Concept design of the megawatt power level gyrotron stabilized by a low-power signal for DEMO project

The specific features of the main components of the new powerful 230GHz/80kV/40A gyrotron aimed to use in the future control fusion facility DEMO are described. The gyrotron design provides a stable output power generation of more than 1 MW using a superconducting magnet with a moderate size warm bore. Furthermore, the new original quasi-optical converter providing the gyrotron operation in three possible regimes  two free oscillation regimes with co-rotating TE33,13 or counter-rotating TE33,-13 mode, and the regime with frequency locking by the stable input signal  is suggested and optimized. The Gaussian content in the output wave-beam in all above-mentioned regimes is about 98%.

Understanding of Controllable Optical Memory Using 1D Inp Based Photonic Structures at Three Communication Windows

The present research realises a controllable optical memory using one dimensional indium phosphate (InP) photonic structures at three optical communication windows (850 nm, 1310 nm and 1550 nm). The photonic structures comprise 21 layers of InP and air material. The memory applications are realised at both single and dual signals of the communication windows. The physics of the research deals with the materials property including the variation of the refractive indices with respect to the input signal. Similarly, mathematics of the works relies on the analysis of reflectance, transmittance and absorbance phenomena. Further, the light from visible spectrum acts as triggering signal to realise optical memory applications. Finally, it is revealed that InP based photonic structures are suitable for controllable memory applications pertaining to the single wavelength (850 nm, 1310 nm, 1550 nm) or dual wavelengths (850 nm and 1310 nm, 1310 nm and 1550 nm, 1550 nm and 850 nm).

Command-Filtered Adaptive Neural Network Backstepping Quantized Control for Fractional-order Nonlinear Systems with Asymmetric Actuator Dead-zone via Disturbance Observer

An adaptive neural network (NN) backstepping quantized control based on command filter and disturbance observer is proposed for fractional-order nonlinear systems with asymmetric actuator dead-zone and unknown external disturbance in this paper. An adaptive NN mechanism is designed to estimate unknown functions, and a command filter is introduced to estimate the virtual control variable as well as its derivative, so the ``explosion of complexity" issue can be avoided existed in the classical backstepping method. To handle the unknown external disturbance, a fractional-order disturbance observer is developed. Moreover, a hysteresis-type quantizer is used to quantify the final input signal to overcome the system performance damage caused by the actuator dead-zone. The quantized input signal can ensure that all the involved signals keep bounded and the tracking error converges to an arbitrarily small region of the origin. Finally, two examples are presented to verify the effectiveness of the proposed method.

Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.

The method of discretization signals to minimize the fallibility of information recovery

The paper proposes a fundamentally new approach to the formulation of the problem of optimizing the discretization interval (frequency). The well-known traditional methods of restoring an analog signal from its discrete implementations consist of sequentially solving two problems: restoring the output signal from a discrete signal at the output of a digital block and restoring the input signal of an analog block from its output signal. However, this approach leads to methodical fallibility caused by interpolation when solving the first problem and by regularizing the equation when solving the second problem. The aim of the work is to develop a method for the signal discretization to minimize the fallibility of information recovery to determine the optimal discretization frequency.The proposed method for determining the optimal discretization rate makes it possible to exclude both components of the methodological fallibility in recovering information about the input signal. This was achieved due to the fact that to solve the reconstruction problem, instead of the known equation, a relation is used that connects the input signal of the analog block with the output discrete signal of the digital block.The proposed relation is devoid of instabilities inherent in the well-known equation. Therefore, when solving it, neither interpolation nor regularization is required, which means that there are no components of the methodological fallibility caused by the indicated operations. In addition, the proposed ratio provides a joint consideration of the properties of the interference in the output signal of the digital block and the frequency properties of the transforming operator, which allows minimizing the fallibility in restoring the input signal of the analog block and determining the optimal discretization frequency.A widespread contradiction in the field of signal information recovery from its discrete values has been investigated. A decrease in the discretization frequency below the optimal one leads to an increase in the approximation fallibility and the loss of some information about the input signal of the analog-to-digital signal processing device. At the same time, unjustified overestimation of the discretization rate, complicating the technical implementation of the device, is not useful, since not only does it not increase the information about the input signal, but, if necessary, its restoration leads to its decrease due to the increase in the effect of noise in the output signal on the recovery accuracy. input signal. The proposed method for signal discretization based on the minimum information recovery fallibility to determine the optimal discretization rate allows us to solve this contradiction.

