The Experts below are selected from a list of 1089 Experts worldwide ranked by ideXlab platform
Robert W. Heath - One of the best experts on this subject based on the ideXlab platform.
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Link adaptation in MIMO-OFDM with non-uniform constellation selection over Spatial Streams through supervised learning
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Robert C. Daniels, Robert W. HeathAbstract:Supervised learning has been used to develop practical link adaptation algorithms for MIMO-OFDM under an equal rate per Stream assumption. In this paper we develop supervised learning algorithms that select from non-uniform rates per Stream. We show that the straightforward application of existing supervised learning link adaptation algorithms exhibits complexity that scales with the number of Spatial Streams. Therefore, we propose a decoupled Stream link adaptation algorithm which reduces the complexity below the original supervised learning algorithm with uniform Spatial Streams. We further show that the performance loss of decoupled link adaptation is reduced in systems with non-uniform constellations per Spatial Stream. IEEE 802.11n and uncoded MIMO-OFDM simulations are used to validate the proposed algorithms.
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ICASSP - Link adaptation in MIMO-OFDM with non-uniform constellation selection over Spatial Streams through supervised learning
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Robert C. Daniels, Robert W. HeathAbstract:Supervised learning has been used to develop practical link adaptation algorithms for MIMO-OFDM under an equal rate per Stream assumption. In this paper we develop supervised learning algorithms that select from non-uniform rates per Stream. We show that the straightforward application of existing supervised learning link adaptation algorithms exhibits complexity that scales with the number of Spatial Streams. Therefore, we propose a decoupled Stream link adaptation algorithm which reduces the complexity below the original supervised learning algorithm with uniform Spatial Streams. We further show that the performance loss of decoupled link adaptation is reduced in systems with non-uniform constellations per Spatial Stream. IEEE 802.11n and uncoded MIMO-OFDM simulations are used to validate the proposed algorithms.
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multiuser mimo downlink with limited feedback using transmit beam matching
International Conference on Communications, 2008Co-Authors: Robert W. Heath, Sunghyun ChoiAbstract:The multiuser multiple-input multiple-output (MIMO) broadcast channel uses multiple antennas at the transmitter to simultaneously deliver information to multiple users. Despite the theoretical capacity gains, the actual throughput of the system with limited feedback of channel state information suffers due to interSpatial Stream interference caused by quantization errors. This paper introduces a new method to mitigate the interference: transmit-beam matching. Using multiple receive antennas, transmit-beam matching minimizes the inter-Spatial Stream interference by each user's effective channel to the transmit-beam. It is evaluated based on a recent MIMO broadcast channel technique with limited feedback, per-user unitary rate control (PU2RC) and shown to effectively mitigate the interference.
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ICC - Multiuser MIMO Downlink with Limited Feedback Using Transmit-Beam Matching
2008 IEEE International Conference on Communications, 2008Co-Authors: Robert W. Heath, Sunghyun ChoiAbstract:The multiuser multiple-input multiple-output (MIMO) broadcast channel uses multiple antennas at the transmitter to simultaneously deliver information to multiple users. Despite the theoretical capacity gains, the actual throughput of the system with limited feedback of channel state information suffers due to interSpatial Stream interference caused by quantization errors. This paper introduces a new method to mitigate the interference: transmit-beam matching. Using multiple receive antennas, transmit-beam matching minimizes the inter-Spatial Stream interference by each user's effective channel to the transmit-beam. It is evaluated based on a recent MIMO broadcast channel technique with limited feedback, per-user unitary rate control (PU2RC) and shown to effectively mitigate the interference.
John C. Middlebrooks - One of the best experts on this subject based on the ideXlab platform.
