This paper presents the performance of outer-loop control for selecting the best modulation and coding scheme (MCS) based on mutual information (MI) for orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) spatial division multiplexing (SDM). We propose an outer-loop control scheme that updates the measured MI per information bit value for selecting the best MCS from a mapping table that associates the block error rate (BLER) and MI per bit instead of directly updating the MCS selection threshold so that the required BLER is satisfied. The proposed outer-loop control is applicable to continuous data transmission including intermittent transmission with a short blank period. Moreover, we compare the measured BLER and throughput performance for two types of outer-loop control methods: instantaneous block error detection and moving-average BLER detection. In the paper, we use maximum likelihood detection (MLD) for MIMO SDM. Computer simulation results optimize the step size for the respective outer-loop control schemes for selecting the best MCS that achieves the higher throughput and the target BLER simultaneously. Computer simulation results also show that by using the most appropriate step size, the outer-loop control method based on the instantaneous block error detection of each physical resource block is more appropriate than that based on the moving-average BLER detection from the viewpoints of achieving the target BLER more accurately and higher throughput.

We provide an overview of techniques for the photonic generation of arbitrary RF waveforms, particularly those suitable for impulse radio or multi-band ultrawideband (UWB)-over-fiber transmission, and chirped microwave waveforms, with an emphasis on microwave photonic filtering and optical spectral shaping followed by wavelength-to-time mapping. We discuss possibilities for integrating the various device and component technologies with silicon photonics.

Due to the fact that natural images are approximately sparse in Discrete Cosine Transform (DCT) or wavelet basis, the Compressive Sensing (CS) can be employed to decode both the host image and watermark with zero error, despite not knowing the host image. In this paper, Limited-Random Sequence (LRS) matrix is utilized to implement the blind CS detection, which benefits from zero error and lower complexity. The performance in Bit Error Rate (BER) and error-free detection probability confirms the validity and efficiency of the proposed scheme.

This paper shows that the induced peak voltage on the short monopole antenna by the EM field radiated from a small gap discharge when the gap width was experimentally changed from 10 to 360µm was not directly proportional to the discharge voltage between the gap. It was found that the 10mm short monopole antenna induced peak voltage had a peak value between 40 and 60µm gap width.

We address a robust algorithm for the interference-plus-noise covariance matrix reconstruction (RA-INCMR) against random arbitrary steering vector mismatches (RASVMs) of the interferences, which lead to substantial degradation of the original INCMR beamformer performance. Firstly, using the worst-case performance optimization (WCPO) criteria, we model these RASVMs as uncertainty sets and then propose the RA-INCMR to obtain the robust INCM (RINCM) based on the Robust Capon Beamforming (RCB) algorithm. Finally, we substitute the RINCM back into the original WCPO beamformer problem for the sample covariance matrix to formulate the new RA-INCM-WCPO beamformer problem. Simulation results demonstrate that the performance of the proposed beamformer is much better than the original INCMR beamformer when there exist RASVMs, especially at low signal-to-noise ratio (SNR).

Electromagnetic analysis (EMA) against public-key cryptographic software on an embedded OS is presented in this paper. First, we propose a method for finding an observation point for EMA, where the EM radiation caused by cryptographic operations can be observed with low noise. The basic idea is to find specific EM radiation patterns produced by cryptographic operations given specific input pattern. During the operations, we scan the surface of the target device(s) with a micro magnetic probe. The scan is optimized in advanced using another compatible device that has the same central processing unit (CPU) and OS as the target device. We demonstrate the validity of the proposed EMAs through some EMA experiments with two types of RSA software on an embedded OS platform. The two types of RSA software have different implementations for modular multiplication algorithms: one is a typical and ready-made implementation using BigInteger class on Java standard library, and another is a custom-made implementation based on the Montgomery multiplication algorithm. We conduct experiments of chosen-message EMA using our scanning method, and show such EMAs successfully reveal the secret key of RSA software even under the noisy condition of the embedded OS platform. We also discuss some countermeasures against the above EMAs.

For the electric demand prediction problem, a modification mechanism of predicted demand data has been proposed in the previous work. In this paper, we analyze the performance of the modification mechanism in power balancing control. Then, we analytically derive an upper bound of the performance, which is characterized by system parameters and prediction precision.

MapReduce and its open software implementation Hadoop are now widely deployed for big data analysis. As MapReduce runs over a cluster of massive machines, data transfer often becomes a bottleneck in job processing. In this paper, we explore the influence of data transfer to job processing performance and analyze the mechanism of job performance deterioration caused by data transfer oriented congestion at disk I/O and/or network I/O. Based on this analysis, we update Hadoop's Heartbeat messages to contain the real time system status for each machine, like disk I/O and link usage rate. This enhancement makes Hadoop's scheduler be aware of each machine's workload and make more accurate decision of scheduling. The experiment has been done to evaluate the effectiveness of enhanced scheduling methods and discussions are provided to compare the several proposed scheduling policies.

