At the stage of software debugging, the effective interaction between software debugging engineers and fault localization techniques can greatly improve fault localization performance. However, most fault localization approaches usually ignore this interaction and merely utilize the information from testing. Due to different goals of testing and fault localization, the lack of interaction may lead to the issue of information inadequacy, which can substantially degrade fault localization performance. In addition, human work is costly and error-prone. It is vital to study and simulate the pattern of debugging engineers as they apply their knowledge and experience to this interaction to promote fault localization effectiveness and reduce their workload. Thus this paper proposes an effective fault localization approach to simulate this interaction via feedback. Based on results obtained from fault localization techniques, this approach utilizes test data generation techniques to automatically produce feedback for interacting with these fault localization techniques, and then iterate this process to improve fault localization performance until a specific stopping condition is satisfied. Experiments on two standard benchmarks demonstrate the significant improvement of our approach over a promising fault localization technique, namely the spectrum-based fault localization technique.

The power of laser radar received echoes varies over a large range due to many factors such as target distance, size, reflection ratio, etc, which leads to the difficulty of decoding codes from the received noise buried signals for spectrum code modulated laser radar. Firstly, a pseudo-random noise (PN) code modulated laser radar model is given, and the problem to be addressed is discussed. Then, a novel method based on Inter Symbol Interference (ISI) is proposed for resolving the problem, providing that only Additive White Gaussian Noise (AWGN) exists. The ISI effect is introduced by using a high pass filter (HPF). The results show that ISI improves laser radar receiver decoding ratio, thus the peak of the correlation function of decoded codes and modulation codes. Finally, the effect of proposed method is verified by a simple experiment.

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 propose a primary traffic based multihop relaying algorithm with cooperative transmission (PTBMR-CT). It enlarges the hop transmission distances to reduce the number of cognitive relays on the route from the cognitive source (CS) to the cognitive destination (CD). In each hop, from the cognitive nodes in a specified area depending on whether the primary source (PS) transmits data to the primary destination (PD), the cognitive node that is farthest away from the cognitive relay that sends data is selected as the other one that receives data. However, when the PS is transmitting data to the PD, from the cognitive nodes in a specified area, another cognitive node is also selected and prepared to be the cognitive relay that receives data of cooperative transmission. Cooperative transmission is performed if the PS is still transmitting data to the PD when the cognitive relay that receives data of the next hop transmission is being searched. Simulation results show that the average number of cognitive relays is reduced by PTBMR-CT compared to conventional primary traffic based farthest neighbor relaying (PTBFNR), and PTBMR-CT outperforms conventional PTBFNR in terms of the average end-to-end reliability, the average end-to-end throughput, the average required transmission power of transmitting data from the CS to the CD, and the average end-to-end transmission latency.

Cloud data center services, such as video on demand (VoD) and sensor data monitoring, have become popular. The quality of service (QoS) between a client and a cloud data center should be assured by satisfying each service's required bandwidth and delay. Multipath traffic engineering is effective for dispersing traffic flows on a network; therefore, an improved k-shortest paths first (k-SPF) algorithm is applied to these cloud data center services to satisfy their required QoS. k-SPF can create a set of multipaths between a cloud data center and all edge routers, to which client nodes are connected, within one algorithm process. Thus, k-SPF can produce k shortest simple paths between a cloud data center and every access router faster than with conventional Yen's algorithm. By using a parameter in the algorithm, k-SPF can also impartially use links on a network and shorten the average hop-count and number of necessary MPLS labels for multiple paths that comprise a multipath.

In this paper, we propose Hidden Conditional Neural Fields (HCNF) for continuous phoneme speech recognition, which are a combination of Hidden Conditional Random Fields (HCRF) and a Multi-Layer Perceptron (MLP), and inherit their merits, namely, the discriminative property for sequences from HCRF and the ability to extract non-linear features from an MLP. HCNF can incorporate many types of features from which non-linear features can be extracted, and is trained by sequential criteria. We first present the formulation of HCNF and then examine three methods to further improve automatic speech recognition using HCNF, which is an objective function that explicitly considers training errors, provides a hierarchical tandem-style feature and includes a deep non-linear feature extractor for the observation function. We show that HCNF can be trained realistically without any initial model and outperforms HCRF and the triphone hidden Markov model trained by the minimum phone error (MPE) manner using experimental results for continuous English phoneme recognition on the TIMIT core test set and Japanese phoneme recognition on the IPA 100 test set.

Digital still cameras emerged following the introduction of the Sony Mavica analog prototype camera in 1981. These early cameras produced poor image quality and did not challenge film cameras for overall quality. By 1995 digital still cameras in expensive SLR formats had 6 mega-pixels and produced high quality images (with significant image processing). In 2005 significant improvement in image quality was apparent and lower prices for digital still cameras (DSCs) started a rapid decline in film usage and film camera sells. By 2010 film usage was mostly limited to professionals and the motion picture industry. The rise of DSCs was marked by a “pixel war” where the driving feature of the cameras was the pixel count where even moderate cost, ∼ $120, DSCs would have 14 mega-pixels. The improvement of CMOS technology pushed this trend of lower prices and higher pixel counts. Only the single lens reflex cameras had large sensors and large pixels. The drive for smaller pixels hurt the quality aspects of the final image (sharpness, noise, speed, and exposure latitude). Only today are camera manufactures starting to reverse their course and producing DSCs with larger sensors and pixels. This paper will explore why larger pixels and sensors are key to the future of DSCs.

