Terahertz-wave detector on silicon carbide platform

We developed a novel terahertz-wave detector fabricated on a SiC platform implementing an InP/InGaAs Fermi-level managed barrier (FMB) diode. The FMB diode epi-layers were transferred on a SiC substrate, and a waveguide coupler and filters were monolithically integrated with an FMB diode. Then, fabricated detector chip was assembled in a fundamental mixer module with a WR-3 rectangular-waveguide input port. It exhibited a minimum noise equivalent power as low as 3e-19 W/Hz at around 300 GHz for a local oscillator power of only 30 microwatts.

A low noise cascaded amplifier for the ultra-wide band receiver in the biosensor

This paper presents the design of an Ultra-Wide Band (UWB) Low Noise cascaded Amplifier (LNA) used for biomedical applications. The designed structure uses a technique which is based on the inductances minimization to reduce the LNA surface while maintaining low power consumption, low noise and high stability, linearity and gain. To prove its robustness, this technique was studied theoretically, optimized and validated through simulation using the CMOS 0.18 µm process. The LNA achieves a maximum band voltage gain of about 17.5 dB at [1-5] GHz frequency band, a minimum noise figure of 2 dB, IIP3 of + 1dBm and consumes only 13mW under a 2 V power supply. It is distinguished by its prominent figure of merit of 0.68.

On the use of light sensors on CubeSat nanosatellites

The paper focuses on the usage of the TCS34725 light sensor in the motion control system of the SamSat-Science nanosatellite platform. The sensor is designed to determine the angle between the sensor normal and the direction to the light emitter center. We developed a technique for calibrating light sensors, carried out a series of experiments, verified the nominal characteristic of the light sensor, and found the dependency of mean squared deviation (MSD) of the sensor values on the angle of incidence of the light flux. Three layouts of light sensors on the lateral faces of the nanosatellite are considered: on a plane; on the faces of a quadrangular pyramid with an inclination angle of 45°; on the faces of a truncated quadrangular pyramid with an angle of inclination of 45°. We have chosen a circuit that provides measurements with minimum noise.

A Terahertz Detector Based on Double-Channel GaN/AlGaN High Electronic Mobility Transistor

A double-channel (DC) GaN/AlGaN high-electron-mobility transistor (HEMT) as a terahertz (THz) detector at 315 GHz frequency is proposed and fabricated in this paper. The structure of the epitaxial layer material in the detector is optimized, and the performance of the GaN HEMT THz detector is improved. The maximum responsivity of 10 kV/W and minimum noise equivalent power (NEP) of 15.5 pW/Hz0.5 are obtained at the radiation frequency of 315 GHz. The results are comparable to and even more promising than the reported single-channel (SC) GaN HEMT detectors. The enhancement of THz response and the reduction of NEP of the DC GaN HEMT detector mainly results from the interaction of 2DEG in the upper and lower channels, which improves the self-mixing effect of the detector. The promising experimental results mean that the proposed DC GaN/AlGaN HEMT THz detector is capable of the practical applications of THz detection.

Smart Resource Allocation Scheme for Mimo Microstrip Wireless Communication

In this paper we have proposed a minimum noise shortest path determination scheme considering the amount of delay and energy consumed with respect to each path. An artificial neural network has been employed for classifying the minimum noise shortest path from the source to destination. A simulation work has been carried out with respect to different Signal-to-Noise (SNR) values in a thirty-node network with one Internet node and 100 bits of message length. Also, a comparison has been made between plain Dynamic Source Routing (DSR) and integrating the minimum noise shortest path algorithm with DSR. The simulation results show that with the increase of SNR, noise constraint in the path reduces, and data throughput increases.

Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. Considering that different objects have different reflection characteristics, we use a superpixel segmentation algorithm to segment the first principal component of original hyperspectral image cube into homogeneous regions. For each superpixel, we construct a corresponding similarity graph to reflect the similarity between band pairs. Then, a multiple graph diffusion strategy with theoretical convergence guarantee is designed to learn a unified graph for partitioning the whole hyperspectral cube into several subcubes via spectral clustering. During the graph diffusion process, the spatial and spectral information of each superpixel are embedded to make spatial/spectral similar superpixels contribute more to each other. Finally, the band containing minimum noise in each subcube is selected to represent the whole subcube. Extensive experiments are conducted on three public datasets to validate the superiority of the proposed method when compared with other state-of-the-art ones.

Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the original data perfectly. However, the conventional KMNF noise estimation (KMNF-NE) uses the local regression residual of neighbourhood pixels, which depends heavily on spatial information. Due to the limited spatial resolution, there are many mixed pixels in HSI, making KMNF-NE unreliable for noise estimation and leading to poor performance in KMNF for classification on HSIs with low spatial resolution. In order to overcome this problem, a mixed noise estimation model (MNEM) is proposed in this paper for optimized KMNF (OP-KMNF). The MNEM adopts the sequential and linear combination of the Gaussian prior denoising model, median filter, and Sobel operator to estimate noise. It retains more details and edge features, making it more suitable for noise estimation in KMNF. Experiments using several HSI datasets with different spatial and spectral resolutions are conducted. The results show that, compared with some other DR methods, the improvement of OP-KMNF in average classification accuracy is up to 4%. To improve the efficiency, the OP-KMNF was implemented on graphics processing units (GPU) and sped up by about 60× compared to the central processing unit (CPU) implementation. The outcome demonstrates the significant performance of OP-KMNF in terms of classification ability and execution efficiency.

Nonlinear Noise Reduction for the Airborne Transient Electromagnetic Method based on Kernel Minimum Noise Fraction

The airborne transient electromagnetic method has become a powerful tool to explore deep resource and tectonic structures. However, aircraft vibrations and flight environments produce very strong and complex nonlinear noise and result in poor data quality compared to ground transient electromagnetic methods. Consequently, the reduction of airborne electromagnetic noises is of vital importance to data inversion and imaging. To suppress and remove the nonlinear noise, we propose using kernel minimum noise fraction (KMNF), which is a nonlinear generalized method of minimum noise fraction. First, an adaptive variable window-width filtering algorithm is used to evaluate the noises and perform the preliminary denoising. Then, we adopt the two filter methods, which are minimum noise fraction (MNF) and KMNF to suppress the noise. The results show that these two methods can both suppress noise and make the decay curves smooth, but kernel MNF is more effective for the nonlinear characteristics of noise and it does not weaken the anomaly. Finally, field data from the Qinling mine area is processed, using the MNF and KMNF methods. The results show that nonlinear noise is suppressed by both methods but the results of KMNF are better than those of the linear MNF method.