Operational modal analysis is based on data collected with a network of sensors installed on the monitored structure to measure its response to the external stimuli. As the instrumentation can be costly, sensors get located at a limited number of spots where hopefully damage-sensitive features can be sensed. Hence, the actual capability to detect in real time a drift from the undamaged structural state might get detrimentally affected. Non-contact measurement methods resting on e.g. digital video cameras, which have gained interest in recent years, can instead provide high-resolution and diffused measurements/information. In this study, moving from videos of a vibrating structure and by combining them with optical flow estimation methods, the vibration response of the said structure is assessed. By means of a phase-based optical flow methodology, a linear correlation between the phase and the structural motion is assumed by e.g. the Gabor filter. Since such a phase extracted from the frames does not result to be linear in every case study, a primary objective is the linearization of the extracted phase, and then the derivation of the motion matrix for all the frames. By applying a dimensional reduction technique, the dimensions of the aforementioned matrix can be reduced to a value close to the number of the excited vibration modes of the structure. By using the blind source separation method, mode shapes and vibration frequencies are finally obtained. The performance of the proposed method is investigated using numerical examples, to testify the accuracy in extracting the dynamic features of the considered structure.

The evaluation of the response of sensors fixed to the print bed in the Fused Filament Fabrication (FFF) process has been the subject of recent studies, due to the increasing use of the FFF process in manufacturing industries. Many of these studies focus on topics related to monitoring the FFF process through the signals collected by sensors, as the response of such sensors is affected by the temperature on the print bed used in the FFF manufacturing process. Thus, this work presents the study of the response of a low-cost electret microphone fixed to the print bed under temperature values within the range for printing with Polylactic Acid (PLA) material, in order to determine the temperature value at which the sensor's response begins to undergo significant changes and whether this influence on the sensor's response is limited to a specific frequency band. To achieve this, tests using the graphite breakage method (PLB) were conducted on the heated print bed at temperature values ranging from 25°C to 65°C. The acoustic waves generated by the tests were captured by the electret microphone attached near the breakage point using the PLB method, and the signals were sampled using an oscilloscope at a frequency of 2 MHz. The signals were processed in the time and frequency domains, followed by comparative analyses between the signals obtained at different temperature values. The results showed that it was not possible to determine a single temperature value at which the response of the electret microphone starts to undergo significant changes, but rather that this value is located close to the extremes of the recommended temperature range for FFF manufacturing with PLA. Furthermore, the results showed that the temperature influence occurs in different frequency bands of the electret microphone's response.

With the rapid population growth of the 20th and 21st centuries, there is an increasing need for improved agricultural management to meet the food demands of this growing population. To address this challenge, the integration of sensors and embedded systems has been widely employed to enhance productivity and optimize resource utilization for meeting the food demands of this growing population. This research aims to develop a sophisticated system capable of collecting essential data on crucial parameters such as the temperature of the soil, temperature of the ambient air, the humidity of the soil, and humidity of the ambient air from the agricultural field. The data will be collected using Microcontrollers and stored in a centralized database. Leveraging this data, the system intends to create detailed two-dimensional maps utilizing Matplotlib and Gaussian Interpolation techniques, effectively portraying the current state of the agricultural field. To ensure seamless information transmission, the microcontroller necessitates remote communication with the server through ESP32, equipped with long-range radio frequency transmitters. This efficient combination ensures reliable data transfer between the microcontroller and the server, facilitating the smooth operation of the system. By integrating embedded systems, Python data processing, and Interpolation, the proposed system provides a reliable tool for data-driven agriculture. This approach will empower farmers with actionable insights, leading to more efficient resource allocation and sustainable farming practices.

In response to escalating global food demand and growing environmental concerns, the incorporation of advanced sensor technologies in agriculture has become paramount. This paper delves into an in-depth exploration of cutting-edge sensor-based technologies, inclusive of IoT applications, machine learning algorithms, and remote sensing, in revolutionizing farming practices for improved productivity, efficiency, and sustainability. The breadth of this exploration encompasses an array of sensors such as soil, weather, light, humidity, and crop health sensors, employed in precision agriculture. Their impact on farming operations and the challenges posed by their implementation are scrutinized. Emphasis is placed on the integral role of IoT-based sensor networks in promoting real-time data acquisition, thereby facilitating efficient decision-making. The paper elaborates on wireless communication protocols crucial for sensor data transmission in smart farming, namely LoRa, ZigBee, WiFi, Bluetooth, and emerging technologies like 5G and NB-IoT. It also highlights the need for interoperability among sensor technologies in diverse technological environments, offering an exhaustive evaluation of data analytics and management techniques for handling the vast data output generated by these systems. Significance is accorded to the robustness of sensor technologies, their ability to withstand harsh environmental conditions, and adaptability to evolving farming landscapes. The paper identifies future perspectives encompassing the application of 5G technology and AI-based predictive modeling techniques to enhance sensor capabilities and optimize data processing functionalities. The challenges encountered in deploying these sensor-based technologies, such as cost, data privacy, system compatibility, and energy management, are discussed in depth with potential solutions and mitigation strategies proposed. This paper, therefore, navigates towards an improved comprehension of the expansive potential of sensor technologies, leading the way to a more sustainable and efficient future for agriculture.

