This paper presents an efficient feedback synchronization technique for joint estimation of integer and fractional carrier frequency offsets in orthogonal frequency division multiplexing (OFDM) systems. The proposed technique has high accuracy, broad estimation range, and works effectively over frequency-selective radio channels. A dynamic self-noise reduction technique is introduced to enhance the estimation accuracy over a wide range of signal-to-noise ratios by changing the synchronizer S-curve.
In this paper, we start by investigating the impact of joint transmit-receive I/Q imbalance on the performance of direct-conversion beamforming OFDM transceivers. We derive a new analytical expression for the average subcarrier SINR of the I/Q imbalance-ignorant beamformer in terms of the I/Q imbalance level at both the transmit and receive sides, the size of the beamforming array, and the input SNR level. This expression motivates the need for I/Q imbalance compensation and provides valuable design insights when examining several special and limiting cases. Next, we derive the throughput-maximizing multiple beamforming transmit/receive coefficients under a general system model with an asymmetric frequency-dependent (FD) joint transmit-receive I/Q imbalance. In addition, we propose a low-complexity pilot-aided scheme for the estimation of the channel and I/Q imbalance parameters. Our simulation results demonstrate that the proposed generalized multiple beamforming scheme is highly effective in mitigating I/Q imbalance effects at practical complexity levels.
Accurate detection and estimation of overlapping fading multipath components is vital for many communication systems, particularly for positioning technologies. Traditional approaches used for channel estimation generally fail in estimating closely-spaced multipath components in code-division multiple access (CDMA) systems. Here, we present a highly efficient technique for asynchronous downlink WCDMA multipath delay estimation with subchip resolution capability based on nonlinear Teager-Kaiser operator concept. The behavior of this technique is influenced considerably by the pulse shape waveform. Both rectangular and root raised cosine pulse shaping filters are considered.
We present a novel and efficient technique for the restoration of color images which are highly corrupted with impulse noise. This is a detection-estimation based approach in which outliers are first detected using a Teager-like operator followed by a locally adaptive threshold. Center pixels whose "energy" exceeds some threshold are replaced with the local marginal median. Simulation results show the superior performance of the proposed filtering algorithm compared to the renowned vector median (VM) and generalized vector directional filter (GVDF), which are commonly used for color image restoration. Monte Carlo simulations show the edge preservation and impulse noise attenuation capabilities of the proposed technique. The efficiency of the algorithm stems from its simple arithmetic operations compared with more demanding ones, e.g. computation of distances and angles in the case of VMF and GVDF, respectively.
Multiple Description Coding (MDC) is a method to solve the problem of noisy channels affecting images. Multiple descriptions (multiple copies) of images are transmitted over different channels while at the receiver; the images are reconstructed using the different copies. In this paper, five MDC methods are proposed and discussed, while results shown their improvement in images quality.
We investigate the physical-layer security of uplink single-carrier frequency-division multiple-access (SC-FDMA) systems. Multiple users, Alices, send confidential messages to a common legitimate base-station, Bob, in the presence of an eavesdropper, Eve. To secure the legitimate transmissions, each user superimposes an artificial noise (AN) signal on the time-domain SC-FDMA data symbol. We reduce the computational and storage requirements at Bob's receiver by assuming simple per-sub-channel detectors. We assume that Eve has global channel knowledge of all links in addition to high computational capabilities, where she adopts high-complexity detectors such as single-user maximum likelihood (ML), multi-user minimum-mean-square-error, and multi-user ML. We analyze the correlation properties of the time-domain AN signal and illustrate how Eve can exploit them to reduce the AN effects. We prove that the number of useful AN streams that can degrade Eve's signal-to-noise ratio is dependent on the channel memories of Alices-Bob and Alices-Eve links. Furthermore, we enhance the system security for the case of partial Alices-Bob channel knowledge at Eve, where Eve only knows the precoding matrices of the data and AN signals instead of knowing the entire Alices-Bob channel matrices, and propose a hybrid security scheme that integrates temporal AN with channel-based secret key extraction.
Video surveillance using drones is both convenient and efficient due to the ease of deployment and unobstructed movement of drones in many scenarios. An interesting application of drone-based video surveillance is to estimate crowd density (both pedestrians and vehicles) in public places. Deep learning using convolution neural networks (CNNs) is employed for automatic crowd counting and density estimation using images and videos. However, the performance and accuracy of such models typically depends upon the model architecture i.e., deeper CNN models improve accuracy at the cost of increased inference time. In this paper, we propose a novel crowd density estimation model for drones (DroneNet) using Self-organized Operational Neural Networks (Self-ONN). Self-ONN provides efficient learning capabilities with lower computational complexity as compared to CNN-based models. We tested our algorithm on two drone-view public datasets. Our evaluation shows that the proposed DroneNet shows superior performance on an equivalent CNN-based model.
Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their "maximum motion displacement" plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over HMC-QU dataset show that the proposed approach can achieve 87.94% accuracy, 92.86% sensitivity and 87.64% precision in MI detection even though the echo quality is quite poor and the temporal resolution is low.
The connection between the Teager energy operator and the ambiguity function is established in this correspondence after defining the Teager operator over complex-valued signals. This relation allows the use of the Teager energy in estimating the second moment angular bandwidth and the moments of a signal duration (spread) and that of its spectrum.
