Neural Network for Metal Detection Based on Magnetic Impedance Sensor.

The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.

Nanoinjection system for precise direct delivery of biomolecules into single cells.

Intracellular delivery of functional molecules such as proteins, transcription factors and DNA is effective and promising in cell biology. However, existing transfection methods are often unsuitable to deliver big molecules into cells or require carriers such as viruses and peptides specific to the target molecules. In addition, the nature of bulk processing does not generally provide accurate dose control of individual cells. The concept of single-cell-based material injection based on electrokinetic pumping through nanocapillaries could overcome these problems, yet the fabrication and operation of nanoscale 3-dimensional structures have remained unsolved. In this research, a hybrid (PDMS/glass) microfluidic chip with a true 3-dimensional nanoinjection structure (called "nanoinjection system") is presented. The nanoinjection structure was fabricated by femtosecond-laser (fs-laser) ablation in a single solid glass, which showed very successful delivery of red fluorescent protein (RFP) and expression of plasmid DNA in several different types of cells. This system is promising in that the amount of molecules to be delivered is controllable and the processed cells are systematically separated into a harvesting chamber, which can radically improve the purity of the processed cells. In addition, it was confirmed that the cells were healthy even after the molecule injection for a few seconds, indicating that the injection time can be significantly elongated, further improving the delivery efficiency of biomolecules without affecting the cell viability. We envision that the nanoinjection system having the major features of being carrier-free and dose-controllable, having an unlimited injection period, and ease of harvesting will greatly contribute to the next-generation research studies in the fields of cell biology and cell therapeutics.

Graphene-Based Thermopile for Thermal Imaging Applications.

In this work, we leverage graphene's unique tunable Seebeck coefficient for the demonstration of a graphene-based thermal imaging system. By integrating graphene based photothermo-electric detectors with micromachined silicon nitride membranes, we are able to achieve room temperature responsivities on the order of ~7-9 V/W (at λ = 10.6 µm), with a time constant of ~23 ms. The large responsivities, due to the combination of thermal isolation and broadband infrared absorption from the underlying SiN membrane, have enabled detection as well as stand-off imaging of an incoherent blackbody target (300-500 K). By comparing the fundamental achievable performance of these graphene-based thermopiles with standard thermocouple materials, we extrapolate that graphene's high carrier mobility can enable improved performances with respect to two main figures of merit for infrared detectors: detectivity (>8 × 10(8) cm Hz(1/2) W(-1)) and noise equivalent temperature difference (<100 mK). Furthermore, even average graphene carrier mobility (<1000 cm(2) V(-1) s(-1)) is still sufficient to detect the emitted thermal radiation from a human target.

Noncontact proximity vital sign sensor based on PLL for sensitivity enhancement.

In this paper, a noncontact proximity vital sign sensor, using a phase locked loop (PLL) incorporated with voltage controlled oscillator (VCO) built-in planar type circular resonator, is proposed to enhance sensitivity in severe environments. The planar type circular resonator acts as a series feedback element of the VCO as well as a near-field receiving antenna. The frequency deviation of the VCO related to the body proximity effect ranges from 0.07 MHz/mm to 1.8 MHz/mm (6.8 mV/mm to 205 mV/mm in sensitivity) up to a distance of 50 mm, while the amount of VCO drift is about 21 MHz in the condition of 60 (°)C temperature range and discrete component tolerance of ± 5%. Total frequency variation occurs in the capture range of the PLL which is 60 MHz. Thus, its loop control voltage converts the amount of frequency deviation into a difference of direct current (DC) voltage, which is utilized to extract vital signs regardless of the ambient temperature. The experimental results reveal that the proposed sensor placed 50 mm away from a subject can reliably detect respiration and heartbeat signals without the ambiguity of harmonic signals caused by respiration signal at an operating frequency of 2.4 GHz.

Electric impedance microflow cytometry for characterization of cell disease states.

The electrical properties of biological cells have connections to their pathological states. Here we present an electric impedance microflow cytometry (EIMC) platform for the characterization of disease states of single cells. This platform entails a microfluidic device for a label-free and non-invasive cell-counting assay through electric impedance sensing. We identified a dimensionless offset parameter δ obtained as a linear combination of a normalized phase shift and a normalized magnitude shift in electric impedance to differentiate cells on the basis of their pathological states. This paper discusses a representative case study on red blood cells (RBCs) invaded by the malaria parasite Plasmodium falciparum. Invasion by P. falciparum induces physical and biochemical changes on the host cells throughout a 48-h multi-stage life cycle within the RBC. As a consequence, it also induces progressive changes in electrical properties of the host cells. We demonstrate that the EIMC system in combination with data analysis involving the new offset parameter allows differentiation of P. falciparum infected RBCs from uninfected RBCs as well as among different P. falciparum intraerythrocytic asexual stages including the ring stage. The representative results provided here also point to the potential of the proposed experimental and analysis platform as a valuable tool for non-invasive diagnostics of a wide variety of disease states and for cell separation.