Managing context data for diverse operating spaces

Context-aware computing is an exciting paradigm in which applications perceive and react to changing environments in an unattended manner. To enable behavioral adaptation, a context-aware application must dynamically acquire context data from different operating spaces in the real world, such as homes, shops and persons. Motivated by the sheer number and diversity of operating spaces, we propose a scalable context data management system in this paper to facilitate data acquisition from these spaces. In our system, we design a gateway framework for all operating spaces and develop matching algorithms to integrate the local context schemas of operating spaces into a global set of domain schemas upon which SQL-based context queries can be issued from applications. The system organizes the operating space gateways as peers in semantic overlay networks and employs distributed query processing techniques over these overlays. Evaluation results on a prototype implementation demonstrate the effectiveness of our system design.     

Convergence analysis of convex incremental neural networks

Recently, a convex incremental algorithm (CI-ELM) has been proposed in Huang and Chen (Neurocomputing 70:3056–3062, 2007), which randomly chooses hidden neurons and then analytically determines the output weights connecting with the hidden layer and the output layer. Though hidden neurons are generated randomly, the network constructed by CI-ELM is still based on the principle of universal approximation. The random approximation theory breaks through the limitation of most conventional theories, eliminating the need for tuning hidden neurons. However, due to the random characteristic, some of the neurons contribute little to decrease the residual error, which eventually increase the complexity and computation of neural networks. Thus, CI-ELM cannot precisely give out its convergence rate. Based on Lee’s results (Lee et al., IEEE Trans Inf Theory 42(6):2118–2132, 1996), we first show the convergence rate of a maximum CI-ELM, and then systematically analyze the convergence rate of an enhanced CI-ELM. Different from CI-ELM, the hidden neurons of the two algorithms are chosen by following the maximum or optimality principle under the same structure as CI-ELM. Further, the proof process also demonstrates that our algorithms achieve smaller residual errors than CI-ELM. Since the proposed neural networks remove these “useless” neurons, they improve the efficiency of neural networks. The experimental results on benchmark regression problems will support our conclusions. The work is under the funding of Singapore MOE AcRF Tier 1 grant WBS No: R 252-000-221-112.     

Microstructure and electrical property of TiNx films deposited by DC reactive magnetron sputtering superimposed electromagnetic field system

TiNx films were successfully deposited by DC reactive magnetron sputtering superimposed EMF system without substrate heating. In case of DCMS 400 W+ EMF 25 W, electrical property, reflectance and crystallinity of the TiN films were clearly improved by the enhancement of nitrification. The lowest sheet resistance of 2.9 Ω/? (resistivity 5.8 x 10(-5) Ωcm) was observed for the film deposited at F(N)2: 16%. Mixture phases of the (111) plane and (200) plane showed lower resistivity than only (200) single phase. Therefore it is confirmed that introduction of EMF system is promising technology to deposit TiN film.     

Preparation and characterization of plasticized high molecular weight PVC-based polymer electrolytes

Poly(vinyl chloride) (PVC)-based polymer electrolytes films consisting of lithium trifluromethanesulfonate (LiCF₃SO₃)-ethylene carbonate (EC) were prepared by the solution-casting method. Ionic conductivities of the electrolytes have been determined by an impedance studies in the temperature range of 298–373 K. Complexation of the prepared electrolytes is studied by X-ray diffraction (XRD) analysis. Thermogravimetric analysis (TGA) was used to confirm the thermal stability of the polymer electrolytes. The conductivity-temperature plots were found to followan Arrhenius nature. All these films are found to be thermally stable until 132–167°C.     

