Jaime Zabalza

Jaime Zabalza
University of Strathclyde · Centre for Excellence in Signal and Image Processing (CeSIP)

PhD EEE

About

44
Publications
7,508
Reads
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1,486
Citations
Introduction
Skills and Expertise

Publications

Publications (44)
Conference Paper
Full-text available
This paper presents some initial results of project HyperSST (Hyperspectral Space Surveillance and Tracking) supported by the UK Space Agency. HyperSST demonstrated the use of hyperspectral and multispectral analysis of the light absorbed, emitted and reflected by space objects to better reconstruct their composition and attitude motion. The key id...
Article
Full-text available
The rich spectral information provided by hyperspectral imaging (HSI) has made this technology very useful in the classification of remotely sensed data. However, classification of hyperspectral data is typically affected by noise and the Hughes phenomenon due to the presence of hundreds of spectral bands and correlation among them, with usually a...
Article
The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral domain and spatial domain feature extraction in hyperspectral images (HSI). However, PCA itself suffers from low efficacy if no spatial information is combined, whilst 2DSSA can extract the spatial information yet has a high computing comp...
Article
Full-text available
Singular spectral analysis (SSA) has recently been successfully applied to feature extraction in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D SSA in spatial domain. However, there are some drawbacks, such as sensitivity to the window size, high computational complexity under a large window, and failing to e...
Article
Full-text available
The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computa...
Article
Full-text available
As a cutting-edge technique for denoising and feature extraction, Singular Spectrum Analysis (SSA) has been applied successfully for feature mining in hyperspectral images (HSI). However, when applying SSA for in-situ feature extraction in HSI, conventional pixel-based 1-D SSA fails to produce satisfactory results, whilst the band-image based 2D-SS...
Article
The layer-by-layer printing process of additive manufacturing methods provides new opportunities to embed identification codes inside parts during manufacture. These embedded codes can be used for product authentication and identification of counterfeits. The availability of reverse engineering tools has increased the risk of counterfeit part produ...
Article
Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still lacking. The lack of proper understanding of the HSI data structure has resulted in the inconsistency...
Article
Full-text available
In an increasingly specialized industry with strong demands from end users, product quality plays a key role in industrial manufacturing, where the quality impact highly depends on the final product and its application. An important parameter for quality control is the surface finish of objects, essential for determining their technical suitability...
Article
Full-text available
Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a case study in which a robotic manipulator, namely a KUKA KR90 R3100, is provided with smart sensing c...
Article
Full-text available
To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel level. First, the HSI is divided into different spatial regions, each region is shape- and size-adapte...
Article
Full-text available
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techni...
Article
Full-text available
For the spatial-spectral classification of hyperspectral images (HSIs), a deep learning framework is proposed in this study, which consists of convolutional neural networks (CNNs) and Markov random fields (MRFs). Firstly, a CNN model to learn the deep spectral feature from the HSI is built and the class posterior probability distribution is estimat...
Article
A cognitive modelling based new inversion method, the successive differential evolution (DE-S) algorithm, is proposed to estimate the Q factor and velocity from the zero-offset vertical seismic profile (VSP) record for oil-gas reservoir exploration. The DE algorithm seeks optimal solutions by simulating the natural species evolution processes and m...
Article
Full-text available
Band selection is an important data dimensionality reduction tool in hyperspectral images (HSIs). To identify the most informative subset band from the hundreds of highly corrected bands in HSIs a novel hyperspectral band selection method using a crossover-based gravitational search algorithm (CGSA) is presented in this study. In this method, the d...
Chapter
Kernel extreme learning machine (ELM) has attracted more and more attentions due to its good performance compared with support vector machine (SVM). Since the original Kernel ELM (KELM) is just a spectral classifier, it can’t extract the rich spatial information of hyperspectral images (HSIs). This hence refrains the performance of KELM. In view of...
Chapter
In order to extend the abilities of current robots in industrial applications towards more autonomous and flexible manufacturing, this work presents an integrated system comprising real-time sensing, path-planning and control of industrial robots to provide them with adaptive reasoning, autonomous thinking and environment interaction under dynamic...
Article
Although singular spectrum analysis (SSA) has been successfully applied for data classification in hyperspectral remote sensing, it suffers from extremely high computational cost, especially for 2D-SSA. As a result, a fast implementation of 2D-SSA namely F-2D-SSA is presented in this paper, where the computational complexity has been significantly...
Article
Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exp...
Article
The authors regret to announce that the affiliation information for Prof H. Zhao should be Guangdong Polytechnic Normal University rather than Guangdong Technic Normal University. The authors would like to apologise for any inconvenience caused.
Article
Hyperspectral remote sensing is experiencing a dazzling proliferation of new sensors, platforms, systems, and applications with the introduction of novel, low-cost, low-weight sensors. Curiously, relatively little development is now occurring in the use of Fourier transform (FT) systems, which have the potential to operate at extremely high through...
