Progress in implantable and ingestible wireless biotelemetry requires versatile and efficient antennas to communicate reliably from a body. We propose an ultraminiature 434 MHz antenna immune to impedance detuning caused by varying electromagnetic properties of the surrounding biological environment. It is designed for a standard input impedance of 50 Ω. The antenna is synthesized and miniaturized using a hybrid analytical-numerical approach, and then optimized to conform to the inner surface of a 17 mm long biocompatible encapsulation (7 mm diameter). The substrate is 50 μm thick. The capsule antenna is analyzed both in simplified and anatomically realistic heterogeneous phantoms. It remains matched at common implantation sites and through the whole gastrointestinal tract. Enhanced robustness allows using the antenna for a wide range of in-body applications. Computed reflection coefficients and radiation performance both show good agreement with measurements. The far field is characterized with the direct illumination technique using an analog fiber optic link. The realized gain (measured max. value -19.6 dBi) exceeds the counterparts by about 3 dBi. The proposed antenna contributes to the further development of a new generation of miniature in-body devices that involve complex and dense integration of sensors, logic, and power source.

The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.

Autonomous wireless body-implanted devices for biotelemetry, telemedicine, and neural interfacing constitute an emerging technology providing powerful capabilities for medicine and clinical research. We study the through-tissue electromagnetic propagation mechanisms, derive the optimal frequency range, and obtain the maximum achievable efficiency for radiative energy transfer from inside a body to free space. We analyze how polarization affects the efficiency by exciting TM and TE modes using a magnetic dipole and a magnetic current source, respectively. Four problem formulations are considered with increasing complexity and realism of anatomy. The results indicate that the optimal operating frequency $f$ for deep implantation (with a depth $d\gtrsim 3\mathrm{cm}$) lies in the (${10}^8–{10}^9$)-Hz range and can be approximated as $f=2.2\times {10}^7/d$. For a subcutaneous case ($d\lesssim 3\mathrm{cm}$), the surface-wave-induced interference is significant: within the range of peak radiation efficiency (about $2\times {10}^8$ to $3\times {10}^9\mathrm{Hz}$), the max-to-min ratio can reach a value of 6.5. For the studied frequency range, 80%–99% of radiation efficiency is lost due to the tissue-air wave-impedance mismatch. Parallel polarization reduces the losses by a few percent; this effect is inversely proportional to the frequency and depth. Considering the implantation depth, the operating frequency, the polarization, and the directivity, we show that about an order-of-magnitude efficiency improvement is achievable compared to existing devices.

Since large power transformers are custom-made, and their design process is a labor-intensive task, their design process is split into different parts. In tendering, the price calculation is based on the preliminary design of the transformer. Due to the complexity of this task, it belongs to the most general branch of discrete, non-linear mathematical optimization problems. Most of the published algorithms are using a copper filling factor based winding model to calculate the main dimensions of the transformer during this first, preliminary design step. Therefore, these cost optimization methods are not considering the detailed winding layout and the conductor dimensions. However, the knowledge of the exact conductor dimensions is essential to calculate the thermal behaviour of the windings and make a more accurate stray loss calculation. The paper presents a novel, evolutionary algorithm-based transformer optimization method which can determine the optimal conductor shape for the windings during this examined preliminary design stage. The accuracy of the presented FEM method was tested on an existing transformer design. Then the results of the proposed optimization method have been compared with a validated transformer design optimization algorithm.