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Spatial Stream Segregation
Springer Handbook of Auditory Research, 2017Co-Authors: John C. MiddlebrooksAbstract:“Stream segregation” refers to a listener’s ability to disentangle interleaved sequences of sounds, such as the ability to string together syllables from one talker in the presence of competing talkers. Spatial separation of sound sources is a key factor that enables the task of segregation. Psychophysical tasks that require listeners to integrate sounds across locations demonstrate that listeners can overcome Spatial separation of sources, suggesting that space is a relatively weak segregating factor. Contrary to that suggestion tasks that require listeners to isolate a sound sequence within a complex background demonstrate robust benefits of Spatial separation of the target from other sources. This chapter reviews psychophysical studies that show weak versus strong Spatial effects on Streaming and shows that the Spatial acuity of Stream segregation can approach the limits of acuity of Spatial hearing. Responses from auditory cortex in anesthetized animals are presented demonstrating that single neurons can exhibit Spatial Stream segregation by synchronizing selectively to one or the other of two interleaved sound sequences. The results from animals imply that perceptually segregated sound sequences are represented in auditory cortex by discrete mutually synchronized neural populations. Human magneto- and electroencephalographic results then are described showing selective enhancement of cortical responses to attended versus unattended sounds. Available results lead to a picture showing bottom-up segregation of sound sources by brainstem mechanisms on the basis of Spatial and other cues, followed by top-down selection of particular neural populations that could underlie perceptual auditory objects of attention.
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Spatial Stream Segregation by Cats
Journal of the Association for Research in Otolaryngology, 2016Co-Authors: Lauren K. Javier, Elizabeth A. Mcguire, John C. MiddlebrooksAbstract:Listeners can perceive interleaved sequences of sounds from two or more sources as segregated Streams. In humans, physical separation of sound sources is a major factor enabling such Stream segregation. Here, we examine Spatial Stream segregation with a psychophysical measure in domestic cats. Cats depressed a pedal to initiate a target sequence of brief sound bursts in a particular rhythm and then released the pedal when the rhythm changed. The target bursts were interleaved with a competing sequence of bursts that could differ in source location but otherwise were identical to the target bursts. This task was possible only when the sources were heard as segregated Streams. When the sound bursts had broad spectra, cats could detect the rhythm change when target and competing sources were separated by as little as 9.4°. Essentially equal levels of performance were observed when frequencies were restricted to a high, 4-to-25-kHz, band in which the principal Spatial cues presumably were related to sound levels. When the stimulus band was restricted from 0.4 to 1.6 kHz, leaving interaural time differences as the principal Spatial cue, performance was severely degraded. The frequency sensitivity of cats in this task contrasts with that of humans, who show better Spatial Stream segregation with low- than with high-frequency sounds. Possible explanations for the species difference includes the smaller interaural delays available to cats due to smaller sizes of their heads and the potentially greater sound-level cues available due to the cat’s frontally directed pinnae and higher audible frequency range.
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emergence of Spatial Stream segregation in the ascending auditory pathway
The Journal of Neuroscience, 2015Co-Authors: Peter Bremen, John C. MiddlebrooksAbstract:Stream segregation enables a listener to disentangle multiple competing sequences of sounds. A recent study from our laboratory demonstrated that cortical neurons in anesthetized cats exhibit Spatial Stream segregation (SSS) by synchronizing preferentially to one of two sequences of noise bursts that alternate between two source locations. Here, we examine the emergence of SSS along the ascending auditory pathway. Extracellular recordings were made in anesthetized rats from the inferior colliculus (IC), the nucleus of the brachium of the IC (BIN), the medial geniculate body (MGB), and the primary auditory cortex (A1). Stimuli consisted of interleaved sequences of broadband noise bursts that alternated between two source locations. At stimulus presentation rates of 5 and 10 bursts per second, at which human listeners report robust SSS, neural SSS is weak in the central nucleus of the IC (ICC), it appears in the nucleus of the brachium of the IC (BIN) and in approximately two-thirds of neurons in the ventral MGB (MGBv), and is prominent throughout A1. The enhancement of SSS at the cortical level reflects both increased Spatial sensitivity and increased forward suppression. We demonstrate that forward suppression in A1 does not result from synaptic inhibition at the cortical level. Instead, forward suppression might reflect synaptic depression in the thalamocortical projection. Together, our findings indicate that auditory Streams are increasingly segregated along the ascending auditory pathway as distinct mutually synchronized neural populations.Listeners are capable of disentangling multiple competing sequences of sounds that originate from distinct sources. This Stream segregation is aided by differences in Spatial location between the sources. A possible substrate of Spatial Stream segregation (SSS) has been described in the auditory cortex, but the mechanisms leading to those cortical responses are unknown. Here, we investigated SSS in three levels of the ascending auditory pathway with extracellular unit recordings in anesthetized rats. We found that neural SSS emerges within the ascending auditory pathway as a consequence of sharpening of Spatial sensitivity and increasing forward suppression. Our results highlight brainstem mechanisms that culminate in SSS at the level of the auditory cortex.
