Change detection and estimation for network-measurement applications

The first study that investigates the characteristics of received probe packets and the reliability of bandwidth estimates when actively measuring the available bandwidth over radio interfaces in mobile communication networks is presented. Knowledge of available bandwidth is very useful in various contexts, e.g. in network management and adaptive streaming applications. Bandwidth measuring tools have so far primarily been designed for and evaluated in wired networks. However, such tools should also be examined in wireless networks since the use of, e.g., mobile broadband is rapidly increasing. The properties of wired and wireless links differ substantially, which affect the performance of the tools. We have made active-probing experiments over a high-speed downlink shared channel, which is used for High-Speed Downlink Packet Access (HSDPA) in the mobile communication technology UMTS, and over a forward traffic channel in CDMA2000 1xEV-DO. Both experiments were performed over commercial networks. They show that one cannot always expect uniform per-packet processing over the radio channel in mobile networks, which is expected by many probing tools. This reduces the reliability of the available-bandwidth estimates, however we suggest how this can be handled. Finally, the mobile-network measurements are compared to experiments performed in an IEEE 802.11 wireless LAN, where the radio channel does not create the same packet-processing behaviour. We also discuss the possibility of using the probe traffic for the purpose of identifying the communication technology at the bottleneck of the network path, assumed this is a wireless broadband link, by mapping specifications of standardised communication technologies to observed probe-traffic characteristics. Copyright © 2011 John Wiley & Sons, Ltd.

In the paper we develop an algorithm based on the Parzen kernel estimate for detection of sudden changes in 3-dimensional shapes which happen along the edge curves. Such problems commonly arise in various areas of computer vision, e.g., in edge detection, bioinformatics and processing of satellite imagery. In many engineering problems abrupt change detection may help in fault protection e.g. the jump detection in functions describing the static and dynamic properties of the objects in mechanical systems. We developed an algorithm for detecting abrupt changes which is nonparametric in nature and utilizes Parzen regression estimates of multivariate functions and their derivatives. In tests we apply this method, particularly but not exclusively, to the functions of two variables.

This study is devoted to detection of abrupt changes in electrocardiogram (ECG). A linear time-variant model with Gaussian white noise is used to describe the real ECG signal, based on the estimated system parameters and tuned covariances of noise, the off-line and on-line generalised likelihood ratio (GLR) tests for ECG signal are developed for change detection. For comparison, the test algorithm uses Levinson, recursive least squares (RLS) methods to obtain the filter models parameters of ECG. Furthermore, windowed on-line GLR test algorithm is developed, which works more effectively in real-time situation. The simulation results with real data show the effectiveness of the application.

High-resolution remote-sensing image change detection plays an important role in areas such as land and resources investigation, natural disaster prediction, and military strategy research. Current change detection methods often focus on extracting more discriminative features, while ignoring the information loss and imbalance problems in the process of feature fusion, which results in weakness in small change objects and edge pixels of change objects. In this letter, a simple but efficient network architecture, extraction, comparison and fusion network (ECFNet), for change detection in remote-sensing images is proposed. By constraining the number of feature channels in the fusion process by the feature comparison module (FCM), ECFNet can better utilize the fine-grained information in the multiscale feature map for result prediction, which not only improves the detection performance of small objects, but also reduces the false detection around the edge pixels of change objects. Experiments on the deeply supervised image fusion network for change detection (DSIFN-CD) test set show that ECFNet achieves state-of-the-art results with a small amount of computation.

The post-transcriptional modification of tRNAs at the wobble position plays a critical role in proper mRNA decoding and efficient protein synthesis. In particular, certain wobble uridines in eukaryotes are converted to 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U). The mcm5s2U modification modulates decoding during translation by increasing the stringency of the wobble uridine to base pair with its canonical nucleotide partner, thereby restricting decoding to its cognate codon. Here, we outline a technique to monitor wobble uridine status in mcm5s2U-containing tRNAs using the gamma-toxin endonuclease from the yeast Kluyveromyces lactis that naturally cleaves tRNAs containing the mcm5s2U modification. This technique is coupled to Northern blotting or reverse transcription-PCR to enable rapid and sensitive detection of changes in mcm5s2U modification state.

