In this paper, we propose a novel method of measuring the perceived picture quality of H.264 coded video based on parametric analysis of the coded bitstream. The parametric analysis means that the proposed method utilizes only bitstream parameters to evaluate video quality, while it does not have any access to the baseband signal (pixel level information) of the decoded video. The proposed method extracts quantiser-scale, macro block type and transform coefficients from each macroblock. These parameters are used to calculate spatiotemporal image features to reflect the perception of coding artifacts which have a strong relation to the subjective quality. A computer simulation shows that the proposed method can estimate the subjective quality at a correlation coefficient of 0.923 whereas the PSNR metric, which is referred to as a benchmark, correlates the subjective quality at a correlation coefficient of 0.793.

The convergence behavior of turbo APPM (TAPPM) decoding is analyzed by using a three-dimensional extrinsic information transfer (EXIT) chart and the decoding trajectory. The signal-to-noise ratio (SNR) threshold, below which iterative decoding fails to converge, is predicted by using the 3-D EXIT chart analysis. Bit error rate performances of TAPPM schemes validate the EXIT-chart-based SNR threshold predictions. Outer constituent codes of TAPPM are chosen to show the lowest SNR threshold with the aid of EXIT chart analysis.

This paper proposes a novel dynamic Scratch-pad Memory allocation strategy to optimize the energy consumption of the memory sub-system. Firstly, the whole program execution process is sliced into several time slots according to the temporal dimension; thereafter, a Time-Slotted Cache Conflict Graph (TSCCG) is introduced to model the behavior of Data Cache (D-Cache) conflicts within each time slot. Then, Integer Nonlinear Programming (INP) is implemented, which can avoid time-consuming linearization process, to select the most profitable data pages. Virtual Memory System (VMS) is adopted to remap those data pages, which will cause severe Cache conflicts within a time slot, to SPM. In order to minimize the swapping overhead of dynamic SPM allocation, a novel SPM controller with a tightly coupled DMA is introduced to issue the swapping operations without CPU's intervention. Last but not the least, this paper discusses the fluctuation of system energy profit based on different MMU page size as well as the Time Slot duration quantitatively. According to our design space exploration, the proposed method can optimize all of the data segments, including global data, heap and stack data in general, and reduce the total energy consumption by 27.28% on average, up to 55.22% with a marginal performance promotion. And comparing to the conventional static CCG (Cache Conflicts Graph), our approach can obtain 24.7% energy profit on average, up to 30.5% with a sight boost in performance.

The performance of English automatic speech recognition systems decreases when recognizing spontaneous speech mainly due to multiple pronunciation variants in the utterances. Previous approaches address this problem by modeling the alteration of the pronunciation on a phoneme to phoneme level. However, the phonetic transformation effects induced by the pronunciation of the whole sentence have not yet been considered. In this article, the sequence-based pronunciation variation is modeled using a noisy channel approach where the spontaneous phoneme sequence is considered as a “noisy” string and the goal is to recover the “clean” string of the word sequence. Hereby, the whole word sequence and its effect on the alternation of the phonemes will be taken into consideration. Moreover, the system not only learns the phoneme transformation but also the mapping from the phoneme to the word directly. In this study, first the phonemes will be recognized with the present recognition system and afterwards the pronunciation variation model based on the noisy channel approach will map from the phoneme to the word level. Two well-known natural language processing approaches are adopted and derived from the noisy channel model theory: Joint-sequence models and statistical machine translation. Both of them are applied and various experiments are conducted using microphone and telephone of spontaneous speech.

We performed the (NH4)2S surface treatments before Al2O3 deposition to improve the Al2O3/III-Nitride interface quality in Al2O3/AlGaN/GaN metal-oxide-semiconductor heterostructure field-effect transistors (MOSHFETs). Interface state density at the Al2O3/GaN interface was decreased by the (NH4)2S treatment. The hysteresis width in ID-VGS and gm-VGS characteristics of the Al2O3/AlGaN MOSHFETs with the (NH4)2S treatment was smaller than that without the (NH4)2S treatment. In addition, transconductance (gm) decrease at a large gate voltage was relaxed by the (NH4)2S treatment. We also performed ultraviolet (UV) illumination during the (NH4)2S treatment for further improvement of the Al2O3/III-Nitride interface quality. Interface state density of the Al2O3/GaN MOS diodes with the UV illumination was smaller than that without the UV illumination.