Article
Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been recently proposed for feature extraction in hyperspectral remote sensing. With the help of hidden nodes in deep layers, a high-level abstraction is achieved for data reduction whilst maintaining the key information of the data. As hidden nodes in SAEs have to deal s...
Conference Paper
Hyperspectral imaging (HSI) devices produce 3-D hyper-cubes of a spatial scene in hundreds of different spectral bands, generating large data sets which allow accurate data processing to be implemented. However, the large dimensionality of hypercubes leads to subsequent implementation of dimensionality reduction techniques such as principal compone...
Conference Paper
Food quality analysis is a key area where reliable, nondestructive and accurate measures are required. Hyperspec-tral imaging is a technology which meets all of these requirements but only if appropriate signal processing techniques are implemented. In this paper, a discussion of some of these state-of-the-art processing techniques is followed by a...
Article
As a recent approach for time series analysis, singular spectrum analysis (SSA) has been successfully applied for feature extraction in hyperspectral imaging (HSI), leading to increased accuracy in pixel-based classification tasks. However, one of the main drawbacks of conventional SSA in HSI is the extremely high computational complexity, where ea...
Conference Paper
Lamb eating quality is related to 3 factors, which are tenderness, juiciness and flavour. In addition to these factors, the surface colour of lamb could influence the purchase decision of consumers. Objective quality evaluation approaches, like nearinfrared spectroscopy (NIRS) and hyperspectral imaging (HSI), have been proved fast and non-destructi...
Article
Full-text available
It is well known that the eating quality of beef has a signifi cant infl uence on the repurchase behavior of consumers. There are several key factors that affect the perception of quality, including color, tenderness, juiciness, and fl avor. To support consumer repurchase choices, there is a need for an objective measurement of quality that could b...
Article
Three factors, including tenderness, juiciness and flavour, are found to have an impact on lamb eating quality, which determines the repurchase behaviour of customers. In addition to these factors, the surface colour of lamb can also influence the purchase decision of consumers. From a long time ago, meat industries have been looking for fast and n...
Article
As a very recent technique for time-series analysis, singular spectrum analysis (SSA) has been applied in many diverse areas, where an original 1-D signal can be decomposed into a sum of components, including varying trends, oscillations, and noise. Considering pixel-based spectral profiles as 1-D signals, in this letter, SSA has been applied in hy...
Article
Presented in a three-dimensional structure called a hypercube, hyperspectral imaging suffers from a large volume of data and high computational cost for data analysis. To overcome such drawbacks, principal component analysis (PCA) has been widely applied for feature extraction and dimensionality reduction. However, a severe bottleneck is how to com...
Article
As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded int...
Article
With numerous and contiguous spectral bands acquired from visible light (400?1,000 nm) to (near) infrared (1,000?1,700 nm and over), hyperspectral imaging (HSI) can potentially identify different objects by detecting minor changes in temperature, moisture, and chemical content. As a result, HSI has been widely applied in a number of application are...
Article
In this paper, a novel feature extraction technique for micro-Doppler classification and its real-time implementation using a support vector machine classifier on a low-cost, embedded digital signal processor are presented. The effectiveness of the proposed technique is improved through exploitation of the outlier rejection capabilities of robust p...
Conference Paper
Full-text available
Based on the well-known Singular Value Decomposition (SVD), Singular Spectrum Analysis (SSA) has been widely employed for time series analysis and forecasting in decomposing the original series into a sum of components. As such, each 1-D signal can be represented with varying trend, oscillations and noise for easy enhancement of the signal. Taking...
Conference Paper
Support Vector Machine (SVM) is a very powerful tool for signal prediction including classification and regression. With Texas Instruments TMS320C6713 DSK, an embedded SVM is implemented, where a user friendly interface is provided via peripherals like the DIPs and LEDs. The C6713 processor in combination with the SDRAM block memory can solve the c...
Article
Full-text available
This paper is focused on the analysis of a Kalman f ilter performance when introduced in the current co ntrol loop of a distributed generation connection inverter. The ide a is to use this kind of filter to reduce the harmo nic content of the currents injected in the point of common coupling. The study has been performed by means of simulation usin...
Article
This paper is focused on the analysis of a Kalman filter performance when introduced in the current control loop of a distributed generator connection inverter. The goal is to use this kind of filter to reduce the harmonic content of the currents injected by this equipment in their point of common coupling. The study has been performed by means of...

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Projects

Project (1)
Project
The project aims to enhance the autonomous manufacturing capability of UK industry in metal forming and forging. The project brings together two departments of the University of Strathclyde, namely DMEM and EEE. With Industry 4.0 being currently widely acknowledged as a key driver of industrial advancement, a strong technologic shift has become apparent within industry to move towards both, more intelligence and more autonomy. Currently, hot forging and forming has benefited only little from this shift beyond traditional automation. There is a vast opportunity to systematically transform the inherently challenging technologies, namely forming and forging into truly smart and flexible manufacturing systems.