In this paper, we present a differential dual-modulus prescaler based on an injection-locked frequency divider (ILFD) for satellite low-noise block (LNB) down-converters. We fabricated three-stage differential latches using an ILFD and a cascaded differential divider in a 130-nm CMOS process. The prototype chip core area occupies 40µm × 20µm. The proposed prescaler achieved the locking range of 2.1-10GHz with both divide-by-10 and divide-by-11 operations at a supply voltage of 1.4V. Normalized energy consumptions are 0.4pJ (=mW/GHz) at a 1.4-V supply voltage and 0.24pJ at a 1.2-V supply voltage. To evaluate the tolerance of phase-difference deviation of the input differential pair from the perfect differential phase-difference, 180 degrees, we measured the operational frequencies for various phase-difference inputs. The proposed prescaler achieved the operational frequency range of 2.1-10GHz with an input phase-difference deviation of less than 90 degrees. However, the range of operational frequency decreases as the phase-difference deviation increases beyond 90 degrees and reaches 3.9-7.9GHz for the phase-difference deviation of 180 degrees (i.e. no phase difference). In addition, to confirm the fully locking operation, we measured the spurious noise and the phase noise degradation while reducing the supply voltage. The sensitivity analysis of the prescaler for various supply voltages can explain the above degradation of spectral purity. Spurious noise arises and the phase noise degrades with decreasing supply voltage due to the quasi- and non-locking operations. We verified the fully-locking operation for the LNB down-converter at a 1.4-V supply voltage.

Parametric resonance based solutions for sub-gigahertz radio frequency transceiver with 0.3V supply voltage are proposed in this paper. As an implementation example, a 0.3V 720µW variation-tolerant injection-locked frequency multiplier is developed in 90nm CMOS. It features a parametric resonance based multi-phase synthesis scheme, thereby achieving the lowest supply voltage with -110dBc@ 600kHz phase noise and 873MHz-1.008GHz locking range in state-of-the-art frequency synthesizers.

The proposed stimulus design for linearity test is embedded in a differential successive approximation register analog-to-digital converter (SAR ADC), i.e. a design for testability (DFT). The proposed DFT is compatible to the pattern generator (PG) and output response analyzer (ORA) with the cost of 12.4-% area of the SAR ADC. The 10-bit SAR ADC prototype is verified in a 0.18-µm CMOS technology and the measured differential nonlinearity (DNL) error is between -0.386 and 0.281 LSB at 1-MS/s.

Four calculation techniques for the Q-factor determination of resonant structures are compared on the basis of the influence of the VNA measurement uncertainty. The influence is evaluated using Monte Carlo calculations. On the basis of the deviation, the dispersion, and the effect of nearby resonances, the circle fitting method is the most appropriate technique. Although the 3dB method is the most popular technique, the Q-factors calculated by this method exhibit deviations, and the sign and amount of the deviation depend on the measurement setup. Comparisons using measurement data demonstrate that the uncertainty of the dielectric loss tangent calculated by the circle fitting method is less than a third of those calculated by the other three techniques.

An RGB-LED driver with a pulse-skipping-modulation boost converter is proposed to fix the reference voltage for lowering down the circuit complexity. A high-voltage LDO and a bandgap reference circuit are built into the chip. The proposed converter outputs a different voltage in response to a different color of LEDs. The output voltages for driving six red, six green, and six blue LEDs in series are 13.5V, 20V, and 21.5V, respectively. The proposed LDO and bandgap reference circuit work with supply voltages from 8V to 12V. The settling time for changing colors is lower than 300µs, better than the unfixed-reference-voltage methods. The proposed circuit was fabricated by using 0.25-µm BCD 60V technology, and the chip area was 1.9 × 1.7mm2.

