Burst Data Research Articles

Spectrum Allocation Scheme for IoMT Devices Using Game Theory

There are a lot of challenges like versatility, network, receptiveness, accuracy, etc… when working on an internet-connected device because of the wide usage of them across the global. Currently there is a huge demand for reliable ways to connect to wireless. Same was conveyed as part of multiple observations and many have explained how spectrum sensing helps to allocate dynamically the unlicensed frequency bands for IoT applications reducing traffic and enabling the IoT technologies. Goal of Cognitive radio is to use the unadopted spectral spaces called spectral holes. A non-cooperative game theoretic strategy is employed to study the same. Also, the formulation of a utility function in terms of spectral usage rate of the users in a CR network. For obtaining the spectral usage rate, a 2-D continuous-time Markov Chain (CTMC) scenario is proposed to obtain the strategy for the users of CR in order to choose the appropriate spectral gaps. The parameters such as percentage utilization of spectrum by varying parameters like Traffic, No. of users and Data Burst rates are studied.

Demonstration of all-digital burst clock and data recovery for symmetrical 50 Gb/s/[formula omitted] PON based on low-bandwidth optics

We experimentally demonstrated all-digital burst clock and data recovery (BCDR) for symmetrical single-wavelength 50 Gb/s four-level amplitude modulation (PAM-4) passive optical network (PON) over the same fiber link at O-band, with the using of 10G-class optics and simple digital signal processing (DSP). The uplink burst frame and BCDR algorithm is used for accurately evaluating PON uplink transmission performance. The results show that over 26 dB link loss budget for continuous-mode uplink transmission and over 25 dB for burst-mode uplink transmission at a bit error rate of 3.8 × 10−3 can be achieved, respectively. For symmetrical 50 Gb/s/λ PON uplink burst-mode, there are 1 dB power budget loss less than uplink continuous-mode.

A reinforcement learning-based sleep scheduling algorithm for cooperative computing in event-driven wireless sensor networks

Emergency event monitoring is an important application of wireless sensor networks (WSNs). In the traditional cloud-assisted WSNs, the monitored data needs to be sent back to the cloud for processing. The round-trip of huge and burst data causes long delay and high energy consumption. With the increase of the computing capability of sensor nodes, local processing of events can be accomplished through cooperative computing between sensor nodes. However, if a node continues to undertake cooperative computing tasks, the energy consumption of nodes will be unbalanced and the network lifetime will be shortened. Sleep scheduling is an effective method to achieve energy balance, which can activate idle nodes alternately to participate in cooperative computing. In this paper, in order to make WSN more efficient to complete the local processing of events, we build a new system model based on node cooperative computing, and propose a multi-node Q learning-based cooperative computing node selection algorithm to obtain the sleep scheduling strategy. Simulation results show that compared with the classical algorithms, the proposed algorithm can complete more event processing, improve the reliability, and prolong the lifetime of WSNs.

Standardizing Platinum Dainotti-correlated gamma-ray bursts, and using them with standardized Amati-correlated gamma-ray bursts to constrain cosmological model parameters.

We show that the Platinum gamma-ray burst (GRB) data compilation, probing the redshift range 0.553 ≤ z ≤ 5.0, obeys a cosmological-model-independent three-parameter Fundamental Plane (Dainotti) correlation and so is standardizable. While they probe the largely unexplored z ∼ 2.3-5 part of cosmological redshift space, the GRB cosmological parameter constraints are consistent with, but less precise than, those from a combination of baryon acoustic oscillation (BAO) and Hubble parameter [H(z)] data. In order to increase the precision of GRB-only cosmological constraints, we exclude common GRBs from the larger Amati-correlated A118 data set composed of 118 GRBs and jointly analyse the remaining 101 Amati-correlated GRBs with the 50 Platinum GRBs. This joint 151 GRB data set probes the largely unexplored z ∼ 2.3-8.2 region; the resulting GRB-only cosmological constraints are more restrictive, and consistent with, but less precise than, those from H(z) +BAO data.

