Input Signal Changes Research Articles

Dynamics of two feed forward genetic motifs in the presence of molecular noise

Understanding the function of common motifs in gene regulatory networks is an important goal of systems biology. Feed forward loops (FFLs) are an example of such a motif. In FFLs, a gene (X) regulates another gene (Z) both directly and via an intermediary gene (Y). Previous theoretical studies have suggested several possible functions for FFLs, based on their transient responses to changes in input signals (using deterministic models) and their fluctuations around steady state (using stochastic models). In this paper we study stochastic models of the two most common FFLs, “coherent type 1” and “incoherent type 1”. We incorporate molecular noise by treating DNA binding, transcription, translation, and decay as stochastic processes. By comparing the dynamics of these loops with models of alternative networks (in which X does not regulate Y), we explore how FFLs act to process information in the presence of noise. This work highlights the importance of incorporating realistic molecular noise in studying both the transient and steady-state behavior of gene regulatory networks.

Optimized position analysis of multi-directional active mounting system based on quantification formula

This paper mainly studies the influence of active mounting system for vehicle engine vibration from both theoretical and experimental aspects. First, an active mounting system that considers both horizontal and vertical directions is proposed, modeled, analyzed and verified. The structure contains a bent beam representing the engine, two sets of actuator and elastomer forming active paths, and a straight beam representing the chassis. Second, the force and phase of the active mounts at each location combination are calculated based on static and dynamic methods. The dynamic analysis method is used to examine how the control signal changes with the input signal to learn the correlation between them. The static analysis is employed to obtain the change in force applied to the paths when the position of the input signal changes and to find the optimized placement criteria. Through the position relationship between force and mounting, a specific position where the applied force is relatively small was considered the optimized position of the active mounting system. Finally, these criteria were verified with experiments and the results are matched well with each other.

Адаптивне оцінювання фінансової безпеки суб’єктів галузі ритейлу в умовах активного розвитку метапросторових бізнес-колаборацій

The article examines the relationship between the development of metaspatial business collaborations in the sphere of retail and the financial security of economic entities (FSEE), which is defined as a state characterized by: balance and quality of financial instruments, technologies and services; resilience to internal and external threats; the ability of the financial system to ensure the achievement of financial goals. An improved methodology for adaptive assessment and analysis of the FSEE has been proposed, which includes three blocks: a block for assessing the state of the FSEE, a block for threat analysis and a block for the development of managerial influences. The FSEE assessment block includes the following four stages: formation of the FSEE scorecard; comprehensive assessment of the level of the FSEE; assessment of the state of individual groups of the FSEE; identification of the FSEE states. The model of formation of the system of indicators is the initial model of the block, the purpose of which is to form and filter preliminary financial indicators that are most significant for such an assessment. The formation of indicators is possible, depending on information security, by expert and statistical methods. The model of integral assessment of the level of the FSEE allows to build an integral indicator that reflects the general level of the financial component of economic security. Its value is computed using the method of constructing a taxonomic indicator of the level of development, which is one of the methods of multivariate analysis. The model for assessing the state of individual groups of the FSEE includes the formation of values of local levels of financial security for certain groups of financial indicators and even an assessment of the impact of these indicators on the corresponding local level of financial security. The developed set of models allows to obtain forecast assessmets that can be used both in the development of the strategy of economic security of enterprise and in the process of operational management of the FSEE. The proposed set of models has been approbated to assess the level of financial security on the basis of data on the activities of 20 retailers in Ukraine. To assess and analyze financial security, neural network models were used, which, due to the property of generalization, insensitivity to small changes in input signals, the ability to learn and abstract, made it possible to solve the problems of the amount of computing resources, as well as the lack of complete information. The use of a neural network approach to the assessment and analysis of the financial component of economic security made it possible to better present a model of the joint influence of factors and threats on the level of the FSEE. In the context of the active development of metaspatial business collaborations, especially in the field of retail, the proposed methodology for adaptive assessment and analysis of financial security of economic entities is extremely relevant.