Manual inspection of concrete structures, such as tall buildings, bridges, and huge infrastructures, is a time-consuming and risky process for human employees. Drones with sensor camera nodes have showed potential in gathering close-range footage, but the problem is rapidly analysing enormous volumes of data to detect and diagnose structural deterioration. This study deals with these challenges by presenting an Internet of Things (IoT), computer vision and deep learning-based automated solution. The primary issue addressed in this research is the requirement for a more efficient and reliable way of identifying structural damage on Concrete Structures. The traditional manual inspection technique is time-consuming and expensive, making timely repairs and maintenance impossible. As a result, an automated solution is necessary to speed up the damage assessment process while reducing dangers to human personnel. The proposed system focuses on detecting various types of damage, such as cracks, Alkali-Silica Reaction (ASR), concrete degradation, and others, on Concrete Structures using drone-captured video footage. The system's scope includes developing a Convolutional Neural Network (CNN) architecture tailored to this specific task and implementing a seamless process for automatically obtaining video data from drones. The primary objective of this work is to create and implement an automated damage detection system capable of identifying structural damage on Concrete Structures in an efficient and accurate manner. The technology intends to expedite the inspection process by utilising IoT, computer vision and deep learning techniques, enabling proactive maintenance and preservation activities. The novelty of the proposed system is a custom-designed CNN architecture that is optimised for detecting damage on Concrete Structures and a system architecture based on IoT to automatically capture data, perform analysis and reporting. The performance of the proposed automated damage detection system was evaluated using a diverse dataset of drone-captured video footage containing various types of damage on Concrete Structures. The CNN architecture demonstrated impressive results, achieving an accuracy of 94% in correctly identifying different types of structural damage. The results showed that the system could accurately detect and identify structural damage on cultural heritage sites. The system is much faster and more efficient than manual inspection, reducing the time and cost required for damage assessment. The proposed system has the potential to revolutionize the way damage assessment is performed on concrete structures. It can help to preserve and protect it by enabling early detection of damage and facilitating timely repairs.

Mycobacterium tuberculosis is an ancient pathogen that has been persistent due to its complex survival strategies in the living host and the environment. It has developed a repertoire of culture filtrate antigens that changes as the disease progresses. These antigens are recognized by specific antibodies that are used for assay development. Electrochemical immunosensors have been developed based on the strong affinity and specificity between an antibody and its target antigen. In this study, an electrochemical immunosensor for detection of the 19 kDa Mycobacterium tuberculosis lipoprotein LpqH was developed using monoclonal antibody immobilized on chitosan-coated iron oxide (Fe₃O₄) bio-composite. The bio-composite is composed of magnetic iron oxide at its core and a non-magnetic thin film on the surface formed by chitosan, providing the chemistry for monoclonal antibody immobilization. Aside from its magnetic properties, iron oxide is a viable electronic source and shows electrocatalytic traits. Cyclic voltammetry and electrochemical impedance spectroscopy were used to characterize and test the immunosensor. Electrochemical measurements showed a strong relationship between the LpqH concentration in phosphate buffered saline solution and the measured peak current. The use of bio-composite offers a huge advantage in handling the nanomaterial, particularly in sequestration and separation of the target antigen from the solution, while also offering the possibility to incorporate more nanomaterials that will further enhance the sensitivity of the immunosensor. The proposed electrochemical detection methodology is promising and has strong provisions for point-of-care testing development.

The growing need for high-performance in-situ biosensing is driving the development of micro/nanobiosensors, which have already shown a significant potential for highly sensitive detection of biological specimens or biologically relevant chemical substances, in applications such as real time monitoring of the biological pollution in air, water and food, or the health conditions of living organisms. Research efforts are being made to push their performance further, beyond the current limits. In this sense, it is important to investigate physical processes and phenomena that inevitably affect the generation of the sensor response and its fluctuations, thus setting the fundamental performance limit. The basis of these investigations is the development and application of mathematical models of sensor time response and noise, which take into account all relevant processes.

