This paper proposes a linear companding transform(CT)using either a single inflection point or two inflection points to reduce the peakto-average power ratio(PAPR)in orthogonal timefrequency space(OTFS)signals.The CT ...This paper proposes a linear companding transform(CT)using either a single inflection point or two inflection points to reduce the peakto-average power ratio(PAPR)in orthogonal timefrequency space(OTFS)signals.The CT strategically compresses higher amplitudes and enhances lower amplitudes based on carefully chosen scaling factors and points of inflection.With these selected parameters,the CT effectively reduces peak power while maintaining average power,leading to a substantial decrease in PAPR.We analyze noise changes in the inverse companding transform(ICT)process.The analysis reveals that the ICT amplifies less than 20%of the total noise.A convolutional encoder and soft decision Viterbi decoding algorithm are utilized in the OTFS system to improve the detection performance.We present simulation results focusing on PAPR reduction and bit error rate(BER)performance.These results demonstrate that the CT with two inflection points outperforms both the single inflection point case and the existingμ-law companding,clipping,peak windowing,unique OTFS frame structure,selected mapping,and partial transmit sequence methods,achieving significant PAPR reduction and BER performance.展开更多
Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by ...Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field,which indicates how the convolution process extracts features in a high dimensional feature space.However,its functionality is restricted to the spatial dimension and network depth,limiting further improvements in network performance due to insufficient information interaction and representation.Crucially,the potential of high dimensional feature space in the channel dimension and the exploration of network width/resolution remain largely untapped.In this paper,we consider nonlinear transforms from the perspective of feature space,defining high-dimensional feature spaces in different dimensions and investigating the specific effects.Firstly,we introduce the dimension increasing and decreasing transforms in both channel and spatial dimensions to obtain high dimensional feature space and achieve better feature extraction.Secondly,we design a channel-spatial fusion residual transform(CSR),which incorporates multi-dimensional transforms for a more effective representation.Furthermore,we simplify the proposed fusion transform to obtain a slim architecture(CSR-sm),balancing network complexity and compression performance.Finally,we build the overall network with stacked CSR transforms to achieve better compression and reconstruction.Experimental results demonstrate that the proposed method can achieve superior ratedistortion performance compared to the existing LIC methods and traditional codecs.Specifically,our proposed method achieves 9.38%BD-rate reduction over VVC on Kodak dataset.展开更多
Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wave...Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wavelet decomposed signals are compared with fractures identified from image logs to determine the fracture-matched mother wavelet.Then the mother wavelet-based decomposed signal combined with the differential curves of conventional well logs create a fracture indicator curve,identifying the fractured zone.Finally the fracture density can be precisely evaluated by the linear relationship of the indicator curve and image log fracture density.This method has been successfully used to evaluate igneous reservoir fractures in the southern Songnan basin and the calculated density from the indicator curve and density from image logs are both basically consistent.展开更多
文摘This paper proposes a linear companding transform(CT)using either a single inflection point or two inflection points to reduce the peakto-average power ratio(PAPR)in orthogonal timefrequency space(OTFS)signals.The CT strategically compresses higher amplitudes and enhances lower amplitudes based on carefully chosen scaling factors and points of inflection.With these selected parameters,the CT effectively reduces peak power while maintaining average power,leading to a substantial decrease in PAPR.We analyze noise changes in the inverse companding transform(ICT)process.The analysis reveals that the ICT amplifies less than 20%of the total noise.A convolutional encoder and soft decision Viterbi decoding algorithm are utilized in the OTFS system to improve the detection performance.We present simulation results focusing on PAPR reduction and bit error rate(BER)performance.These results demonstrate that the CT with two inflection points outperforms both the single inflection point case and the existingμ-law companding,clipping,peak windowing,unique OTFS frame structure,selected mapping,and partial transmit sequence methods,achieving significant PAPR reduction and BER performance.
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.62031013)Guangdong Province Key Construction Discipline Scientific Research Capacity Improvement Project(Grant No.2022ZDJS117).
文摘Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field,which indicates how the convolution process extracts features in a high dimensional feature space.However,its functionality is restricted to the spatial dimension and network depth,limiting further improvements in network performance due to insufficient information interaction and representation.Crucially,the potential of high dimensional feature space in the channel dimension and the exploration of network width/resolution remain largely untapped.In this paper,we consider nonlinear transforms from the perspective of feature space,defining high-dimensional feature spaces in different dimensions and investigating the specific effects.Firstly,we introduce the dimension increasing and decreasing transforms in both channel and spatial dimensions to obtain high dimensional feature space and achieve better feature extraction.Secondly,we design a channel-spatial fusion residual transform(CSR),which incorporates multi-dimensional transforms for a more effective representation.Furthermore,we simplify the proposed fusion transform to obtain a slim architecture(CSR-sm),balancing network complexity and compression performance.Finally,we build the overall network with stacked CSR transforms to achieve better compression and reconstruction.Experimental results demonstrate that the proposed method can achieve superior ratedistortion performance compared to the existing LIC methods and traditional codecs.Specifically,our proposed method achieves 9.38%BD-rate reduction over VVC on Kodak dataset.
基金sponsored by National Science and Technology Major Project of China (No. 2008 ZX 05009-001)
文摘Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wavelet decomposed signals are compared with fractures identified from image logs to determine the fracture-matched mother wavelet.Then the mother wavelet-based decomposed signal combined with the differential curves of conventional well logs create a fracture indicator curve,identifying the fractured zone.Finally the fracture density can be precisely evaluated by the linear relationship of the indicator curve and image log fracture density.This method has been successfully used to evaluate igneous reservoir fractures in the southern Songnan basin and the calculated density from the indicator curve and density from image logs are both basically consistent.