Traditional brain-computer interfaces often exhibit unstable performance over time. It has recently been proposed that passive brain-computer interfaces may provide a way to complement and stabilize these traditional systems. In this study, we investigated the feasibility of a passive brain-computer interface that uses electroencephalography to monitor changes in mental state on a single-trial basis. We recorded cortical activity from 15 locations while 11 able-bodied adults completed a series of challenging mental tasks. Using a feature clustering algorithm to account for redundancy in EEG signal features, we classified self-reported changes in fatigue, frustration, and attention levels with 74.8 ± 9.1%, 71.6 ± 5.6%, and 84.8 ± 7.4% accuracy, respectively. Based on the most frequently-selected features across all participants, we note the importance of the frontal and central electrodes for fatigue detection, posterior alpha band and frontal beta band activity for frustration detection, and posterior alpha band activity for attention detection. Future work will focus on integrating these results with an active brain-computer interface.

The detection of changes in psychological state using a novel pharmacological conditioning procedure

Energy monitoring using statistical process control (SPC) methods makes it more straightforward to identify patterns and trends to decrease energy consumption more effectively. The literature review of energy consumption monitoring with SPC techniques generally focuses on the temporal aspect of variation. However, due to the spatial nature of energy data, enhancing these methods to incorporate temporal and spatial aspects would improve the accuracy of the diagnostic information, underscoring simultaneous detection of the time and location of changes. Thus, the main novelty of this work is the spatial modeling and spatiotemporal monitoring of electricity consumption. For this purpose, the study used actual electricity consumption data from eight western cities of Mazandaran province in the north of Iran for spatial modeling using spatial regression models and a geographically weighted regression (GWR) model. The prediction performance evaluation of spatial models showed GWR as an appropriate model, whose coefficients were monitored through a generalized likelihood ratio (GLR) chart in phase II. The GLR chart detected two changes in consumption, and its performance was confirmed based on the statements from electricity experts relying on meteorological information and floating population data. Furthermore, the performance of the GLR chart was evaluated using out-of-control average run length (ARL1) across three different scenarios. The findings indicate that the GLR chart can effectively detect any sizes of shifts (δ), ranging from 5% to 100% of the model's parameter value. Additionally, with larger values of δ, the ARL1 decreases, resulting in faster detection of changes in the model.

Focused interfaces for end-user network management

Detection of structural changes in machine state has been a long-standing problem in the monitoring and prognosis of complex rotating machinery. In our previous work [1], a novel autoregressive-integrated-moving average (ARIMA) based prediction model has been proposed which however has a relatively low power when detection of a weak change. In this paper, we present a new prediction model based on differential equation which takes into account local trend of data to perform change detection. Final change decision is also made with martingale-based hypothesis testing. We evaluated the proposed method based on an experimental setup and the results demonstrated significant improvements over the ARIMA- based model used in the previous work.

We conducted an exploratory study of the detection of a sudden change of the field time series based on the numerical solution of the Lorenz system. First, the time when the Lorenz path jumped between the regions on the left and right of the equilibrium point of the Lorenz system was quantitatively marked and the sudden change time of the Lorenz system was obtained. Second, the numerical solution of the Lorenz system was regarded as a vector; thus, this solution could be considered as a vector time series. We transformed the vector time series into a time series using the vector inner product, considering the geometric and topological features of the Lorenz system path. Third, the sudden change of the resulting time series was detected using the sliding t-test method. Comparing the test results with the quantitatively marked time indicated that the method could detect every sudden change of the Lorenz path, thus the method is effective. Finally, we used the method to detect the sudden change of the pressure field time series and temperature field time series, and obtained good results for both series, which indicates that the method can apply to high-dimension vector time series. Mathematically, there is no essential difference between the field time series and vector time series; thus, we provide a new method for the detection of the sudden change of the field time series.

