Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

View-invariant gait person re-identification with spatial and temporal attention

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPerson re-identification at a distance across multiple none overlapping cameras has been an active research area for years. In the past ten years, Short term Person Re-Id techniques have made great strides in terms of accuracy using only appearance features in limited environments. However, massive intraclass variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject appearance and clothing might have changed after a significant period has elapsed. Facing these problems, soft biometric features such as Gait have been proposed in the past. Nevertheless, even Gait can vary with illness, ageing and changes in the emotional state, changes in walking surfaces, shoe type, clothes type, objects carried by the subject and even clutter in the scene. Therefore, Gait is considered a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So, extracting discriminative features from both spatial and temporal domains would be very beneficial to this research. Therefore, this thesis focuses on finding the best and most robust method to tackle the gait human Re-identification problem and solve it for practical applications. In real-world surveillance scenarios, the human gait cycle is primarily abnormal. These abnormalities include but not limited to temporal and spatial characteristics changes such as walking speed, broken gait phase and most importantly, varied camera angles. Our work performed an extensive literature study on spatial and temporal gait feature extraction methods with a focus on deep learning. Next, we conducted a comparative study and proposed a spatial-temporal approach for gait feature extraction using the fusion of multiple modalities, including optical-flow, raw silhouettes and RGB images. This approach was tested on two of the most challenging publicly available datasets for gait recognition TUM-GAID and CASIA-B, with excellent results presented in chapter 3. Furthermore, a modern spatial-temporal attention mechanism was proposed and tested on CASIA-B and OULP datasets which learns salient features independent of the gait cycle and view variations. The spatial attention layer in the proposed method extracts the spatial feature maps using a two-layered architecture that are fused using late fusion. It can pay attention to the identity-related salient regions in silhouette sequences discriminatively using the spatial feature maps. The temporal attention layer consists of an LSTM that encodes the temporal motion for silhouette sequences. It uses the encoded output vectors in the temporal attention architecture to focus on the most critical timesteps in the gait cycle and discard the rest. Furthermore, we improved the performance of our method by mapping our extracted spatial-temporal gait features to a discriminative null space for use in our Siamese architecture for crossmatching. We also conducted an element removal experiment on each segment of our spatial-temporal attentional network to gain insight into each component’s contribution to the performance. Our method showed outstanding robustness against abnormal gait cycles as well as viewpoint variations on both benchmark datasets

AI-based design methodologies for hot form quench (HFQ®)

This thesis aims to develop advanced design methodologies that fully exploit the capabilities of the Hot Form Quench (HFQ®) stamping process in stamping complex geometric features in high-strength aluminium alloy structural components. While previous research has focused on material models for FE simulations, these simulations are not suitable for early-phase design due to their high computational cost and expertise requirements. This project has two main objectives: first, to develop design guidelines for the early-stage design phase; and second, to create a machine learning-based platform that can optimise 3D geometries under hot stamping constraints, for both early and late-stage design. With these methodologies, the aim is to facilitate the incorporation of HFQ capabilities into component geometry design, enabling the full realisation of its benefits. To achieve the objectives of this project, two main efforts were undertaken. Firstly, the analysis of aluminium alloys for stamping deep corners was simplified by identifying the effects of corner geometry and material characteristics on post-form thinning distribution. New equation sets were proposed to model trends and design maps were created to guide component design at early stages. Secondly, a platform was developed to optimise 3D geometries for stamping, using deep learning technologies to incorporate manufacturing capabilities. This platform combined two neural networks: a geometry generator based on Signed Distance Functions (SDFs), and an image-based manufacturability surrogate model. The platform used gradient-based techniques to update the inputs to the geometry generator based on the surrogate model's manufacturability information. The effectiveness of the platform was demonstrated on two geometry classes, Corners and Bulkheads, with five case studies conducted to optimise under post-stamped thinning constraints. Results showed that the platform allowed for free morphing of complex geometries, leading to significant improvements in component quality. The research outcomes represent a significant contribution to the field of technologically advanced manufacturing methods and offer promising avenues for future research. The developed methodologies provide practical solutions for designers to identify optimal component geometries, ensuring manufacturing feasibility and reducing design development time and costs. The potential applications of these methodologies extend to real-world industrial settings and can significantly contribute to the continued advancement of the manufacturing sector.Open Acces

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Representation Challenges

Augmented Reality (AR) and Artificial Intelligence (AI) are technological domains that closely interact with space at architectural and urban scale in the broader ambits of cultural heritage and innovative design. The growing interest is perceivable in many fields of knowledge, supported by the rapid development and advancement of theory and application, software and devices, fueling a pervasive phenomenon within our daily lives. These technologies demonstrate to be best exploited when their application and other information and communication technology (ICT) advancements achieve a continuum. In particular, AR defines an alternative path to observe, analyze and communicate space and artifacts. Besides, AI opens future scenarios in data processing, redefining the relationship between man and computer. In the last few years, the AR/AI expansion and relationship have raised deep transdisciplinary speculation. The research experiences have shown many cross-relations in Architecture and Design domains. Representation studies could arise an international debate as a convergence place of multidisciplinary theoretical and applicative contributions related to architecture, city, environment, tangible and intangible Cultural Heritage. This book collects 66 papers and identify eight lines of research that may guide future developments

Volume II Acquisition Research Creating Synergy for Informed Change, Thursday 19th Annual Acquisition Research Proceedings

ProceedingsApproved for public release; distribution is unlimited