Noise generation from interacting high speed axisymmetric jet flows Semiannual status report, 1 Jun. 1968 - 31 Dec. 1969

Far field noise generation from interacting coaxial jet flows, and nozzle operational mode

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks.

In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive decline, which is a mental health disorder that primarily affects cognitive abilities such as learning, memory, etc. As a result, the elderly people may get dependent on caregivers to complete daily life tasks. Detecting the early indicators of dementia before it gets worsen and warning the caregivers and medical doctors would be helpful for further diagnosis. In this paper, the problem of activity recognition and abnormal behaviour detection is investigated for elderly people with dementia. First of all, the paper presents a methodology for generating synthetic data reflecting on some behavioural difficulties of people with dementia given the difficulty of obtaining real-world data. Secondly, the paper explores Convolutional Neural Networks (CNNs) to model patterns in activity sequences and detect abnormal behaviour related to dementia. Activity recognition is considered as a sequence labelling problem, while abnormal behaviour is flagged based on the deviation from normal patterns. Moreover, the performance of CNNs is compared against the state-of-art methods such as Naïve Bayes (NB), Hidden Markov Models (HMMs), Hidden Semi-Markov Models (HSMM), Conditional Random Fields (CRFs). The results obtained indicate that CNNs are competitive with those state-of-art methods

Information propagation in social networks during crises: A structural framework

In crisis situations like riots, earthquakes, storms, etc. information plays a central role in the process of organizing interventions and decision making. Due to their increasing use during crises, social media (SM) represents a valuable source of information that could help obtain a full picture of people needs and concerns. In this chapter, we highlight the importance of SM networks in crisis management (CM) to show how information is propagated through. The chapter also summarizes the current state of research related to information propagation in SMnetworks during crises. In particular three classes of information propagation research categories are identified: network analysis and community detection, role and topic-oriented information propagation, and infrastructure-oriented information propagation. The chapter describes an analysis framework that deals with structural information propagation for crisismanagement purposes. Structural propagation is about broadcasting specific information obtained from social media networks to targeted sinks/receivers/hubs like emergency agencies, police department, fire department, etc. Specifically, the framework aims to identify the discussion topics, known as sub-events, related to a crisis (event) from SM contents. A brief description of techniques used to detect topics and the way those topics can be used in structural information propagation are presented

Verification Of The Kalman Conjecture For Systems Containing Numerator Dynamics

The Kalman conjecture is shown to be true for certain systems of arbitrary order containing numerator dynamics. Simple restrictions on the system parameters are obtained such that the validity of the Kalman conjecture can be easily tested. Copyright © 1981 by The Institute of Electrical and Electronics Engineers. Inc

Social media for crisis management: clustering approaches for sub-event detection

Social media is getting increasingly important for crisis management, as it enables the public to provide information in different forms: text, image and video which can be valuable for crisis management. Such information is usually spatial and time-oriented, useful for understanding the emergency needs, performing decision making and supporting learning/training after the emergency. Due to the huge amount of data gathered during a crisis, automatic processing of the data is needed to support crisis management. One way of automating the process is to uncover sub-events (i.e., special hotspots) in the data collected from social media to enable better understanding of the crisis. We propose in the present paper clustering approaches for sub-event detection that operate on Flickr and YouTube data since multimedia data is of particular importance to understand the situation. Different clustering algorithms are assessed using the textual annotations (i.e., title, tags and description) and additional metadata information, like time and location. The empirical study shows in particular that social multimedia combined with clustering in the context of crisis management is worth using for detecting sub-events. It serves to integrate social media into crisis management without cumbersome manual monitoring