This paper presents an electrolaryngeal (EL) speech enhancement method capable of significantly improving naturalness of EL speech while causing no degradation in its intelligibility. An electrolarynx is an external device that artificially generates excitation sounds to enable laryngectomees to produce EL speech. Although proficient laryngectomees can produce quite intelligible EL speech, it sounds very unnatural due to the mechanical excitation produced by the device. Moreover, the excitation sounds produced by the device often leak outside, adding to EL speech as noise. To address these issues, there are mainly two conventional approached to EL speech enhancement through either noise reduction or statistical voice conversion (VC). The former approach usually causes no degradation in intelligibility but yields only small improvements in naturalness as the mechanical excitation sounds remain essentially unchanged. On the other hand, the latter approach significantly improves naturalness of EL speech using spectral and excitation parameters of natural voices converted from acoustic parameters of EL speech, but it usually causes degradation in intelligibility owing to errors in conversion. We propose a hybrid approach using a noise reduction method for enhancing spectral parameters and statistical voice conversion method for predicting excitation parameters. Moreover, we further modify the prediction process of the excitation parameters to improve its prediction accuracy and reduce adverse effects caused by unvoiced/voiced prediction errors. The experimental results demonstrate the proposed method yields significant improvements in naturalness compared with EL speech while keeping intelligibility high enough.

In this paper, an automatic and unsupervised method using context-dependent hidden Markov models (CD-HMMs) is proposed for the prosodic labeling of speech synthesis databases. This method consists of three main steps, i.e., initialization, model training and prosodic labeling. The initial prosodic labels are obtained by unsupervised clustering using the acoustic features designed according to the characteristics of the prosodic descriptor to be labeled. Then, CD-HMMs of the spectral parameters, F0s and phone durations are estimated by a means similar to the HMM-based parametric speech synthesis using the initial prosodic labels. These labels are further updated by Viterbi decoding under the maximum likelihood criterion given the acoustic feature sequences and the trained CD-HMMs. The model training and prosodic labeling procedures are conducted iteratively until convergence. The performance of the proposed method is evaluated on Mandarin speech synthesis databases and two prosodic descriptors are investigated, i.e., the prosodic phrase boundary and the emphasis expression. In our implementation, the prosodic phrase boundary labels are initialized by clustering the durations of the pauses between every two consecutive prosodic words, and the emphasis expression labels are initialized by examining the differences between the original and the synthetic F0 trajectories. Experimental results show that the proposed method is able to label the prosodic phrase boundary positions much more accurately than the text-analysis-based method without requiring any manually labeled training data. The unit selection speech synthesis system constructed using the prosodic phrase boundary labels generated by our proposed method achieves similar performance to that using the manual labels. Furthermore, the unit selection speech synthesis system constructed using the emphasis expression labels generated by our proposed method can convey the emphasis information effectively while maintaining the naturalness of synthetic speech.

The research reported in this paper is an attempt to elucidate the predictors of pause duration in read-aloud discourse. Through simple linear regression analysis and stepwise multiple linear regression, we examined how different factors (namely, syntactic structure, discourse hierarchy, topic structure, preboundary length, and postboundary length) influenced pause duration both separately and jointly. Results from simple regression analysis showed that discourse hierarchy, syntactic structure, topic structure, and postboundary length had significant impacts on boundary pause duration. However, when these factors were tested in a stepwise regression analysis, only discourse hierarchy, syntactic structure, and postboundary length were found to have significant impacts on boundary pause duration. The regression model that best predicted boundary pause duration in discourse context was the one that first included syntactic structure, and then included discourse hierarchy and postboundary length. This model could account for about 80% of the variance of pause duration. Tests of mediation models showed that the effects of topic structure and discourse hierarchy were significantly mediated by syntactic structure, which was most closely correlated with pause duration. These results support an integrated model combining the influence of several factors and can be applied to text-to-speech systems.

