The growing demand for compact, low-power infrared (IR) sensors necessitates advanced solutions for on-chip spectral selectivity, particularly for integration with Thermal Metal-Oxide-Semiconductor (TMOS) devices. This paper investigates the design and analysis of CMOS-compatible metal-insulator-metal (MIM) metamaterial absorbers tailored for selective absorption in the long-wave infrared (LWIR) region. We present a design methodology utilizing an equivalent-circuit model, which provides intuitive physical insight into the absorption mechanism and significantly reduces computational costs compared to full-wave electromagnetic simulations. An important rule in this design methodology is demonstrating how the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and, critically, by optimizing the dielectric substrate’s refractive index and thickness, which assist in designing small period MIM absorber units which are important in infrared thermal sensor pixels. Our results demonstrate that the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and by optimizing the dielectric substrate’s refractive index and thickness. Specifically, the selection of silicon as the dielectric material, owing to its high refractive index and low losses, facilitates compact designs with high-quality factors. The transmission line model provides intuitive insight into how near-perfect absorption is achieved when the absorber’s input impedance matches the free-space impedance. This work presents a new approach for the methodology of designing MIM absorbers in the mid-infrared and long-wave infrared (LWIR) regions, utilizing the intuitive insights provided by equivalent circuit modeling. This study validates a highly efficient design approach for high-performance, spectrally selective MIM absorbers for LWIR radiation, paving the way for their monolithic integration with TMOS sensors to enable miniaturized, cost-effective, and functionally enhanced IR sensing systems.

Mount Etna is considered as one of the world’s most active volcanoes located In Europe. In this study, we propose to characterize and model physically the geohazard area recently caused by the active Etna volcano. An advanced image processing method is presented, in which the scene is acquired simultaneously by two high-resolution remote sensors NOAA and SUOMI NFP. The proposed experimental protocol for data visualization and analysis is as follows. First, the images are processed with the same spectral reflectance using VIIRS I-bands at 375 m spatial resolution. More in details, the spectral signatures of pixels confirm the environmental changes according to a color visualization coding. In this context, the volcano clouds widespread over Etna mount are estimated approximately through a signal processing measurement algorithm. Second, the images are acquired by two high-resolution sensors, which are the NOAA and SUOMI NFP in the visible Spectrum wavelength. The synchronization of both sensors gives more details about the area occupied by the volcano fires. A spectral wavelength analysis is presented in both cases: (1) non-synchronized (i.e., each sensor separately) and (2) synchronized (i.e., combination of two sensors). Third, the protocol of active fire detection applied to the geohazard Etna Volcano is displayed: fire area detection and estimation, spectral measurement, synchronization of remote sensors, and assessment of the fire spread. Finally, the strengths and limitations of satellite-based active fire detection are presented with respect to the synchronization of different sensors. A theoretical and experimental studies will be presented.

This paper presents a compact 122 GHz Terahertz FMCW radar using a plastic hollow-core dielectric waveguide for non-destructive testing. The guided approach simplifies the system, avoiding complex free-space optics and alignment, while improving signal-to-noise ratio by isolating endpoint reflections from internal ones. Various configurations, including solid immersion lenses, enhance spatial resolution and imaging capabilities. Experiments combine 3D electromagnetic simulations and raster-scanning to image fine details and detect subsurface defects. Applications span aerospace, automotive, and art conservation. Results demonstrate guided FMCW radar as a cost-effective, portable, and reliable alternative to traditional free-space setups, enabling broader, practical implementation across industries.

In recent years, the laser-based ablation of damaged or undesired tooth material has emerged as a highly promising technique for improving dental cavity preparation. While X-ray diffraction and X-ray photoelectron spectroscopy are generally used to characterize the constituents of ablated surfaces, Fourier Transform Infrared (FT-IR) can also be employed for monitoring the changes induced by the femtosecond laser ablation process. In the present study, FT-IR spectroscopy has been adopted to characterize the changes induced in extracted human teeth. The laser ablation was performed in ambient air by using a femtosecond laser source at different fluences in the range of 0.7–1.5 J/cm² to produce regular lines on various samples. Micro-ATR spectroscopy was employed to examine the laser-processed tooth disks. The spectra acquired from different samples reveal the contributions of the various dental components and provide insight into the effect of laser processing under different conditions.