The objective of pool-based incremental active learning is to choose a sample to label from a pool of unlabeled samples in an incremental manner so that the generalization error is minimized. In this scenario, the generalization error often hits a minimum in the middle of the incremental active learning procedure and then it starts to increase. In this paper, we address the problem of early labeling stopping in probabilistic classification for minimizing the generalization error and the labeling cost. Among several possible strategies, we propose to stop labeling when the empirical class-posterior approximation error is maximized. Experiments on benchmark datasets demonstrate the usefulness of the proposed strategy.

A conical area evolutionary algorithm (CAEA) is presented to further improve computational efficiencies of evolutionary algorithms for bi-objective optimization. CAEA partitions the objective space into a number of conical subregions and then solves a scalar subproblem in each subregion that uses a conical area indicator as its scalar objective. The local Pareto optimality of the solution with the minimal conical area in each subregion is proved. Experimental results on bi-objective problems have shown that CAEA offers a significantly higher computational efficiency than the multi-objective evolutionary algorithm based on decomposition (MOEA/D) while CAEA competes well with MOEA/D in terms of solution quality.

This paper proposes a new generative model which can deal with rotational data variations by extending Separable Lattice 2-D HMMs (SL2D-HMMs). In image recognition, geometrical variations such as size, location and rotation degrade the performance. Therefore, the appropriate normalization processes for such variations are required. SL2D-HMMs can perform an elastic matching in both horizontal and vertical directions; this makes it possible to model invariance to size and location. To deal with rotational variations, we introduce additional HMM states which represent the shifts of the state alignments among the observation lines in a particular direction. Face recognition experiments show that the proposed method improves the performance significantly for rotational variation data.

This paper proposes a new first-scan method for two-scan labeling algorithms. In the first scan, our proposed method first scans every fourth image line, and processes the scan line and its two neighbor lines. Then, it processes the remaining lines from top to bottom one by one. Our method decreases the average number of times that must be checked to process a foreground pixel will; thus, the efficiency of labeling can be improved.

One of the most important constraints in wireless sensor networks (WSN) is that their nodes, in most of the cases, are powered by batteries, which cannot be replaced or recharged easily. In these types of networks, data transmission is one of the processes that consume a lot of energy, and therefore the embedded routing algorithm should consider this issue by establishing optimal routes in order to avoid premature death and eventually having partitioned nodes network. This paper proposes a new routing algorithm for WSN called Micro-Economic Routing Algorithm (MERA), which is based on the microeconomic model of supply-demand. In such algorithm each node comprising the network fixes a cost for relay messages according to their residual battery energy; and before sending information to the base station, the node searches for the most economical route. In order to test the performance of MERA, we varied the initial conditions of the system such as the network size and the number of defined thresholds. This was done in order to measure the time span for which the first node dies and the number of information messages received by the base station. Using the NS-2 simulator, we compared the performance of MERA against the Conditional Minimum Drain Rate (CMDR) algorithm reported in the literature. An optimal threshold value for the residual battery is estimated to be close to 20%.

A complete analysis of GaN-based structures with very promising characteristics for future optical waveguide devices, such as modulators, is presented. First the material growth was optimized for low dislocation density and surface roughness. Optical measurements demonstrate excellent waveguide properties in terms of index and temperature dependence while planar propagation losses are below 1 dB/cm. Bias was applied on both sides of the epitaxially grown films to evaluate the refractive index dependence on reverse voltage and a variation of 2.10-3 was found for 30 V. These results support the possibility of using structures of this type for the fabrication of modulator devices such as Mach-Zehnder interferometers.

This paper introduces a novel design method of an asynchronous pipeline based on dual-rail dynamic logic. The overhead of handshake control logic is greatly reduced by constructing a reliable critical datapath, which offers the pipeline high throughput as well as low power consumption. Synchronizing Logic Gates (SLGs), which have no data dependency problem, are used in the design to construct the reliable critical datapath. The design targets latch-free and extremely fine-grain or gate-level pipeline, where the depth of every pipeline stage is only one dual-rail dynamic logic. HSPICE simulation results, in a 65 nm design technology, indicate that the proposed design increases the throughput by 120% and decreases the power consumption by 54% compared with PS0, a classic dual-rail asynchronous pipeline implementation style, in 4-bit wide FIFOs. Moreover, this method is applied to design an array style multiplier. It shows that the proposed design reduces power by 37.9% compared to classic synchronous design when the workloads are 55%. A chip has been fabricated with a 44 multiplier function, which works well at 2.16G data-set/s (Post-layout simulation).

