Curve extension is a useful function in shape modeling for cyberworlds, while a Ball B-spline Curve (BBSC) has its advantages in representing freeform tubular objects. In this paper, an extension algorithm for the BBSC with G2-continuity is investigated. We apply the extending method of B-Spline curves to the skeleton of the BBSC through generalizing a minimal strain energy method from 2D to 3D. And the initial value of the G2-continuity parameter for the skeleton is selected by minimizing the approximate energy function which is a problem with O(1) time complexity. The corresponding radius function of the control ball points is determined through applying the G2-continuity conditions for the skeleton to the scalar function. In order to ensure the radii of the control ball points are positive, we make a decision about the range of the G2-continuity parameter for the radius and then determine it by minimizing the strain energy in the affected area. Some experiments comparing our method with other methods are given. And at the same time, we present the advantage of our method in modeling flexibility from the aspects of the skeleton and radius. The results illustrate our method for extending the BBSC is effective.

Mobile video services are becoming a dominant traffic category in emerging fourth generation (4G) cellular networks such as the 3GPP Long-Term Evolution (LTE) and LTE-Advanced (LTE-A). In particular, mobile video unicasting services based on 3GPP Dynamic Adaptive Streaming over HTTP (DASH) and multicasting/broadcasting services based on 3GPP evolved Multimedia Multicast/Broadcast Service (eMBMS) will require considerable resources for high-quality service delivery with high coverage probability. Faced with the challenge of energy efficient multimedia service provisioning over LTE/LTE-A, in this paper, we present simple analytical tools for evaluation of average service data rates, bandwidth and energy-consumption requirements applicable for different multimedia delivery services and LTE/LTE-A radio access network (RAN) configurations. Moreover, we introduce and evaluate novel energy and bandwidth performance measures defined per unit of service. As a result, we are able to compare the efficiency of different multimedia service delivery configurations over LTE/LTE-A. In particular, in this paper, as a running example we focus on eMBMS and compare the Energy of Service (EoS) of the two macro-cellular LTE/LTE-A configurations recently proposed in 3GPP: i) a single frequency network eMBMS (SFN-eMBMS), and ii) a single-cell eMBMS (SC-eMBMS). Furthermore, we extend this analysis to eMBMS provisioning over Heterogeneous Networks (HetNets) environment. However, the methodology presented is general and targets light-weight system design and comparison of bandwidth/energy costs of different LTE/LTE-A multimedia service delivery configurations.

We consider a unified approach to the tracking analysis of adaptive filters with error and matrix data nonlinearities. Using energy-conservation arguments, we not only derive earlier results in a unified manner, but we also obtain new performance results for more general adaptive algorithms without requiring the restriction of the regression data to a particular distribution. Numerical simulations support the theoretical results.

We propose a power-saving mechanism (PSM) specific to request-and-response-based applications, which simply changes the order of the operating procedure of the legacy PSM by considering the attributes of the request-and-response delay. We numerically analyze the PSM with respect to the energy consumption and buffering delay and characterize this performance by employing a simple energy-delay trade-off (EDT) curve that is determined by the operating parameters. The resulting EDT curve clearly shows that the proposed PSM outperforms the legacy PSM.

In this paper, an accurate player tracking method in far-view soccer videos based on a composite energy function is presented. In far-view soccer videos, player tracking methods that perform processing based only on visual features cannot accurately track players since each player region becomes small, and video coding causes color bleeding between player regions and the soccer field. In order to solve this problem, the proposed method performs player tracking on the basis of the following three elements. First, we utilize visual features based on uniform colors and player shapes. Second, since soccer players play in such a way as to maintain a formation, which is a positional pattern of players, we use this characteristic for player tracking. Third, since the movement direction of each player tends to change smoothly in successive frames of soccer videos, we also focus on this characteristic. Then we adopt three energies: a potential energy based on visual features, an elastic energy based on formations and a movement direction-based energy. Finally, we define a composite energy function that consists of the above three energies and track players by minimizing this energy function. Consequently, the proposed method achieves accurate player tracking in far-view soccer videos.

Extremely low voltage operation near or below threshold voltage is a key circuit technology to improve the energy efficiency of information systems and to realize ultra-low power sensor nodes. However, it is difficult to operate conventional analog circuits based on amplifier at low voltage. Furthermore, PVT (Process, Voltage and Temperature) variation and random Vth variation degrade the minimum operation voltage and the energy efficiency in both digital and analog circuits. In this paper, extremely low power analog circuits based on comparator and switched capacitor as well as extremely low power digital circuits are presented. Many kinds of circuit technologies are applied to cope with the variation problem. Finally, image processing SoC that integrates digital and analog circuits is presented, where improvement of total performance by a cooperation of analog circuits and digital circuits is demonstrated.

k-NN classification has been applied to classify normal tissues in MR images. However, the intensity inhomogeneity of MR images forces conventional k-NN classification into significant misclassification errors. This letter proposes a new interleaved method, which combines k-NN classification and bias field estimation in an energy minimization framework, to simultaneously overcome the limitation of misclassifications in conventional k-NN classification and correct the bias field of observed images. Experiments demonstrate the effectiveness and advantages of the proposed algorithm.