Multiple-input multiple-output (MIMO) transmission is attracting interest for increasing the transmission rates of wireless systems. This paper surveys MIMO transmission technology from the viewpoints of transmission methods, access control schemes, and total transmission efficiency. We consider wireless local area networks (WLAN) systems that use MIMO technology; moreover, we focus on multiuser MIMO (MU-MIMO) technology, which will be introduced in next-generation WLAN systems such as IEEE802.11ac. This paper explains the differences in the detailed access control procedures for MIMO and MU-MIMO transmission, including channel state information (CSI) acquisition. Furthermore, the issues related to CSI feedback and solutions are also discussed. Related works on the medium access control (MAC) protocol in MIMO/MU-MIMO transmission are introduced. In addition, the throughput performance using MIMO/MU-MIMO transmission is evaluated considering an IEEE802.11ac-based WLAN system. From the numerical evaluation, it is shown that the overhead due to CSI feedback from the user terminals to the base station causes a decrease in the throughput. We verified that implicit beamforming, which eliminates CSI feedback, is effective for solving this issue.

In the localization systems based on time difference of arrival (TDOA), multipath fading and the interference source will deteriorate the localization performance. In response to this situation, TDOA estimation based on blind beamforming is proposed in the frequency domain. An additional constraint condition is designed for blind beamforming based on maximum power collecting (MPC). The relationship between the weight coefficients of the beamformer and TDOA is revealed. According to this relationship, TDOA is estimated by discrete Fourier transform (DFT). The efficiency of the proposed estimator is demonstrated by simulation results.

This paper presents an information hiding method for DNA steganography with which a massive amount of data can be hidden in a noncoding strand. Our method maps the encrypted data to the DNA sequence using a numerical mapping table, before concealing it in the noncoding sequence using a secret key comprising sector length and the random number generator's seed. Our encoding algorithm is sector-based and reference dependent. Using modular arithmetic, we created a unique binary-base translation for every sector. By conducting a simulation study, we showed that our method could preserve amino acid information, extract hidden data without reference to the host DNA sequence, and detect the position of mutation error. Experimental results verified that our method produced higher data capacity than conventional methods, with a bpn (bit-per-nucleotide) value that ranged from approximately 1-2, depending on the selected sector length. Additionally, our novel method detected the positions of mutation errors by the presence of a parity base in each sector.

A miniaturized patch hybrid coupler with arbitrary power ratio and impedance transformation is proposed and designed by loading a pair of asymmetric cross slots on a squared patch resonator. To obtain the arbitrary power ratio and impedance transformation, the rectangular size of stepped slot ends should be well designed to be asymmetry and thus to obtain the different inductive loadings along two current paths. Theoretically, the equivalent transmission line model is first developed to understand the physical relationship between the patch and traditional branch-line hybrids. The matching/isolation and power ratio conditions are then derived at center frequency. By following a detailed design guideline, a prototype of the hybrid with 1:2 power ratio and 1:1.3 impedance transformation is designed and fabricated at 4.2 GHz. The measured results show a good agreement with simulated results, where the measured -10 dB impedance bandwidth achieves 18% and the bandwidth of 90°±6° phase difference is about 35% in a frequency range from 3.5 GHz to 5 GHz.

Texture feature descriptors such as local binary patterns (LBP) have proven effective for ground-based cloud classification. Traditionally, these texture feature descriptors are predefined in a handcrafted way. In this paper, we propose a novel method which automatically learns discriminative features from labeled samples for ground-based cloud classification. Our key idea is to learn these features through mutual information maximization which learns a transformation matrix for local difference vectors of LBP. The experimental results show that our learned features greatly improves the performance of ground-based cloud classification when compared to the other state-of-the-art methods.

In cognitive radio (CR), superposition cooperative spectrum sensing (SPCSS) is able to offer a much improved sensing reliability compared to individual sensing. Because of the differences in sensing channel condition, the reporting order for each cognitive radio user (CU) will highly affect the sensing performance of the network. In this paper, we propose an algorithm to assign the best reporting order to each CU in order to maximize sensing performance under SPCSS. The numerical results show that the proposed scheme can obtain the same performance as the optimal scheme.

