Gliomas, the most prevalent primary brain tumors, exhibit complex genetic and epigenetic variations, including ATRX mutations. Existing ATRX status diagnostics, like immunohistochemistry, DNA, and RNA sequencing, face limitations. Terahertz spectroscopy, known for its interaction with biological materials, holds potential for ATRX diagnosis due to its non-invasive genetic and structural insights. This study proposes an innovative methodology integrating deep learning and terahertz spectroscopy for ATRX assessment. The approach begins by transforming one-dimensional terahertz data into two-dimensional images, enhancing data richness. A Deep Convolutional Generative Adversarial Network (DCGAN) augments the image dataset, addressing data scarcity. DCGAN generates realistic images by training a generator and discriminator in tandem. Subsequently, a Residual Network (ResNet) extracts features from augmented images, tackling the vanishing gradient issue. The ResNet model captures crucial complexities essential for accurate ATRX prediction. Extracted features feed into a classifier for final prediction. The study encompasses 22 patients with 440 terahertz spectral data. Dataset contained 220 ATRX-positive and 220 ATRX-negative spectral data. Employing terahertz data and deep learning, the model achieved up to 90.64% accuracy in diagnosing ATRX status. This research introduces a novel approach integrating terahertz spectroscopy and deep learning for enhanced precision in glioma ATRX diagnosis. The method's potential impact extends to personalized treatment and improved prognosis. Moreover, it underscores the broader utility of terahertz spectroscopy and deep learning in advancing genetic alteration diagnostics in diverse cancers.
Extracting spectral parameters of materials is an essential application of terahertz spectroscopy technology, based on the characteristics of coherent detection. However, the commonly used algorithm for extracting spectral parameters requires a parallel and smooth surface as a prerequisite, and the surface roughness will affect the extraction result. Nevertheless, the effect degree and the mechanism are not precise before. Firstly, in this research, optical parameters of samples with different roughness extracted by the algorithm are displayed. Therefore, the effect degree of different roughness on the optical extraction algorithm is clarified. After that, the mechanism of the influence is analyzed through the method of microelement modeling. As a result, it shows that when the sample surface is slightly rough (roughness<60μm), it will not significantly impact the extraction results. The shape of the optical curve will not be significantly distorted. As the roughness increases, the change of the statistical distribution of the Fresnel coefficient and the phase change are the reason for the attenuation of the terahertz wave amplitude and the decrease of the extraction accuracy.
Terahertz time-domain spectroscopy has been widely applied in performing dielectric analysis for various materials. However, air voids trapped in samples will significantly affect the characterization. In this study, the refractive index of mixture samples consisting of iron trioxide and polytetrafluoroethylene in five different mass ratios were measured with terahertz time-domain spectroscopy. In order to extract the intrinsic refractive index of iron trioxide, the effective medium model of CRI was then applied to remove refractive index of PTFE in the sample. The extracted refractive indices were presented as a variable parameter decrease with the increase of iron trioxide content, which also corresponds to the increase of air voids in the tablets. The correlation between trapped air voids and analyte composition roots from the coarse rust particles used in pellets compression. This study shows that the influence of air porosity should be considered when terahertz waves are utilized to characterize dielectric property of coarse material.
Traditional terahertz time-domain spectroscopy (THZ-TDS) can provide a broadband spectral response of the measured object and has been widely used. Notwithstanding, for applications that require real-time quantitative detection such as security inspection, more attention should be paid to the terahertz response around the absorption characteristics of the sample. Terahertz frequency domain spectroscopy (THz-FDS) using frequency modulation continuous wave can better meet the real-time requirements of security applications. In this paper, an analytical method is established to achieve accurate prediction of molar concentration for organics. The absorption coefficient spectra of samples with different molar concentrations are obtained by using traditional THz-TDS in a wide frequency range, and then the THz-FDS based on photomixing technology is applied to locking a narrow band range near the absorption peak for rapid quantitative analysis. The scheme was verified by taking α-lactose monohydrate as an example, and the results showed that the mean square error of concentration prediction was only 0.025 under the interference of water vapor environment. It may shed light on terahertz rapid quantitative detection of organic compounds in realistic security scene.
Quantitative measurement based on THz absorption spectrum is of great importance in THz applications. Several
researchers have worked on it and gained some achievements, but most of them explored pure component or no more
than 2-component s samples. In this paper, a mixture sample consisting of Glutamine, Histidine and Threonine is
investigated in the frequency range from 0.3 to 2.6 THz. The quantitative measurement principle is the Lambert-Beer's
Law which have been accepted in infrared and visible spectra. Our experiments show the validation of the law in THz
region. A Least-Mean-Square algorithm is adopted and measurement errors of Glutamine, Histidine and Threonine are
17.60%, 4.44% and 2.59%.
KEYWORDS: Sensors, Data acquisition, Inspection, Measurement devices, Data processing, Laser applications, Control systems, Chemical oxygen iodine lasers, Mechanical engineering, Computing systems
To improve the hot rolled strip quality and operational stability, a novel tensionmeter based on lever principle is
developed which inspects latent waves and provides real references for flatness control in hot rolling process. The
contact-type tensionmeter including two segmented rolls can get the transverse tension distribution along the strip width.
