Constant Sampling Rate Research Articles

Modeling of forced-vibration systems using continuous-time state-space neural network

Dynamic analysis of forced-vibration systems in civil engineering could be computationally inefficient or even hard to converge if the systems are stiff or highly complicated. Rapid advances in machine learning make it possible to formulate surrogate models for forced-vibration systems using neural networks. The widely used neural networks such as the convolutional neural network (CNN), recurrent neural network (RNN), etc., usually require a constant sampling rate and data length, thus they are difficult to be implemented for real-time calculation of the dynamic system with varying sampling rates. Recently, the continuous-time state-space neural network (CSNN) has shown the capability to lift these restrictions and has been drawing growing attention from the community. In this paper, we propose a generalized CSNN model for various forced-vibration systems (linear and nonlinear). The CSNN model comprises two sets of independent neural networks aiming to compute the state derivative and system response, respectively. Both neural networks adopt linear and nonlinear layers in parallel, instead of only fully connected nonlinear layers as adopted in the literature. This configuration is aimed to enhance the CSNN model with its capability to recognize the linear and nonlinear behaviors of systems. Additionally, the bias options in the CSNN model are all turned off to improve the stability of the model in the long-term time-series forecast, premised on the assumption that the forced-vibration systems are dissipative systems without drift, which is the most common case in civil engineering. Integration on the state derivative at the current time step is executed to obtain the state at the next time step using the explicit 4th-order Runge–Kutta method. Both numerical and experimental illustrative examples are provided, demonstrating that the CSNN model can achieve high performance and training efficiency with a few hyper-parameters, and thus is highly promising for engineering applications.

A novel parallel constrained extended Kalman filter for improving navigation algorithm – case study: gas pipeline

In many real navigation problems, moving objects may have some state or measurement constraints along their way. Using these constraints in conventional Extended Kalman Filter (EKF) equations results large matrices which are computationally time-consuming. In this paper, a new Constrained Navigation Filter (CNF) is proposed as a parallel to reduce the computational burden of the conventional EKF algorithm while increasing the positioning accuracy. So a methodology has been developed for Strap-down Inertial Navigation System (SINS) based on MEMS IMU applied on Pipeline Inspection Gauge (PIG) to sense data at constant sampling rate of 108 km of the pipeline. The results verified that using such a hybrid approach has improved positional accuracy 8.97% in comparison with that of the latest methods like EKF/ Pipe Line Junctions (PLJ). Also, the proposed method is 2.277 times better than EKF/PLJ in the algorithm runtime.

Using the POD sampler for quantitative diffusive (passive) monitoring of volatile and very volatile organics in ambient air: Sampling rates and analytical performance

POD diffusive samplers loaded with Carbopack X and Carbograph 5TD were exposed to certified calibration mixtures containing a total of 110 different ozone precursor and air toxic compounds. Constant sampling rates were identified for 39 ozone precursors and 33 air toxics. As 9 of these compounds were included in both mixtures, this meant a total of 63 different volatile and very volatile compounds were sampled using the POD with overall expanded uncertainties below 30 % for the sampling rate associated with the whole range of sampling times from 2 to 24 h.Carbograph 5TD exhibited superior performance for diffusive sampling of oxygenated and halogenated compounds in the air toxics mixture, while Carbopack X showed higher sampling efficiencies for aliphatic and aromatic hydrocarbons, as well as halogenated compounds derived from benzene and C2 carbon number hydrocarbons.A model has been developed and applied to estimate sampling rates, primarily for the more volatile and weakly adsorbed compounds, as a function of the collected amount of analyte and the exposure time. For an additional 9 ozone precursors on Carbopack X, and 11 air toxics on Carbograph 5TD, the expanded uncertainties of modelled sampling rates were reduced to below 30 % and have a significantly reduced uncertainty compared to those associated with an averaged sampling rate.The paper provides Freundlich’s isotherm parameters for the estimated (modelled) sampling rates and defines a pragmatic approach to their application. It does so by identifying the best sampling time to use for the expected exposure concentrations and associated analyte masses. This allows for expansion of the sampling concentration range from hundreds ng m−3 to mg m−3, while avoiding saturation of the adsorbent.Finally, field measurement comparisons of POD samplers, pumped tube samplers and online gas chromatography (GC), for sampling periods of 3 and 7 days in a semi-rural background area, showed no significant differences between reported concentrations.

Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks

Energy limitation is a major issue in wireless sensor networks where a high volume of redundant data is collected periodically and transmitted through the network. Therefore, efficient energy consumption is the key solution to maximize the network lifetime. This paper proposes an adaptive sampling approach based on spatio-temporal correlation of collected data and on nodes residual energy. This approach aims to optimize sampling rates of sensor nodes while ensuring a high quality of the collected data. In addition, a data reconstruction method based on linear regression is adopted in the sink to reconstruct the missing samples due to the sampling rate reduction and adaptation compared to the case of a constant maximal sampling rate. We compared our approach with recently proposed adaptive sampling benchmark methods in different scenarios of data temporal correlation. Simulation results demonstrate the effectiveness of our proposed method in optimizing energy consumption by reducing the sampling rate while maintaining data quality. Our contribution can be applied to several fields, particularly, the field of water resources management.

Comparative Accuracy Study Of LDV Profile-Sensor Acquisition Modes And Particle Diameters

The laser Doppler velocity profile sensor (LDV-PS) system offers the possibility to measure flow velocities with high spatial resolution and is therefore a promising method for the investigation of flows with high velocity gradients. The present experimental study revolves around the interplay between a broad range of chosen LDV-PS acquisition parameters, the dynamic velocity range within the observed measurement volume, and the accuracy of the velocity and position estimation from an application perspective. In the chosen set-up, thin wires of different diameters rotate at varying velocity levels and are captured instead of tracer particles by the applied system. It is observed that constant sample rate and number for the Fast Fourier Transform (FFT) evaluation of the detected burst signal allow straightforward measurements of unknown velocity profiles, but imply a velocity-dependent spatial measurement uncertainty. In contrast, velocity-adjusted acquisition settings allow to acquire particle bursts with equal signal resolution and length effecting a constant measurement accuracy at all velocity levels when adjusted appropriately. The position standard deviation is furthermore observed to increase with the wire diameter.Hence, the size of the scattering object should be chosen appropriately small during calibration and measurement. The gained knowledge offers the possibility to adapt the evaluation parameters more specifically to a given measurement problem.Consequently, measurements can be conducted in flows with small velocities with a high spacial resolution, when the FFT parameters and processing routines are adjusted accordingly, small particles are chosen and vibrations in the set-up are avoided.

About Partial Reachability Issues in an SEIR Epidemic Model and Related Infectious Disease Tracking in Finite Time under Vaccination and Treatment Controls

This paper studies some basic properties of an SEIR (Susceptible-Exposed-Infectious-Recovered) epidemic model subject to vaccination and treatment controls. Firstly, the basic stability, boundedness, and nonnegativity of the state trajectory solution are investigated. Then, the problem of partial state reachability from a certain state value to a targeted one in finite time is focused on since it turns out that epidemic models are, because of their nature, neither (state) controllable from a given state to the origin nor reachable from a given initial condition. The particular formal statement of the partial reachability is focused on as a problem of output-reachability by defining a measurable output or lower dimension than that of the state. A special case of interest is that when the output is defined as the infectious subpopulation to be step-to-step tracked under suitable amounts being compatible with the required constraints. As a result, and provided that the output-controllability Gramian is nonsingular on a certain time interval of interest, a feedback control effort might be designed so that a prescribed value of the output can be approximately tracked. A linearization approximation is performed to simplify and facilitate the above task which is based on a point-to-point linearization of the solution trajectory. To this end, an “ad hoc” sampled approximate output trajectory is defined as control objective to be targeted through a point-wise calculated Jacobian matrix. A supervised appropriate restatement of the targeted suited sampled output values is redefined, if necessary, to make the initial proposed sampled trajectory compatible with the various needed constraints on nonnegativity and control boundedness. The design can be optionally performed under constant or adaptive sampling rates. Finally, some numerical examples are given to test the theoretical aspects and the design efficiency of the model.

