A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Investigation of Nonlinear Piezoelectric Energy Harvester for Low-Frequency and Wideband Applications

This paper proposes a monostable nonlinear Piezoelectric Energy Harvester (PEH). The harvester is based on an unconventional exsect-tapered fixed-guided spring design, which introduces nonlinearity into the system due to the bending and stretching of the spring. The physical–mathematical model and finite element simulations were performed to analyze the effects of the stretching-induced nonlinearity on the performance of the energy harvester. The proposed exsect-tapered nonlinear PEH shows a bandwidth and power enhancement of 15.38 and 44.4%, respectively, compared to conventional rectangular nonlinear PEHs. It shows a bandwidth and power enhancement of 11.11 and 26.83%, respectively, compared to a simple, linearly tapered and nonlinear PEH. The exsect-tapered nonlinear PEH improves the power output and operational bandwidth for harvesting low-frequency ambient vibrations

MEMS RESONATOR MASS LOADING NOISE MODEL: THE CASE OF BIMODAL ADSORBING SURFACE AND FINITE ADSORBATE AMOUNT

Modeling of adsorption and desorption in microelectromechanical systems (MEMS) generally is crucial for their optimization and control, whether it is necessary to decrease the adsorption-desorption influence (thus ensuring stable operation of ultra-precise micro and nanoresonators) or to increase it (and enhancing in this manner the sensitivity of chemical and biological resonant sensors). In this work we derive and use analytical mathematical expressions to model stochastic fluctuations of the mass adsorbed on the MEMS resonator (mass loading noise). We consider the case where the resonator surface incorporates two different types of binding sites and where non-negligible depletion of the adsorbate occurs in a closed resonator chamber. We arrive at a novel expression for the power spectral density of mass loading noise in resonators and prove the necessity of its application in cases when resonators are exposed to low adsorbate concentrations. We use the novel approach presented here to calculate the resonator performance. In this way we ensure optimization of these MEMS devices and consequentially abatement of adsorption-desorption noise-caused degradation of their operation, both in the case of micro/nanoresonators and resonant sensors. This work is intended for a general use in the design, development and optimization of different MEMS systems based on mechanical resonators, ranging from the RF components to chemical and biological sensors

Artificial intelligence-based optimization of a bimorph-segmented tapered piezoelectric mems energy harvester for multimode operation

This paper presents a study on the design and multiobjective optimization of a bimorph-segmented linearly tapered piezoelectric harvester for low-frequency and multimode vibration energy harvesting. The procedure starts with a significant number of FEM simulations of the structure with different geometric dimensions—length, width, and tapering ratio. The datasets train the artificial neural network (ANN) that provides the fitting function to be modified and used in algorithms for optimization, aiming to achieve minimal resonant frequency and maximal generated power. Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) methods were used to train the ANN, then the goal attainment method (GAM) and genetic algorithm (GA) were used for optimi-zation. The dominant solution resulted from optimization by the genetic algorithm integrated with the ANN fitting function obtained by the SCG training method. The optimal piezoelectric harvester is 121.3 mm long and 71.56 mm wide and has a taper ratio of 0.7682. It ensures over five times greater output power at frequencies below 200 Hz, which benefits the low frequency of the vibration spectrum. The optimized design can harness the power of higher-resonance modes for multimode applications

COVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation

With the periodic rise and fall of COVID-19 and countries being inflicted by its waves, an efficient, economic, and effortless diagnosis procedure for the virus has been the utmost need of the hour. COVID-19 positive individuals may even be asymptomatic making the diagnosis difficult, but amongst the infected subjects, the asymptomatic ones need not be entirely free of symptoms caused by the virus. They might not show any observable symptoms like the symptomatic subjects, but they may differ from uninfected ones in the way they cough. These differences in the coughing sounds are minute and indiscernible to the human ear, however, these can be captured using machine learning-based statistical models. In this paper, we present a deep learning approach to analyze the acoustic dataset provided in Track 1 of the DiCOVA 2021 Challenge containing cough sound recordings belonging to both COVID-19 positive and negative examples. To perform the classification on the sound recordings as belonging to a COVID-19 positive or negative examples, we propose a ConvNet model. Our model achieved an AUC score percentage of 72.23 on the blind test set provided by the same for an unbiased evaluation of the models. The ConvNet model incorporated with Data Augmentation further increased the AUC-ROC percentage from 72.23 to 87.07. It also outperformed the DiCOVA 2021 Challenge's baseline model by 23% thus, claiming the top position on the DiCOVA 2021 Challenge leaderboard. This paper proposes the use of Mel frequency cepstral coefficients as the feature input for the proposed model.Comment: DiCOVA, top 1st, This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Role of Androgens in Cardiovascular Diseases in Men: A Comprehensive Review

