DExIE - An IoT-Class Hardware Monitor for Real-Time Fine-Grained Control-Flow Integrity

The Dynamic Execution Integrity Engine (DExIE) is a lightweight hardware monitor that can be flexibly attached to many IoT-class processor pipelines. It is guaranteed to catch both inter- and intra-function illegal control flows in time to prevent any illegal instructions from touching memory. The performance impact of attaching DExIE to a core depends on the concrete pipeline structure. In some especially suitable cases, extending a processor with DExIE will have no performance penalty. DExIE is real-time capable, as it causes no or only up to 10.4 % additional and then predictable pipeline stalls. Depending on the monitored processor’s size and structure, DExIE is faster than software-based monitoring and often smaller than a separate guard processor. We present not just the hardware architecture, but also the automated programming flow, and discuss compact adaptable storage formats to hold fine-grained control flow information.

Spectrally Extended Line Field Optical Coherence Tomography Angiography

The widespread usage of optical coherence tomography angiography (OCTA) is hindered by technical gaps including limited field of view (FOV), lack of quantitative flow information, and suboptimal motion correction. We introduce spectrally extended line field (SELF) OCTA that provides advanced solutions to these challenges. SELF-OCTA breaks the speed limitation and achieves two-fold gain in FOV without sacrificing microvascular details and signal strength through parallel imaging. It also relieves the requirement for shorter exposure time in wide-field applications, so that sufficient sensitivity to slow flow is maintained, particularly in spectral-domain OCT. Towards quantitative angiography, the ‘frequency flow’ mechanism overcomes the speed bottleneck by obviating the requirement for superfluous B-scans. In addition, this mechanism facilitates OCTA-data based motion tracking. Since it can be implemented in existing OCT devices without significant hardware modification or affecting existing functions, SELF-OCTA will make non-invasive, wide-field, quantitative, and low-cost angiographic imaging available to larger patient populations.

A Spatial–Temporal Similar Graph Attention Network for Cyber Physical System Perception via Traffic Forecasting

Traffic flow forecasting is the basic challenge in intelligent transportation system (ITS). The key problem is to improve the accuracy of model and capture the dynamic temporal and nonlinear spatial dependence. Using real data is one of the ways to improve the spatial–temporal correlation modeling accuracy. However, real traffic flow data are not strictly periodic because of some random factors, which may lead to some deviations. This study focuses on capturing and modeling the temporal perturbation in real periodic data and we propose a spatial–temporal similar graph attention network (STSGAN) to address this problem. In STSGAN, the spatial–temporal graph convolution module is to capture local spatial–temporal relationship in traffic data, and the periodic similar attention module is to treat the nonlinear traffic flow information. Experiments on three datasets demonstrate that our model is best among all methods.

Operational Deflection Shapes Magnification and Visualization Using Optical-Flow-Based Image Processing

Much information can be derived from operational deflection shapes of vibrating structures and the magnification of their motion. However, the acquisition of deflection shapes usually requires a manual definition of an object’s points of interest, while general motion magnification is computationally inefficient. We propose easy extraction of operational deflection shapes straight from vision data by analyzing and processing optical flow information from the video and then, based on these graphs, morphing source data to magnify the shape of deflection. We introduce several processing routines for automatic masking of the optical flow data and frame-wise information fusion. The method is tested based on data acquired both in numerical simulations and real-life experiments in which cantilever beams were subjected to excitation around their natural frequencies.

Considerations When Applying Large-Scale PIV and PTV for Determining River Flow Velocity

Large-scale image velocimetry is a novel approach for non-contact remote sensing of flow in rivers. Research within this topic has largely focussed on developing specific aspects of the image velocimetry work-flow, or alternatively, testing specific tools or software using case studies. This has resulted in the development of a multitude of techniques, with varying practice being employed between groups, and authorities. As such, for those new to image velocimetry, it may be hard to decipher which methods are suited for particular challenges. This research collates, synthesises, and presents current understanding related to the application of particle image velocimetry (PIV) and particle tracking velocimetry (PTV) approaches in a fluvial setting. The image velocimetry work-flow is compartmentalised into sub-systems of: capture optimisation, pre-processing, processing, and post-processing. The focus of each section is to provide examples from the wider literature for best practice, or where this is not possible, to provide an overview of the theoretical basis and provide examples to use as precedence and inform decision making. We present literature from a range of sources from across the hydrology and remote sensing literature to suggest circumstances in which specific approaches are best applied. For most sub-systems, there is clear research or precedence indicating how to best perform analysis. However, there are some stages in the process that are not conclusive with one set method and require user intuition or further research. For example, the role of external environmental conditions on the performance of image velocimetry being a key aspect that is currently lacking research. Further understanding in areas that are lacking, such as environmental challenges, is vital if image velocimetry is to be used as a method for the extraction of river flow information across the range of hydro-geomorphic conditions.

Deep learning model inspired by lateral line system for underwater object detection

Inspired by the lateral line systems of various aquatic organisms that are capable of hydrodynamic imaging using ambient flow information, this study develops a deep learning-based object localization model that can detect the location of objects using flow information measured from a moving sensor array. In numerical simulations with the assumption of a potential flow, a two-dimensional hydrofoil navigates around four stationary cylinders in a uniform flow and obtains two types of sensory data during a simulation, namely flow velocity and pressure, from an array of sensors located on the surface of the hydrofoil. Several neural network models are constructed using the flow velocity and pressure data, and these are used to detect the positions of the hydrofoil and surrounding objects. The model based on a long short-term memory network, which is capable of learning order dependence in sequence prediction problems, outperforms the other models. The number of sensors is then optimized using feature selection techniques. This sensor optimization leads to a new object localization model that achieves impressive accuracy in predicting the locations of the hydrofoil and objects with only 40$\%$ of the sensors used in the original model.

Adaptive Visual Servoing for Obstacle Avoidance of Micro Unmanned Aerial Vehicle with Optical Flow and Switched System Model

A vision-based adaptive switching controller that uses optical flow information to avoid obstacles for micro unmanned aerial vehicles (MUAV) is proposed in this paper. To use the optical flow to indicate the distance between the MUAV and the environment, we propose an algorithm with multi-thread processing such that the optical flow information is obtained reliably and continuously in the entire camera field of view. The flying behavior of considered MUAV is regarded as a switching system when considering different flying modes during the mission of obstacle avoidance. By the required flight direction for obstacle avoidance specified by the detected optical flow, an adaptive control scheme is designed to track the required trajectory in switching modes. The simulation result shows the tracking performances of the adaptive control with the switching system. The experiment of the whole system is completed to verify the obstacle avoidance capability of our system.

Alur Pendaftaran Calon Mahasiswa Baru Politeknik Sekayu Berbasis Multimedia

Multimedia is one form of information technology, namely information technology that combines images, writing, text, sound, video, animation into an information system that is useful in making decisions for its users. This media displays information about the registration flow of prospective new students in a clear and concise manner. The method used is the MDLC (Multimedia Development Life Cycle) method as software development. For an explanation of each registration flow information using audio, in addition to making this media more interesting, characters or animations are displayed.