거친 환경에 최적화된 가속도계 무선 데이터 로깅모듈

A wireless data logging system connected with accelerometer was developed for measuring vibration in harsh environment where conventional vibration equipment could not be used, e.g., on deck of the battleship, surface of the submarine or airplane, etc. To minimize wired connections, the module used a battery to supply power, and the data was recorded by the module itself on the SD card. Data logging was carried out through Arduino. Because the voltage levels of the accelerometers were different, the internal circuits were adjusted by changing the voltage levels through level shifting. Data logging was optimized using the hardware interrupt and double buffering to maintain a constant sampling rate during data logging and to prevent loss of data. The vibration data obtained through the manufactured module was compared with data measured by LMS test to confirm the validity of the signal. It was also confirmed that the module measurement data was normal even during long-time measurements.

Evolutionary Detection Accuracy of Secret Data in Audio Steganography for Securing 5G-Enabled Internet of Things

With the unprecedented growing demand for communication between Internet of Things (IoT) devices, the upcoming 5G and 6G technologies will pave the path to a widespread use of ultra-reliable low-latency applications in such networks. However, with most of the sensitive data being transmitted over wireless links, security, privacy and trust management are emerging as big challenges to handle. IoT applications vary, from self-driving vehicles, drone deliveries, online shopping, IoT smart cities, e-healthcare and robotic assisted surgery, with many applications focused on Voice over IP (VoIP) and require securing data from potential eavesdroppers and attackers. One well-known technique is a hidden exchange of secret data between the devices for which security can be achieved with audio steganography. Audio steganography is an efficient, reliable and low-latency mechanism used for securely communicating sensitive data over wireless links. MPEG-1 Audio Layer 3’s (MP3’s) bit rate falls within the acceptable sound quality required for audio. Its low level of noise distortion does not affect its sound quality, which makes it a good carrier medium for steganography and watermarking. The strength of any embedding technique lies with its undetectability measure. Although there are many detection techniques available for both steganography and watermarking, the detection accuracy of secret data has been proven erroneous. It has yet to be confirmed whether different bit rates or a constant sampling rate for embedding eases detection. The accuracy of detecting hidden information in MP3 files drops with the influence of the compression rate or increases. This drop or increase is caused by either the increase in file track size, the sampling rate or the bit rate. This paper presents an experimental study that evaluates the detection accuracy of the secret data embedded in MP3. Training data were used for the embedding and detection of text messages in MP3 files. Several iterations were evaluated. The experimental results show that the used approach was effective in detecting the embedded data in MP3 files. An accuracy rate of 97.92% was recorded when detecting secret data in MP3 files under 128-kbps compression. This result outperformed the previous research work.

Characteristics and Performances of a Nanostructured Material for Passive Samplers of Gaseous Hg.

Passive air samplers (PASs) have been used for mapping gaseous mercury concentration in extensive areas. In this work, an easy-to-use and -prepare gold nanoparticle (NP)-based PAS has been investigated. The PAS is constituted of a microfibrous quartz disk filter impregnated of gold NP photo-growth on TiO2 NPs (Au@TiO2) and used as gaseous mercury adsorbing material. The disk was housed in a cylinder glass container and subjected to an axial diffusive sampling. The adsorbed mercury was measured by thermal desorption using a Tekran® instrument. Different amounts of Au@TiO2 (ranging between 4.0 and 4.0 × 10−3 mg) were deposited by drop-casting onto the fibrous substrate and assessed for about 1 year of deployment in outdoor environment with a mercury concentration mean of about 1.24 ± 0.32 ng/m3 in order to optimize the adsorbing layer. PASs showed a linear relation of the adsorbed mercury as a function of time with a rate of 18.5 ± 0.4 pg/day (≈1.5% of the gaseous concentration per day). However, only the PAS with 4 mg of Au@TiO2, provided with a surface density of about 3.26 × 10−2 mg/mm2 and 50 μm thick inside the fibrous quartz, kept stability in working, with a constant sampling rate (SR) (0.0138 ± 0.0005 m3/day) over an outdoor monitoring experimental campaign of about 1 year. On the other hand, higher sampling rates have been found when PASs were deployed for a few days, making these tools also effective for one-day monitoring. Furthermore, these PASs were used and re-used after each thermal desorption to confirm the chance to reuse such structured layers within their samplers, thus supporting the purpose to design inexpensive, compact and portable air pollutant sampling devices, ideal for assessing both personal and environmental exposures. During the whole deployment, PASs were aided by simultaneous Tekran® measurements.

