Increase In Sampling Frequency Research Articles

3 Towards Automated Behavior Analysis for Individually Housed Piglets–Sampling Considerations

Abstract Behavior sampling is commonly applied to represent a complete picture of animal behavior. Traditional sampling methods often fail to capture complete sequences of events, diurnal behavior, or behavior patterns. As digital technologies allow more frequent collection of behavior, there is a need to understand if the potential increase in sampling frequency yields an increase in the knowledge we gain from additional observations. In this study, 24-h behavior was labelled for three individually housed research piglets, creating a ground truth dataset. An ethogram with five categories (resting, consumption, maintenance, exploratory, and social interactions) including 23 behavior labels was applied for behavior observation for every frame in the video data set. Using Python to automatically sample behavior data from the complete dataset, the results of instantaneous and continuous sampling strategies at various sampling intervals and times of day were compared with the ground truth continuous data set using a two-sample t-test for: (a) total percentage of observations, (b) average bout length, and (c) normalized number of occurrences. Results were considered significant for alpha< 0.05, which indicated a sampling method was different from ground truth. Continuously sampled data were collected at the start of each hour for 2, 5, 10, 15, and 20-min periods. Instantaneously sampled data were collected at the intervals: 1, 5, 10, 20, and 40-s; 1, 2, 5, 10, 20, and 60-min. Every sampling strategy resulted in misrepresentation of behavior. For example, results for the 1-s instantaneous sampling method (every 15th frame versus every frame) revealed that seven of the 23 behaviors observed were misrepresented in at least one metric: (a) total percentage of observations [P=0.027 (drinking behavior); P=0.027 (sham chewing), P=0.011 (head tossing)]; (b) average bout length [P=0.033 (feeding behavior), P=0.032 (nosing), P=0.040 (exploring pen), P=0.039 (head tossing)]; and (c) normalized number of occurrences [P=0.005 (exploring pen), P=0.018 (head tossing)].

Improved Multichannel Electromyograph Using Off-the-Shelf Components for Education and Research.

Most students and researchers with limited funding are often looking for simple and low-cost devices for the acquisition of the electromyogram signal (EMG) in an educational or research setting. Thus, off-the-shelf devices are used and they have already been described in the literature, but they are used without considering their real performances, which are, in general, quite poor from the electronic and signal processing points of view. It is the purpose of this communication to present the evidence of these issues, and to describe an improved version of the “classical” duo, composed of the common ECG/EMG Olimex board and the Arduino microprocessor board. In this case, the Arduino-DUE is used. Three main points are highlighted in this paper: (a) the bandpass characteristics of the ECG/EMG Olimex board and how they can be improved to cope with EMG bandwidth requirements; (b) the increase in sampling frequency of the signal; and, finally, (c) the possibility of automatic detection of more ECG/EMG Olimex boards installed at the same time as the shields on the Arduino-DUE board. Very simple and low-cost modifications on the ECG/EMG Olimex board could deliver a much better performing multichannel EMG acquisition system, suitable for educational classroom experiments and early proof-of-concept research.

Performance Improvement of Model Predictive Current Control of Fault-Tolerant Five-Phase Flux-Switching Permanent Magnet Motor Drive

To improve the fault-tolerant performance of a five-phase flux-switching permanent magnet (FSPM) motor drive under open-circuit fault (OCF) condition, a model predictive current control (MPCC) with pre-selective method and duty ratio control (DRC) technology is proposed and investigated in this paper. First, on the principle of minimizing harmonic voltages in x–y subspace, two zero switching states and the switching state, which generates a larger voltage vector in α–β subspace are pre-selected. Second, voltage vector references in α–β subspace and x–y subspace are predicted to further select active voltage vector candidates. Consequently, the number of current predictions has been significantly reduced, resulting in the alleviation of the computational complexity and the increase of sampling frequency. Third, the DRC approach is applied in conjunction with the pre-selection-based MPCC to improve the steady-state performance. Finally, the effectiveness of the proposed MPCC method for the OCF tolerant five-phase FSPM motor drive is validated by comparative experiments.

A novel hybrid metaheuristic algorithm for model order reduction in the delta domain: a unified approach

Delta operator parameterization provides a unified framework in modeling, analysis and design of discrete-time systems, in which the resultant model converges to its continuous-time counterpart at high sampling limit. Capitalizing this unique property of delta operator, a new hybrid algorithm combining gray wolf optimizer and firefly algorithm has been proposed for model order reduction of high-dimensional linear discrete-time system. It has been shown that the reduced discrete-time model inherits all the dominant characteristics of the higher-order discrete-time model and with the increase in sampling frequency it converges to the continuous-time reduced model. The effectiveness of the proposed method is illustrated with the help of an example.