The present knowledge on the androgens role in cardiovascular physiology is not fully completed. It remains unclear whether low serum testosterone concentrations in men are an independent risk factor for cardiovascular diseases (CVDs) or a marker of the presence of CVD. However, we demonstrated that endogenous testosterone levels may be implicated in CVDs. Androgens role in modulating cardiovascular function is one of the highest importances, given that its deficiency is strongly associated with hypertension, atherosclerosis, diabetes, obesity, and cardiac hypertrophy. Although significant and independent association between testosterone levels and cardiovascular events in elderly men have not been confirmed in large prospective studies, cross-sectional studies, however, suggested that low testosterone levels in elderly men are associated with CVDs. The results of androgen therapy are not also conclusive. Perhaps, the effects of testosterone treatment of cardiovascular mortality and morbidity have not been extensively examined in control studies. Data on male animal experimentation of the effect of testosterone replacement therapy are either neutral or beneficial on the development of atherosclerosis. Since circulatory androgen levels modulation is expected to cause many other side effects, it seems to be essential to develop a strategy to target androgen receptor for better treating the CVDs

Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method

This work is dedicated to parameter optimization for a self-biased amplifier to be used in preamplifiers for the diagnosis of seizures in neuro-diseases such as epilepsy. For the sake of maximum compactness, which is obligatory for all implantable devices, power is to be supplied by a piezoelectric nanogenerator (PENG). Several meta-heuristic optimization algorithms and an ANN (artificial neural network)-assisted goal attainment method were applied to the circuit, aiming to provide us with the set of optimal design parameters which ensure the minimal overall area of the preamplifier. These parameters are the slew rate, load capacitor, gain–bandwidth product, maximal input voltage, minimal input voltage, input voltage, reference voltage, and dissipation power. The results are re-evaluated and compared in the Cadence 180 nm SCL environment. It has been observed that, among the metaheuristic algorithms, the whale optimization technique reached the best values at low computational cost, decreased complexity, and the highest convergence speed. However, all metaheuristic algorithms were outperformed by the ANN-assisted goal attainment method, which produced a roughly 50% smaller overall area of the preamplifier. All the techniques described here are applicable to the design and optimization of wearable or implantable circuits

DAMPING ANALYSIS TO IMPROVE THE PERFORMANCE OF SHUNT CAPACITIVE RF MEMS SWITCH

This paper describes the significance of the iterative approach and the structure damping analysis which help to get better the performance and validation of shunt capacitive RF MEMS switch. The micro-cantilever based electrostatic ally actuated shunt capacitive RF MEMS switch is designed and after multiple iterations on cantilever structure a modification of the structure is obtained that requires low actuation voltage of 7.3 V for 3 µm deformation. To validate the structure we have performed the damping analysis for each iteration. The low actuation voltage is a consequence of identifying the critical membrane thickness of 0.7 µm, and incorporating two slots and holes into the membrane. The holes to the membrane help in stress distribution. We performed the Eigen frequency analysis of the membrane. The RF MEMS switch is micro machined on a CPW transmission line with Gap-Strip-Gap (G-S-G) of 85 µm - 70 µm - 85 µm. The switch RF isolation properties are analyzed with high dielectric constant thin films i.e., AlN, GaAs, and HfO2. For all the dielectric thin films the RF MEMS switch shows a high isolation of -63.2 dB, but there is shift in the radio frequency. Because of presence of the holes in the membrane the switch exhibits a very low insertion loss of -0.12 dB

Application of machine learning-assisted global optimization for improvement in design and performance of open resonant cavity antenna

Open resonant cavity antenna (ORCA) and its recent advances promise attractive features and possible applications, although the designs reported so far are solely based on the classical electromagnetic (EM) theory and general perception of EM circuits. This work explores machine learning (ML)-assisted antenna design techniques aiming to improve and optimize its major radiation parameters over the maximum achievable operating bandwidth. A state-of-the-art method e.g., parallel surrogate model-assisted hybrid differential evolution for antenna synthesis (PSADEA) has been exercised upon a reference ORCA geometry revealing a fascinating outcome. This modifies the shape of the cavity which was not predicted by EM-based analysis as well as promising significant improvement in its radiation properties. The PSADEA-generated design has been experimentally verified indicating 3dB-11dB improvement in sidelobe level along with high broadside gain maintained above 17 dBi over the 18.5% impedance bandwidth of the ORCA. The new design has been theoretically interpreted by the theory of geometrical optics (GO). This investigation demonstrates the potential and possibilities of employing artificial intelligence (AI)-based techniques in antenna design where multiple parameters need to be adjusted simultaneously for the best possible performances