The human cochlea is undeniably one of the most amazing organs in the body. One of its most intriguing features is its unique capability to convert sound waves into electrical nerve impulses. Humans can generally perceive frequencies between 20 Hz and 20 kHz with their auditory systems. Several studies have been conducted on building an artificial basilar membrane for the human cochlea (cochlear biomodel). It's possible to mimic the active behavior of the basilar membrane using micro-electromechanical systems (MEMS). This paper proposes an array of MEMS bridge beams that are mechanically sensitive to the perceived audible frequency. It was designed to operate within the audible frequency range of a set of bridge beams with 0.65 μm thickness, width of 50 μm and varying lengths between 200 μm and 2000 μm. As the material for bridge beam structures, Platinum (Pt), Molybdenum (Mo), Chromium (Cr), and Aluminium (Al) have been considered. For the cochlear biomodel, platinum has proven to be the best material, closely mimicking the basilar membrane, based on the finite element (FE) and lumped element (LE) models.

Degrading air quality has been a matter of concern nowadays, and monitoring the air quality helps us keep a check on it. Air pollution is a pressing global issue with far-reaching impacts on public health and the environment. The need for effective and real-time monitoring systems has become increasingly apparent to combat this growing concern. Here, an innovative air pollution monitoring system (APMS) that leverages internet of things (IoT) technology to enable comprehensive and dynamic air quality assessment is introduced. The proposed APMS employs a network of IoT-enabled sensors strategically deployed across urban and industrial areas. These sensors are equipped to measure various pollutants, including particulate matter (PM2.5 and PM10), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), carbon monoxide (CO), and volatile organic compounds (VOCs). Here, a regression model is created to forecast air quality using sensor data while taking into account variables including weather information, traffic patterns, and pollutants. Additionally, air quality categories (such as good, moderate, and harmful) are classified using classification algorithms based on preset thresholds. The IoT architecture facilitates seamless data transmission from these sensors to a centralized cloud-based platform. The developed APMS monitors the air quality using a MQ-135 gas sensor, and the data are shared over a web server using the internet. An alarm will trigger when the air quality goes below a certain level. Also, the air quality, which is measured in parts per million (PPM), is displayed on the unit connected to it. Further, an alert message is sent to the air pollution control board when the PPM goes beyond a certain level, which takes preventive measures to control the pollution and also alerts the people, which helps each person in that society save their environment from pollution and have a good air quality environment. Additionally, the APMS offers user-friendly interfaces, accessible through web and mobile applications, to empower citizens with real-time air quality information. The effectiveness of the IoT-based air pollution monitoring system has been validated through successful field trials in urban and industrial environments, and it has the ability to provide real-time data insights and empower stakeholders in promoting environmental sustainability and fostering citizen engagement.

Tool condition monitoring (TCM) systems are essential in milling operations to guarantee the product’s quality, and when those are paired with indirect measure techniques, such as vibration or acoustic emission sensors, the monitoring can happen without sacrificing productivity. Some more advanced techniques in tool wear estimation are based on supervised machine learning algorithms, like several other applications in the Industry 4.0’s context, however, a satisfactory performance can be obtained with simple techniques and low computational power. This work focuses on an application of tool wear estimation using a simple backpropagation neural network in a milling dataset. Statistics techniques, i.e., the mean, variance, skewness and kurtosis were used as features extracted of indirect measurements from vibration and acoustic emission sensors’ data in a real milling testbench dataset containing multiple experiments with sensor data and a direct measure of the flank wear (VB) in most instances. The data was preprocessed, specifically to acquire clean and normalized values for the neural network training, assuming the VB measure as the target variable to predict tool wear, and all incomplete samples without a VB measure, as well as outliers, were removed beforehand. The train and test subsets were chosen randomly after making sure that the maximum values of every variable were represented in the training subset. A multiple topology approach was implemented to test multiple backpropagation neural networks’ configurations to determine the most suitable one based on two performance criteria, i.e., Mean Absolute Percent Error (MAPE) and variance. Although only a simple backpropagation algorithm was used, the results were adequate to demonstrate a balance between accuracy and computational resource usage.

