Object-oriented languages have recently become common, making register indirect jumps more important than ever. In object-oriented languages, virtual functions are heavily used because they improve programming productivity greatly. Virtual function calls usually consist of register indirect jumps, and consequently, programs written in object-oriented languages contain many register indirect jumps. The prediction of the targets of register indirect jumps is more difficult than the prediction of the direction of conditional branches. Many predictors have been proposed for register indirect jumps, but they cannot predict the jump targets with high accuracy or require very complex hardware. We propose a method that resolves jump targets by forwarding execution results. Our proposal dynamically finds the producers of register indirect jumps in virtual function calls. After the execution of the producers, the execution results are forwarded to the processor's front-end. The jump targets can be resolved by the forwarded execution results without requiring prediction. Our proposal improves the performance of programs that include unpredictable register indirect jumps, because it does not rely on prediction but instead uses actual execution results. Our evaluation shows that the IPC improvement using our proposal is as high as 5.4% on average and 9.8% at maximum.

The quantum dot optical frequency comb laser (QD-CML) is an attractive photonic device for generating a stable emission of fine multiple-wavelength peaks. In the present paper, 1.0-GHz and 10-ps-order short optical pulsation is successfully demonstrated from a hybrid mode-locked QD-CML with an ultrabroadband wavelength tuning range in the T+O band. In addition, 10-GHz high-repetition intensity-stable short optical pulse generation with a high S/N ratio is successfully demonstrated using an external-cavity QD-CML with a 10th-harmonic mode-locking technique.

The efficiency of ubiquitous SIMD (Single Instruction Multiple Data) media processors is seriously limited by the bottleneck effect of the scalar kernels in media applications. To solve this problem, a dual-core framework, composed of a micro control unit and an instruction buffer, is proposed. This framework can dynamically decouple the scalar and vector pipelines of the original single-core SIMD architecture into two free-running cores. Thus, the bottleneck effect can be eliminated by effectively exploiting the parallelism between scalar and vector kernels. The dual-core framework achieves the best attributes of both single-core and dual-core SIMD architectures. Experimental results exhibit an average performance improvement of 33%, at an area overhead of 4.26%. What's more, with the increase of the SIMD width, higher performance gain and lower cost can be expected.

To obtain an ultra-short high-intensity pulse source, we investigated the amplification characteristics of two types of pulses (dissipative soliton and stretched pulses) produced by our Yb-doped fiber laser oscillator. Our results show that the dissipative soliton pulse can be amplified with less deterioration than the stretched pulse.

The recent increase in mobile data traffic has resulted in service quality problems. Although an economic approach to control congestion can be achieved by pricing, the current pricing schedule of mobile data services instead causes smartphone users to create more traffic. We establish a pricing model based on the distribution of demand types among heterogeneous users to improve the current tariff structure; our method mixes usage-based and fixed-fee pricing schemes. The results derived from the application of this model to survey data on willingness-to-pay for mobile data service demonstrate that the provider can decrease the amount of data traffic and increase the expected revenue by lowering the price for a unit of data and raising the fixed-fee level for unlimited service. The model also explains the changing weight of usage-based and fixed-fee pricing schemes by considering shifts in the type distribution through service evolution and proposes pricing strategies for future communications services.

In this paper, an extended best linear unbiased estimator (EBLUE) based on a periodic training sequence is proposed and investigated for frequency offset estimation in orthogonal frequency division multiplexing (OFDM) systems. The structure of EBLUE is general and flexible so it adapts to different complexity constraints, and is attractive in practical implementation. Performance analysis and design strategy of EBLUE are provided to realize the best tradeoff between performance and complexity. Moreover, closed-form results of both weight and performance make EBLUE even more attractive in practical implementation. Both the performance and complexity of EBLUE are compared with other proposals and the Cramer-Rao lower bound (CRLB) to demonstrate the merit of EBLUE.

In this letter, we propose a new QoS scheduling algorithm, referred to as the explicit traffic aware scheduling algorithm with explicit queue length notification (ETA-EQN), which aims at supporting multimedia services in HCCA of IEEE 802.11e WLAN. The results from intensive simulations with NS-2 verify that ETA-EQN provides much better network performance than the reference scheduler in terms of throughput, delay, and packet loss.

