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The Incremental Capacity (IC) is a rich source of data for the state-of-health estimation of lithium-ion batteries. This data is typically collected during a low C-rate (dis)charge of the battery which is not representative of many real-world applications outside the research laboratories. Here, this limitation is showcased to be mitigated by employing a new feature-extraction technique applied to a large dataset including 105 batteries with cycle lives ranging from 158 to 1637 cycles. The state-of-health of these batteries is successfully predicted with a mean-absolute-percentage error below 0.7 % by using three regression models of support vector regressor, multi-layer perceptron, and random forest. The methodologies proposed in this work facilitate the development of accurate IC-based state-of-health predictors for lithium-ion batteries in on-board applications.

PurposeThe aim of the research project which resulted in this work is to achieve a cost‐effective approach for instantaneous hyperspectral imaging.Design/methodology/approachThis paper presents a simulation study and an experimental evaluation of a novel imaging spectroscopy technique, where multi‐channel image data are acquired instantaneously and transformed into spectra by using a statistical modelling approach. A digital colour camera equipped with an additional colour filter array was used to acquire an instantaneous single image that was demosaicked to generate a multi‐channel image. A statistical transformation approach was employed to convert this image into a hyperspectral one.FindingsThe feasibility of this method was investigated through extensive simulation and experimental tasks where promising results were obtained.Practical implicationsThe small size of the initially acquired single instantaneous image makes this approach useful for applications where video‐rate hyperspectral imaging is required.Originality/valueFor the first time, a simplified prototype of this novel imaging spectroscopy technique was built and evaluated experimentally. And the results were compared with those of a more ideal simulation study. Recommendations for how to improve the prototype were also suggested as a result of the comparison between the simulation and the prototype evaluation results.

In the medical field, new technologies are incorporated for the sole purpose of enhancing the quality of life for the patients and even for the normal healthy people. Infrared technology is one of the technologies that have some applications in both the medical and biological fields. In this work, the thermal infrared (IR) measurement is used to investigate the potential of skin cancer detection. IR enjoys non-invasive and non-contact advantages as well as favorable cost, apparently. It is also very well developed regarding the technological and methodological aspects. IR per se is an electro-metric radiation that all objects emit when their temperature is above the absolute zero. And the human body is not different in this regard. The IR range extends, ideally, to cover wavelengths from 800 nanometer to few hundred micrometer. Cancer, in modern life, has grown tangibly due to many factors, such as life expectancies increase, personal habits and ultraviolet radiation exposures among others. Moreover, the significant enhancement of technologies has helped identifying more types of cancers than before. The sole purpose of this work is to investigate further IR technology methods and applications not yet matured in skin cancer detection to enhance the detection ability with higher safety level.

A challenging problem in dental implant surgery is to evaluate the stability of the implant. In this simulation study, an experimental phantom is used to represent a jawbone with a dental implant. It is made of a little pool filled with soft-tissue equivalent material and a disc of fresh Oakwood with a metal screw. Varying levels of contact between screw and wood are simulated by screwing in or out the screw. Initially, the screw is screwed in and fixed firmly in wood. Thereafter, the screw is screwed out, a half turn each time, to increase the gap gradually between wood and screw. Pulse-echo ultrasound is used and the power spectra of the received echo-signals are computed. These spectra are normalized then analyzed by using the partial least squares method to estimate the corresponding implant stiffness grade in terms of number of turns when beginning from the initial tight-screw state then screwing out the screw. A coefficient of determination R2 of 96.4% and a mean absolute error of ±0.23 turns are achieved when comparing real and estimated values of stiffness grades, indicating the efficiency of this approach.

Objective: SPECT data from myocardial perfusion imaging (MPI) are normally displayed as a set of three slices orthogonal to the left ventricular (LV) long axis. For data presentation, the images are orientated about the LV long axis. Therefore, radial slices provide a suitable alternative to standard orthogonal slices, with the advantage of requiring fewer slices to adequately represent the data. In this study, a semi-automatic method is developed for displaying MPI SPECT data as a set of radial slices orientated about the LV axis. The aim is to reduce the number of slices viewed without loss of information and independently from the heart size. Method: Standard short axis slices, orientated perpendicular to the LV axis, are utilized.The skeleton of the segmented myocardium is found and the true LV axis is determined in each central long slice. The LV axis of the whole volume is determined by aligning the axes of all slices. Result: Radial slices centered about this axis were generated by integration over a sector equal to the resolution of the imaging system which was of the order of 1.2 cm. Therefore, assuming a mean LV diameter of 8 cm, 20 slices were sufficient to represent a non-gated study. Gated information could be adequately displayed with 4 slices integrated over an angle of 45. Conclusion: A semi-automatic method for generating radial slices from SPECT MPI short axis slices has been developed.

New advances within the recently rediscovered field of Compressed Sensing (CS) have opened for a great variety of new possibilities in the field of image reconstruction and more specifically in medical image reconstruction. In this work, a new approach using a CS-based algorithm is proposed and used in order to solve limited-angle problems (LAPs), like the ones that typically occur in computed tomography or electron microscope. This approach is based on a variant of the Robbins-Monro stochastic approximation procedure, developed by Egaziarian, using regularization by a spatially adaptive filter. This proposal consists on filling the gaps of missing or unobserved data with random noise and enabling a spatially adaptive denoising filter to regularize the data and reveal the underlying topology. This method was tested on different 3D transmission electron microscope datasets that presented different missing data artifacts (e.g, wedge or cone shape). The test results show a great potential for solving LAPs using the proposed technique.

