Structural Health Monitoring (SHM) in Fiber Metal Laminates (FML) is based on the evaluation of Guided Ultrasonic Waves (GUW). At high frequencies, some GUW modes show no displacements at the structure surface. However, vibration information can be acquired from inside the FML. Serving this purpose, sensors need to be integrated into the laminate. State-of-the-art surface-installed piezoelectric ceramics are large and poorly matched to the surrounding material’s acoustic impedance. In this work, a custom structure-integrable Micro-Electro-Mechanical System (MEMS) based acceleration sensors is investigated for its functional compliance and suitability as an ultrasound pickup. The sensor inherent dynamic behavior is experimentally characterized. Consecutively, application-near SHM experiments are conducted and evaluated. Results demonstrate the sensor’s ability and limitations for recording structure-borne ultrasound.

The Cybernetic interface within an Upper-Limb prosthesis facilitates a Human-Machine interaction and ultimately control of the prosthesis limb, a coherent flow between the phantom motion and subsequent actuation of the prosthesis limb to produce the desired gesture hinges heavily hinges heavily upon the physiological sensing source and its ability to acquire quality signal alongside an appropriate decoding of this intent signals with the aid of appropriate signal processing algorithms. In this paper we discuss the sensing and signal processing aspects of the overall prosthesis control cybernetics, with emphasis on transradial, transhumeral and shoulder disarticulate amputations which represent considerable upper-limb amputees typically encountered within the population. We conclude by pinpointing a number of areas within the prosthesis control interface and the ergonomics of the prosthesis limbs that can be subject to further work in order to increase the efficiency, accessibility and overall usage of the bionic upper-limb prosthesis.

In this work, molybdenum trioxide (MoO₃) was synthesized and deposited on tapered optical fiber using the drop-casting technique for hydrogen (H₂) detection at room temperature. A transducing platform in a transmission mode was constructed using multimode optical fiber (MMF) with a 125 µm cladding and a 62.5 µm core diameter. To enhance the evanescent light field surrounding the fiber, the fibers were tapered from 125 µm in diameter to 20 µm in diameter with a 10 mm waist. The microstructures and chemical compositions of the fabricated sensor were analyzed by field emission scanning electron microscopy (FESEM), energy dispersive X-ray (EDX), differential X-ray (XRD), and atomic force microscopy (AFM). In addition, the gas detection properties of the fabricated sensor were studied by exposing it to various concentrations of hydrogen gas from 0.125% to 2.00%. As a result, the sensitivity, response, and recovery time were 11.96 vol%, 220 s, and 200 s, respectively. Overall, the fabricated sensor exhibits good sensitivity as well as repeatability and stability for hydrogen gas detection.

Human Activity Recognition (HAR) has been widely addressed by deep learning techniques. However, most prior research applied a general unique approach (signal processing and deep learning) to deal with different human activities including postures and gestures. These types of activity typically have highly diverse motion characteristics, which could be captured with wearable sensors placed on the user's body. Repetitive movements like running or cycling have repetitive patterns over time and generate harmonics in the frequency domain, while postures like sitting or lying are characterized for a fixed position with some positional changes and gestures or non-repetitive movements are based on an isolated movement usually performed by a limb. This work proposes a classifier module to perform an initial classification among these different types of movements, which would allow applying afterwards the most appropriate approach in terms of signal processing and deep learning techniques for each type of movement. This classifier is evaluated using PAMAP2 and OPPORTUNITY datasets using subject-wise and Leave-One-Subject-Out cross-validation methodologies. These datasets used inertial sensors on hands, arms chest, hip, and ankles, which could collect data in a non-intrusive way. In case of PAMAP2 and subject-wise cross-validation, the direct approach for classifying the 12 activities using 5-second windows in the frequency domain obtained an accuracy of 85.26 ± 0.25 %. However, an initial classifier module could distinguish between repetitive movements and postures using 5-second windows reaching higher performances. Afterwards, specific window size, signal format and deep learning architecture were used for each type of movement module, obtaining a final accuracy of 90.09 ± 0.35 % (an absolute improvement of 4,83%).

Diabetes is a chronic non-communicable disease resulting from pancreatic inability to produce the hormone insulin, or a physiological cellular inability to use this hormone effectively. Insulin is responsible for maintaining biological homeostasis by enabling glucose to enter cells as their primary energy source. In the UK, 4.1 million people live with diabetes, while a further 850,000 are currently undiagnosed. Current global estimates identify 1 in 11 people as having diabetes. Unregulated glucose levels cause significant, and often irreversible, damage to blood vessels in the eyes, kidneys, teeth, and skin. Current means of glucose level monitoring range from infrequent capillary blood glucose sampling to continuous interstitial fluid glucose monitoring. While these methods can minimise their accuracy is limited by the cleanliness of skin, adequate hydration, certain medications and appropriate calibration methods.

