The Experts below are selected from a list of 14493 Experts worldwide ranked by ideXlab platform
Sofiene Affes - One of the best experts on this subject based on the ideXlab platform.
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on optimal frequency domain multiChannel linear filtering for noise reduction
IEEE Transactions on Audio Speech and Language Processing, 2010Co-Authors: Mehrez Souden, Jacob Benesty, Sofiene AffesAbstract:Several contributions have been made so far to develop optimal multiChannel linear filtering approaches and show their ability to reduce the acoustic noise. However, there has not been a clear unifying theoretical analysis of their performance in terms of both noise reduction and speech distortion. To fill this gap, we analyze the frequency-domain (non-causal) multiChannel linear filtering for noise reduction in this paper. For completeness, we consider the noise reduction constrained optimization problem that leads to the parameterized multiChannel non-causal Wiener filter (PMWF). Our contribution is fivefold. First, we formally show that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction. Second, we propose new simplified expressions for the PMWF, the MVDR, and the generalized sidelobe canceller (GSC) that depend on the signals' statistics only. In contrast to earlier works, these expressions are explicitly independent of the Channel Transfer Function ratios. Third, we quantify the theoretical gains and losses in terms of speech distortion and noise reduction when using the PWMF by establishing new simplified closed-form expressions for three performance measures, namely, the signal distortion index, the noise reduction factor (originally proposed in the paper titled ldquoNew insights into the noise reduction Wiener filter,rdquo by J. Chen (IEEE Transactions on Audio, Speech, and Language Processing, Vol. 15, no. 4, pp. 1218-1234, Jul. 2006) to analyze the single Channel time-domain Wiener filter), and the output signal-to-noise ratio (SNR). Fourth, we analyze the effects of coherent and incoherent noise in addition to the benefits of utilizing multiple microphones. Fifth, we propose a new proof for the a posteriori SNR improvement achieved by the PMWF. Finally, we provide some simulations results to corroborate the findings of this work.
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on optimal frequency domain multiChannel linear filtering for noise reduction
IEEE Transactions on Audio Speech and Language Processing, 2010Co-Authors: Mehrez Souden, Jacob Benesty, Sofiene AffesAbstract:Several contributions have been made so far to develop optimal multiChannel linear filtering approaches and show their ability to reduce the acoustic noise. However, there has not been a clear unifying theoretical analysis of their performance in terms of both noise reduction and speech distortion. To fill this gap, we analyze the frequency-domain (non-causal) multiChannel linear filtering for noise reduction in this paper. For completeness, we consider the noise reduction constrained optimization problem that leads to the parameterized multiChannel non-causal Wiener filter (PMWF). Our contribution is fivefold. First, we formally show that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction. Second, we propose new simplified expressions for the PMWF, the MVDR, and the generalized sidelobe canceller (GSC) that depend on the signals' statistics only. In contrast to earlier works, these expressions are explicitly independent of the Channel Transfer Function ratios. Third, we quantify the theoretical gains and losses in terms of speech distortion and noise reduction when using the PWMF by establishing new simplified closed-form expressions for three performance measures, namely, the signal distortion index, the noise reduction factor (originally proposed in the paper titled ldquoNew insights into the noise reduction Wiener filter,rdquo by J. Chen (IEEE Transactions on Audio, Speech, and Language Processing, Vol. 15, no. 4, pp. 1218-1234, Jul. 2006) to analyze the single Channel time-domain Wiener filter), and the output signal-to-noise ratio (SNR). Fourth, we analyze the effects of coherent and incoherent noise in addition to the benefits of utilizing multiple microphones. Fifth, we propose a new proof for the a posteriori SNR improvement achieved by the PMWF. Finally, we provide some simulations results to corroborate the findings of this work.
Matthew S Reynolds - One of the best experts on this subject based on the ideXlab platform.