Tension profile is deduced by different ratio of four force values from the embedded force sensors in tensionmeter
system. The compact mechanical structure ensures the tensionmeter's robust stability in hot rolling process, standard
hardware and software for data acquisition make the system easy to operate and maintain. The trails have proven
tensionmeter successful in improving both strip flatness and mill performance.
In order to overcome the weakness of traditional flatness defect pattern recognition by least squares method (LSM)
proximity algorithm which is illegible on physical meaning and poor robust stability, as long as the low accuracy of
common BP neuron network, a novel parallel flatness defect pattern recognition model based on binary tree hierarchical
BP neural network and Legendre orthodoxy polynomial decomposition were presented, each node in the binary tree has
the same structure but different weights. The precision of novel model was improved dramatically by classifying the
prediction range and setting the binary tree depth. Experiment results show this novel hierarchical BP network
performances are improved not only in precision but also in robust stabilization.
The test technology of electrical safety performance based on IEC international standards is put forward in the paper,
including mainly four test parameters: leakage current, high-voltage withstand, insulation resistance and ground
resistance. The definitions, the types, the testing purposes and methods of these parameters are also proposed. Based on
the technology, we construct a system for data collection, processing and controlling with a PC, a high-performance
microcomputer ADμC842 and a FPGA, and adopts a VI technology to develop an integrated testing system for electrical
safety performance. The system can be applied to both the certification testing of products and quality control in
manufacturing and provides an authenticating measure for the domestic electrical equipments to enter international
markets.
Wireless sensor networks (WSN) have wide applicability to many important applications including environmental
monitoring, military applications and disaster management, etc. In many applications, sensors are assumed to know their
absolute locations. Some localization methods of WSN have been proposed. In these methods, nodes equipped with GPS
to get precise location information, namely the anchor nodes, are employed to derive the locations of other nodes. Most
of the recent work focuses on increasing the accuracy in position estimation. In this paper, aiming at the high
communication cost and average positioning error of DV-hop algorithm, an advanced algorithm which is called ADV-hop
algorithm is proposed. Simulations are made by the network simulator NS2. The simulation results show that ADV-hop
algorithm has lower communication cost and smaller average positioning error than DV-hop algorithm, which
makes ADV-hop algorithm more suitable for the node location of WSN.
In the testing of electrical equipments' leakage currents, impedance networks of human bodies are used to simulate the current's effect on human bodies, and they are key to the preciseness of the testing result. This paper analyses and calculates three human bodies' impedance networks of measuring electric burn current, perception or reaction current, let-go current in IEC60990, by using Matlab, compares the research result of current effect thresholds' change with sine wave's frequency published in IEC479-2, and amends parameters of measuring networks. It also analyses the change of perception or reaction current with waveform by Multisim.
KEYWORDS: Magnetism, Sensors, Magnetic sensors, Digital signal processing, Amplifiers, Power supplies, Resistance, Mathematical modeling, Imaging systems, Imaging arrays
Pipeline transportation, as it is low cost, steady supply and high efficiency, is widely used nowadays. However, pipelines
might be damaged by natural power or human activities. Thus, pipeline status monitoring, including transmogrification,
corrosion, flaw and crack, shows up more and more important. This paper presents a method using magnetic field
measurement system which based on AMR (anisotropy magnetic resistance) sensors array to imaging the pipe's bug.
Compared with single sensor, it can capture more all-around information about the magnetic field distribution on pipe
wall, and it can make the detection more veracity; Compared with the traditional pipeline pig, which based on Hall
elements, it provides greater sensitivity. A mechanical model of relationships between the pipe's bug and the magnetic
field distribution is given; In this AMR measurement system, the hardware includes arrangement sensors, Set/Reset
circuits, amplifiers, multiplexers, DSP device, data radio module and power supply; and software contains an autocalibration
algorithm, and a VC display program of the measured magnetic field. An experimental pipe bug is detected
and the magnetic field is discussed.
KEYWORDS: Digital signal processing, Transducers, Resistance, Signal processing, Neural networks, Complex systems, Digital electronics, Neurons, Analog electronics, Evolutionary algorithms
For the purpose of better application, the nonlinear correction of the transducer is very important. In this
paper, the nonlinear correction system for the thermal resistance transducer is researched to realize the
nonlinear correction. The system consists of five parts including the thermal resistance transducer, the
amplifying circuit, AD converting circuit, digital signal processor (DSP) unit and display module. As for the
algorithms that are applied in the system, the neural network method and linear interpolation method are
discussed. And nonlinear correction experiments by means of the two kinds of algorithms were done. Through
the experiments, it is proved that the nonlinear correction system based on DSP is able to realize the nonlinear correction
of the thermal resistance transducer, and the neural networks algorithm is more effective and accurate than the linear interpolation method.
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