A potential solution to this shortcoming involves the use of wearable sensors which record various information from an individual’s daily activities which have been shown in the medical literature to influence glucose levels and therein serve as potential predictors for estimating overall glucose level. Five features from the wearable device were applied in this work and include daily metrics such as; calories burned, number of steps taken, distance covered, minutes sedentary and activity calories. These features were in turn fused and post-processed with machine learning algorithms to provide a prediction of an individual’s glucose level and showed potential for being able to provide an Artificial Intelligence driven glucose monitoring platform.

In this paper we conduct a comparison case study involving the use of Quadratic Discriminant Analysis (QDA) and Support Vector Machine (SVM) algorithms for the classification of glucose level with data acquired from the wearable sensors of a Type 1 diabetic individual. Preliminary results demonstrate predicted glucose levels with >70% accuracy, providing that the potential for this approach to be used in in the design of an ergonomic glucose prediction platform utilising wearable sensors.

Further work will involve the exploration of additional datasets from affordable wearables to enhance and improve the prediction power of the machine learning algorithms.

The corona discharge characteristics are key factors affecting the electromagnetic environment on the high-voltage direct-current (HVDC) transmission lines and, consequently, may come in contact with humans and equipment outside of the transmission right-of-way. Therefore, this paper presents an initial and feasibility study of corona discharge characterization on HVDC systems through the application of signal processing techniques. This feasibility study is performed through experimental high-voltage discharge tests conducted under controlled environmental conditions. In the corona discharge test, high voltages varying from ±30 kV to ±80 kV were applied through a DC high-voltage generator and corona discharges around the conductor were measured by means of a data acquisition prototype system equipped with a metal electrode device for the purpose of corona current measurement. Subsequently, the signals collected were subjected to digital signal processing parameters to extract the most relevant features related to corona discharge characteristics. The feature extraction process was conducted on the basis of the following metrics: RMS, peak value, and digital filters in specific frequency bands. The results reveal that the proposed method was able to detect the corona discharge characteristics following the changes in the signal content, especially, for narrow frequency ranges. The results indicate the feasibility of the proposed method to detect and characterize the occurrence of corona discharge in a simple way, which expands the research field in corona discharge characterization in HVDC systems by means of digital processing and feature extraction.

Three-Phase Induction Motors (TIMs) are widely applied in industries. Therefore, there is a need to reduce the operational and maintenance costs since their stoppages can impair production lines and lead to financial losses. Among all the TIM components, bearings are crucial in the machine operation once they couple the motor housing to the rotor. Also, they are constantly subjected to friction and mechanical wearing. Consequently, they represent around 41% of the motor fault, according to IEEE. In this context, several studies have sought to develop monitoring systems based on different types of sensors. Therefore, considering the high demand, this article aims to present the state of the art of the past five years concerning the sensing techniques based on current, vibration, and infra-red analysis, which are characterized as promising tools to perform bearing fault detection. The current and vibration analysis is a powerful tool to assess damages in the inner race, outer race, cages, and rolling elements of the bearings. These sensing techniques use current sensors like hall effect-based, Rogowski coils, and current transformers, or vibration sensors like accelerometers. The effectiveness of these techniques is due to the previously developed mathematical models, which relate the current and vibration frequencies to the origin of the fault. Therefore, this article also presents the mathematical models of these bearing failures. The infra-red technique is based on heat emission, and several image processing techniques were developed to optimize the assessment of bearings conditions using thermal images, which are presented in this review. Finally, this work is a contribution to expanding the frontiers of the bearing fault diagnosis area.

Quantum dots (QDs) are colloidal, semiconductor nanocrystals with a diameter in the range of 1-20 nanometres, distinguished by unique physicochemical properties, which are partly the result of the extremely high surface-to-volume ratio and the quantum confinement effect. Due to their extraordinary optical properties, not only have they become an alternative to the commonly used molecular probes in biomedical applications, but they are also extensively studied nanomaterials for the development of sensing systems in analytical chemistry. Therefore, over the last few years quantum dots were employed in sensing systems representing many different detection schemes. One of the promising sensing approach in which QDs can be implemented are Indicator Displacement Assays (IDA), where competitive interactions between sensing system elements are usually utilized.

In this work, simple, quantum dots-based competitive assay for the recognition of nucleotides (AMP, ATP, CMP, CTP, UMP, UTP) is presented. The IDA system was constructed by using single, thiomalic acid (TMA) capped CdTe quantum dots combined with nickel ions. Introduction of nucleotides into the IDA sensing system resulted in a subtle changes of fluorescent properties observed with the use of Excitation-Emission Matrix fluorescence spectroscopy (2D fluorescence). The obtained Excitation-Emission Matrixes (EEMs) were used then as characteristic, fluorescent ‘fingerprints’ and processed by means of chemometric tools for nucleotides recognition. The presented results are solid foundation for the development of simple IDA sensor array, which may serve as a tool for identification an quantification of nucleotides in the future.