A photovoltaic (PV)-assisted CMOS rectifier was developed for efficient energy harvesting from ambient radio waves as one example of the synergistic energy harvesting concept. The rectifier operates truly synergistically. A pn junction diode acting as a PV cell converts light energy to DC bias voltage, which compensates the threshold voltage (Vth) of the MOSFETs and enhances the radio frequency (RF) to DC power conversion efficiency (PCE) of the rectifier even under extremely low input power conditions. The indoor illuminance level was sufficient to generate gate bias voltages to compensate Vths. Although the same PV cell structure for biasing nMOS and pMOS transistors was used, photo-generated bias voltages were found to become unbalanced due to the two-layered pn junction structures and parasitic bipolar transistor action. Under typical indoor lighting conditions, a fabricated PV-assisted rectifier achieved a PCE greater than 20% at an RF input power of -20dBm, a frequency of 920MHz, and an output load of 47kΩ. This PCE value is twice the value obtained by a conventional rectifier without PV assistance. In addition, it was experimentally revealed that if symmetric biasing voltages for nMOS and pMOS transistors were available, the PCE would increase even further.

This letter presents new methods for transforming perfect ternary sequences into perfect 8-QAM+ sequences. Firstly, based on perfect ternary sequences with even period, two mappings which can map two ternary variables to an 8-QAM+ symbol are employed for constructing new perfect 8-QAM+ sequences. In this case, the proposed construction is a generalization of the existing one. Then based on perfect ternary sequence with odd period, perfect 8-QAM sequences are generated. Compared with perfect 8-QAM+ sequences, the resultant sequences have no energy loss.

In this letter, we propose an energy-aware source routing protocol for maximizing the network lifetime in mobile ad hoc networks. We define a new routing cost by considering both transmit and receive power consumption and remaining battery level in each node simultaneously and present an efficient route discovery procedure to investigate the proposed routing cost. Intensive simulation verifies that the proposed routing protocol has similar performance to the conventional routing protocols in terms of the number of transmission hops, transmission rate, and energy consumption while significantly improving the performance with respect to network lifetime.

Packet loss and energy dissipation are two major challenges of designing large-scale wireless sensor networks. Since sensing data is spatially correlated, compressed sensing (CS) is a promising reconstruction scheme to provide low-cost packet error correction and load balancing. In this letter, assuming a multi-hop network topology, we present a CS-oriented data aggregation scheme with a new measurement matrix which balances energy consumption of the nodes and allows for recovery of lost packets at fusion center without additional transmissions. Comparisons with existing methods show that the proposed scheme offers higher recovery precision and less energy consumption on TinyOS.

This paper proposes a new optimization problem and several implementation algorithms for energy-efficient clouds where energy efficiency is measured by the number of physical machines that can be removed from operation and turned off. The optimization problem is formulated is such a way that solutions are considered favorable not only when the number of migrations is minimized but also when the resulting layout has more free physical machines which can therefore be turned off to save electricity.

Cooperative spectrum sensing is an effective approach that utilizes spatial diversity gain to improve detection performance. Most studies assume that the background noise is exactly known. However, this is not realistic because of noise uncertainty which will significantly degrade the performance. A novel weighted hard combination algorithm with two thresholds is proposed by dividing the whole range of the local test statistic into three regions called the presence, uncertainty and absence regions, instead of the conventional two regions. The final decision is made by weighted combination at the common receiver. The key innovation is the full utilization of the information contained in the uncertainty region. It is worth pointing out that the weight coefficient and the local target false alarm probability, which determines the two thresholds, are also optimized to minimize the total error rate. Numerical results show this algorithm can significantly improve the detection performance, and is more robust to noise uncertainty than the existing algorithms. Furthermore, the performance of this algorithm is not sensitive to the local target false alarm probability at low SNR. Under sufficiently high SNR condition, this algorithm reduces to the improved one-out-of-N rule. As noise uncertainty is unavoidable, this algorithm is highly practical.

This paper describes a novel method for the bi-directional transformation between the power consumption patterns of appliances and human living activities. We have been proposing a demand-side energy management system that aims to cut down the peak power consumption and save the electric energy in a household while keeping user's quality of life based on the plan of electricity use and the dynamic priorities of the appliances. The plan of electricity use could be established in advance by predicting appliance power consumption. Regarding the priority of each appliance, it changes according to user's daily living activities, such as cooking, bathing, or entertainment. To evaluate real-time appliance priorities, real-time living activity estimation is needed. In this paper, we address the problem of the bi-directional transformation between personal living activities and power consumption patterns of appliances. We assume that personal living activities and appliance power consumption patterns are related via the following two elements: personal appliance usage patterns, and the location of people. We first propose a Living Activity - Power Consumption Model as a generative model to represent the relationship between living activities and appliance power consumption patterns, via the two elements. We then propose a method for the bidirectional transformation between living activities and appliance power consumption patterns on the model, including the estimation of personal living activities from measured appliance power consumption patterns, and the generation of appliance power consumption patterns from given living activities. Experiments conducted on real daily life demonstrate that our method can estimate living activities that are almost consistent with the real ones. We also confirm through case study that our method is applicable for simulating appliance power consumption patterns. Our contributions in this paper would be effective in saving electric energy, and may be applied to remotely monitor the daily living of older people.