Adsorption-based biosensing relies on the reversible adsorption process of biomolecules on a sensing surface. In addition to producing the response of the sensor, this process, stochastic in nature, is also a source of noise that affects the performance of micro/nanosensors. Spatial rearrangement of adsorbed biomolecules changes the binding/unbinding kinetics to two-step process behavior, and therefore affects both the sensor’s time response and its fluctuations. We present the improved model of the sensor time response and noise, considering biomolecular rearrangement, and evaluate the extent of its influence for various rates of rearrangement and adsorption/desorption processes. The development of improved mathematical models of temporal response and noise of sensors, which include the effects pronounced in the real applications of these devices, is indispensable for both a correct interpretation of the measurement results and estimation of sensor performance limits, and thus for the achievement of reliable detection of the target agent in the analyzed samples.

Aerodynamic testing of various objects consists of pressure measurements at a multitude of points on aerodynamic surfaces or structural elements. Typically, the air pressure from the measurement points is transferred by flexible tubing to multichannel pressure sensing instruments. Existing instruments are usually built around an array of discrete pressure sensors, placed in the same housing together with a few discrete temperature sensors. However, such approach is limiting, especially regarding miniaturization, sensor matching and thermal coupling. In this work, we intend to overcome these limitations by proposing a novel MEMS multisensor chip. Although reports on chips with multiple pressure sensing elements exist in the literature, their concept and design are not suitable for this application. The silicon chip that we have developed has a monolithically integrated matrix of four piezoresistive MEMS pressure sensing elements, and two resistive temperature sensing elements. The thermal coupling between the integrated temperature and pressure sensing elements is much better than it can be between discrete sensors. This is important for temperature compensation of the pressure sensing elements. Another advantage is better matching of the pressure sensing elements characteristics, because they are fabricated in the same technological processes. After finishing the preliminary chip design, we performed computer simulations of mechanical influences that the pressure applied to one or more sensing elements can have on the whole multisensor chip structure. Subsequently, the final chip design was completed, and the first batch was fabricated. Technological processes included photolithography, thermal oxidation, diffusion, sputtering, micromachining (wet chemical etching), anodic bonding, and wafer dicing. Three silicon wafers were processed, each with 30 chips. Electrical tests have been carried out by using a probing station and a semiconductor parameter analyzer, and the full sensor characterization is in progress. Preliminary results indicate that the chip performs as expected.

The use of impedance spectroscopy (IS) to detect and study bacterial growth has increased significantly in recent decades due to the availability of inexpensive and easy-to-use impedance sensors. In general, there is insufficient of information in the literature on impedance studies of bacterial sensors regarding the relationship between the components of the equivalent electrical circuit and the processes occurring on the sensor surface. To increase the sensitivity of measurement methods and sensor parameters, a deep understanding of the sensor response mechanism and its impact on impedance is required. IS method (AC f=4 Hz-8 MHz at a constant amplitude of 1 V) and Au/Pt IDE sensors were used to detect and monitor different concentrations (10³, 10⁶, 10⁹ CFU/ml) of both live and dead bacterial cells (E.coli and S. aureus) prepared in deionized water (DH₂O) and bacteria growth liquid (Mueller Hinton Broth, MHB). All measurements were conducted at temperature 24±1ºC and the immersion sample volume was 1 ml. The analysis of the impedance spectra based on Nyquist and Bode plots shows a significant difference in resistance with increasing concentration for both types of bacteria and the presence of characteristic changes in the low-frequency range. We also observed difference in the time dependences of impedance. The semicircle-shaped portion of the Nyquist plots obtained at high frequencies corresponds to the faradic transfer of electrons on the electrodes, while the spectrum obtained at low frequencies provides information on the diffusion process of transferring bacterial waste products in solution to the electrode surface. Ions formed as a result of bacterial cell growth increase the capacity of the double layer C_dl. The presence of live bacteria led to a decrease in the impedance value compared to dead cells, the value of R_s+R_ct decreased about three times. The proposed method of bacterial cell selective detection can be used to identify two types of bacteria (E.coli and S. aureus), to qualitatively characterize the differences between dead and living cells, and estimate their concentration in samples with unknown number of bacteria per unit volume.

Robot vision, enabled by Deep Learning (DL) breakthroughs, is gaining momentum in the Industry 4.0 digitization process. The present investigation describes a robotic grasp detection application that makes use of a two-finger gripper and an RGB-D camera linked to a collaborative robot. To extract information from an industrial conveyor containing produced components for monitoring, the system leverages a deep convolutional neural network.
The visual recognition system, which is integrated with edge computing units, conducts image recognition for faulty items as well as calculates the position of the robot arm. Identifying deformities in object photos, training and testing the images with a modified version of the You Look Only Once (YOLO) method, and establishing defect borders are all part of the process. Signals are subsequently sent to the robotic manipulator to remove the faulty components. The adopted technique used in this system is trained on custom data and has demonstrated high accuracy and low latency performance as it reached a detection accuracy of 97% for defective pieces.