Cyclic-Auto-Correlation Based Timing Estimation Algorithm for Time-Frequency Overlapping Multi-Carrier Signals

In recent years,the time-frequency overlapping multi-carrier signal has been a novel and valuable topic in blind signal processing,especially in the non-cooperative receiving field.But there is little related research in public published papers.This paper proposes two timing estimation algorithms,which are non-data-aided and based on the cyclic auto-correlation function.In order to evaluate the performance of the proposed algorithms,the theoretical bound of the timing estimation is derived.According to the analyses and simulation results,the effectiveness of the proposed algorithms has been demonstrated.It shows that MethodⅠhas better performance than MethodⅡ.However,MethodⅡdoes not need prior information,so it has a wider range of applications.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

The W transform and its improved methods for time-frequency analysis of seismic data

The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.

Overlap吻合与π吻合在全腹腔镜全胃切除术中的应用

目的:比较Overlap吻合与π吻合在全腹腔镜全胃切除术(TLTG)中的应用效果。方法:选择2019年2月至2022年2月在成都市第五人民医院行TLTG的胃癌患者98例,根据食管空肠吻合方式分为Overlap吻合组(n=56)和π吻合组(n=42)。比较两组围手术期指标(手术时间、吻合时间、术中出血量、淋巴结清扫数目、术后肛门排气时间、术后拔除引流管时间、术后住院时间、肿瘤上缘至食管切缘距离),吻合口相关并发症(吻合口出血、吻合口狭窄、吻合口漏)与随访并发症(吻合口溃疡、倾倒综合征、反流性食管炎、营养不良)发生情况,以及术前与术后3个月整体营养状况主观评估量表(PG-SGA)评分与营养状况客观指标(血红蛋白、白蛋白、总蛋白)水平的变化。结果:Overlap吻合组手术时间[(230.36±25.12)min vs(205.58±20.43)min]、吻合时间[(43.22±6.15)min vs(27.45±5.36)min]均长于π吻合组(P<0.05);两组吻合口相关并发症、随访并发症发生率及术前、术后3个月PG-SGA量表评分与营养状况客观指标水平的变化差异均无统计学意义(P>0.05)。结论:Overlap吻合和π吻合在TLTG术中各有优势,π吻合可缩短手术时间和吻合时间。

右半结肠癌根治术中全腹腔镜下Overlap吻合的短期疗效及并发症分析

目的:通过对比全腹腔镜下顺蠕动侧侧吻合(Overlap吻合)与腹腔外吻合相关临床资料及短期疗效,探究腹腔内吻合在右半结肠癌根治术中的临床疗效及价值。方法:回顾性选择2021年01月至2023年12月我院胃肠与疝外科收治的腹腔镜右半结肠癌根治术患者临床资料,共计92例纳入临床研究。其中采用腹腔内Overlap吻合的患者为ICA组;采用腹腔外端侧吻合为ECA组。收集并分析两组患者临床资料,讨论全腹腔镜下Overlap吻合安全性及短期临床疗效差异。结果:纳入患者一般资料差异无统计学意义(P值>0.05)。手术资料方面,ICA组手术时间[(185.83±27.55)min;P=0.026]与吻合时间[(33.86±4.05)min;P<0.001]均显著长于ECA组,切口长度方面[(4.86±0.84)cm;P<0.001]ICA组显著短于ECA组。而术中出血、清扫淋巴结总数两组差异无统计学意义。术后康复方面,ICA组首次排气时间[(2.55±0.55)天;P=0.001]、首次进流质时间[(3.38±0.62)天;P<0.001]、下床时间[(2.71±0.55)天;P=0.006]、术后住院时间[(7.67±1.26)天;P=0.019]均优于ECA组,且ICA组术后引流量[(277.74±147.68)mL;P=0.017]显著少于ECA组。在术后第3天,ICA组疼痛VAS评分(3.19±0.77;P=0.04)低于ECA组。术后炎症指标方面,两组术后C反应蛋白、中性粒细胞-淋巴细胞比值(NLR)均无统计学差异(P值>0.05)。在手术后并发症方面,总并发症发生率、吻合口瘘、腹腔感染、腹腔出血、吻合口出血、切口感染、肺部感染、术后肠梗阻、术后胃瘫两组并无统计学差异(P值>0.05)。结论:全腹腔镜下Overlap吻合安全可行,具有术后肠道功能恢复更快、创伤更小等优势,手术疗效与传统腔镜辅助手术效果相当,且不会增加短期并发症发生率。