XopZ and ORP1C cooperate to regulate the virulence of Xanthomonas oryzae pv. oryzae on Nipponbare

ABSTRACT Bacterial leaf blight caused by Xanthomonas oryzae pv. oryzae (Xoo) has always been considered to be one of the most severe worldwide diseases in rice. Xoo strains usually use the highly conserved type III secretion system (T3SS) to deliver virulence effectors into rice cells and further suppress the host’s immunity. Previous studies reported that different Xanthomonas outer protein (Xop) effectors include XopZ from one strain appear to share functional redundancies on suppressing rice PAMP-triggered immunity (PTI). But only xopZ, except other xop genes, could significantly impaire Xoo virulence when individually deleting in PXO99 strains. Thus, the XopZ effector should not only suppress rice PTI pathway, but also has other unknown indispensable pathological functions in PXO99–rice interactions. Here, we also found that ∆xopZ mutant strains displayed lower virulence on Nipponbare leaves compared with PXO99 strains. We identified an oxysterol-binding related protein, ORP1C, as a XopZ-interacting protein in rice. Further studies found that rice ORP1C preliminarily played a positive role in regulating the resistance to PXO99 strains, and XopZ–ORP1C interactions cooperated to regulate the compatible interactions of PXO99-Nipponbare rice. The reactive oxygen species (ROS) burst and PTI marker gene expression data indicated that ORP1C were not directly relevant to the PTI pathway in rice. The deeper mechanisms underlying XopZ–ORP1C interaction and how XopZ and ORP1C cooperate for regulating the PXO99–rice interactions require further exploration.

A deep learning method for the recognition of solar radio burst spectrum.

Solar radiation is the excitation source that affects the weather in the atmosphere of the earth, and some solar activities such as flares and coronal mass ejections are often accompanied by radio bursts. The spectrum of solar radio bursts is helpful for astronomers to explore the mechanism of radio bursts. With the development and progress of solar radio spectrum observation methods, the observation of the Sun can be done at almost all times of day. How to quickly and automatically identify the small proportion of burst data from the huge corpus of observation data has become an important research direction. The innovation of this study is to enhance the original radio spectrum dataset with unbalanced sample distribution, and a neural network model for solar radio spectrum image classification is proposed on this basis. This hybrid structure of joint convolution and a memory unit overcomes the shortcoming of the traditional convolution or memory model, which can only extract one-sided features of an image. By extracting the frequency structure features and time-series features at the same time, the sensitivity to the small features of the spectrum image can be enhanced. Based on the data of the Solar Broadband Radio Spectrometer (SBRS) in China, the proposed network model can improve the average classification accuracy of the spectrum image to 98.73%, which will be helpful for related astronomical research.

Detection and analysis of cortical beta bursts in developmental EEG data

Developmental EEG research often involves analyzing signals within various frequency bands, based on the assumption that these signals represent oscillatory neural activity. However, growing evidence suggests that certain frequency bands are dominated by transient burst events in single trials rather than sustained oscillations. This is especially true for the beta band, with adult ‘beta burst’ timing a better predictor of motor behavior than slow changes in average beta amplitude. No developmental research thus far has looked at beta bursts, with techniques used to investigate frequency-specific activity structure rarely even applied to such data. Therefore, we aimed to: i) provide a tutorial for developmental EEG researchers on the application of methods for evaluating the rhythmic versus transient nature of frequency-specific activity; and ii) use these techniques to investigate the existence of sensorimotor beta bursts in infants. We found that beta activity in 12-month-olds did occur in bursts, however differences were also revealed in terms of duration, amplitude, and rate during grasping compared to adults. Application of the techniques illustrated here will be critical for clarifying the functional roles of frequency-specific activity across early development, including the role of beta activity in motor processing and its contribution to differing developmental motor trajectories.