In this letter, an improved channel-adaptive detection scheme based on condition number combined with a QRD-M and CLLL algorithms is presented for MIMO-OFDM systems. The proposed scheme estimates the channel state by using the condition number and then the number of layers for the QRD-M is changed according to the condition number of channel. After the number of layers is determined, the proposed scheme performs the combined QRD-M and CLLL. Simulation results show that the BER curves of the proposed scheme and QRD-M using CLLL have similar performance. However, the complexity of the proposed scheme is about 27% less than QRD-M detection using CLLL.

This study proposes a variable selection linear regression (VSLR) adaptation framework to improve the accuracy of automatic speech recognition (ASR) with only limited and unlabeled adaptation data. The proposed framework can be divided into three phases. The first phase prepares multiple variable subsets by applying a ranking filter to the original regression variable set. The second phase determines the best variable subset based on a pre-determined performance evaluation criterion and computes a linear regression (LR) mapping function based on the determined subset. The third phase performs adaptation in either model or feature spaces. The three phases can select the optimal components and remove redundancies in the LR mapping function effectively and thus enable VSLR to provide satisfactory adaptation performance even with a very limited number of adaptation statistics. We formulate model space VSLR and feature space VSLR by integrating the VS techniques into the conventional LR adaptation systems. Experimental results on the Aurora-4 task show that model space VSLR and feature space VSLR, respectively, outperform standard maximum likelihood linear regression (MLLR) and feature space MLLR (fMLLR) and their extensions, with notable word error rate (WER) reductions in a per-utterance unsupervised adaptation manner.

Mobile Multi-hop Relay (MMR) technology is usually used to increase the transmission rate or to extend communication coverage. In this work, we show that MMR technology can also be used to raise the network capacity. Because Relay Stations (RS) are connected to the Base Station (BS) wirelessly and controlled by the BS, an MMR network can easily be deployed when necessary. High capacity MMR networks thus provide a good candidate solution for coping with temporary traffic surges. For the capacity enhancement of the MMR network, we suggest a novel scheme to parallelize cell transmissions while controlling the interference between transmissions. Using a numerical example for a typical network that is conformant to the IEEE 802.16j, we find that the network capacity increases by 88 percent.

Wireless body area networks (BANs) are attracting great attention as a future technology of wireless networks for healthcare and medical applications. Wireless BANs can generally be divided into two categories, i.e., wearable BANs and implant BANs. However, the performance requirements and channel propagation characteristics of these two kinds of BANs are quite different from each other, that is, wireless signals are approximately transmitted along the human body as a surface wave in wearable BANs, on the other hand, the signals are transmitted through the human tissues in implant BANs. As an effective solution for this problem, this paper first introduces a dual-mode communication system, which is composed of transmitters for in-body and on-body communications and a receiver for both communications. Then, we evaluate the bit error rate (BER) performance of the dual-mode communication system via computer simulations based on realistic channel models, which can reasonably represent the propagation characteristics of on-body and in-body communications. Finally, we conduct a link budget analysis based on the derived BER performances and discuss the link parameters including system margin, maximum link distance, data rate and required transmit power. Our computer simulation results and analysis results demonstrate the feasibility of the dual-mode communication system in wireless BANs.

A ladder lottery, known as the “Amidakuji” in Japan, is a network with n vertical lines and many horizontal lines each of which connects two consecutive vertical lines. Each ladder lottery corresponds to a permutation. Ladder lotteries are frequently used as natural models in many areas. Given a permutation π, an algorithm to enumerate all ladder lotteries of π with the minimum number of horizontal lines is known. In this paper, given a permutation π and an integer k, we design an algorithm to enumerate all ladder lotteries of π with exactly k horizontal lines.

There have been steady demands for a speech segmentation method to handle various speech applications. Conventional segmentation algorithms show reliable performance but they require a sufficient training database. This letter proposes a manner class segmentation method based on the acoustic event and landmark detection used in the knowledge-based speech recognition system. Measurements of sub-band abruptness and additional parameters are used to detect the acoustic events. Candidates of manner classes are segmented from the acoustic events and determined based on the knowledge of acoustic phonetics and acoustic parameters. Manners of vowel/glide, nasal, fricative, stop burst, stop closure, and silence are segmented in this system. In total, 71% of manner classes are correctly segmented with 20-ms error boundaries.

