Department Publications

In this paper, we propose a novel algorithm based on supervised learning for antenna array extrapolation for the purpose of super resolution DoA estimation. We use multiple signal classification (MUSIC) as a DoA estimation technique to estimate the DoA. In order to reduce the computational burden, existing approaches focus on interpolating the missing elements in a virtual array using a sparse array (or non-uniform linear arrays) or employ antenna selection within the same aperture. In contrast, here we utilize an uniform linear array (ULA) using a low number of antennas (within a small array aperture) to extrapolate another ULA with a higher number of antennas (within a bigger aperture). This approach is not restricted to any specific antenna array configuration, in fact it can be generalized to any array configuration. We propose an algorithm which utilizes the advances of supervised learning (dictionary learning …

A combined approach of low-complexity light field depth filtering and deep learning is proposed for object classification in the presence of partial occlusions. The proposed approach exploits depth information embedded in multi-perspective four-dimensional (4-D) light fields via low-complexity 4-D sparse depth filtering and deep-learning. The proposed 4-D depth filter, designed using numerical optimization techniques by formulating as an ℓ 1 - ℓ ∞ minimization problem, is shown to outperform typical light field refocusing based on 4-D shift-sum averaging filters. Experiments conducted using a light field dataset acquired by a Lytro camera verify 45% and 27% better performance in terms of object classification accuracy compared to the cases when no depth filtering is employed and standard shift-sum refocusing is employed, respectively.

A minimax design for 2-D complex-coefficient FIR filters having asymmetric frequency responses is proposed in this paper. We consider the general form of 2-D FIR filters with low group delay and formulate the minimax design as a semidefinite programming problem. The 2-D linear-phase FIR filters with conjugate-symmetric coefficients are a special case of the proposed design. Example filter designs having near-equiripple magnitude responses are presented to verify the effectiveness of the proposed design method.

The objective of this study is to propose a heuristic model of energy propagation due to an anomaly; electromagnetic (EM) field penetration into a continuously covered conducting enclosure (Faraday shield) from an external radio frequency source, violating the accepted model in the EM field theory. In this study, at an arbitrarily selected frequency, range of 26.965–1,800 MHz, of an external frequency source, an EM field inside the conducting enclosure was observed, contrary to expectations, which was followed by a systematic examination. Although no induced voltage could be expected inside the enclosure according to the classical theory, the experiment revealed a clear induced voltage inside, an attenuated induced voltage of −18.0 to −1.0 dB (for the range of frequencies 26.965–1,800 MHz) was observed. Hence, these results apparently contradict the established notion that an EM field cannot …

The enhanced distributed channel access (EDCA) mechanism enables IEEE 802.11p to accommodate differential quality of service (QoS) levels in vehicle-to-vehicle (V2V) communications, through four access categories (ACs). This paper presents multi-dimensional discrete-time Markov chain (DTMC) based model to study the effect of parallel operation of the ACs on the medium access control (MAC) layer performance of ITS-G5 IEEE 802.11p. The overall model consists of four queue models with their respective traffic generators, which are appropriately linked with the DTMCs modeling the operation of each AC. Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for key performance indicators at the MAC layer. An application for a highway scenario is presented to draw insights on the performance. The results show how the …

Reinforcement learning algorithms have been successfully trained for games like GO, Atari, and Chess in simulated environments. However, in cue sport-based games like Carrom, real world is unpredictable unlike in Chess and GO due to the stochastic nature of the gameplay as well as the effect of external factors such as friction combined with multiple collisions. Hence, solely training in a simulated platform for games like Billiard and Carrom, which need precise execution of a shot, would not be ideal in actual gameplay. This paper presents a real-time vision based efficient robotic system to play Carrom against a proficient human opponent. We demonstrate the challenges of adopting a reinforcement learning algorithm beyond simulations in implementing a strategic gameplay for the robotic system. We currently achieve an overall shot accuracy of 70.6% by combining heuristic and reinforcement learning …

A multi-port spatio-temporal noise-shaping ADC is proposed to process plane waves received by spatially-oversampled antenna arrays. In the proposed multi-port ADC, the desired plane waves are processed with a spatial low-pass frequency response whereas the noise and distortion are shaped with a spatial high-pass frequency response. By employing a first-order Butterworth filter, approximately circular passbands and stopbands are achieved for the signal and the noise transfer functions, respectively. The proposed noise-shaping system is designed in the TSMC 180 nm CMOS process, with ADCs and DACs modeled as noise sources. Circuit simulation results show that the proposed system can achieve a bandwidth of 50 MHz.