Municipal sewer networks span across large areas in cities around the world and require regular inspection to identify structural failures, blockages, and other issues that pose public health risks. Traditional inspection methods rely on remote-controlled robotic cameras or CCTV surveys performed by skilled inspectors. These processes are time-consuming, expensive and often inconsistent; for example, the United States alone has more than 1.2 million miles of underground sewer pipes, and up to 75,000 failures are reported annually. Manual CCTV inspections can only cover a small fraction of the network each year, resulting in delayed discovery of defects and costly repairs. To address these limitations, this paper proposes a scalable and low-power fault detection system that integrates embedded machine vision and Tiny Machine Learning (TinyML) on resource-constrained microcontrollers. The system uses transfer learning to train a lightweight TinyML model for defect classification using a dataset of sewer pipe images and deploys the model on battery-powered devices. Each device captures images inside the pipe, performs on-device inference to detect cracks, intrusions, debris and other anomalies, and communicates inference results over a long-range LoRa radio link. Experimental results demonstrate that the proposed system achieves 94% detection accuracy with sub-hundred-millisecond inference time and operates for extended periods on battery power. The research contributes a template for autonomous, scalable, and cost-effective sewer condition assessment that can help municipalities prioritize maintenance and prevent catastrophic failures.

As cities strive to become more sustainable and livable in the age of smart urban develop-ment, there is tendency toward urban landscaping concepts that combine ecological bene-fits and aesthetic appeal. Within this context, artistic landscaping, the deliberate spatial arrangement of plant species to create visual compositions, has emerged as a valuable as-pect of modern urban green infrastructure. While cutting-edge Unmanned Ground Vehicle (UGV) development has primarily focused on large-scale precision agriculture, its poten-tial for artistic and small-scale urban landscaping remains unexplored. Furthermore, inte-grating Internet of Things (IoT) technology into UGVs for autonomous seeding presents an interesting research point. Addressing these challenges, this paper introduces a compact design of an IoT-enabled UGV specifically for artistic landscaping applications. The sys-tem includes an effective full seeding mechanism with dedicated modules for soil digging, sowing, water spraying, and backfilling. These operational modules are coordinated using a microcontroller-based control system to ensure reliability and repeatability. Additional-ly, in this study, a web-based interface has been developed to support both autonomous and manual operation modes, allowing users to customize path planning for geometric seeding patterns as well as real-time monitoring. A fully functional prototype was built and tested under controlled conditions to confirm the core modules’ effectiveness. This development provides a practical solution for supporting the realization of smart and sustainable cities.

In this paper, we have presented the design, prototyping and working of a cost-effective, energy-efficient, and scalable air quality monitoring system (AQMS), enabled by Low power, long Range Wide Area Network (LoRaWAN), an Internet of Things (IoT) technology designed to provide connectivity for massive machine type communication applications. The growing threat of air pollution necessitates outdoor and mobile environmental monitoring systems to provide real-time, location-specific data, which are unfortunately not possible with fixed monitoring devices. For our AQMS, we have developed two custom-built sensor nodes. First node is equipped with Nucleo-WL55JC1 microcontroller and sensors to measure temperature, humidity, and carbon dioxide (CO₂), while other node is equipped with Arduino MKR WAN 1310 controller with sensors to measure carbon monoxide (CO), ammonia (NH₃), and particulate matter (PM2.5 and PM10). These sensor nodes connect to a WisGate Edge LoRaWAN gateway, which aggregates and forwards the sensor data to The Things Network (TTN) for processing and cloud storage. The final visualization is handled via the Ubidots IoT platform, allowing for real-time visualization of environmental data. Besides environmental data, we were able to acquire received signal strength indicator, signal-to-noise ratio as well as frame counter which shows the number of packets received by the gateway. We performed laboratory testing, which confirmed reliable communication, with a packet delivery rate of 98% and minimal average latency of 2.5 seconds. Both nodes operated efficiently on battery power, with the Nucleo-WL55JC1 consuming an average of 20 mA in active mode, while the Arduino MKR WAN 1310 operated at 15 mA. These values ensured extended operation for remote deployment. The system’s low power consumption and modular architecture make it viable for smart city applications and large-scale deployments in resource-constrained areas.