The ability to detect concept drift, i.e., a structural change in the acquired datastream, and react accordingly is a major achievement for intelligent sensing units. This ability allows the unit, for actively tuning the application, to maintain high performance, changing online the operational strategy, detecting and isolating possible occurring faults to name a few tasks. In the paper, we consider a just-in-time strategy for adaptation; the sensing unit reacts exactly when needed, i.e., when concept drift is detected. Change detection tests (CDTs), designed to inspect structural changes in industrial and environmental data, are coupled here with adaptive k-nearest neighbor and support vector machine classifiers, and suitably retrained when the change is detected. Computational complexity and memory requirements of the CDT and the classifier, due to precious limited resources in embedded sensing, are taken into account in the application design. We show that a hierarchical CDT coupled with an adaptive resource-aware classifier is a suitable tool for processing and classifying sequential streams of data.

Frequency Domain Local Tests for Change Detection

Network management has suffered from increases in business, system, and operational complexity. This has been exacerbated by the heterogeneity in management data as well as the high quality requirements of multimedia services. Autonomic networking manages this growing complexity by adding intelligence inside network nodes and network management applications. While most autonomic applications simply use a control loop to monitor and configure entities, our work is aimed at building a self-governing network that is able to fulfill the requirements of current and future services. This means that management applications need a detailed and dynamic view of the contextual status of the network nodes as a whole in order to adapt their behaviour to changing context. In this paper, we propose an algorithm to semi-automatically generate filter rules based on existing information in a network management information model. These filter rules are used to determine the set of contextual data that needs to be exchanged with other nodes. The algorithm exploits the reasoning capabilities of ontologies and relies on the introduction of additional semantic relationships to achieve a fine-grained context exchange model. Large scale evaluations were conducted to characterise the performance of this ontological approach.

My thesis studies how people pay attention to other people and the environment. How does the brain figure out what is important and what are the neural mechanisms underlying attention? What is special about salient social cues compared to salient non-social cues? In Chapter I, I review social cues that attract attention, with an emphasis on the neurobiology of these social cues. I also review neurological and psychiatric links: the relationship between saliency, the amygdala and autism. The first empirical chapter then begins by noting that people constantly move in the environment. In Chapter II, I study the spatial cues that attract attention during locomotion using a cued speeded discrimination task. I found that when the motion was expansive, attention was attracted towards the singular point of the optic flow (the focus of expansion, FOE) in a sustained fashion. The more ecologically valid the motion features became (e.g., temporal expansion of each object, spatial depth structure implied by distribution of the size of the objects), the stronger the attentional effects. However, compared to inanimate objects and cues, people preferentially attend to animals and faces, a process in which the amygdala is thought to play an important role. To directly compare social cues and non-social cues in the same experiment and investigate the neural structures processing social cues, in Chapter III, I employ a change detection task and test four rare patients with bilateral amygdala lesions. All four amygdala patients showed a normal pattern of reliably faster and more accurate detection of animate stimuli, suggesting that advantageous processing of social cues can be preserved even without the amygdala, a key structure of the “social brain”. People not only attend to faces, but also pay attention to others’ facial emotions and analyze faces in great detail. Humans have a dedicated system for processing faces and the amygdala has long been associated with a key role in recognizing facial emotions. In Chapter IV, I study the neural mechanisms of emotion perception and find that single neurons in the human amygdala are selective for subjective judgment of others’ emotions. Lastly, people typically pay special attention to faces and people, but people with autism spectrum disorders (ASD) might not. To further study social attention and explore possible deficits of social attention in autism, in Chapter V, I employ a visual search task and show that people with ASD have reduced attention, especially social attention, to target-congruent objects in the search array. This deficit cannot be explained by low-level visual properties of the stimuli and is independent of the amygdala, but it is dependent on task demands. Overall, through visual psychophysics with concurrent eye-tracking, my thesis found and analyzed socially salient cues and compared social vs. non-social cues and healthy vs. clinical populations. Neural mechanisms underlying social saliency were elucidated through electrophysiology and lesion studies. I finally propose further research questions based on the findings in my thesis and introduce my follow-up studies and preliminary results beyond the scope of this thesis in the very last section, Future Directions.