Coarse-grained Reconfigurable Architecture (CGRA) is a parallel computing platform that provides both high performance of hardware and high flexibility of software. It is becoming a promising platform for embedded and mobile applications. Since the embedded and mobile devices are usually battery-powered, improving battery lifetime becomes one of the primary design issues in using CGRAs. In this paper, we propose a battery-aware task-mapping method to optimize energy consumption and improve battery lifetime. The proposed method mainly addresses two problems: task partitioning and task scheduling when mapping applications onto CGRA. The task partitioning and scheduling are formulated as a joint optimization problem of minimizing the energy consumption. The nonlinear effects of real battery are taken into account in problem formulation. Using the insights from the problem formulation, we design the task-mapping algorithm. We have used several real-world benchmarks to test the effectiveness of the proposed method. Experiment results show that our method can dramatically lower the energy consumption and prolong the battery-life.

Nowadays, with spreads of inexpensive small communication devices, a number of wireless local area networks (WLANs) have been deployed even in the same building for the Internet access services. Their wireless access-points (APs) are often independently installed and managed by different groups such as departments or laboratories in a university or a company. Then, a user host can access to multiple WLANs by detecting signals from their APs, which increases the energy consumption and the operational cost. It may also degrade the communication performance by increasing interferences. In this paper, we present an AP aggregation approach to solve these problems in multiple WLAN environments by aggregating deployed APs of different groups into limited ones using virtual APs. First, we formulate the AP aggregation problem as a combinatorial optimization problem and prove the NP-completeness of its decision problem. Then, we propose its heuristic algorithm composed of five phases. We verify the effectiveness through extensive simulations using the WIMNET simulator.

In this paper, we propose an adaptive voltage huddle-based distributed-register architecture (AVHDR architecture), which integrates dynamic multiple supply voltages and interconnection delay into high-level synthesis. In AVHDR architecture, voltages can be dynamically assigned for energy reduction. In other words, low supply voltages are assigned to non-critical operations, and leakage power is cut off by turning off the power supply to the sleeping functional units. Next, an AVHDR-based high-level synthesis algorithm is proposed. Our algorithm is based on iterative improvement of scheduling/binding and floorplanning. In the iteration process, the modules in each huddle can be placed close to each other and the corresponding AVHDR architecture can be generated and optimized with floorplanning information. Experimental results show that on average our algorithm achieves 43.9% energy-saving compared with conventional algorithms.

Energy-harvesting devices are materials that allow ambient energy sources to be converters into usable electrical power. While a battery powers the modern embedded systems, these energy-harvesting devices power the energy-harvesting embedded systems. This claims a new energy efficient management techniques for the energy-harvesting systems dislike the previous management techniques. The higher entire system efficiency in an energy-harvesting system can be obtained by a higher generating efficiency, a higher consuming efficiency, or a higher transferring efficiency. This paper presents a generalized technique for a dynamic reconfiguration and a task scheduling considering the power loss in DC-DC converters in the system. The proposed technique minimizes the power loss in the DC-DC converter and charger of the system. The proposed technique minimizes the power loss in the DC-DC converters and charger of the system. Experiments with actual application demonstrate that our approach reduces the total energy consumption by 22% in average over the conventional approach.

Nature-inspired devices and architectures are attracting considerable attention for various purposes, including the development of novel computing techniques based on spatiotemporal dynamics, exploiting stochastic processes for computing, and reducing energy dissipation. This paper demonstrates that networks of optical energy transfers between quantum nanostructures mediated by optical near-field interactions occurring at scales far below the wavelength of light could be utilized for solving a constraint satisfaction problem (CSP), the satisfiability problem (SAT), and a decision making problem. The optical energy transfer from smaller quantum dots to larger ones, which is a quantum stochastic process, depends on the existence of resonant energy levels between the quantum dots or a state-filling effect occurring at the larger quantum dots. Such a spatiotemporal mechanism yields different evolutions of energy transfer patterns in multi-quantum-dot systems. We numerically demonstrate that networks of optical energy transfers can be used for solution searching and decision making. We consider that such an approach paves the way to a novel physical informatics in which both coherent and dissipative processes are exploited, with low energy consumption.

This paper presents a unified treatment of the tracking analysis of adaptive filters with data normalization and error nonlinearities. The approach we develop is based on the celebrated energy-conservation framework, which investigates the energy flow through each iteration of an adaptive filter. Aside from deriving earlier results in a unified manner, we obtain new performance results for more general filters without restricting the regression data to a particular distribution. Simulations show good agreement with the theoretical findings.