The Experts below are selected from a list of 4038 Experts worldwide ranked by ideXlab platform
Athina P Petropulu - One of the best experts on this subject based on the ideXlab platform.
-
fps sft a multi dimensional sparse fourier transform based on the fourier projection slice theorem
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Shaogang Wang, Vishal M Patel, Athina P PetropuluAbstract:We propose a multidimensional sparse Fourier transform inspired by the idea of the Fourier projection-slice theorem, called FPS-SFT. FPS-SFT extracts samples along lines (1-dimensional slices from a multidimensional data cube), which are parameterized by random slopes and offsets. The discrete Fourier transform (DFT) along those lines represents projections of multidimensional DFT of the data onto those lines. The multidimensional frequencies that are contained in the signal can be reconstructed from the DFT along lines with a low sample and computational complexity provided that the signal is sparse in the frequency domain and the lines are appropriately designed. The performance of FPS-SFT is demonstrated both theoretically and numerically. A sparse image reconstruction application is illustrated, which shows the capability of the FPS-SFT in solving less sparse scenarios containing non-uniformly distributed frequencies.
-
robust sparse fourier transform based on the fourier projection slice theorem
arXiv: Signal Processing, 2017Co-Authors: Shaogang Wang, Vishal M Patel, Athina P PetropuluAbstract:The state-of-the-art automotive radars employ multidimensional discrete Fourier transforms (DFT) in order to estimate various target parameters. The DFT is implemented using the fast Fourier transform (FFT), at sample and computational complexity of $O(N)$ and $O(N \log N)$, respectively, where $N$ is the number of samples in the signal space. We have recently proposed a sparse Fourier transform based on the Fourier projection-slice theorem (FPS-SFT), which applies to multidimensional signals that are sparse in the frequency domain. FPS-SFT achieves sample complexity of $O(K)$ and computational complexity of $O(K \log K)$ for a multidimensional, $K$-sparse signal. While FPS-SFT considers the ideal scenario, i.e., exactly sparse data that contains on-grid frequencies, in this paper, by extending FPS-SFT into a robust version (RFPS-SFT), we emphasize on addressing noisy signals that contain off-grid frequencies; such signals arise from radar applications. This is achieved by employing a windowing technique and a voting-based frequency decoding procedure; the former reduces the frequency leakage of the off-grid frequencies below the noise level to preserve the sparsity of the signal, while the latter significantly lowers the frequency localization error stemming from the noise. The performance of the proposed method is demonstrated both theoretically and numerically.
-
fps sft a multi dimensional sparse fourier transform based on the fourier projection slice theorem
arXiv: Signal Processing, 2017Co-Authors: Shaogang Wang, Vishal M Patel, Athina P PetropuluAbstract:We propose a multi-dimensional (M-D) sparse Fourier transform inspired by the idea of the Fourier projection-slice theorem, called FPS-SFT. FPS-SFT extracts samples along lines (1-dimensional slices from an M-D data cube), which are parameterized by random slopes and offsets. The discrete Fourier transform (DFT) along those lines represents projections of M-D DFT of the M-D data onto those lines. The M-D sinusoids that are contained in the signal can be reconstructed from the DFT along lines with a low sample and computational complexity provided that the signal is sparse in the frequency domain and the lines are appropriately designed. The performance of FPS-SFT is demonstrated both theoretically and numerically. A sparse image reconstruction application is illustrated, which shows the capability of the FPS-SFT in solving practical problems.
Shaogang Wang - One of the best experts on this subject based on the ideXlab platform.
-
fps sft a multi dimensional sparse fourier transform based on the fourier projection slice theorem
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Shaogang Wang, Vishal M Patel, Athina P PetropuluAbstract:We propose a multidimensional sparse Fourier transform inspired by the idea of the Fourier projection-slice theorem, called FPS-SFT. FPS-SFT extracts samples along lines (1-dimensional slices from a multidimensional data cube), which are parameterized by random slopes and offsets. The discrete Fourier transform (DFT) along those lines represents projections of multidimensional DFT of the data onto those lines. The multidimensional frequencies that are contained in the signal can be reconstructed from the DFT along lines with a low sample and computational complexity provided that the signal is sparse in the frequency domain and the lines are appropriately designed. The performance of FPS-SFT is demonstrated both theoretically and numerically. A sparse image reconstruction application is illustrated, which shows the capability of the FPS-SFT in solving less sparse scenarios containing non-uniformly distributed frequencies.