A sensing efficiency optimization scheme based on two-stage spectrum sensing that maximizes the achievable throughput of the secondary network and minimizes the average sensing time is proposed in this paper. A selection method for the threshold is proposed and proved to ensure optimal sensing performance. An effective iterative algorithm is presented to solve the constructed efficiency optimization problem.

Existing pose estimation algorithms suffer from either low performance or heavy computation cost. In this letter, we present an approach to improve the attractive algorithm called Orthogonal Iteration. A new form of fundamental equations is derived which reduces the computation cost significantly. And paraperspective camera model is used instead of weak perspective camera model during initialization which improves the stability. Experiment results validate the accuracy and stability of the proposed algorithm and show that its computational complexity is favorably compare to the O(n) non-iterative algorithm.

A flexible and computationally efficient method for spectral analysis of sinusoidal signals using the Basis Pursuit De-Noising (BPDN) is proposed. This method estimates a slotted Auto-Correlation Function (ACF) and computes the spectrum as the sparse representation of the ACF in a dictionary of cosine functions. Simulation results illustrate flexibility and effectiveness of the proposed method.

In this paper, we present a speech enhancement technique based on the ambient noise classification that incorporates the Gaussian mixture model (GMM). The principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are set according to the classified context to ensure best performance under each noise. For real-time context awareness, the noise classification is performed on a frame-by-frame basis using the GMM with the soft decision framework. The speech absence probability (SAP) is used in detecting the speech absence periods and updating the likelihood of the GMM.

This letter investigates the training convergence in range-based cooperative positioning with stochastic positional knowledge. Firstly, a closed-form of squared position-error bound (SPEB) is derived with error-free ranging. Using the derived closed-form, it is proved that the SPEB reaches its minimum when at least 2 out of N (> 2) agents send training sequences. Finally, numerical results are provided to elaborate the theoretical analysis with zero-mean Gaussian ranging errors.

In this paper, a dual-band bandpass filter (BPF) of multilayer suspended stripline (SSL) structure and an SSL diplexer composed of a low-pass filter (LPF) and a high-pass filter (HPF) are proposed. Bandstop structure creating transmission zeros is adopted in the BPF and diplexer, enhancing the signal selectivity of the former and increasing the isolation between the diverting ports of the latter. The dual-band BPF possesses two distinct bandpass structures and a bandstop circuit, all laid on different metallic layers. The metallic layers together with the supporting substrates are vertically stacked up to save the circuit dimension. The LPF and HPF used in the diplexer structure are designed by a quasi-lumped approach, which the LC lumped-elements circuit models are developed to analyze filters' characteristics and to emulate their frequency responses. Half-wavelength resonating slots are employed in the diplexer's structure to increase the isolation between its two signal diverting ports. Experiments are conducted to verify the multilayer dual-band BPF and the diplexer design. Agreements are observed between the simulation and the measurement.

A differential pair of convergent and divergent lenses with adjustable lens spacing (“differential lens”) was devised as a varifocal lens and was successfully integrated into an object-space telecentric lens to build a focus mechanism with constant magnification. This integration was done by placing the front principal point of the varifocal lens at the rear focal point of the telecentric lens within a practical tolerance of positioning. Although the constant-magnification focus mechanism is a parallel projection system, a system for perfect perspective projection imaging without shifting the projection center during focusing could be built simply by properly setting this focus mechanism between an image-taking lens with image-space telecentricity and an image sensor. The focus resolution experimentally obtained was 0.92 µm (σ) for the parallel projection system with a depth range of 1.0 mm and this was 0.25 mm (σ) for the perspective projection system with a range from 120 to 350 mm within a desktop space. A marginal image resolution of 100 lp/mm was obtained with optical distortion of less than 0.2% in the parallel projection system. The differential lens could work up to 55 Hz for a sinusoidal change in lens spacing with a peak-to-valley amplitude of 425 µm when a tiny divergent lens that was plano-concave was translated by a piezoelectric positioner. Therefore, images that were entirely in focus were generated at a frame rate of 30 Hz for an object moving at a speed of around 150 mm/s in depth within the desk top space. Thus, three-dimensional (3-D) imaging that provided 3-D resolution based on fast focusing was accomplished in both microscopic and macroscopic spaces.

