Temporal Correlation Method Research Articles

Temporal Data Correlation Providing Enhanced Dynamic Crypto-Ransomware Pre-Encryption Boundary Delineation.

Ransomware is a type of malware that employs encryption to target user files, rendering them inaccessible without a decryption key. To combat ransomware, researchers have developed early detection models that seek to identify threats before encryption takes place, often by monitoring the initial calls to cryptographic APIs. However, because encryption is a standard computational activity involved in processes, such as packing, unpacking, and polymorphism, the presence of cryptographic APIs does not necessarily indicate an imminent ransomware attack. Hence, relying solely on cryptographic APIs is insufficient for accurately determining a ransomware pre-encryption boundary. To this end, this paper is devoted to addressing this issue by proposing a Temporal Data Correlation method that associates cryptographic APIs with the I/O Request Packets (IRPs) based on the timestamp for pre-encryption boundary delineation. The process extracts the various features from the pre-encryption dataset for use in early detection model training. Several machine and deep learning classifiers are used to evaluate the accuracy of the proposed solution. Preliminary results show that this newly proposed approach can achieve higher detection accuracy compared to those reported elsewhere.

Blind source extraction based on EMD and temporal correlation for rolling element bearing fault diagnosis

Purpose Fault diagnosis methods based on blind source separation (BSS) for rolling element bearings are necessary tools to prevent any unexpected accidents. In the field application, the actual signal acquisition is usually hindered by certain restrictions, such as the limited number of signal channels. The purpose of this study is to fulfill the weakness of the existed BSS method. Design/methodology/approach To deal with this problem, this paper proposes a blind source extraction (BSE) method for bearing fault diagnosis based on empirical mode decomposition (EMD) and temporal correlation. First, a single-channel undetermined BSS problem is transformed into a determined BSS problem using the EMD algorithm. Then, the desired fault signal is extracted from selected intrinsic mode functions with a multi-shift correlation method. Findings Experimental results prove the extracted fault signal can be easily identified through the envelope spectrum. The application of the proposed method is validated using simulated signals and rolling element bearing signals of the train axle. Originality/value This paper proposes an underdetermined BSE method based on the EMD and the temporal correlation method for rolling element bearings. A simulated signal and two bearing fault signal from the train rolling element bearings show that the proposed method can well extract the bearing fault signal. Note that the proposed method can extract the periodic fault signal for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other rotating machinery, such as gears or blades.

Distributed High Temperature Monitoring of SMF under Electrical Arc Discharges Based on OFDR.

The distributed high temperature measurement of an optical fiber subjected to electric arc discharges based on optical frequency-domain reflectometry is experimentally demonstrated. The distributed temperature profile is attained in an open glow regime of a few milliamps with maximum detectable temperature up to 2100 ± 20 °C. The discharge arc-induced softened length of the fiber and mechanical stress are measured and statistically analyzed in terms of the correlation of the Rayleigh spectra. The large wavelength scanning range of OFDR enables much higher accuracy for the delay time measurement with a minimum measured delay of 40 fs. The delay shift over the entire heating range for a single discharge duration is statistically calculated by using a temporal correlation method. The reliability of the thermal sensitivity coefficient as 10 pm/°C for telecom single mode fiber (SMF, @1550 nm) is quantitatively analyzed and evaluated by the correlation coefficient. Lastly, a spectral mapping method is employed in spectrum monitoring for discharge dynamic impact on the optical path length (OPL) and local Rayleigh scatter.

Fast digital image correlation using parallel temporal sequence correlation method

Digital image correlation (DIC) is a noncontact technique that is widely used for deformation measurement, but improving the calculation efficiency to achieve real-time DIC calculation has always been a big concern. A parallel temporal sequence DIC method is proposed, which chooses seed points to determine the integer-pixel displacement and applies the moving least-squares fitting technique to acquire the subpixel displacement. This method avoids traditional complex iterations and takes full advantage of the GPU parallel computing. Results of a simulation experiment and an actual experiment demonstrate the accuracy and efficiency of the proposed algorithm. The calculation speed in the simulation experiment of the proposed method achieved 463,320 POI/s, whereas the speed in the actual experiment was 432,866 POI/s, when the speed of the ICGN method was 2700 POI/s and 2074 POI/s under the same accuracy, respectively. Also, the subpixel displacement calculation made up less than 1% of the entire calculation. The computational efficiency could be further enhanced if a faster integer-pixel displacement calculation method is discovered or a parallel algorithm is used.

