With the growth of the world's population and depletion of limited resources, economic development is shifting from ‘take-make-waste’ linear model to a sustainable Circular Economic (CE) model. It is becoming essential to extend the useful life of products to avoid throwaway. In the manufacturing industry, this is especially relevant for long-life expensive capital items. Literature has presented many approaches to extend product life, such as refurbishment, upgrade, repair, and recycling. Depending on a capital item's condition, manufacturers perform a series of activities to extend its useful life, such as refurbishment activities followed by upgrade activities. These life extension activities may involve various stakeholders and systems and significantly impact the capital item's life cycle. It is a challenge to enable cross-collaboration between these different stakeholders and systems, and provide manufacturers a holistic view of the activities performed along the entire life cycle of a capital item. The Digital Thread (DT) concept, which has been applied in product lifecycle management and manufacturing, provides an opportunity to tackle these challenges. However, there is little research about developing and applying DT for extending the useful life of capital items in manufacturing companies. This paper presents ongoing research to develop a DT design tool for product life extension. We take the development of a DT sample for the Refurbishment protocol deployment as an example to illustrate the approach. This tool will pave the way to support manufacturers to extend their large capital items' useful life.

The ‘take-make-dispose’ consumption model that remains shackled to the global economy, is also affecting production assets. Industry is promoting the transition to a circular economy (CE). Deployment of the evolving digital technologies and realisation of Industry 4.0 (I4.0) enhance machines automation, connectivity and provide large amounts of data that can be interpreted in a way that were not possible before. It offers to companies the opportunity to retrofit their legacy equipment that cannot meet the constantly increasing market demands. This helps to extend the useful lifetime of this equipment and accelerate the transition to a CE. However, Industry is still lacking an efficient systemic multi-level approach that enables a cost-effective, holistic, and integrated application of CE principles to the digital uplifting of factory 4.0 capital investments. Companies and particularly SMEs face diverse challenges for implementing I4.0 systems in their businesses due to lack of knowledge, expertise and investment. The translation of CE strategies into concrete actions will help professionals to consider circularity measures in a broad sense. This paper introduces the 10 Circularity Protocols for extending the useful life of large industrial equipment, addressing issues at product, machine, and process level. With guidance on the application of I4.0 technologies, the Protocols help to digitally re-engineer outdated machines to be more productive and energy-efficient, to decrease maintenance downtime, spare-parts storage and off-spec pieces. The Protocols also support on the End-of-Life (EoL) management of the different machine components that are not technically, economically and environmentally feasible to be reused or repaired. As a final step, the future research line and methodology focusing on the digital implementation of the Circularity Protocols guidelines for their deployment and validation in real industrial cases is introduced.

Millimeter communication systems use large antenna arrays to provide good average received power and to take advantage of multi-stream MIMO communication. Unfortunately, due to power consumption in the analog front-end, it is impractical to perform beamforming and fully digital precoding at baseband. Hybrid precoding/combining architectures have been proposed to overcome this limitation. The hybrid structure splits the MIMO processing between the digital and analog domains, while keeping the performance close to that of the fully digital solution. In this paper, we introduce and analyze several algorithms that efficiently design hybrid precoders and combiners starting from the known optimum digital precoder/combiner, which can be computed when perfect channel state information is available. We propose several low complexity solutions which provide different trade-offs between performance and complexity. We show that the proposed iterative solutions perform better in terms of spectral efficiency and/or are faster than previous methods in the literature. All of them provide designs which perform close to the known optimal digital solution. Finally, we study the effects of quantizing the analog component of the hybrid design and show that even with coarse quantization, the average rate performance is good.

Multiple input multiple output (MIMO) systems employing large antenna arrays are the basic architecture for millimeter wave (mmWave) systems. Due to the higher bandwidths to be used at mmWave, the corresponding sampling rates of high-resolution analog-to-digital converters (ADCs) are also very high, so that ADCs become the most power hungry devices in the reception chain. One solution is to employ low resolution, i.e. one-bit, ADCs. We develop an adaptive one-bit compressed sensing scheme that can be used at low-resolution mmWave receivers for channel estimation. The simulation results show that the adaptive one-bit compressed sensing scheme outperforms the fixed one in the context of mmWave channel estimation.

High resolution direction-of-arrival (DOA) estimation is an important problem in many array signal processing applications. This paper proposes an augmented covariance estimator for DOA estimation. The new method exploits the periodicity of the covariance lags when the DOAs are assumed on a discrete grid with a certain resolution. Then, it achieves twice the resolution of typical methods such as the direct augmentable approach or forward backward spatial smoothing. When the sources are not on the discrete grid, an interpolated array manifold technique is proposed to mitigate the grid mismatch error.

Millimeter wave (mmWave) multiple-input multipleoutput (MIMO) communication with large antenna arrays has been proposed to enable gigabit per second communication for next generation cellular systems and local area networks. A key difference relative to lower frequency solutions is that in mmWave systems, precoding/combining can not be performed entirely at digital baseband, due to the high cost and power consumption of some components of the radio frequency (RF) chain. In this paper we develop a low complexity algorithm for finding hybrid precoders that split the precoding/combining process between the analog and digital domains. Our approach exploits sparsity in the received signal to formulate the design of the precoder/combiners as a compressed sensing optimization problem. We use the properties of the matrix containing the array response vectors to find first an orthonormal analog precoder, since sparse approximation algorithms applied to orthonormal sensing matrices are based on simple computations of correlations. Then, we propose to perform a local search to refine the analog precoder and compute the baseband precoder. We present numerical results demonstrate substantial improvements in complexity while maintaining good spectral efficiency.

Airborne pushbroom sensors produce images by acquiring scenes on a line-per-line basis. Depending on the motions of the aircraft carrying the sensor, the line integration time, and the targeted spatial resolution, missing areas may appear on geocorrected images. Missing pixels in geocorrected images are usually tackled by means of interpolation methods, such as nearest neighbor, but these cause visible artifacts that affect the visual quality of the result and also the performance of processing methods working on geocorrected images. We propose the use of an anisotropic diffusion inpainting method specifically devised for hyperspectral images, show some extreme examples, and discuss its convenience.

Semi-implicit schemes have been recently shown to speed up nonlinear diffusion in hyperspectral images while increasing the accuracy of subsequent classifiers in thematic mapping. Here, we show how semi-implicit schemes can be used to implement a truly anisotropic diffusion method for hyperspectral images, and we test the performance of different implementations in terms of computational overhead, speed, numerical accuracy, and thematic mapping performance. In addition, truly anisotropic trace-based diffusion formulations, besides a more precise steering of the diffusion processes, also allow implementation by means of local oriented Gaussian masks. We show how the implementations with the highest numerical accuracy can be also the simplest and fastest while still increasing the classification performance.

Anisotropic diffusion in the hypercube is a powerful image restoration and enhancement tool for hyperspectral imagery. Processing hyperspectral images requires special attention to accuracy, in order to preserve the high spectral precision that characterizes this kind of data. Here, we propose alternative implementations of the method with specific regard to the numerical accuracy of the solution in strict sense. We propose and describe how to apply rotationally invariant finite differences and a local filtering scheme. We also test and discuss their performance on several hyperspectral images.