An 8-bit 100-kS/s successive approximation (SA) analog-to-digital converter (ADC) is proposed for measuring EEG and MEG signals in an 88 point. The architectures of a SA ADC with a single-ended analog input and a split-capacitor-based digital-to-analog converter (SC-DAC) are used to reduce the power consumption and chip area of the entire ADC. The proposed SA ADC uses a time-domain comparator that has an input offset self-calibration circuit. It also includes a serial output interface to support a daisy channel that reduces the number of channels for the multi-point sensor interface. It is designed by using a 0.35-µm 1-poly 6-metal CMOS process with a 3.3 V supply to implement together with a conventional analog circuit such as a low-noise-amplifier. The measured DNL and INL of the SA ADC are +0.63/-0.46 and +0.46/-0.51 LSB, respectively. The SNDR is 48.39 dB for a 1.11 kHz analog input signal at a sampling rate of 100 kS/s. The power consumption and core area are 38.71 µW and 0.059 mm2, respectively.

We extend the Nonparametric Discriminant Analysis (NDA) algorithm to a semi-supervised dimensionality reduction technique, called Semi-supervised Nonparametric Discriminant Analysis (SNDA). SNDA preserves the inherent advantages of NDA, that is, relaxing the Gaussian assumption required for the traditional LDA-based methods. SNDA takes advantage of both the discriminating power provided by the NDA method and the locality-preserving power provided by the manifold learning. Specifically, the labeled data points are used to maximize the separability between different classes and both the labeled and unlabeled data points are used to build a graph incorporating neighborhood information of the data set. Experiments on synthetic as well as real datasets demonstrate the effectiveness of the proposed approach.

In this paper, we propose a new method for wideband spectrum sensing using compressed measurements of the received wideband signal; we can directly separate information of the sub-channels and perform detection in each. Wideband spectrum sensing empowers us to rapidly access the vacant sub-channels in high utilization regime. Regarding the fact that at each time instant some sub-channels are vacant, the received signal is sparse in some bases. Then we could apply the Compressive Sensing (CS) algorithms and take the compressed measurements. On the other hand, the primary user signals in different sub-channels could have different modulation types; therefore, the signal in each sub-channel is chosen among a signal space. Knowing these signal spaces, the secondary user could separate information of different sub-channels employing the compressed measurements. We perform filtering and detection based on these compressed measurements; this decreases the computational complexity of the wideband spectrum sensing. In addition, we model the received wideband signal as a vector which has a block-sparse representation on a basis consisting of all sub-channel bases whose elements occur in clusters. Based on this feature of the received signal, we propose another wideband spectrum sensing method with lower computational complexity. In order to evaluate the performance of the proposed method, we employ the Monte-Carlo simulation. According to simulations if the compression rate is selected appropriately according to the CS theorems and the problem model, the detection performance of our method leads to the performance of the ideal filter bank-based method, which uses the ideal and impractical narrow band filters.

This paper first formulates the optimal instantaneous resource allocation, including path selection, power allocation and subchannel scheduling with proportional fairness in MIMO, OFDMA and relay-enhanced network. The joint optimization problem is a NP-hard one with non-linear constraints. To simplify this problem, we first propose a water-filling method named 'CP-AP w PF' to adaptively allocate power only among transmitting antennas. Then, a modified iterative water-filling algorithm named 'AP-AP w PF' is proposed to achieve adaptive power allocation on each subchannel by using the Jensen's inequality. Simulation shows that 'AP-AP w PF' algorithm improves the throughput for cell-edge users, and achieve a tradeoff between maximizing system throughput and assuring individual QoS.

Precoding is an excellent choice for complementing the MIMO systems. Linear precoding techniques offer better performance at low signal-to-noise ratios (SNRs) while non-linear techniques perform better at higher SNRs. In addition, the non-linear techniques can achieve near optimal capacity at the expense of reasonable levels of complexity. However, precoding depends on the knowledge of the wireless channel. Recent work on MIMO systems have shown that channel-knowledge at the transmitter, in either full or partial forms, can increase the channel capacity and system performance considerably. Therefore, hybrid techniques should be deployed in order to obtain a better trade-off in terms of complexity and performance. In this paper, we present a hybrid precoding technique which deals with the condition of partial channel-knowledge while offering robustness against the effects of correlation and poorly scattered channels while at the same time keeping low levels of complexity and high performance.