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a 1 mbps 158 pj bit bluetooth low energy ble compatible backscatter communication uplink for wireless neural recording in an animal cage environment
International Conference on RFID, 2019Co-Authors: James Rosenthal, Alexandra Pike, Matthew S ReynoldsAbstract:Neuroscience research in non-human primates (NHPs) would benefit from multi-day neural recordings from freely moving animals in unconstrained home cage environments. However, wireless brain-computer interfaces (BCI) face two major challenges. First, a metal animal cage forms a reverberant cavity that leads to dense multipath, impairing the wireless communication Channel. Second, the battery life of existing wireless neural recording devices is limited by the energy consumption of the neural data uplink.In this paper, we characterize the Channel Transfer Function of a metal NHP home cage in the 2.4 GHz industrial, scientific, and medical (ISM) band, and demonstrate that there is adequate signal strength and bandwidth to support low-power Bluetooth Low Energy (BLE) compatible backscatter data uplinks. For a typical cage and antenna system, the measured maximum insertion loss of the cage-antenna system was 27.4 dB and the minimum −3 dB bandwidth was 5.0 MHz. We demonstrate a 1 Mbps BLE compatible backscatter communication link achieving a worst-case packet error rate of 1.05%, yielding an effective bit error rate of 5.6×10−5, exceeding the BLE requirement of ≤ 10−3. The backscatter link has a measured energy consumption of 158 pJ/bit, compared with ≈ 10nJ/bit for existing WiFi and Bluetooth chipsets.
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wideband uhf dqpsk backscatter communication in reverberant cavity animal cage environments
IEEE Transactions on Antennas and Propagation, 2019Co-Authors: Apoorva Sharma, James Rosenthal, Alexandra Pike, Eleftherios Kampianakis, Anissa Dadkhah, Matthew S ReynoldsAbstract:Many neuroscience experiments with animal subjects require free motion of the animal within a metal cage environment. In such cage environments, wireless communication with implanted devices, e.g., neural recording and stimulation is particularly challenging because the metal cage walls form a reverberant cavity with dense multipath. In the case of backscatter communication with the implanted device, the multipath challenge is particularly acute because of the round-trip nature of the backscatter Channel. This paper demonstrates the reverberant cavity effect via measurement of the Channel Transfer Function inside a metal cage used for nonhuman primate research in the 902–928 MHz ultrahigh-frequency industrial, scientific, and medical band. A reduced-size ceramic patch antenna developed for the Neurochip neural recording and stimulation device was affixed to a saline tissue proxy, while a commercial air-dielectric patch antenna was affixed to the ceiling of the cage. A measured 3 dB Channel bandwidth greater than 6.5 MHz with a port-to-port insertion loss between 14 and 37 dB was achieved at 126 surveyed locations within the cage volume. A 6.25 Mb/s backscatter data uplink using a differential quadrature phase shift keying constellation was successfully validated inside the cage, with effectively 0% packet error rate for all but two of the surveyed locations. The simulation and experimental results show good agreement and reveal that wideband backscatter communication systems can perform well despite the significant multipath inside the reverberant cage environment.
Mehrez Souden - One of the best experts on this subject based on the ideXlab platform.
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on optimal frequency domain multiChannel linear filtering for noise reduction
IEEE Transactions on Audio Speech and Language Processing, 2010Co-Authors: Mehrez Souden, Jacob Benesty, Sofiene AffesAbstract:Several contributions have been made so far to develop optimal multiChannel linear filtering approaches and show their ability to reduce the acoustic noise. However, there has not been a clear unifying theoretical analysis of their performance in terms of both noise reduction and speech distortion. To fill this gap, we analyze the frequency-domain (non-causal) multiChannel linear filtering for noise reduction in this paper. For completeness, we consider the noise reduction constrained optimization problem that leads to the parameterized multiChannel non-causal Wiener filter (PMWF). Our contribution is fivefold. First, we formally show that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction. Second, we propose new simplified expressions for the PMWF, the MVDR, and the generalized sidelobe canceller (GSC) that depend on the signals' statistics only. In contrast to earlier works, these expressions are explicitly independent of the Channel Transfer Function ratios. Third, we quantify the theoretical gains and losses in terms of speech distortion and noise reduction when using the PWMF by establishing new simplified closed-form expressions for three performance measures, namely, the signal distortion index, the noise reduction factor (originally proposed in the paper titled ldquoNew insights into the noise reduction Wiener filter,rdquo by J. Chen (IEEE Transactions on Audio, Speech, and Language Processing, Vol. 15, no. 4, pp. 1218-1234, Jul. 2006) to analyze the single Channel time-domain Wiener filter), and the output signal-to-noise ratio (SNR). Fourth, we analyze the effects of coherent and incoherent noise in addition to the benefits of utilizing multiple microphones. Fifth, we propose a new proof for the a posteriori SNR improvement achieved by the PMWF. Finally, we provide some simulations results to corroborate the findings of this work.