This work was financially supported by National Science Centre (Poland) within the framework of the SONATA BIS project No. UMO-2018/30/E/ST4/00481 and by the Warsaw University of Technology under the program Excellence Initiative, Research University (IDUB), BIOTECHMED-1 project no. 504/04496/1020/45.010401. Klaudia Głowacz acknowledges financial support from IDUB project (Scholarship Plus programme).

Over the past several decades, growing research on lipids and lipidomic technologies have shown how the perception of lipids has changed. Lipids are functionally versatile molecules in plants, animals and humans. They are certainly key components of the cell membranes and a source of energy, but they also play an essential role in physiology and pathophysiology, in signal transduction between cells and body metabolism and act as diagnostic and/or prognostic biomarkers of different diseases [1]. Many studies have shown the relationship between altered lipid metabolism and type 2 diabetes mellitus (T2DM) or metabolic disease as Nonalcoholic fatty liver disease (NAFLD) or neurodegenerative disease as Parkinson’s Disease or Atherosclerosis (a risk factor for ischemic stroke) [2-4]. A powerful technique used for lipids detection and characterization in biological tissues is Fourier Transform Infrared (FTIR) spectroscopy [5]. The main goal of the present work is to exploit FTIR spectroscopy as a tool for monitoring lipid extraction efficiency by evaluating three different lipid extraction methods [6]. FTIR spectroscopy is used to monitor the extraction efficiency of the Folch [7], Bume [8] and modified Bume [9] methods in murine liver tissues. In particular, infrared spectra will be obtained in the 4000-600 cm^-1 wavenumber region and the contributions of different functional groups will be evidenced. The ratio values estimated using the absorbance of selected bands related to different liver constituents will be used for a quantitative comparison of the efficiency of the different extraction methods.

[1] Bari M, Bisogno T, Battista N. Bioactive Lipids in Health and Disease. Biomolecules. 2020 Dec; 10(12): 1698.

[2] Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, Yang E, Farrell L, Fox CS, O’Donnell CJ, Carr SA, Vasan RS, Florez JC, Clish CB, Wang TJ, Gerszten RE. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121:1402–11.

[3] Yetukuri L, Katajamaa M, Medina-Gomez G, Seppanen-Laakso T, Vidal-Puig A, Oresic M. Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis. BMC Syst Biol. 2007;1:12.

[4] Chan RB, Oliveira TG, Cortes EP, Honig LS, Duff KE, Small SA, Wenk MR, Shui G, Di Paolo G. Comparative lipidomic analysis of mouse and human brain with Alzheimer disease. J Biol Chem. 2012;287:2678–88.

[5] M. J Baker et al Using Fourier transform IR spectroscopy to analyze biological materials Nature Protocols, 2014, 9(8), 1771-1791.

[6] K. Forfang, B. Zimmermann, G. Kosa, A. Kohler, V. Shapaval FTIR Spectroscopy for Evaluation and Monitoring of Lipid Extraction Efficiency for Oleaginous Fungi PLoS ONE 12(1): e0170611. doi:10.1371/journal.pone.0170611.

[7] Ulmer, Candice Z et al. “Optimization of Folch, Bligh-Dyer, and Matyash sample-to-extraction solvent ratios for human plasma-based lipidomics studies.” Analytica chimica acta vol. 1037 (2018): 351-357. doi:10.1016/j.aca.2018.08.004.

[8] Löfgren, L. et al. The BUME method: a new rapid and simple chloroform-free method for total lipid extraction of animal tissue. Sci. Rep. 6, 27688; doi: 10.1038/srep27688 (2016).

[9] Cruz M, Wang M, Frisch-Daiello J, Han X. Improved Butanol-Methanol (BUME) Method by Replacing Acetic Acid for Lipid Extraction of Biological Samples. Lipids. 2016;51(7):887-896. doi:10.1007/s11745-016-4164-7.

Slope failure and debris flow cause lots of casualties and property loss. Many causalities and economic losses are caused by natural disasters, such as landslides and slope failures, every year. This critical problem motivated that there is a need for an early warning system to mitigate accidents/Failures and economic losses. Most of the studies on real-time early warning systems have been carried out by predicting landslide-prone areas, but studies related to the prediction of landslide occurrence time points by the real-time monitoring of slope displacement are still insufficient and requires further research. In this Project, a three dimensional displacement sensor, rain sensor and water level sensor along with Internet of Things (IoT) monitoring system were combined together, to monitor slope failure through cutting experiments of a real-scale model slope. Real-time monitoring of the slope movement was performed simultaneously via a low-cost, efficient, and easy-to-use IoT system. Based on the obtained displacement data, an advanced data analysis methods was performed. Finally, a slope instrumentation standard was proposed based on the slope of the inverse displacement for early evacuation before slope failure.