In this paper, a novel interference suppression technique from added RoF (Radio-on-Fiber) system is proposed. In general RoF system, received RF (radio frequency) signal intensity is periodically varied depending on chromatic dispersion that is known as fading phenomenon. In proposed technique null points of this fading phenomenon are intentionally applied to minimize signal interferences. This technique can realize two types of multiplexing RoF signal. In the first configuration, a single optical carrier is modulated twice using two optical modulators connected in series. In second configuration, new RoF signal is added to the existing network using individual light source. Multiplexing RoF signals of 10 GHz-band with data of 30 Mbps 64QAM is experimentally demonstrated.

A direction-of-arrival estimation (DoA) scheme that uses a uniform circular array (UCA) is proposed for near-field sources, where multiple pairs-of-subarrays exist with central symmetry. First, multiple generalized ESPRIT (G-ESPRIT) spectrums are obtained by applying the conventional G-ESPRIT algorithm to each of multiple pairs-of-subarrays. Second, a parallel spectrum is found by adding up the reciprocals of these G-ESPRIT spectrums and taking the reciprocal of the total. The locations of peaks in the parallel spectrum give the DoAs being estimated. When a DoA approaches the translation direction of two subarrays, the conventional G-ESPRIT spectrum is broken by a false peak. Since the translation directions of pairs-of-subarrays are different from each other, the false peak, due to the DoA approaching one of translation directions, does not exist simultaneously in all G-ESPRIT spectrums. The parallel concatenation of the spectrums suppresses the false peak and enhances the true DoA peaks. Simulation shows that the proposed scheme reduces the root mean square error of the DoA estimation, compared with the conventional G-ESPRIT algorithm.

We propose a method of the precise frequency tuning in millimeter wave (MMW) generation using a Mach-Zehnder-modulator-based flat comb generator (MZ-FCG). The MZ-FCG generates a flat comb signal where the comb spacing is exactly the same as the frequency of a radio-frequency signal driving the MZ-FCG. Two modes are extracted from the comb signal by using optical filters. One of them was modulated by a phase modulator, creating precisely frequency-controllable sidebands. In the experiment, typical phase modulation was used. By photomixing of the extracted two modes using a high-speed photodiode, MMW signals with precisely frequency-controllable sidebands are generated. By changing the modulation frequency, the frequency of MMW signals can be continuously tuned. In this scheme, there are two methods for the frequency tuning of MMW signals; one is a coarse adjustment which corresponds to the comb spacing, and the other is fine tuning by the phase-modulation. It was demonstrated that the intensity fluctuation of the upper sideband of the modulated MMW signal was less than 1 dB, and the frequency fluctuation was less than the measurement resolution (300 Hz).

In this paper, we propose an optimal supervisory control method for discrete event systems (DESs) that have different preferences. In our previous work, we proposed an optimal supervisory control method based on reinforcement learning. In this paper, we extend it and consider a system that consists of several local systems. This system is modeled by a decentralized DES (DDES) that consists of local DESs, and is supervised by a central supervisor. In addition, we consider that the supervisor and each local DES have their own preferences. Each preference is represented by a preference function. We introduce the new value function based on the preference functions. Then, we propose the learning method of the optimal supervisor based on reinforcement learning for the DDESs. The supervisor learns how to assign the control pattern so as to maximize the value function for the DDES. The proposed method shows the general framework of optimal supervisory control for the DDES that consists of several local systems with different preferences. We show the efficiency of the proposed method through a computer simulation.

A Boolean network model is one of the models of gene regulatory networks, and is widely used in analysis and control. Although a Boolean network is a class of discrete-time nonlinear systems and expresses the synchronous behavior, it is important to consider the asynchronous behavior. In this paper, using a Petri net, a new modeling method of asynchronous Boolean networks with control inputs is proposed. Furthermore, the optimal control problem of Petri nets expressing asynchronous Boolean networks is formulated, and is reduced to an integer programming problem. The proposed approach will provide us one of the mathematical bases of control methods for gene regulatory networks.