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In this study, we generalize a problem of sampling a scalar Gauss Markov Process, namely, the Ornstein-Uhlenbeck (OU) process, where the samples are sent to a remote estimator and the estimator makes a causal estimate of the observed realtime signal. In recent years, the problem is solved for stable OU processes. We present solutions for the optimal sampling policy that exhibits a smaller estimation error for both stable and unstable cases of the OU process along with a special case when the OU process turns to a Wiener process. The obtained optimal sampling policy is a threshold policy. However, the thresholds are different for all three cases. Later, we consider additional noise with the sample when the sampling decision is made beforehand. The estimator utilizes noisy samples to make an estimate of the current signal value. The mean-square error (mse) is changed from previous due to noise and the additional term in the mse is solved which provides performance upper bound and room for a pursuing further investigation on this problem to find an optimal sampling strategy that minimizes the estimation error when the observed samples are noisy. Numerical results show performance degradation caused by the additive noise.

When the parameters of Bayesian Short-time Spectral Amplitude (STSA) estimator for speech enhancement are selected based on the characteristics of the human auditory system, the gain function of the estimator becomes more flexible. Although this type of estimator in acoustic domain is quite effective in reducing the back-ground noise at high frequencies, it produces more speech distortions, which make the high-frequency contents of the speech such as friciatives less perceptible in heavy noise conditions, resulting in intelligibility reduction. On the other hand, the speech enhancement scheme, which exploits the psychoacoustic evidence of frequency selectivity in the modulation domain, is found to be able to increase the intelligibility of noisy speech by a substantial amount, but also suffers from the temporal slurring problem due to its essential design constraint. In order to achieve the joint improvements in both the perceived speech quality and intelligibility, we proposed and investigated a fusion framework by combining the merits of acoustic and modulation domain approaches while avoiding their respective weaknesses. Objective measure evaluation shows that the proposed speech enhancement fusion framework can provide consistent improvements in the perceived speech quality and intelligibility across different SNR levels in various noise conditions, while compared to the other baseline techniques.

Minimizing traffic accidents between vehicles and pedestrians is one of the primary research goals in intelligent transportation systems. To achieve the goal, pedestrian orientation recognition and prediction of pedestrian's crossing or not-crossing intention play a central role. Contemporary approaches do not guarantee satisfactory performance due to limited field-of-view, lack of generalization, and high computational complexity. To overcome these limitations, we propose a real-time predictive pedestrian collision warning service (P2CWS) for two tasks: pedestrian orientation recognition (100.53 FPS) and intention prediction (35.76 FPS). Our framework obtains satisfying generalization over multiple sites because of the proposed site-independent features. At the center of the feature extraction lies 3D pose estimation. The 3D pose analysis enables robust and accurate recognition of pedestrian orientations and prediction of intentions over multiple sites. The proposed vision framework realizes 89.3% accuracy in the behavior recognition task on the TUD dataset without any training process and 91.28% accuracy in intention prediction on our dataset achieving new state-of-the-art performance. To contribute to the corresponding research community, we make our source codes public which are available at //github.com/Uehwan/VisionForPedestrian

Bayesian estimation of short-time spectral amplitude is one of the most predominant approaches for the enhancement of the noise corrupted speech. The performance of these estimators are usually significantly improved when any perceptually relevant cost function is considered. On the other hand, the recent progress in the phase-based speech signal processing have shown that the phase-only enhancement based on spectral phase estimation methods can also provide joint improvement in the perceived speech quality and intelligibility, even in low SNR conditions. In this paper, to take advantage of both the perceptually motivated cost function involving STSAs of estimated and true clean speech and utilizing the prior spectral phase information, we have derived a phase-aware Bayesian STSA estimator. The parameters of the cost function are chosen based on the characteristics of the human auditory system, namely, the dynamic compressive nonlinearity of the cochlea, the perceived loudness theory and the simultaneous masking properties of the ear. This type of parameter selection scheme results in more noise reduction while limiting the speech distortion. The derived STSA estimator is optimal in the MMSE sense if the prior phase information is available. In practice, however, typically only an estimate of the clean speech phase can be obtained via employing different types of spectral phase estimation techniques which have been developed throughout the last few years. In a blind setup, we have evaluated the proposed Bayesian STSA estimator with different types of standard phase estimation methods available in the literature. Experimental results have shown that the proposed estimator can achieve substantial improvement in performance than the traditional phase-blind approaches.

Intelligent reflecting surface (IRS) has emerged as a cost-effective solution to enhance wireless communication performance via passive signal reflection. Existing works on IRS have mainly focused on investigating IRS's passive beamforming/reflection design to boost the communication rate for users assuming that their channel state information (CSI) is fully or partially known. However, how to exploit IRS to improve the wireless transmission reliability without any CSI, which is typical in high-mobility/delay-sensitive communication scenarios, remains largely open. In this paper, we study a new IRS-aided communication system with the IRS integrated to its aided access point (AP) to achieve both functions of transmit diversity and passive beamforming simultaneously. Specifically, we first show an interesting result that the IRS's passive beamforming gain in any direction is invariant to the common phase-shift applied to all of its reflecting elements. Accordingly, we design the common phase-shift of IRS elements to achieve transmit diversity at the AP side without the need of any CSI of the users. In addition, we propose a practical method for the users to estimate the CSI at the receiver side for information decoding. Meanwhile, we show that the conventional passive beamforming gain of IRS can be retained for the other users with their CSI known at the AP. Furthermore, we derive the asymptotic performance of both IRS-aided transmit diversity and passive beamforming in closed-form, by considering the large-scale IRS with an infinite number of elements. Numerical results validate our analysis and show the performance gains of the proposed IRS-aided simultaneous transmit diversity and passive beamforming scheme over other benchmark schemes.