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Spatial Stream segregation by cats, rats, and humans
Journal of the Acoustical Society of America, 2014Co-Authors: John C. Middlebrooks, Peter Bremen, Lauren K. JavierAbstract:Spatial hearing aids a listener in disentangling multiple competing sound sequences. We find that separation of around 10° between target and masker sound sources permits humans and cats to hear interleaved sound sequences as segregated Streams, thus enabling a “rhythmic masking release” task requiring recognition of target rhythms. In cats and rats, neurons in primary auditory cortex (A1) exhibit Spatial Stream segregation in that they synchronize selectively to one of two interleaved sequences of noise burst originating from Spatially separated sources. Cortical Spatial selectivity is markedly sharper under competing-sound conditions compared to that observed with single sound sources. Cortical responses are predicted well by a model that incorporates moderate Spatial selectivity inherited from the brainstem sharpened by forward suppression at the level of thalamocortical synapses. Consistent with that model, Spatial Stream segregation in rats is stronger in cortical area A1 than in the ventral division...
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Spatial Stream segregation by auditory cortical neurons
The Journal of Neuroscience, 2013Co-Authors: John C. Middlebrooks, Peter BremenAbstract:In a complex auditory scene, a “cocktail party” for example, listeners can disentangle multiple competing sequences of sounds. A recent psychophysical study in our laboratory demonstrated a robust Spatial component of Stream segregation showing ∼8° acuity. Here, we recorded single- and multiple-neuron responses from the primary auditory cortex of anesthetized cats while presenting interleaved sound sequences that human listeners would experience as segregated Streams. Sequences of broadband sounds alternated between pairs of locations. Neurons synchronized preferentially to sounds from one or the other location, thereby segregating competing sound sequences. Neurons favoring one source location or the other tended to aggregate within the cortex, suggestive of modular organization. The Spatial acuity of Stream segregation was as narrow as ∼10°, markedly sharper than the broad Spatial tuning for single sources that is well known in the literature. Spatial sensitivity was sharpest among neurons having high characteristic frequencies. Neural Stream segregation was predicted well by a parameter-free model that incorporated single-source Spatial sensitivity and a measured forward-suppression term. We found that the forward suppression was not due to post discharge adaptation in the cortex and, therefore, must have arisen in the subcortical pathway or at the level of thalamocortical synapses. A linear-classifier analysis of single-neuron responses to rhythmic stimuli like those used in our psychophysical study yielded thresholds overlapping those of human listeners. Overall, the results indicate that the ascending auditory system does the work of segregating auditory Streams, bringing them to discrete modules in the cortex for selection by top-down processes.
Sunghyun Choi - One of the best experts on this subject based on the ideXlab platform.
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Linear Precoding with Resource Allocation for MIMO Relay Channels
IEEE Transactions on Wireless Communications, 2013Co-Authors: Edwin Monroy, Sunghyun Choi, Bijan JabbariAbstract:This paper considers a half-duplex relay channel with a single source, relay, and destination, where each node has multiple antennas and the relay operates in decode-and-forward (DF) mode. The additional degrees of freedoms introduced by the MIMO channels entail increased complexity in comparison with the single antenna case. We propose a new transmission strategy for the relay channel that is able to take advantage of the MIMO gains while employing practical techniques that help reduce complexity. In the proposed scheme, the source splits its message in two parts and sends them to the destination, one part directly and the other with help from the relay. For such a transmission strategy, we formulate the problem of obtaining the rate-maximizing linear precoding matrices with time, power, and Spatial Stream allocation and transform the problem into a convex form. We also propose a suboptimal algorithm that provides simplifying expressions to solve the resulting problem with far less computational complexity. The numerical results show that the achievable rate of our scheme is greater than the DF rate in certain scenarios, as well as that of other practical existing strategies. In addition, the rate obtained by the suboptimal method approximates the optimal rate extremely well in all considered scenarios.