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Overlapping approach Proton Pump Inhibitors/Nux vomica-Heel as new intervention for gastro-esophageal reflux management:Delphi consensus study

BACKGROUND Gastro-esophageal reflux disease(GERD)may affect the upper digestive tract;up to 20%of population in Western nations are affected by GERD.Antacids,histamine H2-receptor antagonists,and Proton Pump Inhibitors(PPIs)are considered the referring medications for GERD.Nevertheless,PPIs must be managed carefully because their use,especially chronic,could be linked with some adverse effects.An effective and safe alternative pharmacological tool for GERD is needed.After the identification of potentially new medications to flank PPIs,it is mandatory to revise and improve good clinical practices even through a consensus process.AIM To optimize diagnosis and treatment guidelines for GERD through a consensus based on Delphi method.METHODS The availability of clinical studies describing the action of the multicomponent/multitarget medication Nux vomica-Heel,subject of the consensus,is the basic prerequisite for the consensus itself.A modified Delphi process was used to reach a consensus among a panel of Italian GERD specialists on the overlapping approach PPIs/Nux vomica-Heel as a new intervention model for the management of GERD.The Voting Consensus group was composed of 49 Italian Medical Doctors with different specializations:Gastroenterology,otolaryngology,geriatrics,and general medicine.A scientific committee analyzed the literature,determined areas that required investigation(in agreement with the multiple-choice questionnaire results),and identified two topics of interest:(1)GERD disease;and(2)GERD treatment.Statements for each of these topics were then formulated and validated.The Delphi process involved two rounds of questioning submitted to the panel experts using an online platform.RESULTS According to their routinary GERD practice and current clinical evidence,the panel members provided feedback to each questionnaire statement.The experts evaluated 15 statements and reached consensus on all 15.The statements regarding the GERD disease showed high levels of agreement,with consensus ranging from 70%to 92%.The statements regarding the GERD treatment also showed very high levels of agreement,with consensus ranging from 90%to 100%.This Delphi process was able to reach consensus among physicians in relevant aspects of GERD management,such as the adoption of a new approach to treat patients with GERD based on the overlapping between PPIs and Nux vomica-Heel.The consensus was unanimous among the physicians with different specializations,underlying the uniqueness of the agreement reached to identify in the overlapping approach between PPIs and Nux vomica-Heel a new intervention model for GERD management.The results support that an effective approach to deprescribe PPIs through a progressive decalage timetable(reducing PPIs administration to as-needed use),should be considered.CONCLUSION Nux vomica-Heel appears to be a valid opportunity for GERD treatment to favor the deprescription of PPIs and to maintain low disease activity together with the symptomatology remission.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Overlapping infections of Mycobacterium canariasense and Nocardia farcinica in an immunocompetent patient:A case report

BACKGROUND Infections by non-tuberculous mycobacteria(NTM)have become more common in recent years.Mycobacterium canariasense(M.canariasense)was first reported as an opportunistic pathogen in 2004,but there have been very few case reports since then.Nocardia is a genus of aerobic and Gram-positive bacilli,and these species are also opportunistic pathogens and in the Mycobacteriales order.Conventional methods for diagnosis of NTM are inefficient.Metagenomic next-generation sequencing(mNGS)can rapidly detect many pathogenic microorganisms,even rare species.Most NTM and Nocardia infections occur in immunocompromised patients with atypical clinical symptoms.There are no previous reports of infection by M.canariasense and Nocardia farcinica(N.farcinica),especially in immunocompetent patients.This case report describes an immunocompetent 52-year-old woman who had overlapping infections of M.canariasense,N.farcinica,and Candida parapsilosis(C.parapsilosis)based on mNGS.CASE SUMMARY A 52-year-old woman presented with a productive cough and chest pain for 2 wk,and recurrent episodes of moderate-grade fever for 1 wk.She received antibiotics for 1 wk at a local hospital,and experienced defervescence,but the productive cough and chest pain persisted.We collected samples of a lung lesion and alveolar lavage fluid for mNGS.The lung tissue was positive for M.canariasense,N.farcinica,and C.parapsilosis,and the alveolar lavage fluid was positive for M.canariasense.The diagnosis was pneumonia,and application of appropriate antibiotic therapy cured the patient.CONCLUSION Etiological diagnosis is critical for patients with infectious diseases.mNGS can identify rare and novel pathogens,and does not require a priori knowledge.