Projected extreme climate indices in the java island using cmip5 models

Climate change has brought great environmental impacts that cause economic disruption as it causes extreme climate phenomena such as floods and droughts. The projection of precipitation and temperature is crucial to develop the adaptation and mitigation options, as well as to improve the operational strategies in various sectors. This study used Coupled Model Intercomparison Project Phase 5 (CMIP5) that consists of 29 GCMs to make the projection of precipitation and temperature (2011–2100), along with daily observational data from 16 stations over the Java island for 20 years (1986–2005) to evaluate the models. Spatial and temporal correlation method was used to evaluate the climate models and 5 GCMs with the best performance were selected to project the precipitation and temperature. A bias correction method called Simple Quantile Mapping (SQM) was used to adjust the climate models to better represent the observational data. Representative Concentration Pathway (RCP)4.5 dan RCP8.5 scenarios were chosen and the extreme weather events were depicted using the Expert Team for Climate Change Detection and Indices (ETCCDI), which includes annual total precipitation (Prcptot), consecutive dry days (CDD), consecutive wet days (CWD), monthly maximum temperature (TXx) and monthly minimum temperature (TNn). Using the multi model ensemble (MME) from the 5 best GCMs, the projection of 5 extreme climate indices over Java island shows a relative increase to the historical period.

Physical breakdown of bread and its impact on texture perception: A dynamic perspective

This study aimed to understand the impact of bread structure and its dynamic transformation on people’s chewing behaviour and texture perception. Results from 20 trained panellists showed that bread crust/skin was the dominating factor in oral processing. This ‘outer layer’ introduced a mechanical contrast which increased chewing effort, lowered swallowing thresholds but did not elicit more a complex texture sensation. Furthermore, a single-panellist study was conducted as a proof-of-concept to demonstrate a new temporal correlation method. Progressive fragmentation and hydration diminished the differences between heterogeneous and homogeneous samples, leading to converged bolus properties and chewing effort. However, the differences in texture perception and chewing frequency due to the presence of the crust/skin were not diminished and even became larger throughout oral processing. Hence, chewing force and chewing rhythm might have different modulation mechanisms. Overall, bolus hydrated sensation was largely used by the panellist to gauge the swallowing point while bolus texture was used in a feedback control to regulate the mastication behaviour.

Evaluation of low viscosity variations in fluids using temporal and spatial analysis of the speckle pattern.

The noninvasive detection of a material's viscoelasticity is of great importance in the medical field. In fact, certain diseases cause changes in tissue structure and biological fluid viscosity; tracking those changes allows for detection of these diseases. Rheological measurements are also imperative in the industrial field, where it is necessary to characterize a material's viscoelasticity for manufacturing purposes. In this Letter, we present a noncontact, noninvasive, and low cost method for determining low viscosity values and variations in fluids. Laser speckle and viscometric measurements are performed on test samples having low scattering coefficients and low viscosities. The speckle spatial analysis proved to be as accurate as the speckle temporal correlation method reported in previous studies. Very low viscosities of the order of 1 mPa.s were retrieved for the first time using speckle images with either a frame rate of 1950 fps or a single acquired image.

Comment on “Study on the reliable computation time of the numerical model using the sliding temporal correlation method”

The paper, “Study on the reliable computation time of the numerical model using the sliding temporal correlation method,” was published in Theoretical and Applied Climatology. In that work, the sliding temporal correlation analysis is employed to investigate the predictable time of two typical chaotic numerical models, namely the Lorenz system and the Chen chaotic system. However, it has been recently shown by us when using a linear scaling in time and state variables that generically, the Chen system is only a particular case of the Lorenz system, with time reversion if the parameter c is positive. Consequently, to study the Chen chaotic system is simply to consider the Lorenz system integrated backwards.

