This paper compares two types of microfluidic sensors that are designed for operation in ISM (Industrial, Scientific, Medical) bands at microwave frequencies of 2.45 GHz and 5.8 GHz. In the case of the first sensor, the principle of operation is based on the resonance phenomenon in a microwave circuit filled with a test sample. The second sensor is based on the interferometric principle and makes use of the superposition of two coherent microwave signals, where only one goes through a test sample. Both sensors are monolithic structures fabricated using low temperature co-fired ceramics (LTCCs). The LTCC-based microwave-microfluidic sensor properties are examined and compared by measuring their responses for various concentrations of two types of test fluids: one is a mixture of water/ethanol, and the other is dopamine dissolved in a buffer solution. The experiments show a linear response for the LTCC-based microwave-microfluidic sensors as a function of the concentration of the components in both test fluids.

The constant increase in the number of microfluidic-microwave devices can be explained by various advantages, such as relatively easy integration of various microwave circuits in the device, which contains microfluidic components. To achieve the aforementioned solutions, four trends of manufacturing appear—manufacturing based on epoxy-glass laminates, polymer materials (mostly common in use are polydimethylsiloxane (PDMS) and polymethyl 2-methylpropenoate (PMMA)), glass/silicon substrates, and Low-Temperature Cofired Ceramics (LTCCs). Additionally, the domains of applications the microwave-microfluidic devices can be divided into three main fields—dielectric heating, microwave-based detection in microfluidic devices, and the reactors for microwave-enhanced chemistry. Such an approach allows heating or delivering the microwave power to the liquid in the microchannels, as well as the detection of its dielectric parameters. This article consists of a literature review of exemplary solutions that are based on the above-mentioned technologies with the possibilities, comparison, and exemplary applications based on each aforementioned technology.

Sensors are the foundational components of any smart machine system and are invaluable in all modern technologies. Consequently, faults and errors in sensors can have a significant negative impact on the setup. Intelligent, lightweight, and accurate fault diagnosis and mitigation lie at the crux of modern industries. This study aimed to conceptualize a germane solution in the domain of fault detection, focusing on Hall-effect rotary encoders. Position monitoring through rotary encoders is essential to the safety and seamless functioning of industrial equipment such as lifts and hoists, and commercial systems such as automobiles. This work used multi-strategy learners to accurately diagnose quadrature and offset faults in Hall-effect rotary encoders. The obtained dataset was then run through a lightweight ensemble classifier to train a robust fault detection model. The complete mechanism was simulated through interconnected models simulated in a MATLAB Simulink™ environment. In real time, the developed fault detection algorithm was embedded in an FPGA controller and tested with a 1 kW PMSM drive system. The resulting system is computationally inexpensive and achieves an accuracy of 95.8%, making it a feasible solution for industrial implementation.

Energy harvesting from renewable energy sources is trending in the world due to inventions in modern technology. This paper proposes a grid-tied single-phase modified W-type 81-level inverter. Inverter design equations to calculate various parameters, such as the number of voltage levels and the number of DC power sources, along with the feedback controller equations, are developed to integrate the proposed topology with an electric power grid. The modeling of the control system for the proposed topology is carried out in the synchronously rotating reference frame for single-phase systems. The PWM generation part of the proposed inverter system makes use of the binary search nearest level algorithm to efficiently track the grid voltage signal. The proposed system integrates the inverter with the grid without the need for an output filter. The efficiency analysis shows that the proposed system delivers active and reactive power to the grid with an efficiency of around 90% and a THD of 1.04%. The voltage and current waveforms for the dynamic active and reactive power flow reveal that the proposed system exhibits a good transient and steady-state response. The overall system is simulated in MATLAB/Simulink and the results are verified using a hardware implementation of the prototype circuit.

Photovoltaics (PV), like other renewable sources, is characterized by a steadily growing public interest. However, the efficient generation of green energy from photovoltaic panels requires overcoming a number of barriers, including structural and technological barriers to achieve ever greater efficiency in the conversion of solar energy into electrical current. One of the known reasons leading to efficiency limitations in crystalline silicon photovoltaic cells is the different types of defects and damage, the causes of which can appear already in the production stage of the PV cells themselves. This article describes a brief overview of selected artificial intelligence-based solutions and their application to detect and identify defects in photovoltaic cells and panels using electroluminescence (EL) imaging, and is divided into two main subsections – the first is the comparison of selected solutions of using the AI in imaging the PV cells and panels using EL imaging and infrared (IR) imaging, as the most common ways to determine the presence of inhomogeneities or faults. The second part consists of the exemplary engineering application of the AI algorithms – the binary classification and multi-criteria analysis of the defects’ appearance on the photovoltaic cell EL images. The best results in case of binary classification was the use of Logistic Regression classifier with AUC of 0.9569 with the use of ResNet50 feature extraction. In the case of multicriteria analysis the best performance was achieved for the Faster R-CNN ResNet50 detector with mAP@0.5 of 0.985. The performed analysis shows the usefulness of AI in the field of solar cell diagnostics.

Sensors are the foundational components of any smart machine system and are invaluable in all new-age technologies. Consequently, faults and errors in sensors can have a significant negative impact on the setup they affect. To this end, intelligent, lightweight, and accurate fault diagnosis and mitigation lie at the crux of modern industries. This study aims to conceptualize a germane solution in the domain of fault detection, focusing on hall-effect rotary encoders. Position monitoring through rotary encoders is essential to the safety and seamless function of industrial equipment such as lifts and hoists, as well as commercial systems like automobiles. This work uses multi-strategy learners to accurately diagnose quadrature and offset faults in hall-effect rotary encoders. The resulting system is computationally inexpensive and achieves an accuracy of 95.8%, making it a feasible solution for industrial implementation.