Mini-slot TDM WDM optical networks

In recent years, optical transport networks have evolved from interconnected SONET/WDM ring networks to mesh-based optical WDM networks. Time-slot wavelength switching is to aggregate the lower rate traffic at the time-slot level into a wavelength in order to improve bandwidth utilization. With the advancement of fiber-optics technologies, continual increase of fiber bandwidth and number of wavelengths in each fiber, it is possible to divide a wavelength in a fiber into time-slots, and further divide a time-slot into mini-slots so that the fiber bandwidth can be more efficiently utilized. This article proposes a router architecture with an electronic system controller to support optical data transfer at the mini-slot(s) of a time-slot in a wavelength for each hop of a route. The proposed router architecture performs optical circuit switching and does not use any wavelength converter. Each node in the mini-slot TDM WDM optical network consists of the proposed router architecture. Three different network topologies are used to demonstrate the effectiveness and behavior of this type of network in terms of blocking probability and throughput.     

A real-time flood-routed voice protocol for fibre optic local area networks

A simple real-time flood routing voice (RTFRV) protocol which is suitable for point-to-point arbitrarily connected fibre optic local area networks is described. Simulation on a 3 Mbit/s nine-node mesh network which employs the protocol has shown that it is capable of supporting about 50 simultaneous two-way voice conversations (or 100 users) with packet delay of less than 10 ms and without any loss of information. The overall results suggest that voice transmission on such a network is feasible and that it performs better than voice transmission over Ethernet. Furthermore, the network is also very robust and allows easy network reconfiguration.     

Object relevance weight pattern mining for activity recognition and segmentation

Monitoring daily activities of a person has many potential benefits in pervasive computing. These include providing proactive support for the elderly and monitoring anomalous behaviors. A typical approach in existing research on activity detection is to construct sequence-based models of low-level activity features based on the order of object usage. However, these models have poor accuracy, require many parameters to estimate, and demand excessive computational effort. Many other supervised learning approaches have been proposed but they all suffer from poor scalability due to the manual labeling involved in the training process. In this paper, we simplify the activity modeling process by relying on the relevance weights of objects as the basis of activity discrimination rather than on sequence information. For each activity, we mine the web to extract the most relevant objects according to their normalized usage frequency. We develop a KeyExtract algorithm for activity recognition and two algorithms, MaxGap and MaxGain, for activity segmentation with linear time complexities. Simulation results indicate that our proposed algorithms achieve high accuracy in the presence of different noise levels indicating their good potential in real-world deployment.     

Conserving disk energy in virtual machine based environments by amplifying bursts

Computer systems are now powerful enough to run multiple virtual machines (VMs), each one running a separate operating system (OS) instance. In such an environment, direct and centralized energy management employed by a single OS is unfeasible. Accurately predicting the idle intervals is one of the major approaches to save energy of disk drives. However, for the intensive workloads, it is difficult to find long idle intervals. Even if long idle intervals exist, it is very difficult for a predictor to catch the idle spikes in the workloads. This paper proposes to divide the workloads into buckets which are equal in time length, and predict the number of the forthcoming requests in each bucket instead of the length of the idle periods. By doing so, the bucket method makes the converted workload more predictable. The method also squeezes the executing time of each request to the end of its respective bucket, thus extending the idle length. By deliberately reshaping the workloads such that the crests and troughs of each workload become aligned, we can aggregate the peaks and the idle periods of the workloads. Due to the extended idle length caused by this aggregation, energy can be conserved. Furthermore, as a result of aligning the peaks, resource utilization is improved when the system is active. A trace driven simulator is designed to evaluate the idea. Three traces are employed to represent the workloads issued by three web servers residing in three VMs. The experimental results show that our method can save significant amounts of energy by sacrificing a small amount of quality of service.     

Data/voice integration for fibre optic LANs with arbitrary topology

A high-performance protocol for arbitrary point-to-point fibre optic local area networks with combined voice and data traffic is described. Simulation on a 3 Mbit/s nine-node mesh network has shown that it is capable of supporting a substantial number of data users and more than 100 active voice calls (200 active telephone users, coding rate 64 kbit/s without silence detection) with a packet delay of less than 10 ms and without any loss of information. The network performance is superior to the previous reported voice protocol which used the modified anarchy flood routing (MAFR) technique. Performance limitation due to growing network size for flood-routed networks is also discussed. A method of extending the flood-routing protocols over subnetworks which are connected via bridges is also presented. This method enables a large network to be divided into smaller subnetworks which reduces the number of links required.