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on optimal frequency domain multiChannel linear filtering for noise reduction
IEEE Transactions on Audio Speech and Language Processing, 2010Co-Authors: Mehrez Souden, Jacob Benesty, Sofiene AffesAbstract:Several contributions have been made so far to develop optimal multiChannel linear filtering approaches and show their ability to reduce the acoustic noise. However, there has not been a clear unifying theoretical analysis of their performance in terms of both noise reduction and speech distortion. To fill this gap, we analyze the frequency-domain (non-causal) multiChannel linear filtering for noise reduction in this paper. For completeness, we consider the noise reduction constrained optimization problem that leads to the parameterized multiChannel non-causal Wiener filter (PMWF). Our contribution is fivefold. First, we formally show that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction. Second, we propose new simplified expressions for the PMWF, the MVDR, and the generalized sidelobe canceller (GSC) that depend on the signals' statistics only. In contrast to earlier works, these expressions are explicitly independent of the Channel Transfer Function ratios. Third, we quantify the theoretical gains and losses in terms of speech distortion and noise reduction when using the PWMF by establishing new simplified closed-form expressions for three performance measures, namely, the signal distortion index, the noise reduction factor (originally proposed in the paper titled ldquoNew insights into the noise reduction Wiener filter,rdquo by J. Chen (IEEE Transactions on Audio, Speech, and Language Processing, Vol. 15, no. 4, pp. 1218-1234, Jul. 2006) to analyze the single Channel time-domain Wiener filter), and the output signal-to-noise ratio (SNR). Fourth, we analyze the effects of coherent and incoherent noise in addition to the benefits of utilizing multiple microphones. Fifth, we propose a new proof for the a posteriori SNR improvement achieved by the PMWF. Finally, we provide some simulations results to corroborate the findings of this work.
Raed M Shubair - One of the best experts on this subject based on the ideXlab platform.
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indoor localization for iot using adaptive feature selection a cascaded machine learning approach
IEEE Antennas and Wireless Propagation Letters, 2019Co-Authors: Mohamed I Alhajri, N T Ali, Raed M ShubairAbstract:Evolving Internet-of-things applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This letter presents a novel method based on a cascaded two-stage machine learning approach for highly accurate and robust localization in indoor environments using adaptive selection and combination of radio frequency (RF) features. In the proposed method, machine learning is first used to identify the type of the surrounding indoor environment. Then, in the second stage, machine learning is employed to identify the most appropriate selection and combination of RF features that yield the highest localization accuracy. Analysis is based on $k$ -nearest neighbor machine learning algorithm applied on a real dataset generated from practical measurements of the RF signal in realistic indoor environments. Received signal strength, Channel Transfer Function, and frequency coherence Function are the primary RF features being explored and combined. Numerical investigations demonstrate that prediction based on the concatenation of primary RF features enhanced significantly as the localization accuracy improved by at least 50% to more than 70%.
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indoor localization for iot using adaptive feature selection a cascaded machine learning approach
arXiv: Signal Processing, 2019Co-Authors: Mohamed I Alhajri, N T Ali, Raed M ShubairAbstract:Evolving Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This paper presents a novel method based on a cascaded two-stage machine learning approach for highly-accurate and robust localization in indoor environments using adaptive selection and combination of RF features. In the proposed method, machine learning is first used to identify the type of the surrounding indoor environment. Then, in the second stage, machine learning is employed to identify the most appropriate selection and combination of RF features that yield the highest localization accuracy. Analysis is based on k-Nearest Neighbor (k-NN) machine learning algorithm applied on a real dataset generated from practical measurements of the RF signal in realistic indoor environments. Received Signal Strength, Channel Transfer Function, and Frequency Coherence Function are the primary RF features being explored and combined. Numerical investigations demonstrate that prediction based on the concatenation of primary RF features enhanced significantly as the localization accuracy improved by at least 50% to more than 70%.