Autoimmune hepatitis-primary biliary cholangitis overlap syndrome complicated by various autoimmune diseases:A case report

BACKGROUND Autoimmune hepatitis(AIH)and primary biliary cholangitis(PBC)are two common clinical autoimmune liver diseases,and some patients have both diseases;this feature is called AIH-PBC overlap syndrome.Autoimmune thyroid disease(AITD)is the most frequently overlapping extrahepatic autoimmune disease.Immunoglobulin(IgG)4-related disease is an autoimmune disease recognized in recent years,characterized by elevated serum IgG4 levels and infiltration of IgG4-positive plasma cells in tissues.CASE SUMMARY A 68-year-old female patient was admitted with a history of right upper quadrant pain,anorexia,and jaundice on physical examination.Laboratory examination revealed elevated liver enzymes,multiple positive autoantibodies associated with liver and thyroid disease,and imaging and biopsy suggestive of pancreatitis,hepatitis,and PBC.A diagnosis was made of a rare and complex overlap syndrome of AIH,PBC,AITD,and IgG4-related disease.Laboratory features improved on treatment with ursodeoxycholic acid,methylprednisolone,and azathioprine.CONCLUSION This case highlights the importance of screening patients with autoimmune diseases for related conditions.

Novel method for extraction of ship target with overlaps in SAR image via EM algorithm

The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.

Predictors of survival in autoimmune liver disease overlap syndromes

BACKGROUND Survival in patients with autoimmune liver disease overlap syndromes(AILDOS)compared to those with single autoimmune liver disease is unclear.AIM To investigate the survival of patients with AILDOS and assess the accuracy of non-invasive serum models for predicting liver-related death.METHODS Patients with AILDOS were defined as either autoimmune hepatitis and primary biliary cholangitis overlap(AIH-PBC)or autoimmune hepatitis and primary sclerosing cholangitis overlap(AIH-PSC)and were identified from three tertiary centres for this cohort study.Liver-related death or transplantation(liver-related mortality)was determined using a population-based data linkage system.Prognostic scores for liver-related death were compared for accuracy[including liver outcome score(LOS),Hepascore,Mayo Score,model for end-stage liver disease(MELD)score and MELD incorporated with serum sodium(MELD-Na)score].RESULTS Twenty-two AILDOS patients were followed for a median of 3.1 years(range,0.35-7.7).Fourteen were female,the median age was 46.7 years(range,17.8 to 82.1)and median Hepascore was 1(range,0.07-1).At five years post enrolment,57%of patients remained free from liver-related mortality(74%AIH-PBC,27%AIH-PSC).There was no significant difference in survival between AIH-PBC and AIH-PSC.LOS was a significant predictor of liver-related mortality(P<0.05)in patients with AIH-PBC(n=14)but not AIH-PSC(n=8).A LOS cut-point of 6 discriminated liver-related mortality in AIH-PBC patients(P=0.012,log-rank test,100%sensitivity,77.8%specificity)(Harrell's C-statistic 0.867).The MELD score,MELD-Na score and Mayo Score were not predictive of liver-related mortality in any group.CONCLUSION Survival in the rare,AILDOS is unclear.The current study supports the LOS as a predictor of liver-related mortality in AIH-PBC patients.Further trials investigating predictors of survival in AILDOS are required.

