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.

This study proposes a hybrid CNN-LSTM model for FPGA-accelerated cardiac arrhythmia detection. Detecting cardiac arrhythmias is essential for early intervention, lowering heart disease risk. However, real-time detection necessitates efficient computing because ECG data processing is difficult. The suggested CNN-LSTM model is intended to satisfy the minimal latency and high-throughput requirements of portable medical devices. It is implemented on FPGA via Intel’s oneAPI. Without sacrificing accuracy, quantization and pipelining techniques increase model efficiency even further. The evaluation of key performance parameters, such as efficiency, delay, and power consumption, on FPGA, CPU, and GPU platforms revealed that FPGA had a 5x increase in power efficiency and a 3x decrease in latency. This study shows how FPGA deployment with oneAPI allows for energy-efficient, real-time arrhythmia detection, which makes it appropriate for continuous applications in healthcare.

Smart cities are having the ability to monitor and manage their environments in real time due to the emergence of Internet of Things (IoT) technology. In the context of energy management, energy prediction can be carried out by monitoring and evaluating dynamic environmental data from the energy user side. The decision-making process related to energy production can then be aided by this information in order to achieve flexible production and avoid an excess or insufficient supply of energy. The quantity and variety of IoT data makes it difficult to create an efficient energy forecast system that effectively captures the changing conditions of the IoT environment. This research aims to forecast energy in order to guarantee efficient energy management in networks of smart cities. Here, unique framework, called as, contiguous temporal Chebyshev convolutional optimized network (CoC-TemNet) is developed for the energy management and load forecasting of IoT-enabled smart city applications. For choosing the list of crucial properties for computing the energy function, the Chebyshev Nonspiritual Network Model (CN2M) is used in this instance. Then, the contiguous temporal convolution network (CTCN) model is used to forecast energy with accuracy using the chosen features. The hybrid leaping lizard immune optimization (HLIO) technique is used to calculate the objective function for improving the prediction process. The proposed method was validated on multiple data sets: Southern China, IHEPC, AEP, and ISO-NE. Outperforms baseline models with low RMSE, MSE, MAE values, and 28.1% MAPE. Significantly lower execution times: 0.98 ms for IHEPC and 0.11 ms for the AEP data set.