Mohsen Heidari

Rapid, accurate, and label-free detection of biomolecules and chemical substances remains a challenge in healthcare. Optical biosensors have been considered as biomedical diagnostic tools required in numerous areas including the detection of viruses, food monitoring, diagnosing pollutants in the environment, global personalized medicine, and molecular diagnostics. In particular, the broadly emerging and promising technique of surface plasmon resonance has established to provide real-time and label-free detection when used in biosensing applications in a highly sensitive, specific, and cost-effective manner with small footprint platform. In this study we propose a novel plasmonic biosensor based on biaxially crumpled graphene structures, wherein plasmon resonances in graphene are utilized to detect variations in the refractive index of the sample medium. Shifts in the resonance wavelength of the plasmon modes for a given change in the RI of the surrounding analyte are calculated by investigating the optical response of crumpled graphene structures on different substrates using theoretical computations based on the finite element method combined with the semiclassical Drude model. The results reveal a high sensitivity of 4990 nm/RIU, corresponding to a large figure-of-merit of 20 for biaxially crumpled graphene structures on polystyrene substrates. We demonstrate that biaxially crumpled graphene exhibits superior sensing performance compared with a uniaxial structure. According to the results, crumpled graphene structures on a titanium oxide substrate can improve the sensor sensitivity by avoiding the damping effects of polydimethylsiloxane substrates. The enhanced sensitivity and broadband mechanical tunability of the biaxially crumpled graphene render it a promising platform for biosensing applications.

ABC-metamaterial, structured by repeating atomic layer deposition of three dielectric material-stack layers, opens the new possibility for creating second-order nonlinearity in CMOS-compatible photonics platforms. Here, the design and simulation of Si3N4 waveguide with embedded ABC-metamaterial for generating second-harmonic (SH) of 1.55 μm wave is presented. High-overlap-integral modal phase matching between the fundamental mode of the fundamental frequency and second-order mode of the SH is attained by properly engineering the waveguide geometry. Numerical analyzes show an absolute efficiency of 0.013% for 10 mW pump power in the straight waveguide which can be enhanced to at least 13.4% by employing a microring resonator.

Intrusion detection exerts a crucial influence on securing the IIoT driven by anomaly detection approaches. Dissimilar with the static data, the intrusion detection data is in the form of a dynamic data stream possessing the properties of infiniteness, correlations, and data distribution change.However, these properties cause some issues for current anomaly detection approaches.Firstly, it is impractical to save the whole dataset due to the infiniteness.Secondly, the correlations are hardly considered.Thirdly, the data distribution change cant be appropriately handled due to a lack of model update and change detection strategy. Thus, we propose ASTREAM(anomaly detection in data streams), a novel anomaly detection approach that merges sliding window, model update, and change detection strategies into LSHiForest to achieve accurate and efficient anomaly detection with better scalability. ASTREAM has the following characteristics:(a)the sliding window can be utilized to handle the infiniteness of data streams;(b)the introduced PCA can consider the correlations between different attributes;(c)the change detection and model update can detect data distribution change in time and train the new model. Comprehensive experiments are implemented on the KDDCUP99 dataset to validate ASTREAM performance.Experiment results reveal that ASTREAM outperforms baselines in aspects of accuracy and efficiency and has better scalability.

In this paper, an antenna-coupled bias-less pulsed GaN-based photoconductive THz emitter is designed and investigated numerically. The proposed THz emitter is composed of an asymmetric metal-semiconductor-metal structure, which is enhanced by using both plasmonic and piezotronic effects. The top metal electrode in our design consists of nano-slits and nano-gratings, so that the incident wave coupling to the active region, and the output THz radiation of the photoconductor are improved. Benefiting from both the piezoelectric property of GaN, and the designed metal plasmonic structure on the conventional pulsed GaN-based photoconductive THz emitter, we show that the output current can be enhanced by the factor of about 4.37 (from 40μA to 175μA), while the output bandwidth has been increased about 3.8%. The proposed enhanced plasmonic piezotronic THz emitter can lead to emerging a new generation of efficient and tunable THz photoconductive emitters.

