A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

Fractal compression is the lossy compression technique in the field of gray/color image and video compression. It gives high compression ratio, better image quality with fast decoding time but improvement in encoding time is a challenge. This review paper/article presents the analysis of most significant existing approaches in the field of fractal based gray/color images and video compression, different block matching motion estimation approaches for finding out the motion vectors in a frame based on inter-frame coding and intra-frame coding i.e. individual frame coding and automata theory based coding approaches to represent an image/sequence of images. Though different review papers exist related to fractal coding, this paper is different in many sense. One can develop the new shape pattern for motion estimation and modify the existing block matching motion estimation with automata coding to explore the fractal compression technique with specific focus on reducing the encoding time and achieving better image/video reconstruction quality. This paper is useful for the beginners in the domain of video compression

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

High speed protocols for dual bus and dual ring network architectures

In this dissertation, two channel access mechanisms providing fair and bandwidth efficient transmission on dual bus and dual ring networks with high bandwidth-latency product are proposed. In addition, two effective priority mechanisms are introduced to meet the throughput and delay requirements of the diverse arrays of applications that future high speed networks must support. For dual bus architectures, the Buffer Insertion Bandwidth Balancing (BI_BWB) mechanism and the Preemptive priority Bandwidth Balancing (P_BI_BWB) mechanism are proposed. BI_BWB can significantly improve the delay performance of remote stations. It achieves that by providing each station with a shift register into which the station can temporarily store the upstream stations\u27 transmitted packets and replace these packets with its own transmissions. P_BI_BWB, an enhancement of BI_BWB, is designed to introduce effective preemptive priorities. This mechanism eliminates the effect of low priority on high priority by buffering the low priority traffic into a shift register until the transmission of the high priority traffic is complete. For dual ring architectures, the Fair Bandwidth Allocation Mechanism (FBAM) and the Effective Priority Bandwidth Balancing (EP_BWB) mechanism are introduced. FBAM allows stations to reserve channel bandwidth on a continuous basis rather than wait until bandwidth starvation is observed. Consequently, FBAM does not have to deal with the difficult issue of identifying starvation, a serious drawback of other access mechanisms such as the Local and Global Fairness Algorithms (LFA and GFA, respectively). In addition, its operation requires a significantly smaller number of control bits in the access control field of the slot and its performance is less sensitive to system parameters. Moreover, FBAM demonstrates Max-Min flow control properties with respect to the allocation of bandwidth among competing traffic streams, which is a significant advantage of FBAM over all the previously proposed channel access mechanisms. EP_BWB, an enhancement of FBAM to support preemptive priorities, minimizes the effect of low priority on high priority and supports delay-sensitive traffic by enabling higher priority classes to preempt the transmissions of lower priority classes. Finally, the great potential of EP_BWB to support the interconnection of base stations on a distributed control wireless PCN carrying voice and data traffic is demonstrated

Transport Layer Optimizations for Heterogeneous Wireless Multimedia Networks

The explosive growth of the Internet during the last few years, has been propelled by the TCP/IP protocol suite and the best effort packet forwarding service. However, quality of service (QoS) is far from being a reality especially for multimedia services like video streaming and video conferencing. In the case of wireless and mobile networks, the problem becomes even worse due to the physics of the medium, resulting into further deterioration of the system performance. Goal of this dissertation is the systematic development of comprehensive models that jointly characterize the performance of transport protocols and media delivery in heterogeneous wireless networks. At the core of our novel methodology, is the use of analytical models for driving the design of media transport algorithms, so that the delivery of conversational and non-interactive multimedia data is enhanced in terms of throughput, delay, and jitter. More speciffically, we develop analytical models that characterize the throughput and goodput of the transmission control protocol (TCP) and the transmission friendly rate control (TFRC) protocol, when CBR and VBR multimedia workloads are considered. Subsequently, we enhance the transport protocol models with new parameters that capture the playback buffer performance and the expected video distortion at the receiver. In this way a complete end-to-end model for media streaming is obtained. This model is used as a basis for a new algorithm for rate-distortion optimized mode selection in video streaming appli- cations. As a next step, we extend the developed models for the aforementioned protocols, so that heterogeneous wireless networks can be accommodated. Subsequently, new algorithms are proposed in order to enhance the developed media streaming algorithms when heterogeneous wireless networks are also included. Finally, the aforementioned models and algorithms are extended for the case of concurrent multipath media transport over several hybrid wired/wireless links.Ph.D.Committee Chair: Vijay Madisetti; Committee Member: Raghupathy Sivakumar; Committee Member: Sudhakar Yalamanchili; Committee Member: Umakishore Ramachandran; Committee Member: Yucel Altunbasa