In this paper, we investigate the physics of the positron annihilation process, which occurs in a PET imaging system. In particular, the diffusion of beta particles (positrons) within water was addressed. Beta particles are emitted isotropically from the same source point with random directions and randomly chosen energy levels. After traversing a certain distance within water, beta particles lose a certain amount of its energy. The inelastic collisions with atomic electrons are mainly responsible for the energy dissipation of charged particles, such as electrons and positrons (that have low mass). The energy loss rate due to inelastic process is estimated by using the Beta-Bloch formula. These results help in understanding how to develop and implement a computationally efficient Monte Carlo Simulation of the positron annihilation process.

Myoelectric (EMG) and mechano-myographic (MMG) signals recorded during muscular activities are useful not only for monitoring and assessing these activities but can also help in providing effective rehabilitation for disabled patients as well as constructing and controlling sophisticated prosthesis for various amputees. While the existing training methods using EMG and MMG signal data have compelling benefits, many engineering challenges still remain with regard to the sensory control system. Studies show that prosthesis’ users support arguments considering the continued development of comfortable, reliable and better functionality of the sensors of the available applications. The aim of this paper is to study muscular activity by using an armband with embedded OptoMyoGraphy (OMG) sensors arranged as a one dimensional sensor array. The armband and the signals’ acquisition system are designed and developed at the beginning of the research project. The used sensors are capable of measuring lateral dimensional changes, on the landscape formed by the skin surface, caused due to underlying muscles’ activities around the forearm region. As a novel contribution, the paper discusses a possible methodology to control portable upper limb prosthetic devices using the OMG technique. Furthermore, the developed armband can be used as a biofeedback system for rehabilitation purposes in upper-limb amputees (ULAs) cases with below-the-forearm amputation and help them gain control over their remaining sensory-motor system and get rid of phantom and/or residual limb pain (PLP, RLP).

In Positron Emission Tomography (PET), the coincident emission of gamma photon pairs constitutes the useful signals that should be detected and processed to reconstruct the desired PET images of the studied objects. However, along with the useful signal, noise is also generated and added to the detected signals that are sorted with respect to their line-ofresponse and arranged as a sinogram for each two-dimensional slice. In this paper, the type and properties of noise in PET sinogram data will be evaluated. Furthermore, the effect of the used linear and non-linear image denoising and reconstruction procedures on the type of noise will be analyzed. For this purpose, the Gaussian filter, the Median filter, the Patch Confidence k-Nearest Neighbor filter (PCkNN) and the Block Matching 3D filter (BM3D) were used to denoise PET image data, as well as the maximum likelihood expectation maximization algorithm (MLEM) and the Filtered Back Projection algorithm (FBP) to reconstruct the PET images.

In Positron Emission Tomography (PET), image quality is highly degraded by noise. Therefore, two main PETimage denoising approaches can be used: pre- and postreconstruction denoising. In the pre-reconstruction approach the PET sinogram is denoised before forwarding it to the image reconstruction algorithm. On the other hand, the reconstructed PET-image is denoised in the post-reconstruction approach. In this study, comparison of image quality of the resulting images of the pre- and post-reconstruction approaches is performed. In both types of approaches, the Gaussian filter, the Non-Local Means filter (NLM), the Block-Matching and 3D filter (BM3D), the K-Nearest Neighbors Filter (KNN) and the Patch Confidence K-Nearest Neighbors Filter (PCkNN) are utilized. These approaches are evaluated on a simulated PET-phantom dataset, a real-life physical thorax-phantom PET dataset as well as a reallife MicroPET-scan dataset of a mouse. The performance is measured using the Signal-to-Noise Ratio (SNR) in addition to the Contrast-to-Noise Ratio (CNR) in the resulting images.

Using spectral reflectance to estimate crop status is a method suitable for developing sensors for site-specific agricultural applications. When developing spectral analysis methods, it is important to know the influence of different crop parameters on the spectral reflectance profile. The objective of this report was to present and evaluate a multivariate method for objective hyperspectral analysis in the examination of how different parts of the reflectance spectrum are affected by disease severity and above ground plant density. Data from two field experiments were used; fungal disease severity assessments in wheat 1998 and above ground plant density measurements 2003. The analysis method consisted of two steps: a pre-processing step where the data was normalized and a classification step for estimating the crop variable. Using only 12% of the data as training data, the method resulted in coefficients of determination (R 2) of 94.3% for the disease severity data and 96.9% for the plant density data. The hyperspectral analysis method presented could also be used to extract spectral signatures of disease severity and plant density using the experimental data. In general, two types of spectral signatures for both data sets, with respect to increasing disease severity and decreasing plant density, were observed (1) a flattening of the green reflectance peak together with a general decrease in reflectance in the near infrared region and, (2) a decrease of the shoulder of the near infrared reflectance plateau together with a general increase in the visible region between 550 and 750 nm.

The impact of plant pathological stress on crop reflectance can be measured both in broad-band vegetation indices and in narrow or local characteristics of the reflectance spectra. This work is con ...

Radiological techniques based on X-rays are well established in medical diagnostics and there are known risks associated with the use of ionizing radiation like X-rays. That explains why the X-ray technology is constantly under development in the pursuit of new technologies that can contribute to reduce radiation dose to patients. Since the reduction of a radiation dose generally results in a poorer image quality, we have investigated whether the use of digital image processing can provide panoramic radiographs with enhanced image quality. An automated image processing algorithm was proposed and employed for this purpose. Panoramic X-ray examination is an important and common tool in dental radiology, used especially for children and teenagers. The technique is used to create an overview of a patient's jaw.