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A practical transmission scheme for half-duplex decode-and-forward MIMO relay channels
2012 IEEE Globecom Workshops, 2012Co-Authors: Edwin Monroy, Sunghyun Choi, Bijan JabbariAbstract:We propose a novel and practical transmission strategy for decode-and-forward MIMO relay channels operating in half-duplex mode that is based on precoding with time, power, and Spatial Stream allocation. In the proposed scheme, the message from the source is divided into two parts and sent to the destination, one part directly and the other with the help of the relay. We formulate the problem of finding the rate-maximizing precoding matrices and resource allocation and then transform it into a convex form by employing the proposed suboptimal but simplifying precoding structure and performing other manipulations. The numerical results show that the achievable rate of the proposed scheme is close to a well-known theoretical lower bound and even outperforms it in certain scenarios, despite our transmission strategy being based on a much simpler coding scheme. Additionally, the proposed scheme is able to outperform another scheme based on repetition coding with fixed time slot duration.
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PIMRC - Interference mitigation via scheduling for the MIMO broadcast channel with limited feedback
2009 IEEE 20th International Symposium on Personal Indoor and Mobile Radio Communications, 2009Co-Authors: Sunghyun ChoiAbstract:The multiple-input multiple-output (MIMO) broadcast channel uses multiple antennas at the transmitter to concurrently deliver information to multiple receivers. Despite the theoretical capacity gains over single user MIMO, the actual throughput of the system with limited feedback on channel state information suffers due to interSpatial Stream interference. On top of the orthogonal beamforming with limited feedback [1], this paper introduces two novel Stream schedulers to mitigate the interference. By the proposed adaptive Spatial Stream schedulers, an adaptive number of Spatial Streams are scheduled and thus, reduces the interference, which also achieves power gains by concentrating power into fewer Streams. The two proposed algorithms are evaluated and shown to effectively mitigate the interference.
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Interference mitigation via scheduling for the MIMO broadcast channel with limited feedback
2009 IEEE 20th International Symposium on Personal Indoor and Mobile Radio Communications, 2009Co-Authors: Sunghyun ChoiAbstract:The multiple-input multiple-output (MIMO) broadcast channel uses multiple antennas at the transmitter to concurrently deliver information to multiple receivers. Despite the theoretical capacity gains over single user MIMO, the actual throughput of the system with limited feedback on channel state information suffers due to interSpatial Stream interference. On top of the orthogonal beamforming with limited feedback , this paper introduces two novel Stream schedulers to mitigate the interference. By the proposed adaptive Spatial Stream schedulers, an adaptive number of Spatial Streams are scheduled and thus, reduces the interference, which also achieves power gains by concentrating power into fewer Streams. The two proposed algorithms are evaluated and shown to effectively mitigate the interference.
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multiuser mimo downlink with limited feedback using transmit beam matching
International Conference on Communications, 2008Co-Authors: Robert W. Heath, Sunghyun ChoiAbstract:The multiuser multiple-input multiple-output (MIMO) broadcast channel uses multiple antennas at the transmitter to simultaneously deliver information to multiple users. Despite the theoretical capacity gains, the actual throughput of the system with limited feedback of channel state information suffers due to interSpatial Stream interference caused by quantization errors. This paper introduces a new method to mitigate the interference: transmit-beam matching. Using multiple receive antennas, transmit-beam matching minimizes the inter-Spatial Stream interference by each user's effective channel to the transmit-beam. It is evaluated based on a recent MIMO broadcast channel technique with limited feedback, per-user unitary rate control (PU2RC) and shown to effectively mitigate the interference.
Zhu Xiao - One of the best experts on this subject based on the ideXlab platform.