On the role of lagged exogenous variables and spatio–temporal correlations in improving the accuracy of solar forecasting methods

We propose and analyze a spatio–temporal correlation method to improve forecast performance of solar irradiance using gridded satellite-derived global horizontal irradiance (GHI) data. Forecast models are developed for seven locations in California to predict 1-h averaged GHI 1, 2 and 3 h ahead of time. The seven locations were chosen to represent a diverse set of maritime, mediterranean, arid and semi-arid micro-climates. Ground stations from the California Irrigation Management Information System were used to obtain solar irradiance time-series from the points of interest. In this method, firstly, we define areas with the highest correlated time-series between the satellite-derived data and the ground data. Secondly, we select satellite-derived data from these regions as exogenous variables to several forecast models (linear models, Artificial Neural Networks, Support Vector Regression) to predict GHI at the seven locations. The results show that using linear forecasting models and a genetic algorithm to optimize the selection of multiple time-lagged exogenous variables results in significant forecasting improvements over other benchmark models.

Effect of hypertension on the resting-state functional connectivity in patients with Alzheimer's disease (AD)

Background/ObjectivesAlthough hypertension is known to be a risk factor for AD, the effects of hypertension on brain function in AD patients are not well understood. We investigated alterations in resting-state functional connectivity according to the presence of hypertension in AD patients by using a method of correlation analysis based on a seed region in the posterior cingulate cortex (PCC). We also determined whether differences in resting-state connectivity were associated with gray matter atrophy. MethodsThirty-seven AD patients (18 patients with hypertension and 19 patients without hypertension) underwent the resting-state functional magnetic resonance imaging. We obtained the PCC maps by a temporal correlation method, to identify alterations in the functional connectivity of the PCC in hypertensive group relative to non-hypertensive group. Voxel-based morphometry analysis was also applied to adjust the confounding effect of gray matter atrophy. ResultsWe detected a decreased connectivity to the PCC in the regions of subgenual anterior cingulated cortex (ACC) in hypertensive group relative to non-hypertensive group. However, we observed a pattern of increased connectivity between the PCC and the left inferior parietal cortex in hypertensive group. After correction for gray matter atrophy, all detected regions still remained significant. ConclusionsAltered connectivity in AD patients with hypertension suggests the possibility that hypertension impairs resting-state functional connectivity of the AD brain, inducing a compensational process outside the impaired networks or disequilibrium in brain connectivity. This finding may account for an additional contribution of hypertension to the pathophysiology of AD.

Study on the reliable computation time of the numerical model using the sliding temporal correlation method

A simple and useful method, the sliding temporal correlation (STC) analysis, is employed in the present work to investigate the predictable time (PT) of two typical chaotic numerical models (Lorenz system and Chen chaotic system) and reliable computing times (RCT) of an atmospheric general circulation model (ECHAM5). Through kinds of numerical experiments, results indicate that the maximal prediction time of Lorenz system (and Chen chaotic system) detected by STC method is coherent well with that by classical error limitation method, suggesting the effective role of the STC method. Then, taking the geopotential height for example, the RCT of ECHAM5 and potential impact factors such as the integration time step, initial condition, and model’s resolution are explored. Results reveal that (1) the high-value areas of the RCT are mainly situated in the tropics, and the global mean RCT (GMRCT) decreases from with the time step increasing; (2) the ocean forcing can enlarge the difference of the RCT between that averaged over the Southern Hemisphere (SH) and Northern Hemisphere (NH), which implies the RCT in the NH may be more sensitive to the computation error than that in the SH; (3) the model’s RCT also displays significant seasonality having longer (about 1–2 days) GMRCT in the experiment integrating from winter than that from summer; (4) the RCT of the high-resolution (T106) ECHAM5 shows similar spatial feature to that of low-resolution (T63) ECHAM5, but the GMRCT and hemispheric difference decreases.

