Energy minimization has become one of the primary goals in the embedded real-time domains. Consequently, scratch-pad memory has been employed as partial or entire replacement for cache memory due to its better energy efficiency. However, most previous approaches were not applicable to a preemptive multi-task environment. We propose three methods of partitioning and allocation of scratch-pad memory for fixed-priority-based preemptive multi-task systems. The three methods, i.e., spatial, temporal, and hybrid methods, achieve energy reduction in the instruction memory subsystems. With the spatial method, each task occupies its exclusive space in scratch-pad memory. With the temporal method, the running task uses entire scratch-pad space. The content of scratch-pad memory is swapped out as a task executes or gets preempted. The hybrid method is based on the spatial one but a higher priority task can temporarily use the space of lower priority task. The amount of space is prioritized for higher priority tasks. We formulate each method as an integer programming problem that simultaneously determines (1) partitioning of scratch-pad memory space for the tasks, and (2) allocation of program code to scratch-pad memory space for each task. Our methods not only support the real-time task scheduling but also consider aggressively the periods and priorities of tasks for the energy minimization. Additionally, we implement an RTOS-hardware cooperative support mechanism for runtime code allocation to the scratch-pad memory space. We have made the experiments with the fully functional real-time operating system. The experimental results have demonstrated the effectiveness of our techniques. Up to 73% energy reduction compared to a conventional method was achieved.

Kernel biased discriminant analysis (KBDA), as a subspace learning algorithm, has been an attractive approach for the relevance feedback in content-based image retrieval. Its performance, however, still suffers from the “small sample learning” problem and “kernel learning” problem. Aiming to solve these problems, in this paper, we present a new semi-supervised scheme of KBDA (S-KBDA), in which the projection learning and the “kernel learning” are interweaved into a constrained optimization framework. Specifically, S-KBDA learns a subspace that preserves both the biased discriminant structure among the labeled samples, and the geometric structure among all training samples. In kernel optimization, we directly optimize the kernel matrix, rather than a kernel function, which makes the kernel learning more flexible and appropriate for the retrieval task. To solve the constrained optimization problem, a fast algorithm based on gradient ascent is developed. The image retrieval experiments are given to show the effectiveness of the S-KBDA scheme in comparison with the original KBDA, and the other two state-of-the-art algorithms.

This paper presents the design and implementation of a quadrature voltage-controlled ring oscillator with the improved figure of merit (FOM) using the four single-ended inverter topology. Furthermore, a new architecture to prevent the latch-up in even number of stages composed of single-ended ring inverters is proposed. The design is implemented in 0.18 µm CMOS technology and the measurement results show a FOM of -163.8 dBc/Hz with the phase noise of -125.8 dBc/Hz at 4 MHz offset from the carrier frequency of 3.4 GHz. It exhibits a frequency tuning range from 1.23 GHz to 4.17 GHz with coarse and fine frequency tuning sensitivity of 1.08 MHz/mV and 120 kHz/mV, respectively.

In this letter, coordinated power allocation (PA) is investigated for the downlink of a generalized multi-cluster distributed antenna system (DAS). Motivated by practical applications, we assume only the global large-scale channel state information is known at the transmitter. First, an upper bound (UB) for the ergodic sum capacity of the system is derived and used as a simplified optimization target. Then, a coordinated PA scheme is proposed based on Geometric Programming (GP), which is demonstrated to be nearly optimal by Monte Carlo simulations.

In orthogonal frequency division multiple access (OFD-MA) systems, soft frequency reuse (SFR) and distributed antenna system (DAS) are two effective techniques to avoid excessive inter-cell interference (ICI). To gain the advantages of both, in this letter, we build a new cell architecture by jointly taking DAS and SFR into consideration to achieve the goal of high and well-balanced capacity. Furthermore, to rectify the shortfall in the literature, the capacity and outage probability in the multi-cell environment are derived by taking the complete channel effects into account, including the path loss, shadowing and Rayleigh fading. Simulations verify the superior performance and exactness of the analytical results.

An Energy-efficient Flexible Beacon Scheduling (EFBS) mechanism is proposed to solve the beacon collision problem in cluster-tree healthcare systems. In EFBS, after clustering, BAN Coordinators perform power control. Then they are divided into groups and each group is assigned one contention-free time-slot. The duration of the beacon-only period is flexible. According to the simulation results, EFBS provides better performance than other beacon scheduling approaches.

This paper presents pulse-coupled piecewise constant spiking oscillators (PWCSOs) consisting of two PWCSOs and a coupling method is master-slave coupling. The slave PWCSO exhibits chaos because of chaotic response of the master one. However, if the parameter varies, the slave PWCSO can exhibit the phenomena as a periodicity in the phase plane. We focus on such phenomena and corresponding bifurcation. Using the 2-D return map, we clarify its mechanism.