Optimal Algorithms for Load Balancing in Optical Burst Switching Networks

Data packet drop can happen in Optical Burst-Switched (OBS) when two data bursts are competing on the same wavelength. Recently, many techniques have been developed to solve this problem but they do not consider the congestion. Also, it is necessary to balance the load system in the OBS network. The Ant Colony Optimization (ACO) technique can be applied to determine the straight and the safest route. However, the ACO technique raises both power utilization as well as the execution time. In this study, Cuckoo Search (CS) and ACO methods based approach is proposed to avoid the congestion and load balancing in the OBS network. This strategy evaluates the intensity of hotspot data and then launches the congestion rate optimization process that depends on their load situations. The congestion rate optimization represents the available bandwidth, data distribution rate, queue size, and access rate, and also these factors are optimized through the CS technique. The fitness utility in the CS technique adjusts the distribution rate in the OBS network, and the proposed ACO technique solves the energy utilization problem. The simulation results proved that the presented strategy evades both the end-to-end delay as well as the possibility of packet drop.

Queue Management Algorithm for Satellite Networks Based on Traffic Prediction

In connexion with the effect of the self-similar characteristic of satellite network service traffic on queueing performance, a prediction model with optimised triple exponential smoothing is first established in this paper. This model performs network traffic prediction based on the dynamic triple exponential smoothing model and optimises the smoothing coefficient of the model through the differential evolution algorithm; a cubic function based on traffic prediction is further proposed to improve the adaptive random early detection (ARED) queue management algorithm. Based on the network traffic prediction results and the ARED, this algorithm uses the cubic function to perform nonlinear processing on the packet drop probability function. The simulation results show that the prediction model with optimised triple exponential smoothing has a high prediction accuracy, and the improved ARED algorithm based on the cubic function of traffic prediction in the presence of data bursts in self-similar traffic. It can effectively reduce the packet loss rate and improve the throughput, so as to better control the network congestion caused by self-similar traffic in satellite network.

Fuzzy Congestion Control and Avoidance for CoAP in IoT Networks

In Internet of Things (IoT) networks, congestion is growing with the increasing number of devices, and a large amount of collected data must be transferred. Congestion control is one of the most significant challenges for such networks. The Constrained Application Protocol (CoAP) has been adopted for the IoT to satisfy the demand for smart applications. However, CoAP uses a basic congestion control algorithm that operates only when congestion occurs. Thus, the basic CoAP and most similar loss-based congestion control schemes have remaining issues for burst data transfer in dynamic network environments. This paper proposes a novel rate-based congestion control scheme using fuzzy control for CoAP, called FuzzyCoAP. We use the round-trip time gradient and bottleneck bandwidth gradient as inputs for FuzzyCoAP to infer the degree of congestion. FuzzyCoAP uses this indicator to predict early congestion and adjusts the sending rate to avoid congestion. FuzzyCoAP uses the congestion degree to update the variable RTO for retransmissions. On the other hand, FuzzyCoAP dynamically checks for the available bandwidth to gain high performance for burst data transfer. Various simulation experiments have demonstrated the feasibility of the FuzzyCoAP in different traffic scenarios. We compared the proposed scheme with representative loss-based CoAP schemes, that is, the basic CoAP. The simulation results proved that FuzzyCoAP provides high performance in terms of delay, throughput, loss rate, and retransmissions compared with the basic CoAP.

Applying switching and multiple access model for reducing packet loss and network overheads in watm