With the wide usage of multispectral images, a fast efficient multidimensional clustering method becomes not only meaningful but also necessary. In general, to speed up the multidimensional images' analysis, a multidimensional feature vector should be transformed into a lower dimensional space. The Hilbert curve is a continuous one-to-one mapping from N-dimensional space to one-dimensional space, and can preserves neighborhood as much as possible. However, because the Hilbert curve is generated by a recurve division process, 'Boundary Effects' will happen, which means data that are close in N-dimensional space may not be close in one-dimensional Hilbert order. In this paper, a new efficient approach based on the space-filling curves is proposed for classifying multispectral satellite images. In order to remove 'Boundary Effects' of the Hilbert curve, multiple Hilbert curves, z curves, and the Pseudo-Hilbert curve are used jointly. The proposed method extracts category clusters from one-dimensional data without computing any distance in N-dimensional space. Furthermore, multispectral images can be analyzed hierarchically from coarse data distribution to fine data distribution in accordance with different application. The experimental results performed on LANDSAT data have demonstrated that the proposed method is efficient to manage the multispectral images and can be applied easily.

In this paper, we propose an effective mutation operators for Cooperative Genetic Algorithm (CGA) to be applied to a practical Nurse Scheduling Problem (NSP). The nurse scheduling is a very difficult task, because NSP is a complex combinatorial optimizing problem for which many requirements must be considered. In real hospitals, the schedule changes frequently. The changes of the shift schedule yields various problems, for example, a fall in the nursing level. We describe a technique of the reoptimization of the nurse schedule in response to a change. The conventional CGA is superior in ability for local search by means of its crossover operator, but often stagnates at the unfavorable situation because it is inferior to ability for global search. When the optimization stagnates for long generation cycle, a searching point, population in this case, would be caught in a wide local minimum area. To escape such local minimum area, small change in a population should be required. Based on such consideration, we propose a mutation operator activated depending on the optimization speed. When the optimization stagnates, in other words, when the optimization speed decreases, the mutation yields small changes in the population. Then the population is able to escape from a local minimum area by means of the mutation. However, this mutation operator requires two well-defined parameters. This means that user have to consider the value of these parameters carefully. To solve this problem, we propose a periodic mutation operator which has only one parameter to define itself. This simplified mutation operator is effective over a wide range of the parameter value.

A novel hybrid latch based flip-flop scheme is introduced in this paper. A pulse generator is eliminated to simplify clock distribution and save power. It also achieves high speed by shortening the critical data path. In addition, it avoids output node glitches which exist in conventional hybrid latch based flip-flops. HSPICE simulation results revealed that the proposed PHLFF performs best among referenced schemes. It can reduce 47.5% power dissipation, 16.5% clock-to-output latency and 56.4% PDP, as compared to conventional HLFF.

As ROADMs (Reconfigurable Optical Add/Drop Multiplexers) are becoming widely used in metro/core networks, distributed control of wavelength paths by extended GMPLS (Generalized MultiProtocol Label Switching) protocols has attracted much attention. For the automatic establishment of an arbitrary wavelength path satisfying dynamic traffic demands over a ROADM or WXC (Wavelength Cross Connect)-based network, precise determination of chromatic dispersion over the path and optimized assignment of dispersion compensation capabilities at related nodes are essential. This paper reports an experiment over in-field fibers where GMPLS-based control was applied for the automatic discovery of chromatic dispersion, path computation, and wavelength path establishment with dynamic adjustment of variable dispersion compensation. The GMPLS-based control scheme, which the authors called GMPLS-Plus, extended GMPLS's distributed control architecture with attributes for automatic discovery, advertisement, and signaling of chromatic dispersion. In this experiment, wavelength paths with distances of 24 km and 360 km were successfully established and error-free data transmission was verified. The experiment also confirmed path restoration with dynamic compensation adjustment upon fiber failure.

Mirrored serpentine microstrip lines are proposed for a parallel high speed digital signaling to reduce the peak far-end crosstalk (FEXT) voltage. Mirrored serpentine microstrip lines consist of two serpentine microstrip lines, each one equal to a conventional normal serpentine microstrip line. However, one serpentine microstrip line of the mirrored serpentine microstrip lines is flipped in the length direction, and thus, two serpentine microstrip lines face each other. Time domain reflectometry measurements show that the peak FEXT voltage of the mirrored serpentine microstrip lines is reduced by 56.4% of that of conventional microstrip lines and 30.0% of that of conventional normal serpentine microstrip lines.

We propose a fast and precise transient response and frequency characteristics simulation method for switching converters. This method uses a behavioral simulation tool without using a SPICE-like analog simulator. The nonlinear operation of the circuit is considered, and the nonlinear function is realized by defining the nonlinear formula based on the circuit operation and by applying feedback. To assess the accuracy and simulation time of the proposed simulation method, we designed current-mode buck and boost converters and fabricated them using a 0.18-µm high-voltage CMOS process. The comparison in the transient response and frequency characteristics among SPICE, the proposed program on a behavioral simulation tool which we named NSTVR (New Simulation Tool for Voltage Regulators) and experiments of fabricated IC chips showed good agreement, while NSTVR was more than 22 times faster in transient response and 85 times faster in frequency characteristics than SPICE in CPU time in a boost converter simulation.