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Leadership Effects in Product and Process Management Through Knowledge Management
Publication . Ferreira, Vítor; Espírito Santo, Pedro; Espírito Santo, Lídia
Companies face an increasingly strict demanding and competitive environment. Firms that want stand out in the business world should create strategies to be, day after day, better when compared to with their competitors. Process management in companies is assumed as critical, given that in a constantly changing environment, the adaptation and efficient management of processes with a continuous improvement focus may bring remarkable benefits to the companies. As several studies have highlighted, the continuous improvement of the firm is dependent on the commitment of top management and its leadership. At the same time knowledge management practices are essential for this process. This study analyzes the effects of leadership in process management through knowledge management. Thus, using a sample of 187 companies, this investigation analyzed trough structural equation modeling the impact of this variables on process management efficiency. Despite having had some practical limitations, we find that leadership is a key variable in determining knowledge management. Furthermore, this research showed that knowledge management has influence over the management processes in companies from the viewpoint of continuous improvement.
The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine
Publication . Guarda, André; M. M. Rodrigues, Nuno; Pereira, Fernando
Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing.
Controlling Morphology Using Low Molar Mass Nucleators
Publication . Mitchell, Geoffrey; Wangsoub, Supatra; Nogales, Aurora; Davis, Fred J.; Olley, Robert H.
Crystallisation is a hugely important process in physical sciences and is crucial to many areas of, for example, chemistry, physics, biochemistry, metallurgy and geology. The process is typically associated with solidification, for example in the purification of solids from a heated saturated solution familiar to all chemistry undergraduates. Crystalline solids are also often the end result of cooling liquids, or in some cases gases, but in order to form require nucleation, in the absence of nucleation supercoiling of liquids well below the melting point is possible (Cava-gna, 2009). The quality of crystals, as gauged by size and levels of order is highly variable, and may depend on factors such as material purity and the rate of cool-ing; rapid cooling may result in poor crystallisation, or even the formation of amorphous materials with no long range order. In geological systems rates of cooling may vary over many orders of magnitude, for example obsidian is a large-ly amorphous material produced when lava is rapidly cooled (Tuffen, 2003), while the gypsum crystals found in the Cueva de los Cristales in Chihuahua, Mexico can reach 10 metres in length (Figure 1) and are formed over hundreds of thousands of years. In this latter case the formation of such large spectacular structures as shown in Figure 1 can only be explained by a low nucleation rate (García-Ruiz, 2007; Van Driessche, 2011).
A Double Deep Learning-Based Solution for Efficient Event Data Coding and Classification
Publication . Seleem, Abdelrahman; Guarda, André; M. M. Rodrigues, Nuno; Pereira, Fernando
Event cameras have the ability to capture asynchronous per-pixel brightness changes, usually called "events", offering advantages over traditional frame-based cameras for computer vision tasks. Efficiently coding event data is critical for practical transmission and storage, given the very significant number of events captured. This paper proposes a novel double deep learning-based solution for efficient event data coding and classification, using a point cloud-based representation for events. Moreover, since the conversions from events to point clouds and back to events are key steps in the proposed solution, novel tools are proposed and their impact is evaluated in terms of compression and classification performance. Experimental results show that it is possible to achieve a classification performance for decompressed events which is similar to the one for original events, even after applying a lossy point cloud codec, notably the recent deep learning-based JPEG Pleno Point Cloud Coding standard, with a clear rate reduction. Experimental results also demonstrate that events coded using the JPEG standard achieve better classification performance than those coded using the conventional lossy MPEG Geometry-based Point Cloud Coding standard for the same rate. Furthermore, the adoption of deep learning-based coding offers future high potential for performing computer vision tasks in the compressed domain, which allows skipping the decoding stage, thus mitigating the impact of compression artifact
Dispositivo de Navegación Portable para Personas No Videntes
Publication . Yanez, Daniel Vera; Marcillo, Diego; Pereira, António
La visión es uno de los más importantes sentidos que ayuda a las personas a navegar en nuestro mundo. Comúnmente las personas no videntes desarrollan sus otros sentidos para poder sentir sus alrededores, pero en ciertos casos esto no es
suficiente. Los sentidos pueden ser perturbados por el ruido o enfermedades. Por esta razón se han desarrollado muchos artefactos para ayudar a este grupo de personas. Artefactos como bastones blancos o perros guía ayudan a las personas no videntes a moverse en su entorno. Este artículo propone el uso de un sistema que detecta y reconoce obstáculos cercanos, dando una retroalimentación audible al usuario, evitando una colisión. Es un sistema inalámbrico para que sea cómodo para el usuario. El sistema ayuda a las personas con discapacidad visual a moverse
en escenarios interiores o exteriores. Los objetivos del sistema es detectar los obstáculos que los bastones blancos o los perros guía no pueden, ampliando su rango de detección.