Jacob Benesty - One of the best experts on this subject based on the ideXlab platform.
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on optimal frequency domain multiChannel linear filtering for noise reduction
IEEE Transactions on Audio Speech and Language Processing, 2010Co-Authors: Mehrez Souden, Jacob Benesty, Sofiene AffesAbstract:Several contributions have been made so far to develop optimal multiChannel linear filtering approaches and show their ability to reduce the acoustic noise. However, there has not been a clear unifying theoretical analysis of their performance in terms of both noise reduction and speech distortion. To fill this gap, we analyze the frequency-domain (non-causal) multiChannel linear filtering for noise reduction in this paper. For completeness, we consider the noise reduction constrained optimization problem that leads to the parameterized multiChannel non-causal Wiener filter (PMWF). Our contribution is fivefold. First, we formally show that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction. Second, we propose new simplified expressions for the PMWF, the MVDR, and the generalized sidelobe canceller (GSC) that depend on the signals' statistics only. In contrast to earlier works, these expressions are explicitly independent of the Channel Transfer Function ratios. Third, we quantify the theoretical gains and losses in terms of speech distortion and noise reduction when using the PWMF by establishing new simplified closed-form expressions for three performance measures, namely, the signal distortion index, the noise reduction factor (originally proposed in the paper titled ldquoNew insights into the noise reduction Wiener filter,rdquo by J. Chen (IEEE Transactions on Audio, Speech, and Language Processing, Vol. 15, no. 4, pp. 1218-1234, Jul. 2006) to analyze the single Channel time-domain Wiener filter), and the output signal-to-noise ratio (SNR). Fourth, we analyze the effects of coherent and incoherent noise in addition to the benefits of utilizing multiple microphones. Fifth, we propose a new proof for the a posteriori SNR improvement achieved by the PMWF. Finally, we provide some simulations results to corroborate the findings of this work.
-
on optimal frequency domain multiChannel linear filtering for noise reduction
IEEE Transactions on Audio Speech and Language Processing, 2010Co-Authors: Mehrez Souden, Jacob Benesty, Sofiene AffesAbstract:Several contributions have been made so far to develop optimal multiChannel linear filtering approaches and show their ability to reduce the acoustic noise. However, there has not been a clear unifying theoretical analysis of their performance in terms of both noise reduction and speech distortion. To fill this gap, we analyze the frequency-domain (non-causal) multiChannel linear filtering for noise reduction in this paper. For completeness, we consider the noise reduction constrained optimization problem that leads to the parameterized multiChannel non-causal Wiener filter (PMWF). Our contribution is fivefold. First, we formally show that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction. Second, we propose new simplified expressions for the PMWF, the MVDR, and the generalized sidelobe canceller (GSC) that depend on the signals' statistics only. In contrast to earlier works, these expressions are explicitly independent of the Channel Transfer Function ratios. Third, we quantify the theoretical gains and losses in terms of speech distortion and noise reduction when using the PWMF by establishing new simplified closed-form expressions for three performance measures, namely, the signal distortion index, the noise reduction factor (originally proposed in the paper titled ldquoNew insights into the noise reduction Wiener filter,rdquo by J. Chen (IEEE Transactions on Audio, Speech, and Language Processing, Vol. 15, no. 4, pp. 1218-1234, Jul. 2006) to analyze the single Channel time-domain Wiener filter), and the output signal-to-noise ratio (SNR). Fourth, we analyze the effects of coherent and incoherent noise in addition to the benefits of utilizing multiple microphones. Fifth, we propose a new proof for the a posteriori SNR improvement achieved by the PMWF. Finally, we provide some simulations results to corroborate the findings of this work.