A survey on video compression fast block matching algorithms

Video compression is the process of reducing the amount of data required to represent digital video while preserving an acceptable video quality. Recent studies on video compression have focused on multimedia transmission, videophones, teleconferencing, high definition television, CD-ROM storage, etc. The idea of compression techniques is to remove the redundant information that exists in the video sequences. Motion compensation predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50–80% of video encoding complexity. This technique has been adopted by all of the existing International Video Coding Standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks and compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called Full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm are developed to reduce the computation complexity. This paper focuses on a survey for two video compression techniques: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the Full Search is decreased while the resolution of the predicted frames is the same as for the Full Search. The second is called lossy block matching algorithm process, which reduces the computational complexity effectively but the search result's quality is not the same as for the Full Search

Investigation of Process-Structure Relationship for Additive Manufacturing with Multiphysics Simulation and Physics-Constrained Machine Learning

Metal additive manufacturing (AM) is a group of processes by which metal parts are built layer by layer from powder or wire feedstock with high-energy laser or electron beams. The most well-known metal AM processes include selective laser melting, electron beam melting, and direct energy deposition. Metal AM can significantly improve the manufacturability of products with complex geometries and heterogeneous materials. It has the potential to be widely applied in various industries including automotive, aerospace, biomedical, energy, and other high-value low-volume manufacturing environments. However, the lack of complete and reliable process-structure-property (P-S-P) relationships for metal AM is still the bottleneck to produce defect-free, structurally sound, and reliable AM parts. There are several technical challenges in establishing the P-S-P relationships for process design and optimization. First, there is a lack of fundamental understanding of the rapid solidification process during which microstructures are formed and the properties of solid parts are determined. Second, the curse of dimensionality in the process and structure design space leads to the lack of data to construct reliable P-S-P relationships. Simulation becomes an important tool to enable us to understand rapid solidification given the limitations of experimental techniques for in-situ measurement. In this research, a mesoscale multiphysics simulation model, called phase-field and thermal lattice Boltzmann method (PF-TLBM), is developed with simultaneous considerations of heterogeneous nucleation, solute transport, heat transfer, and phase transition. The simulation can reveal the complex dynamics of rapid solidification in the melt pool, such as the effects of latent heat and cooling rate on dendritic morphology and solute distribution. The microstructure evolution in the complex heating and cooling environment in the layer-by-layer AM process is simulated with the PF-TLBM model. To meet the lack-of-data challenge in constructing P-S-P relationships, a new scheme of multi-fidelity physics-constrained neural network (MF-PCNN) is developed to improve the efficiency of training in neural networks by reducing the required amount of training data and incorporating physical knowledge as constraints. Neural networks with two levels of fidelities are combined to improve prediction accuracy. Low-fidelity networks predict the general trend, whereas high-fidelity networks model local details and fluctuations. The developed MF-PCNN is applied to predict phase transition and dendritic growth. A new physics-constrained neural network with the minimax architecture (PCNN-MM) is also developed, where the training of PCNN-MM is formulated as a minimax problem. A novel training algorithm called Dual-Dimer method is developed to search high-order saddle points. The developed PCNN-MM is also extended to solve multiphysics problems. A new sequential training scheme is developed for PCNN-MMs to ensure the convergence in solving multiphysics problems. A new Dual-Dimer with compressive sampling (DD-CS) algorithm is also developed to alleviate the curse of dimensionality in searching high-order saddle points during the training. A surrogate model of process-structure relationship for AM is constructed based on the PF-TLBM and PCNN-MM. Based on the surrogate model, multi-objective Bayesian optimization is utilized to search the optimal initial temperature and cooling rate to obtain the desired dendritic area and microsegregation level. The developed PF-TLBM and PCNN-MM provide a systematic and efficient approach to construct P-S-P relationships for AM process design.Ph.D

Intelligent strategies for sheep monitoring and management

With the growth in world population, there is an increasing demand for food resources and better land utilisation, e.g., domesticated animals and land management, which in turn brought about developments in intelligent farming. Modern farms rely upon intelligent sensors and advanced software solutions, to optimally manage pasture and support animal welfare. A very significant aspect in domesticated animal farms is monitoring and understanding of animal activity, which provides vital insight into animal well-being and the environment they live in. Moreover, “virtual” fencing systems provide an alternative to managing farmland by replacing traditional boundaries. This thesis proposes novel solutions to animal activity recognition based on accelerometer data using machine learning strategies, and supports the development of virtual fencing systems via animal behaviour management using audio stimuli. The first contribution of this work is four datasets comprising accelerometer gait signals. The first dataset consisted of accelerometer and gyroscope measurements, which were obtained using a Samsung smartphone on seven animals. Next, a dataset of accelerometer measurements was collected using the MetamotionR device on 8 Hebridean ewes. Finally, two datasets of nine Hebridean ewes were collected from two sensors (MetamotionR and Raspberry Pi) comprising of accelerometer signals describing active, inactive and grazing activity of the animal. These datasets will be made publicly available as there is limited availability of such datasets. In respect to activity recognition, a systematic study of the experimental setup, associated signal features and machine learning methods was performed. It was found that Random Forest using accelerometer measurements and a sample rate of 12.5Hz with a sliding window of 5 seconds provides an accuracy of above 96% when discriminating animal activity. The problem of sensor heterogeneity was addressed with transfer learning of Convolutional Neural Networks, which has been used for the first time in this problem, and resulted to an accuracy of 98.55%, and 96.59%, respectively, in the two experimental datasets. Next, the feasibility of using only audio stimuli in the context of a virtual fencing system was explored. Specifically, a systematic evaluation of the parameters of audio stimuli, e.g., frequency and duration, was performed on two sheep breeds, Hebridean and Greyface Dartmoor ewes, in the context of controlling animal position and keeping them away from a designated area. It worth noting that the use of sounds is different to existing approaches, which utilize electric shocks to train animals to adhere within the boundaries of a virtual fence. It was found that audio signals in the frequencies of 125Hz-440Hz, 10kHz-17kHz and white noise are able to control animal activity with accuracies of 89.88%, and 95.93%, for Hebridean and Greyface Dartmoor ewes, respectively. Last but not least, the thesis proposes a multifunctional system that identifies whether the animal is active or inactive, using transfer learning, and manipulates its position using the optimized sound settings achieving a classification accuracy of over 99.95%