Lightweight Real-time Detection of Components via a Micro Aerial Vehicle with Domain Randomization Towards Structural Health Monitoring

Civil structural component detection plays an integral role in Structural Health Monitoring (SHM) pre and post-construction. Challenges including but not limited to labor-intensiveness, cost, and time constraints associated with traditional methods make it a less opti-mal approach in SHM. Despite the success of deep convolutional neural networks in diverse detection problems, the required computational resources are a challenge. This has led to rendering a chunk of resource-constrained edge nodes less applicable with deep convolutional neural networks. In this paper, a computational-efficient deep convolutional neural network is presented based on Gabor filters and a color Canny edge detector. Generic Gabor filters are generated and used as initializers in the computational-efficient deep convolutional neural network presented, afterward trained on building components data. Next, extensive offline and online experimentation with a resource-constrained edge node is conducted and evaluated using diverse metrics. The computational-efficient detection model demonstrates to be effective in detection and via NVIDIA GPU profiler, we observe conservation of around 30% of computational resources during training. The computational-efficient detection model adduces almost a 3% mean average precision higher than two state-of-the-art detectors and records a promising frame processing rate during the online experimentation

A network for multiscale image segmentation

Detecting edges of objects in their images is a basic problem in computational vision. The scale-space technique introduced by Witkin [11] provides means of using local and global reasoning in locating edges. This approach has a major drawback: it is difficult to obtain accurately the locations of the 'semantically meaningful' edges. We have refined the definition of scale-space, and introduced a class of algorithms for implementing it based on using anisotropic diffusion [9]. The algorithms involves simple, local operations replicated over the image making parallel hardware implementation feasible. In this paper we present the major ideas behind the use of scale space, and anisotropic diffusion for edge detection, we show that anisotropic diffusion can enhance edges, we suggest a network implementation of anisotropic diffusion, and provide design criteria for obtaining networks performing scale space, and edge detection. The results of a software implementation are shown

Low complexity video compression using moving edge detection based on DCT coefficients

In this paper, we propose a new low complexity video compression method based on detecting blocks containing moving edges us- ing only DCT coe±cients. The detection, whilst being very e±cient, also allows e±cient motion estimation by constraining the search process to moving macro-blocks only. The encoders PSNR is degraded by 2dB com- pared to H.264/AVC inter for such scenarios, whilst requiring only 5% of the execution time. The computational complexity of our approach is comparable to that of the DISCOVER codec which is the state of the art low complexity distributed video coding. The proposed method ¯nds blocks with moving edge blocks and processes only selected blocks. The approach is particularly suited to surveillance type scenarios with a static camera

Real-Time Detection and Suppression of Malicious Attacks Using Machine Learning and Processor Core Events

Detecting and suppressing malicious attacks continues to challenge designers and users of embedded and edge processing systems. Embedded systems and IoT devices are becoming more prevalent and they are evolving to accommodate the increased complexity requirements of edge computing by incorporating increasing levels of advanced security, energy efficiency, connectivity, performance, and increased computational power to support, for example, machine learning intelligence. These capabilities can be used in a collaborative way to provide a means for detecting a family of side channel malware attacks based upon the exploitation of timing side channels arising from cache and branch prediction circuitry. The SPECTRE exploit serves as the exemplary attack based on data cache timing side channels; however, many variants of this attack have emerged and continue to emerge. Due to the increasing proliferation of this class of devices and the continuing emergence of new variants of timing side channel attacks, there is motivation to develop a malware detection approach that is suitable for embedded and edge processing-based systems that requires minimal computational resources, is robust under varying load conditions, and that is capable of detecting any of a number of different variants of this attack, including zero-day versions. The detection approach is demonstrated to be applicable to variants of the classic SPECTRE attack including the micro-ops cache attack that exploits X86 architectures. The method monitors concurrent processes running on a Linux-based system operating in an edge-computing device to detect if one or more of the processes implements a timing-based side channel attack . Furthermore, the malware detection approach is designed to be lightweight in the sense that it requires minimal computing resources and offers rapid detection times since it uses existing on-chip hardware, pre-programmed event or performance counters, as a data source combined with a simple but effective SVM to detect variants of malicious exploits that may be present within a standard application process. Upon detection of a malicious process, the edge device could automatically suspend or kill the detected and offending process. A feature selection technique is used to select the most appropriate CPU events that indicate the presence of the targeted malware family and to improve performance results and system efficiency. Analysis results are included that evaluated a number of different detection approaches to justify the selection of an SVM due to the tradeoff of accuracy versus computational resource requirements. This approach is demonstrated through implementations on both ARM and X86 instruction set architectures and provide experimental results regarding its accuracy and performance. Detection performance is characterized by a number of metrics including ROC curves. Experimental results assess the robustness of the malware detection approach. The detection of one variant of the cache timing attack is evaluated when the SVM is trained using a different variant. The detection accuracy over a variety of different and varying load conditions is evaluated. Finally, an evaluation of robustness is evaluated by injecting noise into the event counter data at increasing levels until significant detection failures are observed

Modularity detection in protein-protein interaction networks

BACKGROUND: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. RESULTS: We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, C.Elegans and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. CONCLUSIONS: We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the application of those measures to efficiently guide the modularity detection process. We have empirically evaluated our approach in the specific context of real-world large scale biological networks, and have demonstrated significant savings in computational time while maintaining comparable quality of detected modules