Digital Closed-Loop Control Strategy to Maintain the Phase Shift of a Multi-Channel BCM Boost Converter for PFC Applications

This paper presents a novel closed-loop digital control scheme to maintain proper interleaving operation of a multi-channel boundary-conduction-mode boost converter used in power-factor-correction applications. The proposed control scheme is suitable for implementation on a low-cost microcontroller. This is made possible by executing the control scheme at a constant sampling rate that is much slower than the maximum switching frequency of the converter. The performance of the control scheme is further improved by using an adaptive gain that scales with the on -time of the converter, which provides optimal phase-shift control and stability under all operating conditions. The digital closed-loop control scheme is validated experimentally on a three-channel 1-kW prototype ac–dc converter. The converter has an output voltage of 400 V and a universal input voltage range of 85–265 V. The prototype converter uses a low-cost microcontroller while demonstrating correct interleaving operation.

Nonuniform Fourier-decomposition MRI for ventilation- and perfusion-weighted imaging of the lung.

PurposeTo improve the robustness of pulmonary ventilation‐ and perfusion‐weighted imaging with Fourier decomposition (FD) MRI in the presence of respiratory and cardiac frequency variations by replacing the standard fast Fourier transform with the more general nonuniform Fourier transform.Theory and MethodsDynamic coronal single‐slice MRI of the thorax was performed in 11 patients and 5 healthy volunteers on a 1.5T whole‐body scanner using a 2D ultra‐fast balanced steady‐state free‐precession sequence with temporal resolutions of 4‐9 images/s. For the proposed nonuniform Fourier‐decomposition (NUFD) approach, the original signal with variable physiological frequencies that was acquired with constant sampling rate was retrospectively transformed into a signal with (ventilation or perfusion) frequency‐adapted sampling rate. For that purpose, frequency tracking was performed with the synchro‐squeezed wavelet transform. Ventilation‐ and perfusion‐weighted NUFD amplitude and signal delay maps were generated and quantitatively compared with regularly sampled FD maps based on their signal‐to‐noise ratio (SNR).ResultsVolunteers and patients showed statistically significant increases of SNR in frequency‐adapted NUFD results compared to regularly sampled FD results. For ventilation data, the mean SNR increased by 43.4%±25.3% and 24.4%±31.9% in volunteers and patients, respectively; for perfusion data, SNR increased by 93.0%±36.1% and 75.6%±62.8%. Two patients showed perfusion signal in pulmonary areas with NUFD that could not be imaged with FD.ConclusionThis study demonstrates that using nonuniform Fourier transform in combination with frequency tracking can significantly increase SNR and reduce frequency overlaps by collecting the signal intensity onto single frequency bins.

Unique faster‐than‐Nyquist transceiver of the ACM system

The link adaptive transmission system chooses the optimum combinations of modulation order and code rate according to the characteristics of wireless channels to make use of the channel resources. To improve the spectral efficiency further, the authors apply the faster-than-Nyquist (FTN) signalling to the adaptive coding and modulation (ACM) system. The ACM system based on FTN signalling can take advantage of the channel resources by adjusting the packing ratios, modulation orders and code rates. However, the variable packing ratios correspond to variable sampling rate, which challenges the receiver of the ACM system. They propose a new transceiver of the FTN-based ACM system which can detect the FTN signal at a constant sampling rate. Moreover, they search for the optimum modulation and coding schemes of ACM based on FTN by analysing the extrinsic information transfer diagram. The simulation results show that the FTN-based ACM system has additional gains compared with the traditional way and the proposed transceiver may be a better choice in the ACM system.

Low-frequency vibration measurement based on camera-projector system

A low-frequency vibration measurement based on a camera-projector system is proposed and verified by experiment at several hertz. A CCD camera gathered deformed fringes modulated by a vibrating trumpet at a constant sampling rate. Information on amplitude and frequency are contained in a series of observed images. The height value of each pixel point in the image can be restored by using Fourier transform profilometry. Connecting the height value of every sampling frame into a motion trajectory, the frequency and amplitude of the vibrating object can be obtained from the trajectory curve. The experiment restored the vibration process of a trumpet in half a cycle while the vibrating frequency was 3 Hz. Subsequently, the measurement of different frequencies and different amplitudes are comparatively studied. The results show that slight vibration at 35 µm and above can be detected by the system, which confirms the feasibility of the proposed system in the field of industrial vibration measurement.