We propose a novel bias-free adaptive beamformer employing an affine projection algorithm with the optimal regularization parameter. The generalized sidelobe canceller affine projection algorithm suffers from a bias of a weight vectors under the condition of no reference signals for output of an array in the beamforming application. First, we analyze the bias in the algorithm and prove that the bias can be eliminated through a large regularization parameter. However, this causes slow convergence at the initial state, so the regularization parameter should be controlled. Through the optimization of the regularization parameter, the proposed method achieves fast convergence without the bias at the steady-state. Experimental results show that the proposed beamformer not only removes the bias but also achieves both fast convergence and high steady-state output signal-to-interference-plus-noise ratio.

The correct detection of the start of burst is very important in wideband networking radio operation as it directly affects the Time Division Multiple Access (TDMA) adaptive time slot algorithm. In this paper, we propose a robust Data Aided (DA) algorithm for burst detection in a hybrid CDMA/Adaptive TDMA based wideband networking waveform of a software defined radio. The proposed algorithm is based on a novel differentially modulated training sequence designed by using precoding sequence. The training sequence structure and precoding sequence are exploited in the calculation of proposed timing metric which is normalized by the signal energy. The precoding sequence is adequately designed for the timing metric to have a sharp peak. The algorithm shows excellent performance for multiuser scenario. It is shown through computer simulations that by increasing the active users from 1 to 8, the performance degradation is only about 1∼2dB. The proposed algorithm is compared with other algorithms and found to outperform them even in the presence of multipath fading effects. The proposed algorithm has been implemented on Field Programmable Gate Array (FPGA) platform for high data rate applications and it is shown that the results from hardware are identical to the simulation results.

Recently, the growing concepts that information communication technologies apply to social infrastructures have caused deep interests with wireless sensor networks (WSNs). WSNs can be used for various application areas such as home, health, factory and so on. For the different application areas, there are different technical issues (e.g., security, reliability, real time gathering, long life time, scalability). Efficient information gathering can be potentially obtained if we take a suitable information gathering method with considering the requirements of each WSN application. Thus, we have not persisted all information gathering perfectly and have proposed one of simple information gathering methods in response to the requirements of WSN applications in this paper. In the proposed method, the information is converted to physical-layer parameters of wireless communications, such as frequency and time. Also, simulations are performed to validate the effectiveness of the proposed method in real time gathering and estimating with high precision.

Cloud computing is a widely used computing platform in business and academic communities. Performance is an important issue when a user runs an application in the cloud. The user may want to estimate the application-execution time beforehand to guarantee the application performance or to choose the most suitable cloud. Moreover, the cloud system architect and the designer need to understand the application performance characteristics, such as the scalability or the utilization of cloud platforms, to improve performance. However, because the application performance in clouds sometime fluctuates, estimation of the application performance is difficult. In this paper, we discuss the performance fluctuation of Hadoop jobs in both a public cloud and a community cloud for one to three months. The experimental results indicate phenomena that we cannot see without long-term experiments and phenomena inherent in Hadoop. The results suggest better ways to estimate Hadoop application performances in clouds. For example, we should be aware of application characteristics (CPU intensive or communication intensive), datacenter characteristics (busy or not), and time frame (time of day and day of the week) to estimate the performance fluctuation due to workload congestion in cloud platforms. Furthermore, we should be aware of performance degradation due to task re-execution in Hadoop applications.

A practical model-checking (MC) approach for fault analysis, that is one of the most cost-effective tasks in software development, is proposed. The proposed approach is based on a technique, named “Program-oriented Modeling” (POM) for extracting a model from source code. The framework of model extraction by POM provides configurable abstraction based on user-defined transformation rules, and it supports trial-and-error model extraction. An environment for MC called POM/MC was also built. POM/MC analyzes C source code to extract Promela models used for the SPIN model checker. It was applied to an industrial software system to evaluate the efficiency of the configurable model extraction by POM for fault analysis. Moreover, it was shown that the proposed MC approach can reduce the effort involved in analyzing software faults by MC.

In this paper, the multicell distributed beamforming (MDBF) design problem of suppressing intra-cell interference (InCI) and inter-cell interference (ICI) is studied. To start with, in order to decrease the InCI and ICI caused by a user, we propose a gradient-iteration altruistic algorithm to derive the beamforming vectors. The convergence of the proposed iterative algorithm is proved. Second, a metric function is established to restrict the ICI and maximize cell rate. This function depends on only local channel state information (CSI) and does not need additional CSIs. Moreover, an MDBF algorithm with the metric function is proposed. This proposed algorithm utilizes gradient iteration to maximize the metric function to improve sum rate of the cell. Finally, simulation results demonstrate that the proposed algorithm can achieve higher cell rates while offering more advantages to suppress InCI and ICI than the traditional ones.