As an effectual simple wireless equivalent created in the telecommunications (telephone) industry, Wireless Asynchronous Transfer Mode (WATM) is utilized to stream unified traffics like video, data, and voice data. In the asynchronous data transfer mode, voice data transfer a packet with the same medium, and data share the networks and burst data. Effective WATM data transmission requires an extensive array of designs, techniques used for control, and simulation methodologies. The congestion of the network is among the key challenges that lower the entire WATM performance during this procedure, in addition to the delay in cell and the overload of traffic. The congestions cause cell loss, and it requires expensive switches compared to the LAN. Consequently, in this current study, the application of an effectual switching model together with a control mechanism that possesses multiple accesses is employed. The multiple access process and switching model are utilized to establish an effective data sharing process with minimum complexity. The switching model uses the synchronous inputs and output ports with buffering to ensure the data sharing process. The traffic in the network is decreased, and the loss of packets in the cells is efficiently kept to a minimum by the proposed technique. The system being discussed is employed through the utilization of software employed using OPNET 10.5 simulation, with the valuation of the WATM along with the investigational outcomes accordingly. The system's efficiency is assessed by throughput, latency, cell loss probability value (CLP), overhead network, and packet loss. Thus, the system ensures the minimum packet loss (0.1 %) and high data transmission rate (96.6 %)

Predicting Depths of Burst at Denied Access Sites Using Bayesian Data Synthesis

AbstractAnthropogenic radioisotopes are the only definitive signature that a below ground nuclear test has been conducted. The depth at which a test occurs is known to be a critical parameter, affecting not only the migration time for gases to reach the surface but also their subsequent isotopic ratios. Suspected tests typically occur at sites to which the international community has no access, and their suspected location and depth must be computed. Teleseismic methods are typically used for doing this but can have uncertainties larger than a kilometer. Here, we present an alternative approach that uses Bayesian data synthesis to combine information on local topology, geology, the presence of surface deformation, and yield. The method is tested against actual depth of burst data from 36 Soviet tests and 14 in the United States with the mean error to <53 m if the yield can be accurately estimated.

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals.

Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer learning with dynamic associate domain adaptation is proposed for human activity recognition. In order to improve the CSI quality and denoising of CSI, we carried out missing packet filling, burst noise removal, background estimation, feature extraction, feature enhancement, and data augmentation in the data pre-processing stage. This paper considers the problem of environment-independent human activity recognition, also known as domain adaptation. The pre-trained model is trained from the source domain by collecting a complete labeled dataset of all of the CSI of human activity patterns. Then, the pre-trained model is transferred to the target environment through the semi-supervised transfer learning stage. Therefore, when humans move to different target domains, a partial labeled dataset of the target domain is required for fine-tuning. In this paper, we propose a dynamic associate domain adaptation called DADA. By modifying the existing associate domain adaptation algorithm, the target domain can provide a dynamic ratio of labeled dataset/unlabeled dataset, while the existing associate domain adaptation algorithm only allows target domains with the unlabeled dataset. The advantage of DADA is that it provides a dynamic strategy to eliminate different effects on different environments. In addition, we further designed an attention-based DenseNet model, or AD, as our training network, which is modified by an existing DenseNet by adding the attention function. The solution we proposed was simplified to DADA-AD throughout the paper. The experimental results show that for domain adaptation in different domains, the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes.

Cognition and Subjective Age Predict Physical Activity Engagement: A Longitudinal Study of Impaired Older Adults

Physical activity is an important factor in preventing or slowing cognitive decline. However, the predictors of fluctuations in physical activity in a population that is already experiencing cognitive impairment is not well understood. Subjective age, such as how old one feels, has been tied to many health indicators in cognitively intact populations. Thus, we focused on the within-person associations between subjective age and physical activity as they unfold over time within a sample of cognitively impaired participants. The current study examined 400 reports from measurement burst data consisting of 5 weekly surveys conducted twice across 6 months from 68 cognitively impaired participants (M age = 70.14 (6.63), range = 60-92). Participants completed a battery of cognitive tests at baseline. At each weekly assessment, participants reported on their physical activity (e.g., exercise, outdoor, flexibility, and strength activities; Yes/No) and subjective age with respect to how old they feel overall and how old they feel mentally. There were longitudinal decreases in physical activity across the bursts, but on occasions when participants felt younger overall or younger mentally there were increases in physical activity. In addition, the effects of mental subjective age depended on cognitive ability, with those scoring lower in cognitive ability appearing to benefit the most from decreases (feeling younger) in mental subjective age. These findings suggest that perceptions of aging, especially within the domain of mental age, are tied to physical activity engagement for older adults with cognitive impairment.