BIM and Facility Management for smart data management and visualization

Il BIM è per tutti gli edifici. Riconosciuta tra le disruptive technologies, la metodologia BIM cambia completamente il modo tradizionale di lavorare dell’industria delle costruzioni, a partire dalla fase di progettazione. In questo scenario, la sfida più interessante è quella di stabilire un framework, che riunisca metodi e strumenti per il ciclo di vita degli edifici, per la gestione del costruito. Il paradigma di Smart city si declina anche nella disponibilità di smart data, includendo, quindi, l’utilizzo intelligente delle informazioni riguardanti il patrimonio immobiliare. Il coinvolgimento proattivo del Facility Management nel processo edilizio è la chiave per garantire la disponibilità di un dataset appropriato di informazioni, supportando l’idea di un sistema di gestione della conoscenza basato sul BIM. In linea con questo approccio, un processo di management impostato a partire dal BIM è conseguibile attraverso una re-ingegnerizzazione complessiva della filiera atta a garantire l’efficacia del BIM ed a fornire servizi intelligenti di Facility 4.0.BIM is for all buildings. As a disruptive technology, BIM completely changes the traditional way of working of the Construction Industry, starting from the design stage. However, the challenging issue is to establish a framework that brings together methods and tools for the buildings lifecycle, focusing on the existing buildings management. Smart city means smart data, including, therefore, intelligent use of Real Estate information. Involving Facility Management in the process is the key to ensure the availability of the proper dataset of information, supporting the idea of a BIM-based knowledge management system. According to this approach, BIM Management is achievable applying a reverse engineering process to guarantee the BIM effectiveness and to provide Facility 4.0 smart services

Utilization of computational planning and geo-design as a tool for having interactive conversations in scenario planning

In the process of planning and designing in an urban context, building and calculating information to report for the substantial number of elements in the urban environment is a very time consuming and labor-intensive process. Advancement of computational or parametric design tools has enabled designers and planners to script in automated tools for the repetitive procedures that should have been done manually in the past. Although more time consuming at the beginning, this approach accelerates modeling numerous elements in a certain project greatly, as it provides the opportunity to create multiple varying iterations and alternatives for a certain project in a matter of seconds. Attempting to automate the recurring and time-consuming process of building out masses and reporting back data, an automated scenario builder is created which utilizes procedural modeling to do those tasks. This could help create an automated scenario builder that can be utilized as a tool for engaging conversations with clients and stakeholders during visioning exercises. Multiple development typologies are coded within the scenario builder, so the tool could create alternative scenarios in real time based on feedback from the clients. The automated scenario builder can have a reporting function built in to evaluate various scenarios on the go to see how each alternative meets the vision that has been intended for the future of a place. The main advantage of this approach is that it allows for creation of an ever-growing comprehensive library of toolsets which could be adapted for each project consultants encounter based on the context and individualities they have. Growth of the library leads to having a better toolset which with the advancement of technology will be able to take on a larger number of tasks within the planning process. However, it is crucial to note that automating the process of building out scenarios is not a computerization of the design and it is just a tool for planners or designers to streamline the process.Community and Regional Plannin

Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition

Modern machine learning models are beginning to rival human performance on some realistic object recognition tasks, but we still lack a full understanding of how the human brain solves this same problem. This thesis combines knowledge from machine learning and computational neuroscience to create models of human object recognition that are increasingly realistic both in their treatment of low-level neural mechanisms and in their reproduction of high-level human behaviour. First, I present extensions to the Neural Engineering Framework to make its preferred type of model---the “fixed-encoding” network---more accurate for object recognition tasks. These extensions include better distributions---such as Gabor filters---for the encoding weights, and better loss functions---namely weighted squared loss, softmax loss, and hinge loss---to solve for decoding weights. Second, I introduce increased biological realism into deep convolutional neural networks trained with backpropagation, by training them to run using spiking leaky integrate-and-fire (LIF) neurons. These models have been successful in machine learning, and I am able to convert them to spiking networks while retaining similar levels of performance. I present a novel method to smooth the LIF rate response function in order to avoid the common problems associated with differentiating spiking neurons in general and LIF neurons in particular. I also derive a number of novel characterizations of spiking variability, and use these to train spiking networks to be more robust to this variability. Finally, to address the problems with implementing backpropagation in a biological system, I train spiking deep neural networks using the more biological Feedback Alignment algorithm. I examine this algorithm in depth, including many variations on the core algorithm, methods to train using non-differentiable spiking neurons, and some of the limitations of the algorithm. Using these findings, I construct a spiking model that learns online in a biologically realistic manner. The models developed in this thesis help to explain both how spiking neurons in the brain work together to allow us to recognize complex objects, and how the brain may learn this behaviour. Their spiking nature allows them to be implemented on highly efficient neuromorphic hardware, opening the door to object recognition on energy-limited devices such as cell phones and mobile robots

Actas da 10ª Conferência sobre Redes de Computadores

Universidade do MinhoCCTCCentro AlgoritmiCisco SystemsIEEE Portugal Sectio