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WiFiMap+: High-Level Indoor Semantic Inference With WiFi Human Activity and Environment
IEEE Transactions on Vehicular Technology, 2019Co-Authors: Wei Zhang, Siwang Zhou, Liang Yang, Lu Ou, Zhu XiaoAbstract:Existing indoor semantic recognition schemes are capable of discovering patterns through smartphone sensing; however, there is a lack of a device-free indoor semantic recognition system. In this paper, we propose WiFiMap+, which is a first-ever automatical inference system using WiFi signals to recognize high-level indoor semantics from human activities and environments, where the high-level indoor semantics consist of indoor facilities and environments. To characterize the static indoor environments and dynamical human activities separately with channel state information (CSI), we propose a novel two-Stream architecture to generate the Spatial Streams and the movement Streams independently. Compared to the recent research on activity recognition, this two-Stream architecture can make the content area of CSI samples extend from human activities to indoor environments. For obtaining accurate indoor environment detection, we propose a CSI-environment model with a Spatial Stream generation algorithm, which can reduce the effect of human activities on environment detection. For stable activity recognition, we also propose an environment-based testing sample representation method, which can utilize the environment knowledge to overcome the diversity of CSI caused by the environment changes. Finally, we implement WiFiMap+ using commercial WiFi devices and evaluate its performance for seven common semantic detection cases in six-room scenarios. The experimental results show that our proposed WiFiMap+ is robust to the multi-room scenario and can achieve the average accuracy of $\text{92.8}\%$ and the lowest accuracy of about $\text{82}\%$ .
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WiFiMap+: High-Level Indoor Semantic Inference With WiFi Human Activity and Environment
IEEE Transactions on Vehicular Technology, 2019Co-Authors: Wei Zhang, Siwang Zhou, Liang Yang, Lu Ou, Zhu XiaoAbstract:Existing indoor semantic recognition schemes are capable of discovering patterns through smartphone sensing; however, there is a lack of a device-free indoor semantic recognition system. In this paper, we propose WiFiMap+, which is a first-ever automatical inference system using WiFi signals to recognize highlevel indoor semantics from human activities and environments, where the high-level indoor semantics consist of indoor facilities and environments. To characterize the static indoor environments and dynamical human activities separately with channel state information (CSI), we propose a novel two-Stream architecture to generate the Spatial Streams and the movement Streams independently. Compared to the recent research on activity recognition, this two-Stream architecture can make the content area of CSI samples extend from human activities to indoor environments. For obtaining accurate indoor environment detection, we propose a CSI-environment model with a Spatial Stream generation algorithm, which can reduce the effect of human activities on environment detection. For stable activity recognition, we also propose an environment-based testing sample representation method, which can utilize the environment knowledge to overcome the diversity of CSI caused by the environment changes. Finally, we implement WiFiMap+ using commercial WiFi devices and evaluate its performance for seven common semantic detection cases in sixroom scenarios. The experimental results show that our proposed WiFiMap+ is robust to the multi-room scenario and can achieve the average accuracy of 92.8% and the lowest accuracy of about 82%.
Zhenzhong Chen - One of the best experts on this subject based on the ideXlab platform.
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Video Saliency Prediction Based on Spatial-Temporal Two-Stream Network
IEEE Transactions on Circuits and Systems for Video Technology, 2019Co-Authors: Kao Zhang, Zhenzhong ChenAbstract:In this paper, we propose a novel two-Stream neural network for video saliency prediction. Unlike some traditional methods based on hand-crafted feature extraction and integration, our proposed method automatically learns saliency related spatiotemporal features from human fixations without any pre-processing, post-processing, or manual tuning. Video frames are routed through the Spatial Stream network to compute static or color saliency maps for each of them. And a new two-stage temporal Stream network is proposed, which is composed of a pre-trained 2D-CNN model (SF-Net) to extract saliency related features and a shallow 3D-CNN model (Te-Net) to process these features, for temporal or dynamic saliency maps. It can reduce the requirement of video gaze data, improve training efficiency, and achieve high performance. A fusion network is adopted to combine the outputs of both Streams and generate the final saliency maps. Besides, a convolutional Gaussian priors (CGP) layer is proposed to learn the bias phenomenon in viewing behavior to improve the performance of the video saliency prediction. The proposed method is compared with state-of-the-art saliency models on two public video saliency benchmark datasets. The results demonstrate that our model can achieve advanced performance on video saliency prediction.