Adaptive detection method of FTN signalling at constant Nyquist sampling rate

Faster-than-Nyquist (FTN) signalling, as a candidate technology of fifth generation, can transmit more data with the same bandwidth. In the adaptive transmission system based on FTN signalling (FNS), the transmitter changes the packing ratios to adapt to the wireless channels. The scheme that combines the adaptive coding and modulation with the FNS has higher spectral efficiency. However, the symbol rate changing with the packing ratio challenges the receiver of adaptive transmission system based on FNS. Moreover, the decoding complexity based on viterbi algorithm (VA) is increasing exponentially when the packing ratios of FNS decrease. In this study, the authors proposed an adaptive detection method to detect FTN signal at constant sampling rate. The proposed method adopted an linear minimum mean square error (LMMSE) equaliser to solve the mismatch of the symbol rate and the sampling rate. Their simulation results show that the adaptive detection method of FNS can approach the optimal bit error rate performance of the coded FTN system. At the same time, the proposed method can avoid the complexity induced by the variable sampling rate in the adaptive transmission system based on FNS.

GPS-based analysis of physical activities using positioning and heart rate cycling data

This paper addresses the use of multichannel signal processing methods in analysis of heart rate changes during cycling using the global positioning system (GPS) to record the route conditions. The main objectives of this work are in monitoring of physiological activities, cycling features extraction, their classification and visualization. Real data were acquired from 41 cycling rides of the same 11.48-km long route divided into 2460 segments of approximately 60 s. The data were recorded with a varying sampling period within the range of 1–22 s depending on the route profile. The pre-processing stage included preparatory analysis, filtering and resampling of the data to a constant sampling rate. The proposed algorithm includes the evaluation of the cross-correlation between the heart rate and the altitude gradient as recorded by a GPS satellite system. A Bayesian approach was then applied to classify the cycling segment features into two classes (specifying cycling up and down) with the classification accuracy better than 93 %. A comparison with other classification methods is presented in the paper as well. The results include the following relationships: (1) the heart rate and altitude gradient, which shared a positive correlation coefficient of 0.62; (2) the heart rate and speed, which shared a negative correlation coefficient of −0.72 over all of the analysed segments; and (3) the mean heart rate change delay (6.8–11.5 s) in relation to the changes in the altitude gradients associated with cycling up and down. The paper forms a contribution to the use of computational intelligence and visualization for data processing both in cycling and fitness physical activities as well.

Development of Soft Sensors for the Case Where the Time Delay is Random

Time delay and sampling rate are two conditions that can make the design and implementation of soft sensors difficult due limiting the amount of information that can be obtained about the true process. Another wrinkle is the fact that neither of these two parameters is necessarily constant, with the value changing due to changes in the process. One such area of concern is the hot steel mill rolling process, where variable time delay can lead to problems with controlling the process. This paper seeks to examine the appropriate design of the bias update term of the soft sensor in light of variable, but known, time delays for a process operating with constant sampling rate and for both open- and closed-loop conditions. Using a mathematical approach to the problem, the previous results are extended to consider such a case. It is shown that if the process has variable time delay, then designing the bias update term in the soft sensor is very important, as it can have implications for process stability and trackability. Simulations are included to show the impact of different bias update terms.

REVITALIZATION OF PROCESSOR FOR CYBER PHYSICAL SYSTEMS USING RTOS

Cyber-physical systems (CPS) are physical and engineered systems whose operations are monitored, coordinated, controlled and integrated by a computing and communication core. Just as the internet transformed how humans interact with one another, cyberphysical systems will transform how we interact with the physical world around us. These systems will have a constant sensor sampling rate and processor response time. Hence when they are deployed to processors that are shared amongst control and noncontrol tasks it will potentially increases CPU usage, such as cache, interrupts and task management through schedulers and results in interference between tasks. This reduces system stability. . Hence Experiments are done in control and computing communities with the scope of maximizing CPU utilization and maintaining system stability. In this paper we analyze how a Processor maintains its stability when maximum resource is utilized. It can perform various operations such as border security, Rescue operation, Firing, Obstacle detection, Vibration detection and they communicate with the Tankers through RF and also uses Zigbee technology to communicate with the server.