Adaptive Noise-Resistant Low-Power ASK Demodulator Design in UHF RFID Chips

This paper presents a new signal demodulator for ultra-high frequency (UHF) radio frequency identification (RFID) tag chips. The demodulator is used to demodulate amplitude shift keying (ASK) modulated signals with the advantages of high noise immunity, large input range and low power consumption. The demodulator consists of a charge pump, an envelope detector, and a comparator. In particular, the demodulator provides a hysteresis input signal to the comparator through two envelope detectors, resulting in better noise immunity. The demodulator is based on a standard 0.13 µm CMOS process. The demodulator is suitable for demodulating high frequency signals at 900 MHz with a data rate of 128 Kbps and can operate up to 78 °C. The input signal has a peak of 1.2 V and consumes as little as 113.6 nW. The demodulator also has a noise immunity threshold of approximately 3.729 V.

Drosulfakinin signaling modulates female sexual receptivity in Drosophila

Female sexual behavior as an innate behavior is of prominent biological importance for survival and reproduction. However, molecular and circuit mechanisms underlying female sexual behavior is not well understood. Here, we identify the Cholecystokinin-like peptide Drosulfakinin (DSK) promotes female sexual behavior in Drosophila. Manipulation both Dsk and DSK neuronal activity impact female sexual receptivity. In addition, we reveal that Dsk-expressing neurons receive input signal from R71G01GAL4 neurons to promote female sexual receptivity. Based on intersectional technique, we further found the regulation of female sexual behavior relies mainly on medial DSK neurons rather than lateral DSK neurons, and medial DSK neurons modulate female sexual behavior by acting on its receptor CCKLR-17D3. Thus, we characterized DSK/CCKLR-17D3 as R71G01GAL4 neurons downstream signaling to regulate female sexual behavior.

DYNAMICS OF THE TEMPERATURE SENSOR OF THE «SMART HOME» HEATING SYSTEM

The article deals with the issues of modeling the control of room heating. The dynamics of the object-the heating system of a residential building-is obtained. The equation of the dynamics of the temperature sensor is derived. Dynamic errors are determined by the dynamic characteristics of the temperature sensor. The dynamic error is determined for a stepwise change in the input signal.

Application of Dynamic Input Shaping for a Dual-Mirror System

The task of trajectory planning for a dual-mirror optical pointing system greatly benefits from carefully designed dynamic input signals. This paper summarizes the application of multivariable input shaping (IS) for a dual-mirror system, starting from initial open-loop step-response data. The optical pointing system presented consists of two Fast Steering Mirrors (FSM) for which dynamically coupled input signals are designed, while adhering to mechanical and input signal constraints. For the solution, the planned trajectories for the dual-mirrors are determined via (inverse) kinematic analysis. A linear program (LP) problem is used to compute the dynamic input signal for each of the FSMs, with one of the mirrors acting as an image motion compensation device that guarantees tracking of a planned trajectory within a specified accuracy and the operating constraints of the FSMs.

Numerical simulation of the frequency response and phase response of combined UWB antennas in the receiving-transmitting mode

Results of a simulation and experimental study of a combined ultra-wideband antenna, which is a combination of electric and magnetic dipoles, are presented. The input signal recovery of the transceiver path of combined antennas made by the MathCad code and the experimental one were compared. The reconstruction took place according to the frequency and phase responses of the transceiver path of these antennas.