Research on the Predictive Algorithm of Wear Degree Based on Adaptive Feature Learning

In the prediction and modeling analysis of wear degree in the field of industrial parts processing, there are problems such as poor prediction ability for long sequence data and low sensitivity of output feedback to changes in input signals. In this paper, a combined prediction model is proposed that integrates dual attention mechanisms and self-regressive correction. Firstly, pre-processing is performed on the collected wear data to eliminate noise and aberrant mutation data. Then, the feature attention mechanism is introduced to analyze the input data sequence, and the weights of each feature under the temporal condition are set based on the contribution of the prediction results, thereby obtaining the LSTM hidden state at the current time. Subsequently, the temporal attention mechanism is introduced to perform a weighted calculation of the hidden state information, analyze the correlation of long-term sequential wear data, and decode and output the analysis results. Finally, the ARIMA model is used to perform linear correction on the predicted results to improve the accuracy of wear degree prediction. The proposed model is compared and analyzed with the models that are highly related in recent research on real-world wear degree datasets. The experimental results show that the improved model has a better ability to improve the corresponding problems and has a significant increase in prediction accuracy.

Hardware implementation of Boolean functions based on the automaton model

Objectives. Currently, electronic control devices are increasingly being introduced into various household and production products. Microcontrollers of a wide variety of configurations are widely used as such devices. Another approach can be proposed where a control device with a standard structure is synthesized from typical integrated circuits and implements a Boolean function describing the required control actions.The purpose of the work is to investigate the possibility of implementing Boolean functions using devices with a standard structure, the design of which is based on the use of a discrete automaton model.Methods. The original Boolean function to be implemented is given as a disjunctive normal form. A binary decision diagram (BDD) is built for such function, optimized by the number of vertices, on the basis of which a graph of transitions of a synchronous Moore automaton with an abstract state is formed. Further, after performing the state encoding step of the machine, input information for flashing (programming) of the matrix memory of the read-only memory (ROM) is generated based on its transition table.Results. A device that implements a Boolean function based on an automaton model is synthesized from typical microcircuits. The main component is ROM, which, according to the standard structure of the device, is supplemented by a shift register, a state register, a trigger and three selectors of the initial and two final states.Conclusion. The process of designing a device with standard structure that implements the Boolean function, as a result, comes down to programming the ROM matrix memory based on an automaton transition table. The use of a reprogrammable ROM allows to change the functionality of the device while maintaining the previous circuit implementation. The disadvantage of such a device, as well as devices implemented on the basis of microcontrollers, is the low speed, the advantage is the possibility of use it in various products and devices, primarily for household purposes, which do not require a high-speed response to the change of input signal.

Ways of ensuring effective detection of multiposition OFDM signals

The problem of processing various stages of signal detection in interference conditions was investigated, statistical processing methods were analyzed. The task of information processing is to detect the moment of change in the input signal (change in signal level, change in frequency, occurrence of a pulse signal, etc.). External interference distorts the input signal, so it can be distinguished based on the statistical difference between interference and mixing. Additional interference that distorts the input signal in the equipment of the telecommunications system is introduced during the conversion of the signal before entering the digital processing equipment. Various methods of statistical processing are studied: optimal, adaptive, non-classical optimization methods based on the principles of non-displacement, invariance, similarity, etc. The application of non-parametric methods was investigated when the functional form of the distribution of the input data was unknown and only the general difference between the presence and absence of the signal was indicated. The ranking method of signal detection processing deserves special attention. Adaptive methods are used if the distribution of input data is known with accuracy up to the array of unknown parameters. Nonparametric methods are used when the functional form of the distribution of input data is unknown, and only the general differences between the situations of the presence and absence of a signal are specified. The analysis of signal detection methods shows that one of the promising directions of research is the development of methods that use the invariant properties of the system and ensure the maximum possible storage of signal information. It was studied that the flexibility of the ranking procedure makes it possible to solve a wide range of problems in the detection of multi-position signals under conditions of non-parametric a priori uncertainty.