We give a unified view to greedy geometric routing algorithms in ad hoc networks. For this, we first present a general form of greedy routing algorithm using a class of objective functions which are invariant under congruent transformations of a point set. We show that several known greedy routing algorithms such as Greedy Routing, Compass Routing, and Midpoint Routing can be regarded as special cases of the generalized greedy routing algorithm. In addition, inspired by the unified view of greedy routing, we propose three new greedy routing algorithms. We then derive a sufficient condition for our generalized greedy routing algorithm to guarantee packet delivery on every Delaunay graph. This condition makes it easier to check whether a given routing algorithm guarantees packet delivery, and it is closed under convex linear combination of objective functions. It is shown that Greedy Routing, Midpoint Routing, and the three new greedy routing algorithms proposed in this paper satisfy the sufficient condition, i.e., they guarantee packet delivery on Delaunay graphs. We also discuss merits and demerits of these methods.

Existing fault localization approaches usually do not provide a context for developers to understand the problem. Thus, this paper proposes a novel approach using the dynamic backward slicing technique to enrich contexts for existing approaches. Our empirical results show that our approach significantly outperforms five state-of-the-art fault localization techniques.

For the sake of privacy preservation, services that are offered with reference to individual user preferences should do so with a sufficient degree of anonymity. We surveyed various tools that meet requirements of such services and decided that group signature schemes with weakened anonymity (without unlinkability) are adequate. Then, we investigated a theoretical gap between unlinkability of group signature schemes and their other requirements. We show that this gap is significantly large. Specifically, we clarify that if unlinkability can be achieved from any other property of group signature schemes, it becomes possible to construct a chosen-ciphertext secure cryptosystem from any one-way function. This result implies that the efficiency of group signature schemes can be drastically improved if unlinkability is not taken into account. We also demonstrate a way to construct a scheme without unlinkability that is significantly more efficient than the best known full-fledged scheme.

Consider a simple undirected graph G = (V,E) with vertex set V and edge set E. Let G-u be a subgraph induced by the vertex set V-{u}. The distance δG(x,y) is defined as the length of the shortest path between vertices x and y in G. The vertex u ∈ V is a hinge vertex if there exist two vertices x,y ∈ V-{u} such that δG-u(x,y)>δG(x,y). Let U be a set consisting of all hinge vertices of G. The neighborhood of u is the set of all vertices adjacent to u and is denoted by N(u). We define d(u) = max{δG-u(x,y) | δG-u(x,y)>δG(x,y),x,y ∈ N(u)} for u ∈ U as detour degree of u. A maximum detour hinge vertex problem is to find a hinge vertex u with maximum d(u) in G. In this paper, we proposed an algorithm to find the maximum detour hinge vertex on an interval graph that runs in O(n2) time, where n is the number of vertices in the graph.

Polarimetric coherence strongly relates to the types and orientations of local scatterers. An optimization scheme is proposed to optimize the coherence between two polarimetric channels for polarimetric SAR (PolSAR) data. The coherence magnitude (correlation coefficient) is maximized by rotating a polarimetric coherence matrix in the rotation domain around the radar line of sight. L-band E-SAR and X-band Pi-SAR PolSAR data sets are used for demonstration and validation. The coherence of oriented manmade targets is significantly enhanced while that of forests remains relatively low. Therefore, the proposed technique can effectively discriminate these two land covers which are easily misinterpreted by the conventional model-based decomposition. Moreover, based on an optimized polarimetric coherence parameter and the total backscattered power, a simple manmade target extraction scheme is developed for application demonstration. This approach is applied with the Pi-SAR data. The experimental results validate the effectiveness of the proposed method.