We propose a novel, to the best of our knowledge, plasmonic-based methodology for the purpose of fast DNA sequencing. The interband surface plasmon resonance and field-enhancement properties of graphene nanopore in the presence of the DNA nucleobases are investigated using a hybrid quantum/classical method (HQCM), which employs time-dependent density functional theory and a quasistatic finite difference time domain approach. In the strong plasmonic–molecular coupling regime where the plasmon and DNA absorption frequencies are degenerated, the optical response of DNA molecule in the vicinity of the nanopore is enhanced. In contrast, when the plasmon and nucleobases resonances are detuned the distinct peaks and broadening of the molecular resonances represent the inherent properties of the nucleobase. Due to the different optical properties of DNA nucleobases in the ultraviolet (UV) region of light, the signal corresponding to the replacement of nucleobases in a DNA block can be determined by considering the differential absorbance. Results show the promising capability of the present mechanism for practical DNA sequencing.

Autonomous unmanned aerial vehicles (UAVs) are essential for detecting and tracking specific events, such as automatic navigation. The intelligent monitoring of people's social distances in crowds is one of the most significant events caused by the coronavirus. The virus is spreading more quickly among the crowds, and the disease cycle continues in congested areas. Due to the error that occurs when humans monitor their activity, an automated model is required to alert to social distance violations in crowds. As a result, this article proposes a two-step framework based on autonomous UAV videos, including human tracking and deep learning-based recognition of the crowd's social distance. The deep architecture is a modified-fast and lightweight ShuffleNet learning structure. First, the Kalman filter is used to determine the positions of individuals, and then the modified ShuffleNet is used to refine the bounding boxes obtained and determine the social distance. The social distance is calculated using the initial refinement of the bounding box obtained during the tracking step and the scale in frames of the human body. The observed average accuracy, average processing time (APT), and processed frame per second (FPS) for three congestion datasets were 97.5%, 84 milliseconds, and 11.5 FPS, respectively. Real-time decision-making was achieved by reducing the size and resolution of the frames. Additionally, the frames were re-labeled to reduce the computational complexity associated with detecting social distancing. The experimental results demonstrated that the proposed method could operate more quickly and accurately on various resolution frames of UAV videos with difficult conditions.

Light–matter interactions of guided modes strongly depend on mode shape. This Letter presents an ultrahigh extinction ratio (ER) polarizer/modulator at λ = 1.55 µ m , enabled by selective mode reshaping for different orthogonal modes. This bi-function device benefits from a designed side-polished fiber (SPF) with a three-layer stack of graphene/ V O 2 /PMMA overlay. Thermally induced insulator-metal transition (IMT) in V O 2 via a graphene microheater leads to the polarization-selective mode shaping (PSMS), which is used to manipulate orthogonal modes separately. As a result, a TE modulator (TM-pass polarizer) with an ultrahigh E R = 168.4 d B / m m (175.1 dB/mm) and low insertion loss (IL) of I L = 3.38 d B / m m (0.34 dB/mm) is achieved.

A high-performance electro-absorption optical modulator based on the epsilon-near-zero (ENZ) effect is proposed. The structure consists of a waveguide with a silicon (Si) core over which a stack of graphene/HfO ₂ /graphene/ITO/HfO ₂ /graphene is grown, covered by a Si cladding. An external voltage is applied across the graphene layers to change the carrier concentration in the indium tin oxide (ITO) layers. Using a self-consistent theory, the required voltage to achieve the ENZ points in the ITO layers is calculated up to 3.42 V for an ITO thickness of 5 nm. The operation of the modulator is investigated using a three-dimensional finite-difference time-domain (FDTD) method, resulting in a modulation depth as high as 5.23 dB/μm (5.36 dB/μm) at a wavelength of 1.55 μm for the TE (TM) polarization, which ensures the polarization-insensitivity of our proposed modulator. It is also calculated that the insertion loss of the modulator is in the order of 2 . 5 × 10 ^-3 dB/μm that yields the figure of merit (FOM) of more than 1800. The outstanding features of our proposed modulator are mainly attributed to using the Si cladding layer instead of metal cladding. Furthermore, in contrast to the previously studied structures with metal electrodes, graphene layers significantly reduce the insertion loss.