Performance Management of IEEE 802.15.4 Wireless Sensor Network for Precision Agriculture

The monitoring and control of crops in precision agriculture sometimes requires a high collection frequency of information (e.g., temperature, humidity, and salinity) due to the variability in crops. Data acquisition and transmission are generally achieved thanks to wireless sensor networks. However, sensor nodes have limited resources. Thus, it is necessary to adapt the increase in sampling frequency for different crops, under application constraints (reliability, packet delay, and lifetime duration). In this paper, we propose to properly tune IEEE 802.15.4 MAC parameters ( macMinBE and macMaxCSMABackoffs ) and the sampling frequency of deployed sensor nodes. An analytical model of network performance is derived and used to perform the tuning of these tradeoff parameters. Simulation analysis shows that our scheme provides an efficient increase of sampling frequency of sensor nodes while satisfying application requirements.

Analysis on multiple-component synchronization of ultra-fast time-interleaved analog-to-digital conversion systems and its novel parameterized hardware solution.

Parallelism-based technique of time-interleaved analog-to-digital conversion (TIADC) has become an effective solution for the higher sampling rate acquisition system to acquire non-repetitive waveforms. With the increase of sampling frequency, the indeterminacy of combining sequence of sampled data among multiple components has become a highlighted barrier for the reset operation of high-speed acquisition systems, and this is especially obvious for the ultra-fast TIADC systems. In this paper, we clarify the root of the problem in multiple-component synchronization (MCS) caused by such reset operation. Also we propose a novel and reliable hardware solution to precisely condition each reset signal, including three key circuit design parameters, i.e., the best time interval, required edge uncertainty, and the minimum delay precision. Besides, the designing scheme and debugging procedures are presented in detail in a generalized platform of this system type. Finally, in order to demonstrate the feasibility, parametric materialization and testing verification are gradually accomplished in a 20 Giga Samples Per Second (GSPS) TIADC system composed of four 5 GSPS ADC components. The results show that the proposed method is feasible and effective for ensuring the combined determinacy of multiple groups of sampled data and solving the MCS problem. In comparison with other existing solutions, it adopts some simple logic components more easily and flexibly, and this is significant for the development of congeneric systems or instruments featuring the MCS.

A trade-off analysis between penetration rate and sampling frequency of mobile sensors in traffic state estimation

The rapid-growth of smartphones with embedded navigation systems such as GPS modules provides new ways of monitoring traffic. These devices can register and send a great amount of traffic related data, which can be used for traffic state estimation. In such a case, the amount of data collected depends on two variables: the penetration rate of devices in traffic flow (P) and their data sampling frequency (z). Referring to data composition as the way certain number of observations is collected, in terms of P and z, we need to understand the relation between the amount and composition of data collected, and the accuracy achieved in traffic state estimation. This was accomplished through an in-depth analysis of two datasets of vehicle trajectories on freeways. The first dataset consists of trajectories over a real freeway, while the second dataset is obtained through microsimulation. Hypothetical scenarios of data sent by equipped vehicles were created, based on the composition of data collected. Different values of P and z were used, and each unique combination defined a specific scenario. Traffic states were estimated through two simple methods, and a more advanced one that incorporates traffic flow theory. A measure to quantify data to be collected was proposed, based on travel time, number of vehicles, penetration rate and sampling frequency. The error was below 6% for every scenario in each dataset. Also, increasing data reduced variability in data count estimation. The performance of the different estimation methods varied through each dataset and scenario. Since the same number of observations can be gathered with different combinations of P and z, the effect of data composition was analyzed (a trade-off between penetration rate and sampling frequency). Different situations were found. In some, an increase in penetration rate is more effective to reduce estimation error than an increase in sampling frequency, considering an equal increase in observations. In other areas, the opposite relationship was found. Between these areas, an indifference curve was found. In fact, this curve is the solution to the optimization problem of minimizing the error given any fixed number of observations. As a general result, increasing sampling frequency (penetration rate) is more beneficial when the current sampling frequency (penetration rate) is low, independent of the penetration rate (sampling frequency).

발효공정 자동제어를 위한 포도주 발효 중 포도당 농도 온라인 측정

A flow injection analysis method (FIA), which analyzes sample conditions after injecting a sample and reagents into a continuous stream, are recognized as the most adequate analyzing method according to the increase of sampling frequency, the decrease of measuring time and the diversity of measuring targets. Specially, the FIA is considered to be used effectively for the control of a fermentation process to produce fermentation food and useful microbial production by activation of a fermentation industry for development of biological materials. In this study, a flow injection analysis sensor unit was developed for on-line monitoring of the fermentation process. The performance was verified by on-line measuring the concentration of glucose of the fermentation process of wine. The glucose concentrations of the samples were measured every 12 hours during the whole fermentation process and compared with those by a HPLC. The concentration relative errors of glucose on the fermentation process of wine showed below 30% within 72 hours and over 50% after the 72 hours. The sensor unit had potential to on-line monitoring of the fermentation process but some problems to overcome for an commercial application.

