The advancements in technology have brought about significant changes in the automobile industry. A system that combines the control of a physical process with computing technology and communication networks is called a cyber-physical system (CPS). The enhancement of network communication has transitioned vehicles from purely mechanical to software-controlled technologies. The controller area network (CAN) bus protocol controls the communication network of autonomous vehicles. The convergence of technologies in autonomous vehicles (AVs) and connected vehicles (CVs) within Connected and Autonomous Vehicles (CAVs) leads to improved traffic flow, enhanced safety, and increased reliability. CAVs development and deployment have gained momentum, and many companies and research organizations have announced their initiatives and begun road trials. Governments worldwide have also implemented policies to facilitate and expedite the deployment of CAVs. Nevertheless, the issue of CAV cyber security has become a prevalent concern, representing a significant challenge in deploying CAVs. This study presents an intelligent cyber threat detection system (ICTDS) for CAV that utilizes transfer learning to detect cyberattacks on physical components of autonomous vehicles through their network infrastructure. The proposed security system was tested using an autonomous vehicle network dataset. The dataset was preprocessed and used to train and evaluate various pre-trained convolutional neural networks (CNNs), such as ResNet-50, MobileNetV2, GoogLeNet, and AlexNet. The proposed security system demonstrated exceptional performance, as demonstrated by its results in precision, recall, F1-score and accuracy metrics. The system achieved an accuracy rate of 99.60%, indicating its high level of performance.

At present, the cardiovascular magnetic resonance imaging (MRI) is an important imaging technique used for investigation of heart structure and function. On the other hand, in this type of a non-invasive examining device, the pulsating current in the gradient coil system generates mechanical vibration and acoustic noise. Such a vibration is often accompanied by a local heating effect which can be measured by a contactless method using a thermal imaging camera. The shape of the peripheral pulse wave of the photoplethysmography (PPG) signal reflects the current state of a human cardiovascular system including changes in the arterial stiffness, the arterial blood pressure, and the heart rate. These parameters can be also used for detection of the stress effect during examination in the MRI device.

The quality of the sensed PPG signals and the determined PPG wave features depend also on the actual state of the skin at the position of the optical sensor. Human age and gender as well as the skin color and the temperature of the skin surface can have influence on the PPG signal, too. For precise determination of PPG wave parameters, the current temperature should be measured at the same time as the PPG signal is sensed.

This paper describes the process of design, realization, and testing of a special prototype of a wearable PPG sensor with the contact thermometer to carry out a detailed measurement of the skin temperature in the place where the optical part of the PPG sensor touches a finger/wrist. To enable proper and safe function in the weak magnetic field environment of the MRI device, the whole wearable PPG sensor must consist of non-ferromagnetic materials and all parts must be fully shielded by aluminum boxes against the radiofrequency disturbance.

Over the past few decades, there has been a significant rise in wearable healthcare technologies that have been playing a major role all over the world in monitoring health and alerting individuals during deviation from their normal health conditions and assisting them to stay fit and healthy. The present healthcare technologies proven to track many health parameters such as Heart Rate, Blood Oxygen Saturation, Body Temperature, Blood Pressure, Physical Activity, Sleeping patterns, etc. Due to the modern lifestyle and consumption of unhealthy food products, there’s been an adverse effect in digestive health standards. According to study report on 2020, in every 1 million, 4 lakh people are suffering from the Gastrointestinal (GI) tract disorders.

In this work, a wearable device with textile electrodes is designed and developed to analyze the digestive conditions namely pre-prandial and post-prandial using Electrogastrogram (EGG) signals. Further, the proposed device will be comprised of cloth wear with textile electrodes as a sensor, Analog to Digital Converter (ADC) with Programmable Gain Amplifier (PGA), Microcontroller with inbuilt WiFi module and Internet of Things (IoT) cloud platform. Any person can wear the proposed cloth wear inside their regular clothing. Also, the proposed cloth wear with three different textile electrodes made of silver-plated nylon which will be fabricated in three distinct places of the clothing according to the standard electrode placement protocol. Once the clothing is worn, the electrode will rest at the stomach area so that it will be ready to collect EGG signals from the abdomen over the stomach. Further, the acquired signals will be fed to the controller for further analysis.

The objective of this work is to design and develop a smart wearable device to alert/notify the persons once they skipped their food habits on time. Also, the day wise food intake with respect to time will be stored in the IoT Cloud platform for later references. Since the proposed device is compact and can be integrated in usual cloth wear, the device shall be used to monitor the digestive habits namely pre-prandial and post-prandial conditions effectively which leads to healthy life.

The interaction of nanostructured metal particles with the molecular components of biosystems differ significantly from the processes that take place in the presence of ions of the same metals [1]. This unequivocally indicates the need to take into account not only the course of chemical processes, but also implies to discuss certain physical effects that are usually neglected when considering such interactions.

