A metamaterial-based microwave sensor with complementary split ring resonator (CSRR) is implemented for dielectric characterization of liquids. The novelty in the proposed contactless sensor is the use of liquid samples placed normal to the sensor surface. Placed inside of glass capillary tubes, it is quickly possible to analyze the dielectric properties of liquids simply by replacing the capillary tubes with new samples. The liquid samples inside the glass capillary tubes modify the resonant frequency and Q factor of the CSRR sensor. Thereby a relation between the sensor resonant frequency, Q factor, and complex permittivity of the liquid samples can be estimated. A measurement setup was used to test the proposed sensor that exhibited successfully detection of 10% steps in binary mixtures of ethanol and water. The proposed sensor is compact, low-cost, contactless, reusable, easily fabricated, and easy to work.

The excellent visual quality and compression rate of fractal image coding have limited applications due to exhaustive inherent encoding time. This paper presents a new fast and efficient image coder that applies the speed of the wavelet transform to the image quality of the fractal compression. Fast fractal encoding using Fisher's domain classification is applied to the lowpass subband of wavelet transformed image and a modified set partitioning in hierarchical trees (SPIHT) coding, on the remaining coefficients. Furthermore, image details and wavelet progressive transmission characteristics are maintained, no blocking effects from fractal techniques are introduced, and the encoding fidelity problem common in fractal-wavelet hybrid coders is solved. The proposed scheme promotes an average of 94% reduction in encoding-decoding time comparing to the pure accelerated Fractal coding results. The simulations also compare the results to the SPIHT wavelet coding. In both cases, the new scheme improves the subjective quality of pictures for high-medium-low bitrates.

Sensors for detecting wear particles in fluid lubricants have been widely studied owing to their ability to diagnose mechanical machine conditions and to help in preventing mechanical failures caused by wear particles or contamination particles. The proposed novelty sensor was fabricated using photolithography to make the complementary split ring resonator (CSSR) on PCB and it utilized neodymium magnets at the center of the CSRR structure to attract ferrous wear particles. The proposed sensor was tested, and we found that it is highly sensitive and could detect 50 ppm of ferrous wear particles larger than 10 μm dissolved in mineral white oil ISO VG 15. The sensor of this article has a low-cost and can be used in different situations to measure the ferrous wear particles while monitoring the real-time mechanical equipment wear, as it can be easily integrated into them. The sensor offers good accuracy, and is insensitive to fluid transparency and bubble formation, unlike other sensors for detection of wear particles. Therefore, the proposed novelty sensor of this work is in line with Industry 4.0, which requires many low-cost sensors for continuous monitoring to apply predictive maintenance techniques for extending the lifespan of infrastructures.

The 2019 coronavirus disease (COVID-19) pandemic has contaminated millions of people, resulting in high fatality rates. Recently emerging artificial intelligence technologies like the convolutional neural network (CNN) are strengthening the power of imaging tools and can help medical specialists. CNN combined with other sensors creates a new solution to fight COVID-19 transmission. This paper presents a novel method to detect coughs (an important symptom of COVID-19) using a K-band continuous-wave Doppler radar with most popular CNNs architectures: AlexNet, VGG-19, and GoogLeNet. The proposed method has cough detection test accuracy of 88.0% using AlexNet CNN with people 1 m away from the microwave radar sensor, test accuracy of 80.0% with people 3 m away from the radar sensor, and test accuracy of 86.5% with a single mixed dataset with people 1 m and 3 m away from the radar sensor. The K-band radar sensor is inexpensive, completely camera-free and collects no personally-identifying information, allaying privacy concerns while still providing in-depth public health data on individual, local, and national levels. Additionally, the measurements are conducted without human contact, making the process proposed in this work safe for the investigation of contagious diseases such as COVID-19. The proposed cough detection system using microwave radar sensor has environmental robustness and dark/light-independence, unlike traditional cameras. The proposed microwave radar sensor can be used alone or in group with other sensors in a fusion sensor system to create a robust system to detect cough and other movements, mainly if using CNNs.

The printed circuit board (PCB) plays a critical role in any electronic product, and its manufacturing quality assurance is responsible for substantially impacting the final product’s price. Hence, research and development (R&D) for better inspection methods have been an internationally growing subject. Regarding the multiple PCB inspection paradigms, the visual investigation of these boards represents the majority of modern techniques. Thus, the present survey collects the most impacting studies over the last 25 years, highlighting their operation strategies and how they evolved until recently, including introducing artificial intelligence (AI) approaches to increase their overall performance and lower operating costs. Furthermore, this work calls attention to the importance of public PCB datasets to contribute to developing such methods and provide benchmarking references. Finally, the most recent challenges in the field are described and discussed.