The exploration of the Solar system from Earth in search of new living spaces will provide new options and possibilities for the survival and development of humankind, where the understanding of their environment, and their resource utilization are essential stages to develop. In the future, when space technology is highly developed and the cost of interplanetary transportation is greatly reduced, planets such as Saturn, Mars and Jupiter will become the "islands" in the Solar system for human settlements. The identification of such resources usually done through exploration, monitoring with sensors and spectral analysis is needed. The development of techniques for satellite surveying to organize the surface of such celestial bodies to generate maps with resource information is part of the first steps for the exploration. For the purpose of a highly accurate 3D modeling of the celestial body and a flat 2D map presentation, the surface structure of the celestial body should be understood by using geometry as well as different types of map projection methods, that will be exploited by the satellites. Different geometrical models and algorithms together with information from sensing systems can be used as much as possible in order to accurately locate the position of on-surface vehicles that could perform in-situ analysis of surface samples. Thus, the combination of the 2D/3D techniques with localization information obtained from sensors, and its use through the satellites, creates a map of the distribution of the celestial body resources. In this article, a projection method based on such a combination and other conventional techniques to achieve better accuracy and efficiency during the process of mapping and projection of a celestial body surface is presented.

Over the past few decades, a significant interest in space exploration has emerged, driven by the lack of resources and the quest for answers to issues like climate change on Earth. New technologies have brought us closer to the possibility of exploring and mapping our solar system and its surroundings in greater detail. Alongside these new horizons, important challenges also emerge. Current methods of space communications operate with a notable lack of efficiency. The vast distances between celestial bodies within our solar system and Earth result in data transmission and reception that fall far short of real-time capabilities. Factors such as bandwidth asymmetry contribute to disruptions in the satellite communications network, e.g., intermittency of service. This paper proposes the development of an interplanetary satellite communication network, which is built upon a communications protocol featuring dynamic routing. This network architecture aims to optimize information transportation by adapting its communications algorithm to environment conditions as quickly as possible. The envisioned satellite infrastructure involves strategically placing satellites at key Lagrange points around each moon and planet within the asteroid belt. The elements within the infrastructure must be aware of their position in space through the integration of sensing capabilities and intelligent algorithms. Near each planet, a satellite with more capabilities will be tasked with gathering and transmitting the information from nanosatellites orbiting a planet, which will relay the respective signals back to our planet. This architecture will enable faster decision-making processes based on exploration data of the most significant celestial bodies within the asteroid belt, providing valuable insights such as constant monitoring of the dark side of the moon and difficult to reach zones in the Solar system.

The presentation aims to share our experiences gained from working on multiple industrial-academic collaborative projects within the Digital Innovation for Growth (DIfG) regional program. This initiative provided academic expertise to low-resource SMEs. The projects primarily revolved around measuring various process or structural health variables. The subsequent wireless reporting of these results to an online dashboard and generating alert messages when variables exceeded predefined thresholds were central to our work.

Due to the diverse nature of our partners' requirements, there was no one-size-fits-all solution for the considered use cases. We will delve into our utilization and insights regarding various IoT-related tools and technologies. These include ESP32 WiFi-enabled microcontrollers, WiFi Manager, NTP time service, watchdog timers, Adafruit IO dashboards, the Twilio SMS gateway, as well as LoRa modules and networks such as TNT and Helium.

ESP32 microcontrollers were employed when an on-site enterprise WiFi network was accessible. We established a secure connection using the WiFi Manager Arduino library, achieved low-power operation through deep sleep mode with periodic watchdog timer wake-ups, and synchronized time after wake-up using the NTP service. Data was submitted to the Adafruit IO dashboard for storage and presentation. Alerts were implemented using the Twilio online SMS gateway. When a WiFi network connection was unavailable, we resorted to using a LoRa module for data communication. Regrettably, robust communication between the available modules proved elusive, eliminating the possibility of an on-site gateway. Among the two global LoRa networks available (the free The Things Network and the commercial Helium), we established a robust connection with the latter.

By effectively combining these tools and technologies, we successfully completed prototypes that enabled testing of the devices on-site.

