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This research proposes an inkjet printed dual-band dual-sense circularly polarized antenna using CPW-feeding on PET substrate. The antenna is designed and optimized using ANSYS HFSS, which operates at 4.01 GHz - 5.05 GHz (22.96%) and 6.23 GHz - 7.58 GHz (19.55%) with a return loss of $0.44\lambda $ x $0.52\lambda $ x $0.002\lambda $ , where $\lambda $ is the free-space wavelength at the lowest circular polarization frequency. Measurement of the fabricated version shows good agreement with the simulated version. To the best of author’s knowledge, this proposed design is the first circularly polarized antenna integrating flexibility with dual-band and dual-sensing characteristics.

In this article, an inkjet-printed circular-shaped monopole ultra-wideband (UWB) antenna with an inside-cut feed structure was implemented on a flexible polyethylene terephthalate (PET) substrate. The coplanar waveguide (CPW)-fed antenna was designed using ANSYS high-frequency structural simulator (HFSS), which operates at 3.04–10.70 GHz and 15.18–18 GHz (upper Ku band) with a return loss < −10 dB and a VSWR < 2. The antenna, with the dimensions of 47 mm × 25 mm × 0.135 mm, exhibited omnidirectional radiation characteristics over the entire impedance bandwidth, with an average peak gain of 3.94 dBi. The simulated antenna structure was in good agreement with the experiment’s measured results under flat and bending conditions, making it conducive for flexible and wearable Internet of things (IoT) applications.

The field of flexible antennas is witnessing an exponential growth due to the demand for wearable devices, Internet of Things (IoT) framework, point of care devices, personalized medicine platform, 5G technology, wireless sensor networks, and communication devices with a smaller form factor to name a few. The choice of non-rigid antennas is application specific and depends on the type of substrate, materials used, processing techniques, antenna performance, and the surrounding environment. There are numerous design innovations, new materials and material properties, intriguing fabrication methods, and niche applications. This review article focuses on the need for flexible antennas, materials, and processes used for fabricating the antennas, various material properties influencing antenna performance, and specific biomedical applications accompanied by the design considerations. After a comprehensive treatment of the above-mentioned topics, the article will focus on inherent challenges and future prospects of flexible antennas. Finally, an insight into the application of flexible antenna on future wireless solutions is discussed.

In this article, the optimization of printing properties on a new, flexible ceramic substrate is reported for sensing and antenna applications encompassing internet of things (IoT) devices. E-Strate® is a commercially available, non-rigid, thin ceramic substrate for implementing in room temperature and high-temperature devices. In this substrate, the printing parameters like drop spacing, number of printed layers, sintering temperature, and sintering time were varied to ensure an electrically conductive and repeatable pattern. The test patterns were printed using silver nanoparticle ink and a Dimatix 2831 inkjet printer. Electrical conductivity, high-temperature tolerance, bending, and adhesion were investigated on the printed samples. The three-factor factorial design analysis showed that the number of printed layers, sintering temperature, sintering time, and their interactions were significant factors affecting electrical conductivity. The optimum printing parameters for the thin E-Strate® substrate were found to be 20 μm drop spacing, three layers of printing, and 300 °C sintering temperature for 30 min. The high-temperature tolerance test indicated a stable pattern without any electrical degradation. Repetitive bending, adhesion test, and ASTM tape tests showed adequate mechanical stability of the pattern. These results will provide insight for investigators interested in fabricating new IoT devices.

In this study, a low voltage solid-state circuit breaker (SSCB) was implemented for a DC distribution system using commercially available components. The design process of the high-side static switch was enabled through a voltage bias. Detailed functional testing of the current sensor, high-side switch, thermal ratings, analog to digital conversion (ADC) techniques, and response times of the SSCB was evaluated. The designed SSCB was capable of low-end lighting protection applications and tested at 50 V. A 15 A continuous current rating was obtained, and the minimum response time of the SSCB was nearly 290 times faster than that of conventional AC protection methods. The SSCB was implemented to fill the gap where traditional AC protection schemes have failed. DC distribution systems are capable of extreme faults that can destroy sensitive power electronic equipment. However, continued research and development of the SSCB is helping to revolutionize the power industry and change the current power distribution methods to better utilize clean renewable energy systems.

In this article, a new investigation on a low-temperature electrochemical hydrocarbon and NOx sensor is presented. Based on the mixed-potential-based sensing scheme, the sensor is constructed using platinum and metal oxide electrodes, along with an Yttria-Stabilized Zirconia (YSZ)/Strontium Titanate (SrTiO3) thin-film electrolyte. Unlike traditional mixed-potential sensors which operate at higher temperatures (>400 °C), this potentiometric sensor operates at 200 °C with dominant hydrocarbon (HC) and NOx response in the open-circuit and biased modes, respectively. The possible low-temperature operation of the sensor is speculated to be primarily due to the enhanced oxygen ion conductivity of the electrolyte, which may be attributed to the space charge effect, epitaxial strain, and atomic reconstruction at the interface of the YSZ/STO thin film. The response and recovery time for the NOx sensor are found to be 7 s and 8 s, respectively. The sensor exhibited stable response even after 120 days of testing, with an 11.4% decrease in HC response and a 3.3% decrease in NOx response.

Considering precision agriculture, recent technological developments have sparked the emergence of several new tools that can help to automate the agricultural process. For instance, accurately detecting and counting apples in orchards is essential for maximizing harvests and ensuring effective resource management. However, there are several intrinsic difficulties with traditional techniques for identifying and counting apples in orchards. To identify, recognize, and detect apples, apple target detection algorithms, such as YOLOv7, have shown a great deal of reflection and accuracy. But occlusions, electrical wiring, branches, and overlapping pose severe issues for precisely detecting apples. Thus, to overcome these issues and accurately recognize apples and find the depth of apples from drone-based videos in complicated backdrops, our proposed model combines a multi-head attention system with the YOLOv7 object identification framework. Furthermore, we provide the ByteTrack method for apple counting in real time, which guarantees effective monitoring of apples. To verify the efficacy of our suggested model, a thorough comparison assessment is performed with several current apple detection and counting techniques. The outcomes adequately proved the effectiveness of our strategy, which continuously surpassed competing methods to achieve exceptional accuracies of 0.92, 0.96, and 0.95 with respect to precision, recall, and F1 score, and a low MAPE of 0.027, respectively.

Horticulture and agriculture are considered as the important pillars of any economy. Current technological advancements have led to the development of several new technologies which are useful in atomizing the agriculture process. Apple farming has a significant role in Italy’s agriculture domain where manual labor is widely employed for apple picking which can be replaced by automated robot mechanisms. However, these mechanisms are based on computer vision methods. These methods focus on detection, localization and tracking the apple fruits in given video frames. Later, appropriate actions can be taken to enhance the production and harvesting. Several techniques have been presented for apple detection, but complex background, noise and image blurriness are the major causes which can deteriorate the performance of the system. Thus, in this work, we present a deep learning-based scheme to detect apples which uses Yolov5 architecture in live apple farm images. We further improve the Yolov5 architecture by incorporating an adaptive pooling scheme and attribute augmentation model. This model detects the smaller objects and improves the feature quality to detect the apples in complex backgrounds. Moreover, a loss function is also incorporated to obtain the accurate bounding box which helps to maximize the detection accuracy. The comparative study shows that the proposed approach with the improved Yolov5 architecture achieves overall accuracy of 0.97, 0.99, and 0.98 in terms of precision, recall, and F1-score, respectively.