With exponentially increasing power densities due to technology scaling and ever increasing demand for performance, chip temperature has become an important issue that limits the performance of computer systems. Typically, it is essential to use a set of on-chip thermal sensors to monitor temperatures during the runtime. The runtime thermal measurements are then employed by dynamic thermal management techniques to manage chip performance appropriately. In this paper, we propose an inverse distance weighting method based on a dynamic Voronoi diagram for the reconstruction of full thermal characterization of integrated circuits with non-uniform thermal sensor placements. Firstly we utilize the proposed method to transform the non-uniformly spaced samples to virtual uniformly spaced data. Then we apply three classical interpolation algorithms to reconstruct the full thermal signals in the uniformly spaced samples mode. To evaluate the effectiveness of our method, we develop an experiment for reconstructing full thermal status of a 16-core processor. Experimental results show that the proposed method significantly outperforms spectral analysis techniques, and can obtain full thermal characterization with an average absolute error of 1.72% using 9 thermal sensors per core.

A multiple classifier system can be a powerful solution for robust pattern recognition. It is expected that the appropriate combination of multiple classifiers may reduce errors, provide robustness, and achieve higher performance. In this paper, high performance Chinese syllable recognition is presented using combinations of multiple classifiers. Chinese syllable recognition is divided into base syllable recognition (disregarding the tones) and recognition of 4 tones. For base syllable recognition, we used a combination of two multisegment vector quantization (MSVQ) classifiers based on different features (instantaneous and transitional features of speech). For tone recognition, vector quantization (VQ) classifier was first used, and was comparable to multilayer perceptron (MLP) classifier. To get robust or better performance, a combination of distortion-based classifier (VQ) and discriminant-based classifier (MLP) is proposed. The evaluations have been carried out using standard syllable database CRDB in China, and experimental results have shown that combination of multiple classifiers with different features or different methodologies can improve recognition performance. Recognition accuracy for base syllable, tone, and tonal syllable is 96.79%, 99.82% and 96.24% respectively. Since these results were evaluated on a standard database, they can be used as a benchmark that allows direct comparison against other approaches.

This invited paper is dedicated to introduce recent activities of Japanese microwave industries. 7 topics are introduced from major microwave companies in Japan. All topics are from invited talks in 2014 Asia-Pacific microwave conference (APMC2014) held in Sendai, November, 2014.

In this paper, we study low complexity transceiver for double space time transmit diversity (DSTTD) and orthogonal frequency division multiplexing (OFDM) system with antenna shuffling. Firstly, we propose a novel antenna shuffling method based on the criterion of minimizing the condition number of channel correlation matrix. The condition number is an indicator about the quality of the channel. By selecting the minimum of condition number which has better channel quality, consequently, a linear detector with respect to this new channel may achieve better performance results. A low complexity variant of the condition number calculation is also proposed, and it is shown that this criterion can be reduced to the minimum mean square error (MMSE) based criterion. Furthermore, the weighted soft decision Viterbi decoding is applied to mitigate noise enhancement inherent to zero forcing (ZF) and MMSE linear receivers and improve error rate performance. Next, we propose an algorithm to reduce the amount of feedback by exploiting the fact that the channel frequency responses across OFDM subcarriers are correlated. In the proposed algorithm, subcarriers are clustered in blocks, which are allocated the same shuffling pattern with the largest number of the shuffling patterns in the cluster. This way, the signaling overhead can be reduced in comparison with each subcarrier based feedback. Extensive simulations show that the proposed techniques for DSTTD-OFDM system outperform other existing techniques under both uncorrelated and highly spatial correlated frequency selective MIMO fading channels.

In this paper, we propose a new approach to speaker independent isolated-word speech recognition using multisegment multiple vector quantization (VQ) codebooks. In this approach, words are recognized by means of multisegment multiple VQ codebooks, a separate multisegment multiple VQ codebooks are designed for each word in the recognition vocabulary by dividing equally the word into multiple segments which is correlative with number of syllables or phonemes of the word, and designing two individual VQ codebooks consisting of both instantaneous and transitional speech features for each segment. Using this approach, the influence of the within-word coarticulation can be minimized, the time-sequence information of speech can be used, and the word length differences in the vocabulary or speaking rates variations can be adapted automatically. Moreover, the mel-cepstral coefficients based on unbiased estimation of log spectrum (UELS) are used, and comparison experiment with LPC derived mel cepstral coefficients is made. Recognition experiments Using testing databases consisting of 100 Japanese words (Waseda database) and 216 phonetically balanced words (ATR database), confirmed the effectiveness of the new method and the new speech features. The approach is described, computational complexity as well as memory requirements are analyzed, the experimental results are presented.

To decrease memory access of the decoder for double binary convolutional turbo code (DB CTC), an iterative decoding scheme is proposed. Instead of accessing all of the backward state metrics from the state metric cache (SMC), a part of them is computed by the recalculation unit (RU) in the forward direction. By analysis and simulations, both the amount of memory access and the size of SMC are reduced by about 45%. Moreover, combined with the scaling technique, the proposed scheme gets decoding performance near to that of the well-known Log-MAP algorithm.

In wireless networks, how to provide reliable data transfer is an important and challenging issue due to channel fading and interference. Several approaches, e.g., Automatic Repeat reQuest (ARQ), Hybrid ARQ (HARQ) and Network Coding (NC), are used to enhance reliability of transmission in wireless networks. However, we note that these schemes implement the data recovery process for mixed unicast and multicast (MUM) communications by simply separating the process into two phases, unicast and multicast phase. This is inefficient and expensive. In this paper, we propose an efficient retransmission scheme with network coding for MUM transmission, aiming at improving bandwidth utilization. UMNC searches for coding opportunities from both unicast and multicast flows, which offer the potential benefit of improved recovery in the event of packet loss. We theoretically prove that UMNC can effectively reduce the total number of retransmissions and thus improve bandwidth efficiency, compared with existing schemes.

An upper bound is established for certain exponential sums on the rational points of an elliptic curve over a residue class ring ZN , N=pq for two distinct odd primes p and q. The result is a generalization of an estimate of exponential sums on rational point groups of elliptic curves over finite fields. The bound is applied to showing the pseudorandomness of a large family of binary sequences constructed by using elliptic curves over ZN .

The dramatic growth in wireless data traffic has triggered the investigation of fifth generation (5G) wireless communication systems. Small cells will play a very important role in 5G to meet the 5G requirements in spectral efficiency, energy savings, etc. In this paper, we investigate low complexity millimeter-wave communication systems with uniform circular arrays (UCAs) in line-of-sight (LOS) multiple-input multiple-output (MIMO) channels, which are used in fixed wireless access such as small cell wireless backhaul for 5G. First, we demonstrate that the MIMO channel matrices for UCAs in LOS-MIMO channels are circulant matrices. Next, we provide a detailed derivation of the unified optimal antenna placement which makes MIMO channel matrices orthogonal for 3×3 and 4×4 UCAs in LOS channels. We also derive simple analytical expressions of eigenvalues and capacity as a function of array design (link range and array diameters) for the concerned systems. Finally, based on the properties of circulant matrices, we propose a high performance low complexity LOS-MIMO precoding system that combines forward error correction (FEC) codes and spatial interleaver with the fixed IDFT precoding matrix. The proposed precoding system for UCAs does not require the channel knowledge for estimating the precoding matrix at the transmitter under the LOS condition, since the channel matrices are circulant ones for UCAs. Simulation results show that the proposed low complexity system is robust to various link ranges and can attain excellent performance in strong LOS environments and channel estimation errors.