The Practical Path of Labor Curriculum in Compulsory Education in China from the Perspective of Overlapping Influence Domain Theory

Since the new curriculum reform,labor education has gradually shed its marginalized position in the“five educations,”and the labor curriculum has become an independent course officially separated from comprehensive practical activity courses.Exploring the practical path of labor curriculum in compulsory education in China has become the primary task of labor education in China.Based on the practical situation of labor curriculum in compulsory education in China,drawing on the theory of overlapping influence domains,and from the perspective of collaborative education among family,school,and community,this paper proposes a curriculum practical path of“school-led”family-school-community collaboration and a curriculum practical path guided by“student-centered”sentiment,in order to provide references for the practice of labor curriculum in compulsory education in China.

Overlap吻合术式在腹腔镜结直肠手术重建中的临床研究

目的研究腹腔镜结直肠手术重建中运用侧侧吻合(Overlap)术式的临床价值。方法选择2022年1月至2023年2月期间龙游县人民医院收治的102例结直肠癌患者为研究对象,均行腹腔镜结直肠癌根治术,以吻合术式不同将其分为两组,其中行功能性端端吻合为对照组(n=51),采用Overlap吻合术式则为观察组(n=51),比较两组患者的围术期指标、炎症指标以及术后并发症发生情况,术后随访12个月,评价两组患者生活质量。结果观察组患者的术后住院、进食、手术以及排气时间均短于对照组,差异均有统计学意义(t分别=5.26、3.49、3.43、3.55,P均<0.05);观察组术后3 d的C反应蛋白(CRP)、白细胞介素-6(IL-6)以及肿瘤坏死因子-α(TNF-α)水平均低于对照组,差异均有统计学意义(t分别=13.14、15.79、12.49,P均<0.05);观察组患者的并发症发生率明显低于对照组,差异有统计学意义(χ^(2)=15.86,P<0.05);两组3、6、9、12个月的生活质量评分比较,差异均无统计学意义(t分别=0.19、0.50、0.60、0.62,P均>0.05)。结论在腹腔镜结直肠癌根治术中,通过采用Overlap吻合术式,不仅可以缩短手术时间,恢复胃肠道功能,还可以减少术后并发症,并且对患者生活质量无明显影响,可以作为有效且安全的一种吻合方式。

Pseudo-Semi-Overlap Functions-Based Fuzzy Rough Sets Applied to Image Edge Extraction

As an extension of overlap functions, pseudo-semi-overlap functions are a crucial class of aggregation functions. Therefore, (I, PSO)-fuzzy rough sets are introduced, utilizing pseudo-semi-overlap functions, and further extended for applications in image edge extraction. Firstly, a new clustering function, the pseudo-semi-overlap function, is introduced by eliminating the symmetry and right continuity present in the overlap function. The relaxed nature of this function enhances its applicability in image edge extraction. Secondly, the definitions of (I, PSO)-fuzzy rough sets are provided, using (I, PSO)-fuzzy rough sets, a pair of new fuzzy mathematical morphological operators (IPSOFMM operators) is proposed. Finally, by combining the fuzzy C-means algorithm and IPSOFMM operators, a novel image edge extraction algorithm (FCM-IPSO algorithm) is proposed and implemented. Compared to existing algorithms, the FCM-IPSO algorithm exhibits more image edges and a 73.81% decrease in the noise introduction rate. The outstanding performance of (I, PSO)-fuzzy rough sets in image edge extraction demonstrates their practical application value.

Application of sparse time-frequency decomposition to seismic data

The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results.

TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft

Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.

Intelligibility evaluation of enhanced whisper in joint time-frequency domain

Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyzed. Experimental results show that for a spectrum of some density, the joint time-frequency gain-modification based speech enhancement algorithm achieves significant improvement in intelligibility. Additionally, the spectrum region where the estimated spectrum is smaller than the clean spectrum, is the most important region contributing to intelligibility improvement for the enhanced whisper. The spectrum region where the estimated spectrum is larger than twice the size of the clean spectrum is detrimental to speech intelligibility perception within the whisper context.

Effects of Gabor transform parameters on signa time-frequency resolution

In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.