-
robust sparse fourier transform based on the fourier projection slice theorem
arXiv: Signal Processing, 2017Co-Authors: Shaogang Wang, Vishal M Patel, Athina P PetropuluAbstract:The state-of-the-art automotive radars employ multidimensional discrete Fourier transforms (DFT) in order to estimate various target parameters. The DFT is implemented using the fast Fourier transform (FFT), at sample and computational complexity of $O(N)$ and $O(N \log N)$, respectively, where $N$ is the number of samples in the signal space. We have recently proposed a sparse Fourier transform based on the Fourier projection-slice theorem (FPS-SFT), which applies to multidimensional signals that are sparse in the frequency domain. FPS-SFT achieves sample complexity of $O(K)$ and computational complexity of $O(K \log K)$ for a multidimensional, $K$-sparse signal. While FPS-SFT considers the ideal scenario, i.e., exactly sparse data that contains on-grid frequencies, in this paper, by extending FPS-SFT into a robust version (RFPS-SFT), we emphasize on addressing noisy signals that contain off-grid frequencies; such signals arise from radar applications. This is achieved by employing a windowing technique and a voting-based frequency decoding procedure; the former reduces the frequency leakage of the off-grid frequencies below the noise level to preserve the sparsity of the signal, while the latter significantly lowers the frequency localization error stemming from the noise. The performance of the proposed method is demonstrated both theoretically and numerically.
-
fps sft a multi dimensional sparse fourier transform based on the fourier projection slice theorem
arXiv: Signal Processing, 2017Co-Authors: Shaogang Wang, Vishal M Patel, Athina P PetropuluAbstract:We propose a multi-dimensional (M-D) sparse Fourier transform inspired by the idea of the Fourier projection-slice theorem, called FPS-SFT. FPS-SFT extracts samples along lines (1-dimensional slices from an M-D data cube), which are parameterized by random slopes and offsets. The discrete Fourier transform (DFT) along those lines represents projections of M-D DFT of the M-D data onto those lines. The M-D sinusoids that are contained in the signal can be reconstructed from the DFT along lines with a low sample and computational complexity provided that the signal is sparse in the frequency domain and the lines are appropriately designed. The performance of FPS-SFT is demonstrated both theoretically and numerically. A sparse image reconstruction application is illustrated, which shows the capability of the FPS-SFT in solving practical problems.
Vishal M Patel - One of the best experts on this subject based on the ideXlab platform.
-
fps sft a multi dimensional sparse fourier transform based on the fourier projection slice theorem
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Shaogang Wang, Vishal M Patel, Athina P PetropuluAbstract:We propose a multidimensional sparse Fourier transform inspired by the idea of the Fourier projection-slice theorem, called FPS-SFT. FPS-SFT extracts samples along lines (1-dimensional slices from a multidimensional data cube), which are parameterized by random slopes and offsets. The discrete Fourier transform (DFT) along those lines represents projections of multidimensional DFT of the data onto those lines. The multidimensional frequencies that are contained in the signal can be reconstructed from the DFT along lines with a low sample and computational complexity provided that the signal is sparse in the frequency domain and the lines are appropriately designed. The performance of FPS-SFT is demonstrated both theoretically and numerically. A sparse image reconstruction application is illustrated, which shows the capability of the FPS-SFT in solving less sparse scenarios containing non-uniformly distributed frequencies.
-
robust sparse fourier transform based on the fourier projection slice theorem
arXiv: Signal Processing, 2017Co-Authors: Shaogang Wang, Vishal M Patel, Athina P PetropuluAbstract:The state-of-the-art automotive radars employ multidimensional discrete Fourier transforms (DFT) in order to estimate various target parameters. The DFT is implemented using the fast Fourier transform (FFT), at sample and computational complexity of $O(N)$ and $O(N \log N)$, respectively, where $N$ is the number of samples in the signal space. We have recently proposed a sparse Fourier transform based on the Fourier projection-slice theorem (FPS-SFT), which applies to multidimensional signals that are sparse in the frequency domain. FPS-SFT achieves sample complexity of $O(K)$ and computational complexity of $O(K \log K)$ for a multidimensional, $K$-sparse signal. While FPS-SFT considers the ideal scenario, i.e., exactly sparse data that contains on-grid frequencies, in this paper, by extending FPS-SFT into a robust version (RFPS-SFT), we emphasize on addressing noisy signals that contain off-grid frequencies; such signals arise from radar applications. This is achieved by employing a windowing technique and a voting-based frequency decoding procedure; the former reduces the frequency leakage of the off-grid frequencies below the noise level to preserve the sparsity of the signal, while the latter significantly lowers the frequency localization error stemming from the noise. The performance of the proposed method is demonstrated both theoretically and numerically.
-
fps sft a multi dimensional sparse fourier transform based on the fourier projection slice theorem
arXiv: Signal Processing, 2017Co-Authors: Shaogang Wang, Vishal M Patel, Athina P PetropuluAbstract:We propose a multi-dimensional (M-D) sparse Fourier transform inspired by the idea of the Fourier projection-slice theorem, called FPS-SFT. FPS-SFT extracts samples along lines (1-dimensional slices from an M-D data cube), which are parameterized by random slopes and offsets. The discrete Fourier transform (DFT) along those lines represents projections of M-D DFT of the M-D data onto those lines. The M-D sinusoids that are contained in the signal can be reconstructed from the DFT along lines with a low sample and computational complexity provided that the signal is sparse in the frequency domain and the lines are appropriately designed. The performance of FPS-SFT is demonstrated both theoretically and numerically. A sparse image reconstruction application is illustrated, which shows the capability of the FPS-SFT in solving practical problems.
Hiromi Wada - One of the best experts on this subject based on the ideXlab platform.