Millimeter-wave (mm-wave) radar has become a key technology in wireless sensor net-works (WSNs) due to its high spatial resolution and penetration capability, enabling ap-plications such as smart traffic control and non-contact health monitoring. Achieving fine range resolution necessitates wide signal bandwidth, which places stringent demands on power amplifier (PA) performance in terms of bandwidth, efficiency, and output power. Therefore the design of the power amplifier for WSN poses significant challenges. This paper presents a broadband mm-wave PA implemented in a 40-nm CMOS process, utiliz-ing transformer-based power combining to enhance efficiency and bandwidth simultane-ously, which can adequately meet the requirements of WSN systems. The PA achieves a 3-dB flat power bandwidth up to 62% from 13 to 24.8 GHz. At 19 GHz, it delivers a satu-rated output power (Psat) of 12.3 dBm, a 1-dB compression point (P1dB) of 10.15 dBm, and exhibits a peak power-added efficiency (PAE) of 23%, with 17.2% PAE at P1dB. The PA consumes 43 mW from a 1.1 V supply and occupies an active area of only 0.06 mm2. These results validate the effectiveness of transformer-based combining for achieving compact, high-performance broadband PAs in CMOS, and demonstrate its suitability for mm-wave radar systems requiring high range resolution. The amplifier provides a high stability, with output return losses better than −10 dB.

The fast expansion of the Internet of Things (IoT) has accelerated the advancement of smart home technologies. However, secure communication and access control remain sig-nificant challenges. This paper presents a fully implemented FPGA-based IoT gateway that utilizes the Zynq-7000 SoC, integrating sensing, processing, wireless communication, and hardware-level authentication. Analog temperature data from an LM35 sensor is dig-itized via a 12-bit XADC and transmitted over Wi-Fi using an ESP8266-01 module. An SPI-based OLED provides real-time feedback. To ensure device-level trust, an XOR-based Physically Unclonable Function (PUF) enables lightweight challenge–response authenti-cation with over good uniqueness and a latency of under 10 ms. The system demonstrates ±0.5 °C sensing accuracy, <50 ms transmission delay, and low power consumption. It offers a scalable and secure platform suitable for real-time smart home and facility auto-mation.

Electric bicycles (E-Bikes) are gaining popularity as a sustainable mode of transportation
due to their energy efficiency and zero-emission operation. However, challenges such as
battery overcharging, overheating, and degradation from improper use can reduce battery
lifespan and increase maintenance costs. To address these issues, this paper presents the
design and implementation of a Battery Management System (BMS) tailored for E-Bike
applications, with a focus on enhancing safety, reliability, and performance. The proposed
BMS includes core functionalities such as State of Charge (SoC) estimation, temperature
monitoring, under-voltage, and overcharge protection. Different approaches including
Open-Circuit Voltage (OCV), Coulomb Counting (CC), and Kalman Filter techniques is
employed to improve SoC estimation accuracy. The circuit for CC based BMS was first simulated using Proteus, and system behavior was modeled in MATLAB Simulink to validate
design assumptions before hardware implementation. An Arduino Uno microcontroller
was used to control the system, interfacing with an LM35 temperature sensor, a voltage
divider, and an ACS712 current sensor. The BMS controls battery charging based on SoC
levels and activates a cooling fan when the battery temperature exceeds 45 °C. It disconnects
the charger at 100% SoC and triggers a beep alarm when SoC falls below 40%. An
external charger and regenerative charging from four electrodynamometers on the bicycle
chain recharge the battery when SoC drops below 20%, provided the load is disconnected.
Measurement results closely matched simulation data, with the MATLAB model showing
44% SoC after 3 h, compared to the actual real-time 45.85%. The system accurately tracked
charging/discharging patterns, validating its effectiveness. This compact and cost-effective
BMS design ensures safe operation, improves battery longevity, and supports broader
adoption of E-Bikes as an eco-friendly transportation solution.