In this paper, a comprehensive performance analysis of a distributed intelligent reflective surfaces (IRSs)-aided communication system is presented. First, the optimal signal-to-noise ratio (SNR), which is attainable through the direct and reflected channels, is quantified by controlling the phase shifts of the distributed IRS. Next, this optimal SNR is statistically characterized by deriving tight approximations to the exact probability density function (PDF) and cumulative distribution function (CDF) for Nakagami-$m$ fading. The accuracy/tightness of this statistical characterization is investigated by deriving the Kullback-Leibler divergence. Our PDF/CDF analysis is used to derive tight approximations/bounds for the outage probability, achievable rate, and average symbol error rate (SER) in closed-form. To obtain useful insights, the asymptotic outage probability and average SER are derived for the high SNR regime. Thereby, the achievable diversity order and array gains are quantified. Our asymptotic performance analysis reveals that the diversity order can be boosted by using distributed passive IRSs without generating additional electromagnetic (EM) waves via active radio frequency chains. Our asymptotic rate analysis shows that the lower and upper rate bounds converge to an asymptotic limit in large reflective element regime. Our analysis is validated via Monte-Carlo simulations. We present a rigorous set of numerical results to investigate the performance gains of the proposed system model. Our analytical and numerical results reveal that the performance of single-input single-output wireless systems can be boosted by recycling the EM waves generated by a transmitter through distributed passive IRS reflections to enable constructive signal combining at a receiver.

Modern wireless channels are increasingly dense and mobile making the channel highly non-stationary. The time-varying distribution and the existence of joint interference across multiple degrees of freedom (e.g., users, antennas, frequency and symbols) in such channels render conventional precoding sub-optimal in practice, and have led to historically poor characterization of their statistics. The core of our work is the derivation of a high-order generalization of Mercer's Theorem to decompose the non-stationary channel into constituent fading sub-channels (2-D eigenfunctions) that are jointly orthogonal across its degrees of freedom. Consequently, transmitting these eigenfunctions with optimally derived coefficients eventually mitigates any interference across these dimensions and forms the foundation of the proposed joint spatio-temporal precoding. The precoded symbols directly reconstruct the data symbols at the receiver upon demodulation, thereby significantly reducing its computational burden, by alleviating the need for any complementary decoding. These eigenfunctions are paramount to extracting the second-order channel statistics, and therefore completely characterize the underlying channel. Theory and simulations show that such precoding leads to ${>}10^4{\times}$ BER improvement (at 20dB) over existing methods for non-stationary channels.

Large margin nearest neighbor (LMNN) is a metric learner which optimizes the performance of the popular $k$NN classifier. However, its resulting metric relies on pre-selected target neighbors. In this paper, we address the feasibility of LMNN's optimization constraints regarding these target points, and introduce a mathematical measure to evaluate the size of the feasible region of the optimization problem. We enhance the optimization framework of LMNN by a weighting scheme which prefers data triplets which yield a larger feasible region. This increases the chances to obtain a good metric as the solution of LMNN's problem. We evaluate the performance of the resulting feasibility-based LMNN algorithm using synthetic and real datasets. The empirical results show an improved accuracy for different types of datasets in comparison to regular LMNN.

This study considers the 3D human pose estimation problem in a single RGB image by proposing a conditional random field (CRF) model over 2D poses, in which the 3D pose is obtained as a byproduct of the inference process. The unary term of the proposed CRF model is defined based on a powerful heat-map regression network, which has been proposed for 2D human pose estimation. This study also presents a regression network for lifting the 2D pose to 3D pose and proposes the prior term based on the consistency between the estimated 3D pose and the 2D pose. To obtain the approximate solution of the proposed CRF model, the N-best strategy is adopted. The proposed inference algorithm can be viewed as sequential processes of bottom-up generation of 2D and 3D pose proposals from the input 2D image based on deep networks and top-down verification of such proposals by checking their consistencies. To evaluate the proposed method, we use two large-scale datasets: Human3.6M and HumanEva. Experimental results show that the proposed method achieves the state-of-the-art 3D human pose estimation performance.

In this paper, we study the optimal convergence rate for distributed convex optimization problems in networks. We model the communication restrictions imposed by the network as a set of affine constraints and provide optimal complexity bounds for four different setups, namely: the function $F(\xb) \triangleq \sum_{i=1}^{m}f_i(\xb)$ is strongly convex and smooth, either strongly convex or smooth or just convex. Our results show that Nesterov's accelerated gradient descent on the dual problem can be executed in a distributed manner and obtains the same optimal rates as in the centralized version of the problem (up to constant or logarithmic factors) with an additional cost related to the spectral gap of the interaction matrix. Finally, we discuss some extensions to the proposed setup such as proximal friendly functions, time-varying graphs, improvement of the condition numbers.