Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks

Robust and computationally efficient anomaly detection in videos is a problem in video surveillance systems. We propose a technique to increase robustness and reduce computational complexity in a Convolutional Neural Network (CNN) based anomaly detector that utilizes the optical flow information of video data. We reduce the complexity of the network by denoising the intermediate layer outputs of the CNN and by using powers-of-two weights, which replaces the computationally expensive multiplication operations with bit-shift operations. Denoising operation during inference forces small valued intermediate layer outputs to zero. The number of zeros in the network significantly increases as a result of denoising, we can implement the CNN about 10% faster than a comparable network while detecting all the anomalies in the testing set. It turns out that denoising operation also provides robustness because the contribution of small intermediate values to the final result is negligible. During training we also generate motion vector images by a Generative Adversarial Network (GAN) to improve the robustness of the overall system. We experimentally observe that the resulting system is robust to background motion

Design and Optimization of Cooperative Wireless Networks

With increasing usage of wireless networks, data traffic is rapidly increasing. For instance, cellular traffic is growing exponentially and it is expected to remain so for the foreseeable future. Thus device-to-device (D2D) networking/connections are promising to relieve traffic over traditional networks and improve throughput in wireless networks. Furthermore, such D2D connections could lead to applications such as (i) information gathering from sensor networks and detection in the fusion node, (ii) error recovery from the cellular links using D2D connection support, and (iii) cooperative computation in distributed systems. In particular, we solve the following problems in this thesis: (i) information gathering and detection in wireless sensor networks, (ii) content awareness for cooperative mobile devices that employ network coding, (iii) network coding design for multiple interfaces in cooperative mobile devices, and (iv) cooperative computation in heterogeneous wireless networks