Tunable Mid-Infrared Graphene Plasmonic Cross-Shaped Resonator for Demultiplexing Application

A tunable narrow single-mode bandpass filter using graphene nanoribbons for THz applications

This paper presents a tunable, single-mode narrowband optical filter designed for terahertz applications utilizing graphene nanoribbons. To attain optimal conditions, the filter was devised in three steps. It is composed of two input and output waveguides and a T-shaped resonator with nanoscale dimensions. The transmission spectrum analysis employs the three-dimensional finite difference time domain and coupled mode theory methods. Tunability is achieved through the adjustment of the nanoribbon size and the chemical potential of graphene. The filter demonstrates remarkable performance metrics, including a maximum transmission spectrum efficiency of 99%, a full width at half maximum (FWHM) of 0.115 THz, a quality factor (Q-factor) of 100, and a free spectral range (FSR) of 45 THz. The presented structure holds significant promise for integrated optical components and compact optical devices, showcasing its applicability in the terahertz frequency range. Furthermore, the inherent sensitivity of this structure to changes in the refractive index of the substrate positions it as a potential sensor.

Optical biosensors using plasmonic and photonic crystal band-gap structures for the detection of basal cell cancer

One of the most interesting topics in bio-optics is measuring the refractive index of tissues. Accordingly, two novel optical biosensor configurations for cancer cell detections have been proposed in this paper. These structures are composed of one-dimensional photonic crystal (PC) lattices coupled to two metal-insulator-metal (MIM) plasmonic waveguides. Also, the tapering method is used to improve the matching between the MIM plasmonic waveguides and PC structure in the second proposed topology. The PC lattices at the central part of the structures generate photonic bandgaps (PBGs) with sharp edges in the transmission spectra of the biosensors. These sharp edges are suitable candidates for sensing applications. On the other hand, the long distance between two PBG edges causes that when the low PBG edge is used for sensing mechanism, it does not have an overlapping with the high PBG edge by changing the refractive index of the analyte. Therefore, the proposed biosensors can be used for a wide wavelength range. The maximum obtained sensitivities and FOM values of the designed biosensors are equal to 718.6, 714.3 nm/RIU, and 156.217, 60.1 RIU^-1, respectively. The metal and insulator materials which are used in the designed structures are silver, air, and GaAs, respectively. The finite-difference time-domain (FDTD) method is used for the numerical investigation of the proposed structures. Furthermore, the initial structure of the proposed biosensors is analyzed using the transmission line method to verify the FDTD simulations. The attractive and simple topologies of the proposed biosensors and their high sensitivities make them suitable candidates for biosensing applications.

Reverse Design of On-Chip Terahertz Demultiplexers

The reverse design method (RDM) is a frontier direction in the optical research field. In this work, RDM is applied to the design of terahertz demultiplexers, including two-port and three-port terahertz demultiplexers, with areas of 3 mm × 3 mm and 5 mm × 5 mm, respectively. The Finite-Difference Time-Domain (FDTD) simulation results show that the terahertz waves at frequencies of 0.5 THz and 0.417 THz can be well separated by the two-port demultiplexer, and the transmittances of the two outputs reach as high as 0.75 after bandwidth optimization. Meanwhile, the three-port terahertz demultiplexer can have terahertz waves separated from three Ports, and the crosstalk between adjacent channels is less than -18 dB.

Deep Learning-Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors

BACKGROUND

Multimodal wearable technologies have brought forward wide possibilities in human activity recognition, and more specifically personalized monitoring of eating habits. The emerging challenge now is the selection of most discriminative information from high-dimensional data collected from multiple sources. The available fusion algorithms with their complex structure are poorly adopted to the computationally constrained environment which requires integrating information directly at the source. As a result, more simple low-level fusion methods are needed.

OBJECTIVE

In the absence of a data combining process, the cost of directly applying high-dimensional raw data to a deep classifier would be computationally expensive with regard to the response time, energy consumption, and memory requirement. Taking this into account, we aimed to develop a data fusion technique in a computationally efficient way to achieve a more comprehensive insight of human activity dynamics in a lower dimension. The major objective was considering statistical dependency of multisensory data and exploring intermodality correlation patterns for different activities.

METHODS

In this technique, the information in time (regardless of the number of sources) is transformed into a 2D space that facilitates classification of eating episodes from others. This is based on a hypothesis that data captured by various sensors are statistically associated with each other and the covariance matrix of all these signals has a unique distribution correlated with each activity which can be encoded on a contour representation. These representations are then used as input of a deep model to learn specific patterns associated with specific activity.

RESULTS

In order to show the generalizability of the proposed fusion algorithm, 2 different scenarios were taken into account. These scenarios were different in terms of temporal segment size, type of activity, wearable device, subjects, and deep learning architecture. The first scenario used a data set in which a single participant performed a limited number of activities while wearing the Empatica E4 wristband. In the second scenario, a data set related to the activities of daily living was used where 10 different participants wore inertial measurement units while performing a more complex set of activities. The precision metric obtained from leave-one-subject-out cross-validation for the second scenario reached 0.803. The impact of missing data on performance degradation was also evaluated.

CONCLUSIONS

To conclude, the proposed fusion technique provides the possibility of embedding joint variability information over different modalities in just a single 2D representation which results in obtaining a more global view of different aspects of daily human activities at hand, and yet preserving the desired performance level in activity recognition.

Equivalent circuit model of graphene chiral multi-band metadevice absorber composed of U-shaped resonator array

In this study, we have designed an equivalent circuit model (ECM) by use of a simple MATLAB code to analyze a single-layered graphene chiral multi-band metadevice absorber which is composed of U-shaped graphene resonator array in terahertz (THz) region. In addition, the proposed metadevice absorber is analyzed numerically by the finite element method (FEM) in CST Software to verify the ECM analysis. The proposed device which is the first tunable graphene-based chiral metadevice absorber can be used in polarization sensitive devices in THz region. It is single-layered, tunable, and it has strong linear dichroism (LD) response of 94% and absorption of 99% for both transverse electric (TE) and transverse magnetic (TM) electromagnetic waves. It has four absorption bands with absorption >50% in 0.5-4.5 THz : three absorption bands for TE mode and one absorption band for TM mode. Proposed ECM has good agreement with the FEM simulation results. ECM analysis provides a simple, fast, and effective way to understand the resonance modes of the metadevice absorber and gives guidance for the analysis and design of the graphene chiral metadevices in the THz region.