In this work, we studied the interaction of silver nanoparticles dispersion (Ag-NP) in ethylene glycol with particle size less than 100 nm (Sigma-Aldrich 658804) with glutathione in an water and carbonate buffer (pH 10). The choice of glutathione (GSH) is due to the fact that it plays a significant role in intracellular processes, participating in the protection of intracellular components from the toxic effects of heavy metal ions; at the same time, differences in its interaction with silver ions and nanoparticles were experimentally demonstrated [2].

A series of optical studies of the absorption and emission spectra of solutions of silver nanoparticles with GSH was carried out in order to establish the dominant processes in the system. It was shown that the above mentioned silver nanoparticles in aqueous solutions spontaneously decompose over time, while glutathione differently affects these processes in water and carbonate buffer. It was shown that not only the local surface plasmon resonance bands, but also the emission spectra of Ag-NP~GSH solutions in the region of 350-550 nm change with time. The sources of such radiation can be carbon quantum dots (CQD), which, according to published data, can be formed during the synthesis of silver nanoparticles and effectively luminesce in this region of the spectrum. Raman spectroscopy data confirm the presence of CQD in the used dispersion nanoparticles of silver [3]. The presence of quantum dots in the system makes it possible to indirectly track the presence of silver nanoparticles, which are booster centers, enhancing the emission of CQDs.

The studies allow us to state that the interaction of glutathione with silver nanoparticles is a complex topochemical process in which, in addition to chemical reactions, the processes of transformation of silver nanoparticles and changes in the distribution of their sizes and chemical/physical functionality take place.

1. Snopok, B.A., Snopok, O.B. (2020). Nanoscale–Specific Analytics: How to Push the Analytic Excellence in Express Analysis of CBRN. In: Bonča, J., Kruchinin, S. (eds) Advanced Nanomaterials for Detection of CBRN. NATO Science for Peace and Security Series A: Chemistry and Biology. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-2030-2_13
2. Mei Jing Piao 1, Kyoung Ah Kang, In Kyung Lee, Hye Sun Kim, Suhkmann Kim, Jeong Yun Choi, Jinhee Choi, Jin Won Hyun Silver nanoparticles induce oxidative cell damage in human liver cells through inhibition of reduced glutathione and induction of mitochondria-involved apoptosis Toxicol Lett . (2011) 201(1):92-100. doi: 10.1016/j.toxlet.2010.12.010.
3. Kravchenko S., Boltovets P., Snopok B. Chemical Transformation of Typical Biological Recognition Elements in Reactions with Nanosized Targets: A Study of Glutathione Coated Silver Nanoparticles, Engineering Proceedings (2023) 35 (1), 31

This paper presents a novel microfluidic dielectrophoresis (DEP) system to focus and separate cells of similar size based on their structural differences, which is more challenging than separation by size. Because, in this case, the DEP force is only proportional to the polarizabilities of cells. We used live and dead yeast cells as bioparticles to investigate the chip efficiency. Our designed chip consists of three sections. First, focusing cells in the center of the microchannel by employing a negative DEP phenomenon. After that, cells are separated due to the different deflection from high electric field areas. Finally, a novel outlet design was utilized to facilitate separation by increasing the gap between the two groups of cells. The proposed sheath-free design has one inlet for target cell injection requiring only one pump to control the flow rate, which reduces costs and complexity. Successful discrimination of the particles was achieved by using DEP force as a label-free and highly efficient technique. As an accessible and cost-effective method, soft lithography by 3D printed resin mold was used to fabricate microfluidic parts. Microchannel is made of Polydimethylsiloxane (PDMS) material that is biocompatible. The electrodes are made of gold due to its biocompatibility and non-oxidation, and a titanium layer is sputtered as the buffer layer for the adhesion of the sputtered gold layer to the glass. A standard microfabrication process is employed to create the electrode pattern. O2 plasma treatment yielded a leakage-free bonding between patterned glass and PDMS structure containing microfluidic channel. The maximum voltage applied to the electrodes (26 V) is lower than the threshold value for cell electroporation. Simulations and experimental results both confirm the effectiveness of the proposed microfluidic chip.

When it comes to growing lettuce, specific nutrients play vital roles in its growth and development. These essential nutrients include full nutriments (FN), nitrogen (N), phosphorus (P), and potassium (K). Insufficient or excess levels of these nutrients can have negative effects on lettuce plants, resulting in various deficiencies that can be observed in the leaves. To better understand and identify these deficiencies, a deep learning approach is employed to improve these tasks. For the study, YOLOv8 Nano, a lightweight deep network, is chosen to classify the observed deficiencies in lettuce leaves. Several enhancements to the baseline algorithm are made, the backbone is replaced with VGG16 to improve the classification accuracy, and depthwise convolution is incorporated into it to enrich the features while keeping the head unchanged. The proposed network, incorporating these modifications, achieved superior classification results with a top-1 accuracy of 99%. This performance outperformed other state-of-the-art classification methods, demonstrating the effectiveness of the approach in identifying lettuce deficiencies. The objective of the research was to improve a baseline algorithm that could achieve the classification task above 85% of top-1 accuracy, with a FLOP inferior to 10G, and classification latency below 170 ms per image.