Self-Tuning PLL: A New, Easy, Fast and Highly Efficient Phase-Locked Loop Algorithm

A phase-locked loop or phase lock loop (PLL) is, essentially, a closed loop control system, where its output signal (regulated) maintains a direct (chosen) relationship to the input signal (unregulated). If the phases of the input and output signals remain the same, their frequencies will also match. In its usual way, a PLL is an electronic circuit with filters and controllers that must be tuned each time, according to the input signal parameters. As a contribution and novelty, this paper introduces a new, easy, fast and highly efficient PLL algorithm, that it does not need to adjust every time the input signal changes. This makes it independent of the input signal it receives and, therefore, and in a certain way, universally applicable. In addition, the proposal is implemented exclusively by software, housed in a microcontroller, which also represents another novelty. As another remarkable feature, the proposed algorithm is very simple, with a very low computational cost, which makes it very stable, practically insensitive to noise, and very fast. These characteristics and the performance shown over a wide range of input signal frequencies, make the proposed algorithm suitable for use in different applications, from the electricity grid synchronization to the demodulation of frequency modulation, DC motor drives, etc. In addition to frequency and phase tracking, the developed algorithm generates the signal corresponding to the fundamental frequency of the input signal. Furthermore, the algorithm behavior is not affected by the input signal distortion and is independent of its initial phase. The performance and excellent behavior of the developed algorithm are evaluated through simulations and experimental tests.

Monitoring Junction Temperature of RF MOSFET under Its Working Condition Using Fiber Bragg Grating.

When a high-power radio frequency (RF) metal oxide semiconductor field effect transistor (MOSFET) works in low-efficiency situations, considerable power is dissipated into heat, resulting in an excessive junction temperature and a likely failure. In this study, an optical fiber Bragg grating (FBG) sensor is installed on the die of a high-power RF MOSFET. The temperature change of RF MOSFET with the change of input signal is obtained by using the temperature frequency shift characteristic of the FBG reflected signal. Furthermore, the fast and repetitive capture of junction temperature by FBG reveals details of the temperature variation within each RF pulse, which is correctly correlated with input signals. The results show that besides monitoring the temperature accumulation of the chip for a long time, the FBG can also capture junction temperature details of the chip within each pulse period. Finally, a Cauer-type thermal model of the RF MOSFET was constructed based on the temperature information captured by the FBG.

A Novel TRNG Based on Traditional ADC Nonlinear Effect and Chaotic Map for IoT Security and Anticollision

In the rapidly developing Internet of Things (IoT) applications, how to achieve rapid identification of massive devices and secure the communication of wireless data based on low cost and low power consumption is the key problem to be solved urgently. This paper proposes a novel true random number generator (TRNG) based on ADC nonlinear effect and chaotic map, which can be implemented by traditional processors with built-in ADCs, such as MCU, DSP, ARM, and FPGA. The processor controls the ADC to sample the changing input signal to obtain the digital signal DADC and then extracts some bits of DADC to generate the true random number (TRN). At the same time, after a delay based on DADC, the next time ADC sampling is carried out, and the cycle continues until the processor stops generating the TRN. Due to the nonlinear effect of ADC, the DADC obtained from each sampling is stochastic, and the changing input signal will sharply change the delay time, thus changing the sampling interval (called random interval sampling). As the input signal changes, DADC with strong randomness is obtained. The whole operation of the TRNG resembles a chaotic map, and this method also eliminates the pseudorandom property of chaotic map by combining the variable input signal (including noise) with the nonlinear effect of ADC. The simulation and actual test data are verified by NIST, and the verification results show that the random numbers generated by the proposed method have strong randomness and can be used to implement TRNG. The proposed TRNG has the advantages of low cost, low power consumption, and strong compatibility, and the rate of generating true random number is more than 1.6 Mbps (determined by ADC sampling rate and processor frequency), which is very suitable for IoT sensor devices for security encryption algorithms and anticollision.