Efficient Predictable Probe of Optical Burst Switched For Wireless Feeler Bond

Optical Burst Switching (OBS) is a promising paradigm for high speed transmission of data. In OBS, a key problem is to schedule bursts with minimum loss. Single method is not sufficient to improve performance. So, our performance model includes some feasible methods to improve OBS performance without significantly increasing the implementation complexity. The methods are addition of simple fiber delay lines (FDLs), increasing random extra offset time, window based channel scheduling (WBS) and Burst Delay Feedback scheduling (BDFS). Additional FDLs can only eliminate the negative impact caused by the variation of the offset time between control packets and data bursts. The random extra offset time approach does not require any additional hardware in the nodes. WBS provides better throughput improvement when FDLs are used in the nodes to compensate the processing time. Finally Burst Delay Feedback Scheduling in addition with these methods can significantly improve OBS throughput and reduce transmission delay.

Standardizing Dainotti-correlated gamma-ray bursts, and using them with standardized Amati-correlated gamma-ray bursts to constrain cosmological model parameters

ABSTRACT We show that each of the three Dainotti-correlated gamma-ray burst (GRB) data sets recently compiled by Wang et al. and Hu et al., that together probe the redshift range 0.35 ≤ z ≤ 5.91, obey cosmological-model-independent Dainotti correlations and so are standardizable. We use these GRB data in conjunction with the best currently available Amati-correlated GRB data, that probe 0.3399 ≤ z ≤ 8.2, to constrain cosmological model parameters. The resulting cosmological constraints are weak, providing lower limits on the non-relativistic matter density parameter, mildly favouring non-zero spatial curvature, and largely consistent with currently accelerated cosmological expansion as well as with constraints determined from better-established data.

A PSO-SVM for Burst Header Packet Flooding Attacks Detection in Optical Burst Switching Networks

An Optical Burst Switching (OBS) network is vulnerable to Burst Header Packet (BHP) flooding attack. In flooding attacks, edge nodes send BHPs at a high rate to reserve bandwidth for unrealized data bursts, which leads to a waste of bandwidth, a decrease in network performance, and massive data loss. Machine learning techniques are utilized to detect this attack in the OBS network. In this paper, we propose a particle swarm optimization–support vector machine (PSO-SVM) model for detecting BHP flooding attacks, in which the PSO is used to optimize the parameters of the SVM. We use the dataset provided by the UCI warehouse to train and test the model. The experimental results show that the detection accuracy of the PSO-SVM model reaches 95.0%, which is 9.4%, 9.6%, 20.7%, 8% higher than naïve Bayes, SVM, k-nearest neighbor, and decision tree. Although DCNN outperforms our model, it requires more processing and training time. Collectively, our approach is effective and high-efficiency in detecting flooding attacks in optical burst switching networks and maintaining network stability and security.

Re-examination of the Time Structure of the SN1987A Neutrino Burst Data in Kamiokande-II

Abstract The seven seconds’ gap in the Kamiokande-II SN1987A neutrino data is reexamined.

Spectral Analysis of Electromagnetic Compatibility

The report's main aim was to transmit the frequency signals without any disturbances and compute the time and frequencies of energies. Conduct the data burst system with the help of an anechoic chamber to study the electromagnetic interference and utilise the spectrum analyser to identify the required frequency during the conduction of signal transmission and minimise the noise disturbances.

Spectral Analysis of Electromagnetic Compatibility

The report's main aim was to transmit the frequency signals without any disturbances and compute the time and frequencies of energies. Conduct the data burst system with the help of an anechoic chamber to study the electromagnetic interference and utilise the spectrum analyser to identify the required frequency during the conduction of signal transmission and minimise the noise disturbances.