As a global feature of fingerprint patterns, the Orientation Field (OF) plays an important role in fingerprint recognition systems. This paper proposes a fast binary pattern based orientation estimation with nearest-neighbor search, which can reduce the computational complexity greatly. We also propose a classified post processing with adaptive averaging strategy to increase the accuracy of the estimated OF. Experimental results confirm that the proposed method can satisfy the strict requirements of the embedded applications over the conventional approaches.

We propose a system that, using video information, segments the mouth region from a face image and then detects the protrusion of the tongue from inside the oral cavity. Initially, under the assumption that the mouth is closed, we detect both mouth corners. We use a set of specifically oriented Gabor filters for enhancing horizontal features corresponding to the shadow existing between the upper and lower lips. After applying the Hough line detector, the extremes of the line that was found are regarded as the mouth corners. Detection rate for mouth corner localization is 85.33%. These points are then input to a mouth appearance model which fits a mouth contour to the image. By segmenting its bounding box we obtain a mouth template. Next, considering the symmetric nature of the mouth, we divide the template into right and left halves. Thus, our system makes use of three templates. We track the mouth in the following frames using normalized correlation for mouth template matching. Changes happening in the mouth region are directly described by the correlation value, i.e., the appearance of the tongue in the surface of the mouth will cause a decrease in the correlation coefficient through time. These coefficients are used for detecting the tongue protrusion. The right and left tongue protrusion positions will be detected by analyzing similarity changes between the right and left half-mouth templates and the currently tracked ones. Detection rates under the default parameters of our system are 90.20% for the tongue protrusion regardless of the position, and 84.78% for the right and left tongue protrusion positions. Our results demonstrate the feasibility of real-time tongue protrusion detection in vision-based systems and motivates further investigating the usage of this new modality in human-computer communication.

In this article, we study different methods to enhance the capacity of Voice-over-IP (VoIP) in Evolved UTRAN (E-UTRAN) downlink. According to previous E-UTRAN system level simulation results, VoIP capacity with dynamic scheduling and semi-persistent scheduling is limited by available Physical Downlink Control CHannel (PDCCH) resources and Physical Downlink Shared CHannel (PDSCH) resources respectively. The different behavior of these two packet scheduling schemes and specific bottleneck on capacity lead us to formulate two distinctly different performance enhancement strategies, which are the main focus of this paper. For dynamic scheduling, we propose a bi-directional power control algorithm to rationalize PDCCH resource allocation, thus greatly improving VoIP capacity. For semi-persistent scheduling, we introduce Adaptive Transmission Bandwidth (ATB) to rationalize PDSCH resource allocation and further apply dynamic packet bundling in highly-loaded network, thus improving VoIP capacity in a considerable manner. The proposed enhancement methods are validated through large-scale system level simulations and the obtained system simulation results further confirm effectiveness of these enhancement methods.

The VoIP-based Internet Phonesystem is now seen as one of the killer applications in the high speed and broadband internet environment. Given the wide-spread use of the Internet Phone, it is necessary to provide security services for guaranteeing users' privacy. However, providing security service in Internet Phone has the possibility of incurring additional overheads such as call setup delay time. In this paper, we present a one-way key agreement model based on VoIP in order to reduce call setup time as well as protecting user privacy. The proposed approach decreases the delay time of the call setup in comparison with the previous models because our model enables the key generation in caller side without waiting the response from the receiver.

We consider a queuing model with applications to electric vehicle (EV) charging systems in smart grids. We adopt a scheme where an Electric Service Company (ESCo) broadcasts a one bit signal to EVs, possibly indicating 'on-peak' periods during which electricity cost is high. EVs randomly suspend/resume charging based on the signal. To model the dynamics of EVs we propose an M/M/∞ queue with random interruptions, and analyze the dynamics using time-scale decomposition. There exists a trade-off: one may postpone charging activity to 'off-peak' periods during which electricity cost is cheaper, however this incurs extra delay in completion of charging. Using our model we characterize achievable trade-offs between the mean cost and delay perceived by users. Next we consider a scenario where EVs respond to the signal based on the individual loads. Simulation results show that peak electricity demand can be reduced if EVs carrying higher loads are less sensitive to the signal.

Numerous noise suppression methods for speech signals have been developed up to now. In this paper, a new method to suppress noise in speech signals is proposed, which requires a single microphone only and doesn't need any priori-information on both noise spectrum and pitch. It works in the presence of noise with high amplitude and unknown direction of arrival. More specifically, an adaptive noise suppression algorithm applicable to real-life speech recognition is proposed without assuming the Gaussian white noise, which performs effectively even though the noise statistics and the fluctuation form of speech signal are unknown. The effectiveness of the proposed method is confirmed by applying it to real speech signals contaminated by noises.

