Fourier Transform (FFT) technique for frequency domain analysis (FDA) was also applied. Furthermore, the comparison and evaluation system among different techniques to find an adequate technique incorporating for accuracy and to increase the reliability of detection and diagnosing different levels of cavitation within a centrifugal pump were also investigated.

Experimental Analysis of Streamer Currents Under Lightning Impulse Voltage Using Fast Fourier and Discrete Wavelet Transform Methods

This paper presents an experimental comparative study of streamer currents propagating in mineral oil, natural esters (olive oil and rapeseed oil) and synthetic ester (tetra-ester) under both positive and negative lightning impulse voltages. Because of the similarity in the shapes of streamer currents in investigated liquids, it is difficult to differentiate between the current signals corresponding to each liquid. For that purpose, two techniques are used for the analysis of these signals. The first one is the Fast Fourier Transform (FFT) technique, which is based on the frequency spectrum analysis. The second one is the Discrete Wavelet Transform (DWT) method, which has demonstrated its ability for the analysis of the non-stationary signals. The obtained experimental results show spectral signature specific to each liquid, thus making it possible to differentiate the various streamer currents. Overall, the two techniques show good performance. However, the accuracy and effectiveness of the DWT method is better than those of the FFT technique.

Training FFT to Select Beams in Massive MIMO

In this letter, we propose new trainable Fast Fourier Transform (FFT) structures to increase the accuracy of beamspace transformation in the multi-user (MU) mode of Massive Multiple Input Multiple Output (MIMO) receiver. The FFT is a widely employed beamspace transformation technique, thanks to its low transforming complexity. Unfortunately, there is a significant performance loss of MU signal detection in the FFT beamspace, as default FFT beams are not co-directed with angles of arrivals. We address this issue with trainable FFT that outperforms not only fixed FFT approaches, but also other trainable FFT techniques.

Flows for the masses: A multi-fluid non-linear perturbation theory for massive neutrinos

Velocity dispersion of the massive neutrinos presents a daunting challenge for non-linear cosmological perturbation theory. We consider the neutrino population as a collection of non-linear fluids, each with uniform initial momentum, through an extension of the Time Renormalization Group perturbation theory. Employing recently-developed Fast Fourier Transform techniques, we accelerate our non-linear perturbation theory by more than two orders of magnitude, making it quick enough for practical use. After verifying that the neutrino mode-coupling integrals and power spectra converge, we show that our perturbation theory agrees with N-body neutrino simulations to within 10% for neutrino fractions Ω ν,0 h 2 ≤ 0.005 up to wave numbers of k = 1 h/Mpc, an accuracy consistent with ≤ 2.5% errors in the neutrino mass determination. Non-linear growth represents a > 10% correction to the neutrino power spectrum even for density fractions as low as Ω ν,0 h 2 = 0.001, demonstrating the limits of linear theory for accurate neutrino power spectrum predictions. Our code FlowsForTheMasses is avaliable online at github.com/upadhye/FlowsForTheMasses.

"THERMOELASTIC DISPLACEMENT AND TEMPERATURE RISE IN A HALF-SPACE DUE TO A STEADY-STATE HEAT FLUX "

Due to model complexity, classical contact mechanics theory assumes isothermal contact processes, involving bodies with uniform temperatures and no heat transmitted or generated through or near the contact interface. This paper addresses the problem of frictional heating in non-conforming or rough contacts by investigating the thermoelastic behaviour of asperities. The heat generated in a sliding contact by interfacial friction leads to thermoelastic distortion of the contact surface, further modifying contact parameters such as pressure, gap or temperature. The thermal expansion of the contacting bodies must therefore be accounted for when solving the contact problem. The thermoelastic displacement is computed with the aid of the half-space theory and of fundamental solutions for point sources of heat located at the free surface, derived in the literature of heat conduction in solids. The linearity of conduction equations encourages the use of superposition principle in the same way as for the elastic displacement. As the thermoelastic displacement is expressed mathematically as a convolution product, methods derived in contact mechanics for elastic displacement calculation are adapted to the heat conduction equations. The influence coefficients needed to efficiently compute the convolution products are derived, and the Discrete Convolution Fast Fourier Transform technique is applied to improve the algorithm computational efficiency. A similar method is then advanced for the temperature rise on the contact interface due to arbitrary heat input. The predictions of the newly advanced computer programs are tested against existing closed-form solutions for uniform circular or ring heat sources, and a good agreement is found.