A bi-dimensional approach to assessing the volumetric evolution of an exploited sandbank

Abstract We analyse the multi-annual evolution of bathymetry along several cross-sections of the Kwintebank (one of the Flemish Banks). An important issue in the area is the intensive exploitation under regulations imposed by the Belgian authorities that aim to guarantee sustainability of extractions. All bathymetric data collected during the period 1987–2000 at a frequency of approximately four surveys per year are analysed, to identify volumetric temporal trends. The errors inherent in the measurements taken with a single-beam instrument are examined. The lack of indisputable quality criteria led to the choice of robust statistical methods. A statistically significant annual decline of approximately 1.5% in bank volume is shown. Another practical conclusion is that a big increase in sampling frequency is necessary if the authorities set a criterion regarding the long-term sustainability of the bank and wish to have a swift guarantee that this criterion is being observed.

Facilitating brownfields transactions using Triad and environmental insurance

AbstractA significant hindrance to reuse of brownfields properties is the risk associated with redevelopment,specifically the uncertainty associated with environmental cleanup. This article explores an approach tomanaging environmental risk through a combination of risk quantification, environmental insurance, and theTriad Approach to site sampling and data interpretation. The expected costs of environmental liabilities areestimated using the Marsh Peer ReviewSM risk quantification process that employs statistical techniques andhighly experienced technical staff. The outputs of the process indicate premiums and attachment points forinsurance products, but they also point to “critical uncertainties” that drive the insurancepremiums. Insurance premiums are often linked to site delineation deficiencies, such as the magnitude ofimpacted soil or the size of a groundwater plume. The Triad Approach is an integrated site characterizationprocess developed by the Environmental Protection Agency that combines systematic planning, dynamic or adaptivefield decision making and field analytical methods (FAMs). The real‐time data produced by FAMsallow for in‐field resolution of uncertainty about sample location, which in turn provides morerepresentative delineation of contaminant distribution. The trade‐off of using slightly less accuratebut substantially lower cost FAMs is an increase in sampling frequency or density, thereby reducing the risk ofincomplete detection or delineation while yielding a “data set” that is more powerful than fewerindividual data points analyzed through traditional methods. Employing the Triad Approach to analyze the“critical uncertainties” identified in the Peer Review Process can impact insurance premiums andallow for better terms of coverage. The combination of using the Triad Approach and environmental insuranceproducts can lead to more predictable and profitable Brownfield transactions. © 2003 Wiley Peridicals,Inc.

Direct data compression techniques for ECG signals: effect of sampling frequency on performance

The existing techniques for data compression can be divided into three main groups, namely direct, transformation and parameter extraction methods. The present paper deals with the direct data compression (DDC) methods as applied to ECG data. The performance has been evaluated on the basis of compression ratio, percent-root-mean-square difference and fidelity of the reconstructed signal. Further, in order to know the extent to which the diagnostic information is preserved during compression, peak and boundary measurements have been made both on the reconstructed and original ECG signal and compared. The objective of the present paper is to report the effect of sampling frequency on the aforementioned parameters as studied by the authors. The experimental results show that with the increase in sampling frequency the compression ratio increases and the percent-root-mean-square difference generally decreases. Further, the reconstructed signal is of higher quality having larger bandwidth and higher resolution. The clinical information is also better preserved in the reconstructed signal. The results of peak and boundary measurements show that the errors in the peaks, onset and end points are within the acceptable limits up to a certain value of compression ratio, above which the errors become unacceptably large. It is also observed that because of smoothing, high frequency noise is rejected, which helps in reducing the electromyographic noise.

Short-term variations of circulating melatonin in the ewe.

Melatonin levels have been studied in venous blood sampled at different frequencies (0.5-, 2-, and 60-min intervals) form intact ewes. All samples were taken during the dark phase of either natural or artificial photoperiods. In one experiment samples were taken simultaneously from both jugular veins to investigate the possible effects of "streaming" on the levels measured. Plasma cortisol was measured to ascertain whether or not the frequent removal of blood activated the ACTH stress axis. Plasma melatonin levels showed considerable variation with peaks of up to 365 pg/ml on a baseline of between 30 and 60 pg/ml. There was consistent evidence of intermittent peaks, the frequency of which increased with an increase in sampling frequency. Plasma cortisol showed no correlation with either the frequency or the amplitude of the melatonin peaks. When plasma samples were taken from both jugular veins a similar melatonin pattern was seen in the samples from both sides, but samples taken from the left jugular vein invariably showed higher levels than those taken from the right vein. This may be due to differential vascular drainage of the pineal to the two sides.

Non-recursive methods for on-line estimation of voltages or currents and symmetrical components using Kalman filter theory

Using Kalman filter theory, new non-recursive algorithms for estimating the fundamental voltage and current waveforms from noise signals are developed and investigated. For non-harmonic random noise, these procedures have much better filter properties than Fourier algorithms, especially with increase in filter order and sampling frequency. The properties of the new methods in the time domain are also better than the Fourier ones. New algorithms for estimation of the symmetrical components are also developed and investigated. The use of the elaborated algorithms for protecting electric power systems has some advantages, owing to the convenient transient response time.