Hayder Radha - Academia.edu (original) (raw)

Papers by Hayder Radha

In this paper, we analyze statistical and rate-distortion (R-D) properties of MPEG-4 Fine-Granula... more In this paper, we analyze statistical and rate-distortion (R-D) properties of MPEG-4 Fine-Granular Scalability (FGS), which has recently become an important scalable compression framework and a de-facto standard for Internet video streaming. We first propose a novel statistical model of DCT residue that accurately captures the properties of the input to the MPEG-4 FGS enhancement layer. Our results show that FGS residue concentrates a lot of probability mass near zero and cannot be accurately modeled by Gaussian or Laplacian distributions. We then model the distortion of each bitplane based on the proposed statistical framework and further demonstrate that our R-D model significantly outperforms current distortion models.

In this work we examine the effect of having independent radial and angular velocity radar measur... more In this work we examine the effect of having independent radial and angular velocity radar measurements on 2D tracking performance. With this, we provide design considerations for systems using interferometric angular velocity estimation. We found that on average as the number of clutter detections increases, the reduction in error by adding an additional angular velocity measurement also increases, implying a greater need for orthogonal velocity measurements in high-clutter environments. In particular, in simulation at a clutter rate of 50 detections per measurement, we obtained an error reduction of 14% by adding a radial velocity measurement and a further reduction of 3% from an additional angular velocity measurement. We also tested this method on a publicly available autonomous driving dataset by synthesizing angular velocity measurements and found a similar reduction of 13% and 6% by adding Doppler and angular velocity measurements, respectively.

CRC Press eBooks, Mar 22, 2000

Multiple object tracking (MOT) is a critical module for enabling autonomous vehicles to achieve s... more Multiple object tracking (MOT) is a critical module for enabling autonomous vehicles to achieve safe planing and navigation in cluttered environments. In tracking-by-detection systems, there are inevitably many false positives and misses among learning-based input detections. The challenge for MOT is to combine these detections into tracks, and filter them based on their uncertainties, states, and temporal consistency to achieve accurate and persistent tracks. In this paper, we propose to solve the 3D MOT problem for autonomous driving applications using a random finite set-based (RFS) Multiple Measurement Models filter (RFS-M3). In partiuclar, we propose multiple measurement models for a Poisson multi-Bernoulli mixture (PMBM) filter in support of different application scenarios. Our RFS-M3 filter can naturally model these uncertainties accurately and elegantly. We combine the learning-based detections with our RFS-M3 tracker through incorporating the detection confidence score into the PMBM prediction and update step. The superior experimental results of our RFS-M3 tracker on Waymo, Argoverse and nuSceness datasets illustrate that our RFS-M3 tracker outperforms state-of-the-art deep learning-based and traditional filter-based approaches. To the best of our knowledge, this represents a first successful attempt for employing an RFS-based approach in conjunction with 3D learning-based amodal detections for 3D MOT applications with comprehensive validation using challenging datasets made available by industry leaders.

Under traditional IP multicast, application-level FEC can only be implemented on an end-to-end ba... more Under traditional IP multicast, application-level FEC can only be implemented on an end-to-end basis between the sender and the clients. Emerging overlay and peer-to-peer (p2p) networks open the door for new paradigms of network FEC. The deployment of FEC within these emerging networks has received very little attention (if any). In this paper, we analyze and optimize the impact of Network-Embedded FEC (NEF) in overlay and p2p multimedia multicast networks. Under NEF, we place FEC codecs in selected intermediate nodes of a multicast tree. The NEF codecs detect and recover lost packets within FEC blocks at earlier stages before these blocks arrive at deeper intermediate nodes or at the final leaf nodes. This approach significantly reduces the probability of receiving undecodable FEC blocks. In essence, the proposed NEF codecs work as signal regenerators in a communication system and can reconstruct most of the lost data packets without requiring retransmission. We develop an optimization algorithm for the placement of NEF codecs within random multicast trees. Our theoretical analysis and simulation results show that a relatively small number of NEF codecs placed in (sub-)optimally selected intermediate nodes of a network can improve the throughput and overall reliability dramatically.

arXiv (Cornell University), Sep 1, 2020

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Jun 1, 2022

SAE technical paper series, Apr 14, 2020

arXiv (Cornell University), May 7, 2014

arXiv (Cornell University), Jun 30, 2020

Abstract—Reliable transmission is a challenging task over wireless LANs since wireless links are ... more Abstract—Reliable transmission is a challenging task over wireless LANs since wireless links are known to be susceptible to errors. Although the current IEEE802.11 standard ARQ error control protocol performs relatively well over channels with very low bit error rates (BERs), this performance deteriorates rapidly as the BER increases. This paper investigates the problem of reliable transmission in a contention free wireless LAN and introduces a Packet Embedded Error Control (PEEC) protocol, which employs packet-embedded parity symbols instead of ARQbased retransmission for error recovery. Specifically, depending on receiver feedback, PEEC adaptively estimates channel conditions and administers the transmission of (data and parity) symbols within a packet. This enables successful recovery of both new data and old unrecovered data from prior transmissions. In addition to theoretically analyzing PEEC, the performance of the proposed scheme is extensively analyzed over real channel traces collected on 802.11b WLANs. We compare PEEC performance with the performance of the IEEE802.11 standard ARQ protocol as well as contemporary protocols such as enhanced ARQ and the hybrid ARQ/FEC. Our analysis and experimental simulations show that PEEC outperforms all three competing protocols over a wide range of actual 802.11b WLAN collected traces. Finally, the design and implementation of PEEC using an Adaptive Low-Density-Parity-Check (A-LDPC) decoder is presented. Index Terms—Forward error correction, Channel coding, Wireless LAN, Feedback communication, Automatic repeat request.

This paper considers the problem of single image super-resolution (SR). Previous example-based SR... more This paper considers the problem of single image super-resolution (SR). Previous example-based SR approaches mainly focus on analyzing the co-occurrence properties of low resolution (LR) and high resolution (HR) patches via dictionary learning. In our recent work [1], a novel approach (SR via sparse subspace clustering-based linear approximation of manifold or SLAM) has been proposed. In this paper, we further improve the SLAM method by considering and analyzing each tangent subspace as one point in a Grassmann manifold to select an optimal subset of tangent spaces. Furthermore, the optimal subset is clustered hierarchically, which helps in reducing the proposed algorithm's complexity significantly while still preserving the quality of the reconstructed HR image.