Different resting-state functional connectivity alterations in smokers and nonsmokers with Internet gaming addiction.

This study investigated changes in resting-state functional connectivity (rsFC) of posterior cingulate cortex (PCC) in smokers and nonsmokers with Internet gaming addiction (IGA). Twenty-nine smokers with IGA, 22 nonsmokers with IGA, and 30 healthy controls (HC group) underwent a resting-state fMRI scan. PCC connectivity was determined in all subjects by investigating synchronized low-frequency fMRI signal fluctuations using a temporal correlation method. Compared with the nonsmokers with IGA, the smokers with IGA exhibited decreased rsFC with PCC in the right rectus gyrus. Left middle frontal gyrus exhibited increased rsFC. The PCC connectivity with the right rectus gyrus was found to be negatively correlated with the CIAS scores in the smokers with IGA before correction. Our results suggested that smokers with IGA had functional changes in brain areas related to motivation and executive function compared with the nonsmokers with IGA.

Altered Default Network Resting-State Functional Connectivity in Adolescents with Internet Gaming Addiction

PurposeExcessive use of the Internet has been linked to a variety of negative psychosocial consequences. This study used resting-state functional magnetic resonance imaging (fMRI) to investigate whether functional connectivity is altered in adolescents with Internet gaming addiction (IGA).MethodsSeventeen adolescents with IGA and 24 normal control adolescents underwent a 7.3 minute resting-state fMRI scan. Posterior cingulate cortex (PCC) connectivity was determined in all subjects by investigating synchronized low-frequency fMRI signal fluctuations using a temporal correlation method. To assess the relationship between IGA symptom severity and PCC connectivity, contrast images representing areas correlated with PCC connectivity were correlated with the scores of the 17 subjects with IGA on the Chen Internet Addiction Scale (CIAS) and Barratt Impulsiveness Scale-11 (BIS-11) and their hours of Internet use per week.ResultsThere were no significant differences in the distributions of the age, gender, and years of education between the two groups. The subjects with IGA showed longer Internet use per week (hours) (p<0.0001) and higher CIAS (p<0.0001) and BIS-11 (p = 0.01) scores than the controls. Compared with the control group, subjects with IGA exhibited increased functional connectivity in the bilateral cerebellum posterior lobe and middle temporal gyrus. The bilateral inferior parietal lobule and right inferior temporal gyrus exhibited decreased connectivity. Connectivity with the PCC was positively correlated with CIAS scores in the right precuneus, posterior cingulate gyrus, thalamus, caudate, nucleus accumbens, supplementary motor area, and lingual gyrus. It was negatively correlated with the right cerebellum anterior lobe and left superior parietal lobule.ConclusionOur results suggest that adolescents with IGA exhibit different resting-state patterns of brain activity. As these alterations are partially consistent with those in patients with substance addiction, they support the hypothesis that IGA as a behavioral addiction that may share similar neurobiological abnormalities with other addictive disorders.

A proficient wavelet-based video encoder through the motion estimation algorithm

In several multimedia applications, image and video coding plays a significant role. In many applications, sceneries such as broadcast services over satellite and terrestrial channels, digital video storage, wires and wireless conversational services and digital video communication are used. It is not possible to store full digital video without processing. Video compression reduces the data used to represent digital video images and is a combination of spatial image compression and temporal motion compensation. The main aim of our research is to develop an efficient video compression system. The proposed system consists of three steps. At first, wavelet decomposition is applied to the I–frame and the resulting coefficients are quantised using the listless SPECK (LSK) algorithm. In the next step, motion estimation is done using adaptive rood search with the spatio–temporal correlation method (ARS–ST) and it calculates the distance between the P–frame and I–frame blocks. In the final step, the difference between the original and the predicted P–frame is evaluated and is known as residual. To produce good quality predictive frames, this residual is transmitted along with motion vectors (MVs) and to decrease its size, it is coded using LSK. The proposed video compression technique uses different videos for assessment and by determining the PSNR values, compression efficiency is estimated. By comparing the proposed system with existing systems, it is seen that our system effectively compresses videos with remarkable PSNR measurements.