High output power AlGaN/GaN metal-insulator-semiconductor (MIS) hetero-junction field effect transistor (HFET) on Si substrate for millimeter-wave application has developed. High temperature chemical vapor deposition (HT-CVD) grown SiN as a gate insulator improves the breakdown characteristics which enables the operation at high drain voltage of 55 V. The device exhibits high drain current of 1.1 A/mm free from the current collapse and high RF gain of 10.4 dB. The amplifier module developed AlGaN/GaN MIS-HFET with the gate width of 5.4 mm exhibits an output power of 10.7 W and a linear gain of 4 dB at 26.5 GHz. The resultant high output power is very promising for long-distance communication at millimeter-wave in the future which would enable high speed and high density data transmission.

Two types of similarities between words have been studied in the natural language processing community: synonymy and relational similarity. A high degree of similarity exist between synonymous words. On the other hand, a high degree of relational similarity exists between analogous word pairs. We present and empirically test a hypothesis that links these two types of similarities. Specifically, we propose a method to measure the degree of synonymy between two words using relational similarity between word pairs as a proxy. Given two words, first, we represent the semantic relations that hold between those words using lexical patterns. We use a sequential pattern clustering algorithm to identify different lexical patterns that represent the same semantic relation. Second, we compute the degree of synonymy between two words using an inter-cluster covariance matrix. We compare the proposed method for measuring the degree of synonymy against previously proposed methods on the Miller-Charles dataset and the WordSimilarity-353 dataset. Our proposed method outperforms all existing Web-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset.

This paper clarifies the bifurcation structure of the chaotic attractor in an interrupted circuit with switching delay from theoretical and experimental view points. First, we introduce the circuit model and its dynamics. Next, we define the return map in order to investigate the bifurcation structure of the chaotic attractor. Finally, we discuss the dynamical effect of switching delay in the existence region of the chaotic attractor compared with that of a circuit with ideal switching.

Template matching for image sequences captured with a moving camera is very important for several applications such as Robot Vision, SLAM, ITS, and video surveillance systems. However, it is difficult to realize accurate template matching using only visual feature information such as HSV histograms, edge histograms, HOG histograms, and SIFT features, because it is affected by several phenomena such as illumination change, viewpoint change, size change, and noise. In order to realize robust tracking, structure information such as the relative position of each part of the object should be considered. In this paper, we propose a method that considers both visual feature information and structure information. Experiments show that the proposed method realizes robust tracking and determine the relationships between object parts in the scenes and those in the template.

This paper describes high-temperature electron transport properties of AlGaN-channel HEMT fabricated on a free-standing AlN substrate, estimated at temperatures between 25 and 300. The AlGaN-channel HEMT exhibited significantly reduced temperature dependence in DC and RF device characteristics, as compared to those for the conventional AlGaN/GaN HEMT, resulting in larger values in both saturated drain current and current gain cutoff frequency at 300. Delay time analyses suggested that the temperature dependence of the AlGaN-channel HEMT was primarily dominated by the effective electron velocity in the AlGaN channel. These results indicate that an AlGaN-channel HEMT fabricated on an AlN substrate is promising for high-performance device applications at high temperatures.

Our research is focused on examining the video quality assessment model based on the MPEG-7 descriptor. Video quality is estimated by using several features based on the predicted frame quality such as average value, worst value, best value, standard deviation, and the predicted frame rate obtained from descriptor information. As a result, assessment of video quality can be conducted with a high prediction accuracy with correlation coefficient=0.94, standard deviation of error=0.24, maximum error=0.68 and outlier ratio=0.23.

Reduction of the intensity noise in semiconductor lasers is an important subject for the higher performance of an application. Simultaneous usage of the superposition of high frequency current and the electric negative feedback loop was proposed to suppress the noise for the higher power operation of semiconductor lasers. Effective noise reduction of more than 25 dB with 80 mW operation was experimentally demonstrated.

A new training algorithm for the chaotic Adachi Neural Network (AdNN) is investigated. The classical training algorithm for the AdNN and it's variants is usually a “one-shot” learning, for example, the Outer Product Rule (OPR) is the most used. Although the OPR is effective for conventional neural networks, its effectiveness and adequateness for Chaotic Neural Networks (CNNs) have not been discussed formally. As a complementary and tentative work in this field, we modified the AdNN's weights by enforcing an unsupervised Hebbian rule. Experimental analysis shows that the new weighted AdNN yields even stronger dynamical associative memory and pattern recognition phenomena for different settings than the primitive AdNN.