A High Conversion Gain Envelope Detector with Wide Input Range for Simultaneous Wireless Information and Power Transfer System

Wireless sensors networks (WSN) have been gradually facilitating the pervasive connectivity of wireless sensor nodes. A greater number of wireless sensors have been used in different aspects of our life. However, limited device battery life restricts the applications of large-scale WSN. This paper presents a batteryless envelope detector with radio frequency energy harvesting (RFEH) for wireless sensor nodes, which enables simultaneous wireless information and power transfer (SWIPT). The envelope detector is designed for small modulation index AM signals with large amplitude variations. Therefore, the envelope detector is supposed to have wide input range while achieving a high conversion gain. We proposed an adaptive biasing technique in order to extend the input range of envelope detector. The input differential pair is adaptively biased through a feedback loop to overcome the variation of bias point when the amplitude of input signal changes. The cross coupled rectifier and DC-DC boost converter with maximum power point tracking (MPPT) are presented against power conversion efficiency (PCE) degradation of RF rectifier with the input power varying. The adaptive biased envelope detector is theoretically analyzed by square law MOSFET model. Designed with 0.18 μm complementary-metal-oxide-semiconductor (CMOS) standard process, the power consumption of proposed envelope detector is 9 μW. Simulated with a 915 MHz AM input signal with 2 Mbps data rate and 0.05 modulation index, the proposed envelope detector achieves 20.37 dB maximum conversion gain when the amplitude of input signal is 0.5 V, and the PCE of energy harvesting circuits achieves 55.2% when input power is –12.5 dBm.

Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components

Ball bearings are one of the most common components used in rotating machines. They reduce the rotational friction between the shaft and fixed components and maintain the center line of rotation of the shaft. A damaged bearing will cause abnormal vibration and noise, and often results in machine failure and loss of production. In this study the public database on ball bearings, provided by the Vibration Institute of Machinery Failure Prevention Technology (MFPT), was used for data retrieval and analysis, and a diagnosis model was created according to the data sets of the bearing in the database. Three different approaches were used for the extraction of features and a classifier was used to implement a diagnostic system. The aim of this study was a comparison of three approaches. The first was the Short-time Fourier Transform (STFT) where the time-frequency domain image is extracted as the feature used for status identification. The second and third approaches were based on the Chen-Lee Chaotic and the Lorenz Chaotic Systems and chaotic dynamic error maps were used in analysis and feature status identification. Chaotic systems are particularly sensitive to the slightest changes in input signals, and the time domain signals from bearings in different conditions were mapped onto individual images. The feature images extracted by the three different approaches were then used for training and verification in a Convolutional Neural Network (CNN). From the results of the experiments, it can be seen that all three approaches gave high identification rates. The interactive verification identification rate of the Chen-Lee chaotic system with CNN under three statuses reached 98.33%, and it also had the best computational efficiency in the condition without losing any classification accuracy. This will make a substantial contribution to real-time ball bearing fault diagnosis.

Bio-Inspired Video Enhancement for Small Moving Target Detection.

Moving targets at a very large distance from a camera appear small and of low contrast. The low signal-to-noise-ratio and the presence of clutter in the background degrade the detection performance of conventional moving object detection techniques. To address these challenges, we propose temporal pre-processing of video frames using a biologically-inspired vision model. The bio-inspired model consists of multiple layers of processing analogous to the photoreceptor cells in the visual system of small insects. The adaptive filtering mechanism in the photoreceptor cells suppresses clutter and expands the possible range of input signal changes which improves the target background contrast. We perform experiments on real world video sequences of small moving targets captured with a high bit depth, high resolution and high frame-rate camera. Experimental results show that the biological vision based pre-processing leads to improved detection performance when used in conjunction with a variety of computer vision based moving object detection algorithms. The temporal bio-processing alone has improved the area under the receiver operating characteristic (AUROC) curve of the best performing algorithm by 75.4%. Our results suggest that the bio-inspired pre-processing has strong potential to become a key component of a practical small target detection system.