A preliminary study of alterations in default network connectivity in post-traumatic stress disorder patients following recent trauma

This study used resting-state functional magnetic resonance imaging (fMRI) to investigate whether functional connectivity is altered in people developing post-traumatic stress disorder (PTSD) following recent trauma. Sixty-two participants who had experienced recent acute traumatic events underwent a 7.3 min resting fMRI scan within 2 days post accident. Of these, 22 participants were diagnosed with PTSD within 1 to 6 months. Nineteen age- and sex-matched subjects without PTSD were selected as the trauma-exposed control group. Posterior cingulate cortex connectivity was determined from 17 PTSD patients and 15 control subjects by investigating synchronic low-frequency fMRI signal fluctuations using a temporal correlation method. To assess the relationship between PTSD symptom severity and PCC connectivity, the contrast image representing areas correlated with the PCC was correlated with the 17 PTSD subjects' Clinician Administered PTSD Scale (CAPS) scores at diagnosis. Compared with the control group, PTSD patients exhibited decreased functional connectivity in the right lingual and right middle temporal gyri, and left lingual/posterior cingulate cortex. The left inferior temporal gyrus, right middle temporal gyrus, left middle temporal gyrus/insula, left medial frontal lobe/anterior cingulate cortex, and right medial frontal gyrus also showed increased connectivity within two days post accident. A negative correlation was found between PCC connectivity and CAPS scores in the left medial prefrontal cortex (mPFC). These results suggest that patients who develop PTSD exhibit different resting-state patterns of neuronal activity following recent trauma. Abnormal FC of mPFC may be a major risk factor predisposing patients to the development of PTSD.

Abnormal functional connectivity in patients with vascular cognitive impairment, no dementia: A resting-state functional magnetic resonance imaging study

The functional connectivity (FC) method was used to investigate the changes in the resting state of patients with vascular cognitive impairment, no dementia (VCIND). Resting-state functional magnetic resonance images (fMRIs) were acquired from 16 patients with subcortical ischemic vascular disease (SIVD) who fulfilled the criteria for VCIND, as well as 18 age- and sex-matched subjects with SIVD with no cognitive impairment (control group). Posterior cingulate cortex connectivity was gathered by investigating synchronic low-frequency fMRI signal fluctuations with a temporal correlation method. Compared with the control group, the patients showed FC decrease in the left middle temporal gyrus, the left anterior cingulate/left middle frontal gyrus, the right caudate, the right middle frontal gyrus, and the left medial frontal gyrus/paracentral lobule. There were also some regions that showed increased connectivity. These regions included the right inferior temporal gyrus, the left middle temporal gyrus, the left precentral gyrus, and the left superior parietal lobule. Our findings revealed the change in resting-state patterns of neuronal activity in patients with VCIND. This change may be caused by subcortical white matter lesions that destroyed direct and indirect fiber tract connectivity across the cerebral white matter and influenced the cortical FC and hypoperfusion resulted from small vascular disease. The results of the increased connectivity may be evoked by the compensatory recruitment and plasticity mechanism. Our findings suggest that the simplicity and noninvasiveness of this method makes it a potential tool to help thoroughly understand the pathogenesis of VCIND.