-
a randomized phase iii trial of adjuvant chemotherapy with UFT for completely resected pathological stage i non small cell lung cancer the west japan study group for lung cancer surgery wjsg the 4th study
Annals of Oncology, 2005Co-Authors: M Nakagawa, Fumihiro Tanaka, N Tsubota, M Ohta, Motoshi Takao, Hiromi WadaAbstract:Purpose: To examine the efficacy of UFT, an oral 5-fluorouracil derivative agent, as post-operative adjuvant therapy for pathologic (p-) stage I non-small-cell lung cancer (NSCLC), because a previous randomized study had suggested it was efficacious for early-stage NSCLC patients. Patients and methods: Patients with completely resected p-stage I, adenocarcinoma or squamous cell carcinoma were eligible. A total of 332 patients were randomized to the surgery-alone group (control group) and the treatment group (UFT 400 mg/m 2 for 1 year after surgery, UFT group) after stratification by the histologic types. Results: For all patients, the 5- and 8-year survival rates for the UFT group were 82.2% and 73.0%, and those for the control group were 75.9% and 61.2%, respectively; no statistically significant improvement of survival was achieved by UFT administration (P = 0.105). For Ad patients, the 5- and 8-year survival rates of the UFT group (n = 120) were 85.2% and 79.5%, respectively, which seemed better than those of the control group (n = 121) (79.2% and 64.0%, respectively; P = 0.081). For squamous cell carcinoma patients, there was also no difference in survival between the control group (n = 48) and the UFT group (n = 43) (P = 0.762). For all pT1 patients, the 5- and 8-year survival rates of the UFT group were 83.6% and 82.1%, respectively, significantly better than those of the control group (77.9% and 57.6%, respectively, P = 0.036); UFT was not significantly effective for pT2 patients. For pT1 adenocarcinoma patients, UFT administration markedly improved the survival (P = 0.011). Conclusion: Post-operative UFT administration did not significantly improve post-operative survival of p-stage I NSCLC patients. Subset analyses suggested that UFT might be effective in pT1N0M0 adenocarcinoma patients.
-
apoptosis and p53 status predict the efficacy of postoperative administration of UFT in non small cell lung cancer
British Journal of Cancer, 2001Co-Authors: F Tanaka, Yosuke Otake, Kazuhiro Yanagihara, T Yamada, Ryo Miyahara, Yozo Kawano, Kenji Inui, Hiromi WadaAbstract:To examine whether efficacy of postoperative oral administration of UFT, a 5-fluorouracil derivative chemotherapeutic agent, may be influenced by incidence of apoptosis (apoptosis index) or apoptosis-related gene status (p53 and bcl-2) of the tumour, a total of 162 patients with pathologic stage I non-small cell lung cancer were retrospectively reviewed. UFT was administrated postoperatively to 44 patients (UFT group), and not to the other 118 patients (Control group). For all patients, 5-year survival rate of the UFT group (79.9%) seemed higher than that of the Control group (69.8%), although without significant difference (P = 0.054). For patients with higher apoptotic index, 5-year survival rate of the UFT group (83.3%) was significantly higher than that of the Control group (67.6%, P = 0.039); for patients with lower apoptotic index, however, there was no difference in the prognosis between these two groups. Similarly, UFT was effective for patients without p53 aberrant expression (5-year survival rates: 95.2% for the UFT group and 74.3% for the Control group, P = 0.022), whereas not effective for patients with p53 aberrant expression. Bcl-2 status did not influence the efficacy of UFT. In conclusion, apoptotic index and p53 status are useful factors to predict the efficacy of postoperative adjuvant therapy using UFT.
Maureen Ohara - One of the best experts on this subject based on the ideXlab platform.
-
the volume clock insights into the high frequency paradigm
The Journal of Portfolio Management, 2012Co-Authors: David Easley, Marcos Lopez De Prado, Maureen OharaAbstract:Over the last two centuries, technological advantages have allowed some traders to be faster than others. In this article, the authors argue that contrary to popular perception, speed is not the defining characteristic that sets high-frequency trading (HFT) apart. HFT is the natural evolution of a new trading paradigm that is characterized by strategic decisions made in a volume-clock metric. Even if the speed advantage disappears, HFT will evolve to continue exploiting structural weaknesses of low-frequency trading (LFT).LFT practitioners are not defenseless against HFT players, however, and this article offers options that can help them survive and adapt to this new environment.
-
the volume clock insights into the high frequency paradigm
Social Science Research Network, 2012Co-Authors: David Easley, Marcos Lopez De Prado, Maureen OharaAbstract:Over the last two centuries, technological advantages have allowed some traders to be faster than others. We argue that, contrary to popular perception, speed is not the defining characteristic that sets High Frequency Trading (HFT) apart. HFT is the natural evolution of a new trading paradigm that is characterized by strategic decisions made in a volume-clock metric. Even if the speed advantage disappears, HFT will evolve to continue exploiting Low Frequency Trading’s (LFT) structural weaknesses. However, LFT practitioners are not defenseless against HFT players, and we offer options that can help them survive and adapt to this new environment.