In this letter, we derive another exact bit error rate (BER) for decode-and-forward (DF) relay systems over Rayleigh fading channels. At first, our focus is on fixed-DF (FDF) relay schemes in which the probability density function (PDF) is derived based on error-events at relay nodes. Some insight into how erroneous detection and transmission at relay nodes affect both the combined signal-to-noise ratio (SNR) and the averaged BER is obtained, and cooperative diversity is observed from the closed-form BER expression. In addition, the developed analytical method is extended to adaptive-DF (ADF) schemes and the exact BER expressions are derived. Simulation results are finally presented to validate the analysis.

Sender-based message logging (SBML) with checkpointing has its well-known beneficial feature, lowering highly failure-free overhead of synchronous logging with volatile logging at sender's memory. This feature encourages it to be applied into many distributed systems as a low-cost transparent rollback recovery technique. However, the original SBML recovery algorithm may no longer be progressing in some transient communication error cases. This paper proposes a consistent recovery algorithm to solve this problem by piggybacking small log information for unstable messages received on each acknowledgement message for returning the receive sequence number assigned to a message by its receiver. Our algorithm also enables all messages scheduled to be sent, but delayed because of some preceding unstable messages to be actually transmitted out much earlier than the existing ones.

This paper provides conceptual and experimental analysis of a new approach in the study of kanji, our “Learner's Visualization (LV) Approach”. In a previous study we found that the LV Approach assists beginning learners in significantly updating their personal kanji deconstruction visualization. Additionally, in another study our findings provided evidence that beginning learners also receive a significant impact in the ability to acquire vocabulary. In this study, our research problem examines how beginning and intermediate students use visualization to cognitively deconstruct (divide) kanji in different ways, and how this affects their learning progress. We analyze the cognitive differences in how kanji learners explore and deconstruct novel kanji while using the LV Approach and how these differences affect their learning process while using the LV Approach. During the learning experience, our LILES System (Learner's Introspective Latent Envisionment System), based on the LV Approach, guides learners to choose from a set of possible “kanji deconstruction layouts” (layouts showing different ways in which a given kanji can be divided). The system then assists learners in updating their “kanji deconstruction level” (the average number of parts they visualize within kanji according to their current abilities). Statistical analysis based on achieved performance was conducted. The analysis of our results proves that there are cognitive differences: beginners deconstruct kanji into more parts (“blocks”) than intermediate learners do, and while both improve their kanji deconstruction scores, there is a more significant change in “kanji deconstruction level” in beginners. However, it was also found that intermediate learners benefit more in “kanji retention score” compared with beginners. Suggestions for further research are provided.

This letter provides a tight upper bound on the bit error rate (BER) over the Nakagami-m fading channel for the dual carrier modulation (DCM) scheme, which is adopted by the multi-band orthogonal frequency division multiplexing (MB-OFDM) ultra-wideband (UWB) system. Its tightness is verified with the existing result for Rayleigh fading channel, i.e., for m=1, which would be also valid for a more general fading environment.

The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.

Manufacturing defects in the deep sub-micron VLSI process and aging resulted problems of devices during lifecycle are inevitable, and fault-tolerant routing algorithms are important to provide the required communication for NoCs in spite of failures. The proposed algorithm, referred to as scalable and reconfigurable fault-tolerant distributed routing (RFDR), partitions the system into nine regions using the concept of divide-and-conquer. It is a distributed algorithm, and each router guarantees fault-tolerance within one's own region and the system can be still sustained with multiple fault areas. The proposed RFDR has excellent scalability with hardware cost keeping constant independent of system size. Also it is completely reconfigurable when new nodes fail. Simulations under various synthetic traffic patterns show its better performance compared to Extended-XY routing algorithm. Moreover, there is almost no hardware overhead compared to Logic-Based Distributed Routing (LBDR), but the fault-tolerance capacity is enhanced in the proposed algorithm. Hardware cost is reduced 37% compared to Reconfigurable Distributed Scalable Predictable Interconnect Network (R-DSPIN) which only supports single fault region.

We describe a concept and realization of distance-adaptive (DA) resource allocation in spectrum-sliced elastic optical path network (SLICE). We modify the modulation format and cross-connection bandwidth of individual fixed-bit rate optical paths to optimize performance with respect to transmission distance. The shorter paths are allocated a smaller amount of resources which allows reducing the spectrum occupied by the channel. We show in calculation a reduction in required spectral resources of more than 60% when compared to the traditional traffic allocation schemes based on ITU-T grid. The concept is verified experimentally.