Light field saliency detection—important due to utility in many vision tasks—still lack speed and can improve in accuracy. Due to the formulation of the saliency detection problem in light fields as a segmentation task or a “memorizing” tasks, existing approaches consume unnecessarily large amounts of computational resources for (training and) testing leading to execution times is several seconds. We solve this by aggressively reducing the large light-field images to a much smaller three-channel feature map appropriate for saliency detection using an RGB image saliency detector. We achieve this by introducing a novel convolutional neural network based features extraction and encoding module. Our saliency detector takes 0.4 s to process a light field of size 9× 9× 512× 375 in a CPU and is significantly faster than existing systems, with better or comparable accuracy. Our work shows that extracting features from light fields through aggressive size reduction and the attention results in a faster and accurate light-field saliency detector.

The PET-laparoscope system has been proposed to diagnose lymph node metastases intraoperatively in gastric cancer surgeries. The system uses two detector units, a fixed detector array and a movable detector probe to acquire PET images. The detector probe can be inserted into a patient’s stomach through laparoscopic ports. A surgeon can move the detector inside the patient’s body but the movement is limited. In this study the system was evaluated with an abdomen phantom so that the probe movement was realistic. Moreover, performance of an MLEM algorithm was investigated for the PET-laparoscope system. Experiments with the abdomen phantom confirmed that the system performs well when the probe movement is similar to that achievable during a laparoscopic surgery. MLEM algorithm provided better images. Image artifacts were reduced and the spatial resolution in lateral directions were better …

A moving object in a five-dimensional (5-D) light field video (LFV) can be selectively enhanced using the depth and the velocity of the object. In this paper, a 5-D depth-velocity (DV) filter is proposed to enhance multiple moving objects at different depths and with different velocities in an LFV. The 5-D DV filter is designed as a cascade of an infinite-extent impulse response multi-depth filter and a finite-extent impulse response multi-velocity filter. Experimental results obtained with numerically generated and real LFVs indicate that more than 15 dB improvement in signal-to-interference ratio can be achieved with the proposed 5-D multi DV filter compared to previously proposed multi depth-only filters.

Bleeding-edge technological pursuits ranging from self-guided robots at the research stage to mass scale industrial applications such as augmented reality, intelligent security systems and self-driving vehicles heavily rely on perception through vision. Vision based perception of the environment in autonomous systems extensively use object detection, segmentation and tracking as fundamental components. Despite the recent advancements in deep learning-based object detection on monocular images, several highly publicized accidents involving self-driving vehicles and critical failures in monitoring systems highlight the need for significant further improvement on real-time tracking systems in practice. We identify two such key areas with room for improvement and introduce two separate novel frameworks to tackle each problem. We observe that trackers often perform poorly in object dense situations where occlusions and crossovers are prevalent. We identify that in order to perform better in these scenarios both appearance and motion information should be incorporated. Siamese networks have recently become highly successful at appearance based single object tracking while Recurrent Neural Networks (RNNs) have started dominating motion-based tracking. Our work focuses on combining Siamese networks and RNNs to exploit both (temporally varying) appearance and motion information to build a robust framework that can also operate in real-time. We further explore heuristics-based constraints for tracking in the Bird’s Eye View Space for efficiently exploiting 3D information. Our segmentation approach is based on one of the most …

We present a compressive sensing based defect detection by multiple input multiple output (MIMO) wireless radar. Here, defects are inside a layered material structure, therefore, due to reflections from the surface of the layered material structure the defect detection is challenging. By utilizing a low-rank nature of the reflections of the layered material structure and sparse nature of the defects, we propose a method based on rank minimization and sparse recovery. To improve the accuracy in the recovery of low-rank and sparse components, we propose a non-convex approach based on the iteratively reweighted nuclear norm and iteratively reweighted norm algorithm. Our numerical results show that the proposed method is able to demix and recover the signalling responses of the defects and layered structure successfully from substantially reduced number of observations. Further, the proposed approach outperforms the state-of-the-art clutter reduction approaches