Resource Allocation for Heterogeneous Traffic in LTE Virtual Networks

Cellular network virtualization is being considered as a key trend in future mobile networks towards improved resource utilization. However, virtualization scenarios need investigation to understand the considerations which should be taken into account when deploying virtualized wireless networks in practice. Towards this, we address the performance of a virtualized network in the presence of heterogeneous classes of traffic. In previous cellular network virtualization literature, both Real time (RT) and Non-Real time (NRT) traffic requests have been included without distinction. Both types are provisioned using the same algorithm for allocation of resources specified by the Network Scheduler [1]. However, different types of traffic have different characteristics [2], e.g., RT requests are delay sensitive but may need fixed bandwidth, and hence should be treated differently, especially when wireless channel conditions are factored into the scheduling. We recognize this difference and in this paper, we propose a new approach to improve scheduling of resources for RT and NRT traffic. In particular, we prioritize the traffic belonging to different virtual slices from all service providers (SP/VEs) at the Network Scheduler before allocating resources to different SP/VEs, i.e., We form a Virtual Prioritized Slice (VPS). The virtual prioritized slice is forwarded to the VPS scheduler to serve all RT requests first. Only after the RT traffic is scheduled, the NRT traffic is provisioned using proportional fairness (PF) scheduling. We show by simulation results that this new VPS approach helps outperform recently proposed resource allocation schemes

Online indexing and clustering of social media data for emergency management

Social media becomes a vital part in our daily communication practice, creating a huge amount of data and covering different real-world situations. Currently, there is a tendency in making use of social media during emergency management and response. Most of this effort is performed by a huge number of volunteers browsing through social media data and preparing maps that can be used by professional first responders. Automatic analysis approaches are needed to directly support the response teams in monitoring and also understanding the evolution of facts in social media during an emergency situation. In this paper, we investigate the problem of real-time sub-events identification in social media data (i.e., Twitter, Flickr and YouTube) during emergencies. A processing framework is presented serving to generate situational reports/summaries from social media data. This framework relies in particular on online indexing and online clustering of media data streams. Online indexing aims at tracking the relevant vocabulary to capture the evolution of sub-events over time. Online clustering, on the other hand, is used to detect and update the set of sub-events using the indices built during online indexing. To evaluate the framework, social media data related to Hurricane Sandy 2012 was collected and used in a series of experiments. In particular some online indexing methods have been tested against a proposed method to show their suitability. Moreover, the quality of online clustering has been studied using standard clustering indices. Overall the framework provides a great opportunity for supporting emergency responders as demonstrated in real-world emergency exercises

Abnormal Behaviour Detection for Dementia Sufferers via Transfer Learning and Recursive Auto-Encoders

—Cognitive impairment is one of the crucial problems elderly people face. Tracking their daily life activities and detecting early indicators of cognitive decline would be necessary for further diagnosis. Depending on the decline magnitude, monitoring may need to be done over long periods of time to detect abnormal behaviour. In the absence of training data, it would be helpful to learn the normal behaviour and daily life patterns of a (cognitively) healthy person and use them as a basis for tracking other patients. In this paper, we propose to investigate Recursive Auto-Encoders (RAE)-based transfer learning to cope with the problem of scarcity of data in the context of abnormal behaviour detection. We present a method for generating synthetic data to reflect on some behavior of people with dementia. An RAE model is trained on data of a healthy person in a source household. Then, the resulting RAE is used to detect abnormal behavior in a target house. To evaluate the proposed approach, we compare the results with the-state-ofthe-art supervised methods. The results indicate that transfer learning is promising when there is lack of training data

Combustion contribution to noise in jet engines

The relative importance of combustion as a source of noise in a flow regime representative of a subsonic jet engine exhaust was investigated. The combustion noise source characteristics were obtained from pressure and temperature fluctuation measurements in the combustor and exhaust nozzle. The similarity between the fluctuations in this source region and the far field noise were compared. In the jet exhaust velocity range between 450 and 660 ft/sec investigated in detail, the frequencies of dominant pressure and temperature fluctuations in the combustor were also the frequencies of the dominant far field noise. The overall noise levels were 14 to 20 dB higher than from a corresponding clean jet in the same velocity range. Thus it seemed clear that the unsteadiness associated with the combustion process was responsible for the dominant noise in the far field. A simple analysis to predict the far field noise due to the internal pressure fluctuations causing exit plane velocity fluctuations produced trends closely resembling the measured results, but under predicted the far field noise over the spectral range examined. The possible reason for the higher far field noise is direct transmission of acoustic waves through the nozzle, which was not accounted for in the prediction scheme