Resting Brain Connectivity: Changes during the Progress of Alzheimer Disease

To investigate alterations in functional connectivity in the resting brain networks in healthy elderly volunteers and patients with mild, moderate, or severe Alzheimer Disease (AD). This study was approved by the institutional ethics committee, and informed consent was obtained. Forty-six patients with AD and 16 healthy elderly volunteers were prospectively examined. Resting-state functional magnetic resonance imaging was used to detect alterations in posterior cingulate cortex (PCC) functional connectivity through a comparison of the healthy control group with three separate AD groups-mild, moderate, and severe AD. A temporal correlation method was used to obtain PCC connectivity maps. Dissociated functional connectivity between the PCC and a set of regions, including the visual cortices bilaterally, the inferior temporal cortex, the hippocampus, and especially the medial prefrontal cortex and the precuneus and/or cuneus, was observed in all AD groups. The disruption of connectivity intensified as the stage of AD progression increased. There were also regions that exhibited increased connectivity; these regions extended from left lateralized frontoparietal regions and spread to bilateral frontoparietal regions along with AD progression. Changes in PCC functional connectivity comprised bidirectional alterations in the resting networks in AD-affected brains, and the impaired resting functional connectivity seemed to change with AD progression. Therefore, alterations in functional connectivity in the default mode network might play a role in the progression of AD.

Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer’s disease

Resting-state networks dissociate in the early stage of Alzheimer’s disease (AD). The posterior cingulate cortex (PCC) in AD brain is vulnerable to isolation from the rest of brain. However, it remains unclear how this functional connectivity is related to PCC changes. We employed resting-state functional MRI (fMRI) to examine brain regions with a functional connection to PCC in a mild AD group compared with matched control subjects. PCC connectivity was gathered by investigating synchronic low frequency fMRI signal fluctuations with a temporal correlation method. We found asymmetric PCC-left hippocampus, right dorsal–lateral prefrontal cortex and right thalamus connectivity disruption. In addition, some other regions such as the bilateral visual cortex, the infero-temporal cortex, the posterior orbital frontal cortex, the ventral medial prefrontal cortex and the precuneus showed decreased functional connectivity to the PCC. There were also some regions, primarily in the left frontal–parietal cortices, that showed increased connectivity. These regions included the medial prefrontal cortex, bilateral dorsal–lateral prefrontal cortex, the left basal ganglia and the left primary motor cortex. Impairments to memory, high vision-related functions and olfaction in AD can be explained by a disruption to the functional connection of resting-state networks. The results of increased connectivity may support the compensatory-recruitment hypothesis. Our findings suggest that the characteristics of resting-state functional connectivity could plausibly provide an early imaging biomarker for AD.

Fine Temporal Properties of Center–Surround Interactions in Motion Revealed by Reverse Correlation

Center-surround interactions are a key property of visual motion mechanisms. Using a temporal reverse correlation method with human observers, we investigated perceptual interactions between a brief center motion (approximately 20 ms) and a surround that moved up-down with a new direction chosen randomly every 5 ms. The aim was to reveal interactions between center and surround motions and their dependency on relative direction, contrast, and timing. Hypothesizing that surround computation involves different neural circuitry than the center response, we manipulated surround contrast to affect the relative timing of center and surround signals. The reverse correlation analysis yielded temporal profiles of surround influence indicating, in 5 ms steps, the time course of the effect of the surround on the discriminability of center motion. The resulting temporal profiles varied systematically with contrast: as surround contrast decreased, both the latency and duration of its influence increased. This finding, consistent with longer and variable neural response latencies at low contrast, psychophysically reveals fine-scale temporal interactions between center and surround signals. Additionally, the strength of surround influence was correlated with psychophysical thresholds for discriminating center motion. The directionality of this relationship, however, depended only on center contrast. When center motion was high contrast, poor direction discrimination was associated with an increased probability of same-direction surround motions. Low-contrast center motion, however, was more discriminable when surrounded by motion in the same direction, regardless of surround contrast. This suggests that the previously reported adaptive nature of center-surround interactions in motion is driven primarily by the visibility of the center motion signals.

A recursive full search algorithm based on temporal correlation

Although many fast searching-algorithms for motion estimations aim at reducing the computational complexity, the optimal accuracy is hardly achieved. Hence these methods are not suitable for bit-rate constrained environments. Due to a lack of regularity, VLSI implementation of these algorithms is not easy. In this study, we propose a new adaptive full search algorithm using the temporal correlation method and iterative layer process. Our algorithm is useful to decrease the computational complexity and achieves an optimal level of accuracy. Simulations demonstrate that our method can at least halve the computational complexity for full search estimation.