Logical Chaotic Resonance in a Bistable System

In this work, we demonstrate a new chaotic signal-induced phenomenon that the output of a chaotic signal-driven bistable system can be consistently mapped to specific logic gate operation in an optimal window of chaotic signal intensity. We term this phenomenon logical chaotic resonance (LCR). Then, an intuitive interpretation for LCR phenomenon is given based on potential well map and mean first-passage time. Through LCR mechanism, the chaotic signal with proper intensity is used to obtain reliable logical gate in the bistable system. Besides, appropriately increasing the chaotic signal intensity can effectively improve the response speed of the bistable system to the change of input signal. Finally, the role of chaotic signal in enhancing the capacity of resisting disturbance of parameters is demonstrated.

Unjuk Kerja Filter Pasif dalam Mereduksi Distorsi Gelombang pada AC/DC Konverter

This paper shows the use of passive filters to reduce the distortion of input waves in a single phase uncontrolled rectifier. Simulations are carried out with various passive filter models to get a decrease in the value of Total Harmonic Distortion (THD) and correct the distortion of the uncontrolled single phase rectifier input waveform. The results of the simulation show the shape of the input signal changes in the uncontrolled single-phase rectifier, analyzing the value of power factor improvement and also the THD value.

An Auto-Tuning Continuous-Time Bandpass Sigma-Delta Modulator with Signal Observation for MEMS Gyroscope Readout Systems.

This paper presents the design and implementation of an auto-tuning continuous-time bandpass sigma-delta (ΣΔ) modulator for micro-electromechchanical systems (MEMS) gyroscope readout systems. Its notch frequency can well match the input signal frequency by adding a signal observation to the traditional ΣΔ modulator. The filter of the observation adopts the same architecture as that of the traditional ΣΔ modulator, allowing the two filters to have the same response to input signal change, which is converted into a control voltage on metal-oxide semiconductor (MOS) resistance in the filters. The automatic tuning not only works to solve the mismatch problem caused by process error and temperature variation, but can also be applied to the interface circuit of gyroscopes with different resonant frequencies. The circuit is implemented in a 0.18-μm complementary metal-oxide semiconductor (CMOS) process with a core area of 2.4 mm2. The improved modulator achieves a dynamic range of 106 dB, a noise floor below 120 dB and a maximum signal-to-noise and distortion ratio (SNDR) of 86.4 dB. The tuning capability of the chip is relatively stable under input signals from 6 to 15 kHz at temperatures ranging from −45 to 60 °C.

A Simple Quasi-Static Compact Model of Bipolar ReRAM Memristive Devices

In this brief, we present a quasi-static compact model for bipolar ReRAM memristive devices. This model is based on a piecewise model in flux-charge space for reset and set transitions that has been extended to build a compact model of reset/set transitions for different slopes. In order to show its functionality, we use it to reproduce the behavior of a device fabricated by the CNR-IMM, MDM Laboratory. We discuss the needed parameters extraction procedure for the device. As shown in the results, the implemented model is able to capture the effects of the slope change in the ramp input signal for reset and set by using a set of technological parameters related to the device and information related to the slope of the ramp input voltage signal.

Synthesis of smart sensors based on the introduction of minimal structural redundancy

