Non-coherent detection for ultraviolet communications with inter-symbol interference

Ultraviolet communication (UVC) serves as a promising supplement to share the responsibility for the overloads in conventional wireless communication systems. One challenge for UVC lies in inter-symbol-interference (ISI), which combined with the ambient noise, contaminates the received signals and thereby deteriorates the communication accuracy. Existing coherent signal detection schemes (e.g. maximum likelihood sequence detection, MLSD) require channel state information (CSI) to compensate the channel ISI effect, thereby falling into either a long overhead and large computational complexity, or poor CSI acquisition that further hinders the detection performance. Non-coherent schemes for UVC, although capable of reducing the complexity, cannot provide high detection accuracy in the face of ISI. In this work, we propose a novel non-coherent paradigm via the exploration of the UV signal features that are insensitive to the ISI. By optimally weighting and combining the extracted features to minimize the bit error rate (BER), the optimally-weighted non-coherent detection (OWNCD) is proposed, which converts the signal detection with ISI into a binary detection framework with a heuristic decision threshold. As such, the proposed OWNCD avoids the complex CSI estimation and guarantees the detection accuracy. Compared to the state-of-the-art MLSD in the cases of static and time-varying CSI, the proposed OWNCD can gain ∼1 dB and 8 dB in signal-to-noise-ratio (SNR) at the 7% overhead FEC limit (BER of 4.5×10 −3 , respectively, and can also reduce the computational complexity by 4 order of magnitud

Speeding up Adaboost object detection with motion segmentation and Haar feature acceleration

A key challenge in a surveillance system is the object detection task. Object detection in general is a non-trivial problem. A sub-problem within the broader context of object detection which many researchers focus on is face detection. Numerous techniques have been proposed for face detection. One of the better performing algorithms is proposed by Viola et. al. This algorithm is based on Adaboost and uses Haar features to detect objects. The main reason for its popularity is very low false positive rates and the fact that the classifier network can be trained for any detection task. The use of Haar basis functions to represent key object features is the key to its success. The basis functions are organized as a network to form a strong classifier. To detect objects, this technique divides each input image into non-overlapping sub-windows and the strong classifier is applied to each sub-window to detect the presence of an object. The process is repeated at multiple scales of the input image to detect objects of various sizes. In this thesis we propose an object detection system that uses object segmentation as a preprocessing step. We use Mixture of Gaussians (MoG) proposed by Staffer et. al. for object segmentation. One key advantage with using segmentation to extract image regions of interest is that it reduces the number of search windows sent to detection task, thereby reducing the computational complexity and the execution time. Moreover, owing to the computational complexity of both the segmentation and detection algorithms we used in the system, we propose hardware architectures for accelerating key computationally intensive blocks. In this thesis we propose hardware architecture for MoG and also for a key compute intensive block within the adaboost algorithm corresponding to the Haar feature computation

Efficient and effective human action recognition in video through motion boundary description with a compact set of trajectories

Human action recognition (HAR) is at the core of human-computer interaction and video scene understanding. However, achieving effective HAR in an unconstrained environment is still a challenging task. To that end, trajectory-based video representations are currently widely used. Despite the promising levels of effectiveness achieved by these approaches, problems regarding computational complexity and the presence of redundant trajectories still need to be addressed in a satisfactory way. In this paper, we propose a method for trajectory rejection, reducing the number of redundant trajectories without degrading the effectiveness of HAR. Furthermore, to realize efficient optical flow estimation prior to trajectory extraction, we integrate a method for dynamic frame skipping. Experiments with four publicly available human action datasets show that the proposed approach outperforms state-of-the-art HAR approaches in terms of effectiveness, while simultaneously mitigating the computational complexity

Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574