REDEEMING OF PROCESSOR FOR CYBER PHYSICAL SYSTEMS USING RTOS

Cyber Physical Systems (CPS) are designed to make a software application directly interact with the events in the physical world. These systems will have a constant sensor sampling rate and processor response time. When they are deployed to processors that are shared amongst control and non-control tasks it will potentially increases CPU usage, such as cache, interrupts and task management through schedulers, which results in interference between tasks. This response time jitter reduces system stability. Hence Experiments are done in control and computing communities with the scope of maximizing CPU utilization and maintaining System stability. In this paper we develop a Microcontroller Based Multifunctional Mobile Robot with real time movement is demonstrated which replaces huge man power for various applications. It is designed for Military purpose and functions at critical time. Navigation of the Robot is done through Zigbee technology and it uses RF to communicate with the Tankers. A Wireless AV receiver at the Base station displays the images captured by the Camera. To make the Robot highly secured it is coupled with a Laser gun and it interacts with the physical world through sensors such as Obstacle sensor and Vibration sensor. All these functions of the Robot are completed within their deadline through Salvo Rtos.

Methods for Analysis of Spatio-Temporal Bluetooth Tracking Data

Analysis of people's movements represented by continuous sequences of spatio-temporal data tuples have received lots of attention in recent years. The focus of those studies was mostly GPS data recorded on a constant sample rate. However, the creation of intelligent location-aware models and environments also requires reliable localization in indoor environments as well as in mixed indoor/outdoor scenarios. In these cases, signal loss makes usage of GPS infeasible; therefore other recording technologies evolved. Our approach is analysis of episodic movement data. This data contains some uncertainties among time (continuity), space (accuracy), and the number of recorded objects (coverage). Prominent examples of episodic movement data are spatio-temporal activity logs, cell-based tracking data, and billing records. To give one detailed example, Bluetooth tracking monitors the presence of mobile phones and intercoms within a sensor's footprints. Usage of multiple sensors provides flows among the sensors. Most existing data mining algorithms use interpolation and therefore are infeasible for this kind of data. For example, speed and movement direction cannot be derived directly from episodic data; trajectories may not be depicted as a continuous line; and densities cannot be computed. Still, the data hold much information on group movement. Our approach is to aggregate movement in order to overcome the uncertainties. Deriving a number of objects for the spatio-temporal compartments and transitions among them gives interesting insights on the spatio-temporal behavior of moving objects. As a next step to support analysts, we propose clustering the spatio-temporal presence and flow situations. This work focuses as well on creation of a descriptive probability model for the movement based on Spatial Bayesian Networks. We present our methods on a real world data set collected during a football game in Nîmes, France in June 2011.

A bio-inspired asynchronous skin system for crack detection applications

In many applications of structural health monitoring (SHM) it is imperative or advantageous to have large sensor arrays in order to properly sense the state of health of the structure. Typically these sensor networks are implemented by placing a large number of sensors over a structure and running individual cables from each sensor back to a central measurement station. Data is then collected from each sensor on the network at a constant sampling rate regardless of the current timescales at which events are acting on the structure. These conventional SHM sensor networks have a number of shortfalls. They tend to have a large number of cables that can represent a single point of failure for each sensor as well as add significant weight and installation costs. The constant sampling rate associated with each sensor very quickly leads to large amounts of data that must be analyzed, stored, and possibly transmitted to a remote user. This leads to increased demands on power consumption, bandwidth, and size. It also taxes our current techniques for managing large amounts of data. For the last decade the goal of the SHM community has been to endow structures with the functionality of a biological nervous system. Despite this goal the community has predominantly ignored the biological nervous system as inspiration for building structural nervous systems, choosing instead to focus on experimental mechanics and simulation techniques. In this work we explore the use of a novel, bio-inspired, SHM skin. This skin makes use of distributed computing and asynchronous communication techniques to alleviate the scale of the data management challenge as well as reduce power. The system also periodically sends a ‘heat beat’ signal to provide state-of-health updates. This conductive skin was implemented using conductive ink resistors as well as with graphene-oxide capacitors.