Beyond traditional surveillance applications, sensor-based human action recognition and segmentation responds to a growing demand in the health and safety sector. Among state-of-the-art methods for sensor data analytics, deep learning approaches to human action segmentation can be broadly categorized by two main approaches, namely algorithms that input skeletal sequences, as well as video-based models with RGB, depth, and infrared inputs. Recently, skeletal action recognition has largely been dominated by spatio-temporal graph convolutional neural networks (ST-GCN), while video-based action segmentation has seen great performance using 3D convolutional neural networks (3D-CNN), as well as vision transformers. In this paper, we argue that these two inputs are complementary, and develop an approach that achieves superior performance with a multi-modal ensemble. Video action segmentation models typically compute features in an offline phase due to memory constraints inherent to 3D-CNNs, however graph CNNs do not suffer from this problem. Hence, a multi-task GCN is developed that can predict both frame-wise actions as well as sequence-wise action timestamps, allowing for the use of fine-tuned video classification models to classify action segments and achieve refined predictions. Symmetrically, a multi-task video approach is presented that uses a video action segmentation model to predict framewise labels and timestamps, augmented with a skeletal action classification model, yielding improved performance. Finally, an ensemble of segmentation methods for each modality (skeletal, RGB, depth, and infrared) is formulated. Experimental results yield 86% accuracy on the PKU-MMD v2 dataset, representing state-of-the-art performance while also addressing the related over-segmentation problem.

Damage detection and fault diagnosis systems based on condition monitoring, feature extraction and sensor data guidance have achieved notoriety in the field of industrial automation for enabling the prediction of the remaining useful life of industrial assets. For that reason, this project aims to explore an alternative methodology for detecting damage in machining tools based on data from acoustic emission sensors. The study was validated from an experimental analysis carried out in the milling process. The proposed approach consists of designing condition indicators that quantify damage to the milling cutter based on the implementation of the root mean square deviation (RMSD) and correlation coefficient deviation metric (CCDM) indices. The study was carried out by testing different frequency bands of the acoustic signals collected during the process, by calculating the fast Fourier transform (FFT), seeking the most suitable to determine the wear of the material, which proved to be between 5 and 8 kHz. Finally, the results arising from the implementation of the proposed method proved to be very important for the optimization of the manufacturing process, being able to help in the automation of the exchange of the milling cutter or to alert the operators that this must be done.

Fault occurrence in practical systems, if not addressed, can cause diminished performance or even system breakdown. Therefore, fault detection has emerged as a crucial challenge in ensuring system safety and reliability. This paper presents a novel fuzzy observer aimed at reconstructing actuator and sensor faults in nonlinear systems, even when subjected to external disturbances. The approach we propose utilizes the Takagi-Sugeno fuzzy model and Lyapunov function. Initially, by filtering the system output, we construct a system where actuator faults correspond to the original actuator and sensor faults. Subsequently, the impact of disturbance on state estimations is minimized by employing the H-infinity performance criteria. We demonstrate that, for non-disturbed systems, these estimations gradually converge to their true values. In designing the observer gains, transformation matrices are derived by solving linear matrix inequalities. Our approach boasts some advantages over existing methods. By assuming that the premise variables are immeasurable, we enhance the usability of our approach. As a proof of concept, we evaluate two practical systems. The simulation results underline the benefits of our proposed method in terms of rapid and accurate fault detection performance.

A recent survey depicts that across the globe there are nearly 36 million visually impaired people facing serious issues in accessibility, education, navigating public spaces, safety concerns, and mental health. Now the evolution of obstacle detectors for blind people have been from the usage of people, sticks, smart glasses, and smart shoes. Among the above, the major problem faced by all blind people is to walk independently to every place, so to make them feel independent while they walk, here is a proposal for an intelligent shoe. The proposed intelligent shoe consists of a controller connected with an ultrasonic sensor, buzzer, vibration patterns, GPS navigation, connectivity with a smart-phone or smart-watch, voice assistance, feedback on gait and posture, and emergency features that are embedded with each other to communicate the presence of obstacles in the directions of the path of the blind. The sensor identifies an obstacle in the direction present then it passes the signal to the controller that activates the buzzer and the vibration patterns present in that direction. Therefore by the proposed concept of vibration sense and buzzing sound with GPS navigation, connectivity with a smartphone or smart-watch make the system easy access for the blind to identify the obstacle present on their way and make them social inclusion.