An adaptive policy-based framework for network services management

This paper presents a framework for specifying policies for the management of network services. Although policy-based management has been the subject of considerable research, proposed solutions are often restricted to condition-action rules, where conditions are matched against incoming traffic flows. This results in static policy configurations where manual intervention is required to cater for configuration changes and to enable policy deployment. The framework presented in this paper supports automated policy deployment and flexible event triggers to permit dynamic policy configuration. While current research focuses mostly on rules for low-level device configuration, significant challenges remain to be addressed in order to:a) provide policy specification and adaptation across different abstraction layers; and, b) provide tools and services for the engineering of policy-driven systems. In particular, this paper focuses on solutions for dynamic adaptation of policies in response to changes within the managed environment. Policy adaptation includes both dynamically changing policy parameters and reconfiguring the policy objects. Access control for network services is also discussed.Accepted versio

Effect of Electron Energy Distribution Function on Power Deposition and Plasma Density in an Inductively Coupled Discharge at Very Low Pressures

A self-consistent 1-D model was developed to study the effect of the electron energy distribution function (EEDF) on power deposition and plasma density profiles in a planar inductively coupled plasma (ICP) in the non-local regime (pressure < 10 mTorr). The model consisted of three modules: (1) an electron energy distribution function (EEDF) module to compute the non-Maxwellian EEDF, (2) a non-local electron kinetics module to predict the non-local electron conductivity, RF current, electric field and power deposition profiles in the non-uniform plasma, and (3) a heavy species transport module to solve for the ion density and velocity profiles as well as the metastable density. Results using the non-Maxwellian EEDF model were compared with predictions using a Maxwellian EEDF, under otherwise identical conditions. The RF electric field, current, and power deposition profiles were different, especially at 1mTorr, for which the electron effective mean free path was larger than the skin depth. The plasma density predicted by the Maxwellian EEDF was up to 93% larger for the conditions examined. Thus, the non-Maxwellian EEDF must be accounted for in modeling ICPs at very low pressures.Comment: 19 pages submitted to Plasma Sources Sci. Techno

Tasking networked CCTV cameras and mobile phones to identify and localize multiple people

We present a method to identify and localize people by leveraging existing CCTV camera infrastructure along with inertial sensors (accelerometer and magnetometer) within each person’s mobile phones. Since a person’s motion path, as observed by the camera, must match the local motion measurements from their phone, we are able to uniquely identify people with the phones ’ IDs by detecting the statistical dependence between the phone and camera measurements. For this, we express the problem as consisting of a twomeasurement HMM for each person, with one camera measurement and one phone measurement. Then we use a maximum a posteriori formulation to find the most likely ID assignments. Through sensor fusion, our method largely bypasses the motion correspondence problem from computer vision and is able to track people across large spatial or temporal gaps in sensing. We evaluate the system through simulations and experiments in a real camera network testbed

Exploring Radio Frequency Techniques for Bone Fracture Detection: A Comprehensive Review of Low Frequency and Microwave Approaches

YesThis comprehensive review paper examines bone fracture detection techniques based on time-domain low-frequency and microwave radiofrequency (RF). Early and accurate diagnosis of bone fractures remains critical in healthcare, as it can significantly improve patient outcomes. This review focuses on the potential of low-frequency and microwave RF methods, particularly their combination and application of time-domain analysis for enhanced fracture detection. We begin by providing an overview of the fundamental concepts of RF techniques and then by examining biological tissues' dielectric properties. We then compare the advantages and limitations of various bone fracture detection techniques, such as low-frequency RF methods, microwave RF methods, ultrasonography, X-ray, and CT scans. The discussion then shifts to hybrid approaches that combine low-frequency and microwave techniques, emphasising the advantages of such combinations in fracture detection. Machine learning techniques, their applications in bone fracture detection, and the role of time-domain analysis in hybrid approaches are also investigated. Finally, we examine the accuracy and reliability of simulated models for bone fracture detection. We discuss recent advancements and future directions, such as novel sensor technologies, improved signal processing techniques, integration with medical imaging modalities, and personalised fracture detection approaches. This review aims to comprehensively understand the landscape and future potential of time-domain analysis in low-frequency and microwave RF techniques for bone fracture detection.EU Horizon Europe H2020-MSCA-RISE-2022-2027 (ID: 101086492) and H2020-MSCA-RISE-2019-2024 (ID 872878), Marie Skłodowska-Curie, Research and Innovation Staff Exchange (RISE), and the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/ X039366/1