We present experimental results of temperature measurements obtained using UOTSes in an office environment over prolonged periods of time (tens of hours). The aim of the experiments was to assess the unattended long-term performance of the UOTSes compared to a conventional reference sensor. Specifically, we examined the relationship between the output frequency and the reference temperature, the degree of scatter, and the presence of any undesired readings. The experiments were conducted both in an open office space and within an enclosure that minimized air movements around the UOTS.

While some undesired artifacts were observed, the output frequency of the UOTSes closely tracked the reference temperatures for the majority of the experiments conducted over extended durations (including overnight, weekends, and even Christmas periods). Notably, both the conventional sensor and UOTS readings exhibited increased scatter when removed from the enclosure. This behavior was consistent with previous data reported by various researchers.

These results demonstrate the viability of using UOTSes for temperature monitoring in dwellings. The unique advantages of UOTSes include their ability to sense temperature across complete ultrasonic pathways (rather than at a single point like conventional sensors), their rapid response to changes due to the air between transducers serving as part of the sensor, and their high resolution (approximately 50 Hz per degree Celsius).

Around 339 million people are suffering from asthma worldwide. An acute asthma attack causes difficulties in daily life activities and sometimes it can be fatal. The unnecessary challenges faced by asthmatics signifies the need for a device that helps people monitor and control asthma to prevent possible attacks. A number of studies have reported an elevation of carbon monoxide in exhaled breath (eCO) of asthma patients and suggests it as an effective bio-marker of lung inflammation. By making use of the reported results, this projects aims to make use of the eCO bio-marker to design a carbon monoxide (CO) asthma monitoring system. The system consists of raspberry Pi 3 microcontroller and a MQ 7 CO sensor for processing and detecting the carbon monoxide concentration in parts per million. For accurate results a face mask is attached to the sensor to neglect the environmental CO. The working of the sensor circuit is validated using carbon monoxide source. With more researchers on the threshold level of CO for an imminent asthma attack this CO sensor could eventually save lives and improves the standard of living while being an affordable and user friendly device for active lifestyle.

Medical cargo drones carry containers usually subjected to many external parameters such as temperature, pressure, humidity etc. Also, the drones carrying medical units flying at different altitudes result change in temperature which may affect the organs. To tackle such difficulties, in this work a smart container embedded with a Peltier module and a temperature sensor is developed to maintain the temperature thereby providing safety for healthcare products or organs. Further, the relay module and ESP8266 WIFI Microcontroller (MCU) is utilized to control the Peltier module which enables the user to send live data to the cloud, allowing the user to monitor and control the temperature remotely. The Blynk Internet of Things (IoT) platform is used to monitor the temperature of the smart medical container box. Results demonstrate that the proposed system is highly efficient on monitoring and controlling the smart medical container box temperature according to the set values. Also, the desired temperature range of the respective medical products or organs shall be maintained in an accurate manner. The drone equipped with smart medical container is tested in real time at different altitude levels to examine the performance of the developed system.

Global freshwater excessive usage poses severe threat to human well-being and the environment. Maintaining the use of water becomes challenging due to population growth, changing hydrology, and climate change. With increasing land development and human activities in nearby and distant watersheds, the supplies of drinking water for cities are at threat due to potential contamination. India's 35% urban population generates almost 65% of the country's wastewater (NITI Aayog, 2022). This study focused the urban river management using geospatial techniques of the Dehradun Municipal Corporation (DMC) which has an area of 183.70 km² and its associated watersheds of Bindal River and Rispana River having an area of 44.40 km² and 58.09 km², respectively. The Shutter Radar Topography Mission (SRTM) DEM data with a spatial resolution of 30m has been used for the delineation of watershed boundary, drainage network, and identifying topographic features. Additionally, Sentinel-2 data with a spatial resolution of 10m has been utilized to analyse change in land use in 2017 and 2022. The drainage pattern in Bindal and Rispana watersheds were dendritic in shape with moderate relief. This study found, a significant decline in Agricultural Land from 17.94% in 2017 to 14.66% in 2022. This decline was accompanied by increase in Built-up area from 32.53% to 35.44%. The increased biotic pressure poses a critical threat to river health and biodiversity. This study highlights the urgent need of comprehensive river management strategies to efficiently monitor the biotic pressure due to transformation of land use. This research will be beneficial to diverse stakeholders, including decision-makers and urban planners engaged in sustainable management of water resources and urban development.