The problems of operation of measuring systems and changes in their parameters due the influence of various kinds of destabilizing factors, including time, are considered, and the primary elements of the measuring path – sensors and converting equipment – are most susceptible to such an influence. The deviation of the values of the parameters of the measurers from the nominal values leads to significant errors in the assessment of the unknown input signal, which necessitates the current (during operation) identification of the measurer. The problem of the current identification of the measurer parameters under unknown input influences is solved. The identification procedure is carried out by introducing an additional channel for transforming the measured value in the spatial domain. In this case, the transformation of the measured value in the additional channel of the structurally redundant measurer has the form of a preliminary functional (nonlinear) transformation of the m-th power over the input signal. The solution to the current identification problem is considered for the linear static characteristic of the main channel of the measuring transducer. An additional equation in solving the problem of current identification and conducting metrological self-control in an intelligent sensor is presented in the form of a regression relationship between the output signals of the additional and main channels of the structurally redundant measurer. The unknown parameters of the regression equation are determined from the results of processing the time samples of the output signals of the additional and main channels using the least squares method. The dependences of the rms measurement error of the input quantity on the rms value of the measurement noise in the output signals of the structurally redundant measurer, the dynamics of the input signal change and the power of nonlinearity m of the preliminary functional transformation in the additional measuring channel are presented. In the course of research, it was revealed that the shape and spectrum of the input signal do not significantly affect the measurement accuracy. It is shown that the highest measurement accuracy is provided by the preliminary quadratic conversion of the input signal in the additional channel of the minimum-redundant sensor. The research results can be used for metrological selfcontrol in smart sensors or smart measuring systems.

Robustness of the digital filter to differing calibration flows

The use of a tubing system is a common means of measuring surface pressures for wind engineering experiments. However, it introduces signal distortions on the pressure fluctuations. This is primarily addressed before data acquisition by altering the physical tube to produce a more suitable behaviour, or post-acquisition by applying a spectral filter. More recently, digital filters have been suggested for correcting the pressure distortions, providing faster correction of data by working in the time-domain. However, the reproducibility, or stability, of the filter coefficients to differing calibration flows has not been assessed. This includes the effect of different dominant frequencies and mean pressures in the calibration signal.Digital filters were derived for a 1500 ​mm flexible tube, sampled at 400 ​Hz with a variety of input signals. These included a controlled signal from a speaker and a turbulent wind tunnel environment. It was found that the digital filter coefficients were stable to the change in input signal, indicating the tube behaviour has been properly characterised by the filter. It was also seen to reject signal noise more effectively than the equivalent spectral filter, while being more computationally-efficient in derivation and application than the latter. The effectiveness of the filter is also discussed with an assessment on the resulting mean, peak and fluctuating error.

The Precompression Processing of LMS Algorithm in Noise Elimination

In this study, the authors propose a novel precompression processing (PCP) of the least mean squares (LMS) algorithm based on a regulator factor. The novelty of the PCP algorithm is that the compressed input signals vary from each other on different components at each iteration. The input signal of the improved LMS algorithm is precompressed based on the regulator factor. The precompressed input signal is not only related to the regulator factor α and the current value of the input signal at each iteration but also related to the amplitude of the input signal before this iteration. The improved algorithm can eliminate the influence of input signal mutation on the filter performance. In the numerical simulations, we compare the improved LMS algorithm and NLMS algorithm in the cases of normal input signal and input signal with mutation and the influence of different regulator factors on the noise elimination. Results show that the PCP algorithm has good noise elimination effect when the input signal changes abruptly and the regulator factor α = 0.01 can meet the requirements.

Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning

To achieve persistent systems in the future, autonomous underwater vehicles (AUVs) will need to autonomously dock onto a charging station. Here, reinforcement learning strategies were applied for the first time to control the docking of an AUV onto a fixed platform in a simulation environment. Two reinforcement learning schemes were investigated: one with continuous state and action spaces, deep deterministic policy gradient (DDPG), and one with continuous state but discrete action spaces, deep Q network (DQN). For DQN, the discrete actions were selected as step changes in the control input signals. The performance of the reinforcement learning strategies was compared with classical and optimal control techniques. The control actions selected by DDPG suffer from chattering effects due to a hyperbolic tangent layer in the actor. Conversely, DQN presents the best compromise between short docking time and low control effort, whilst meeting the docking requirements. Whereas the reinforcement learning algorithms present a very high computational cost at training time, they are five orders of magnitude faster than optimal control at deployment time, thus enabling an on-line implementation. Therefore, reinforcement learning achieves a performance similar to optimal control at a much lower computational cost at deployment, whilst also presenting a more general framework.