Although the Marching Cube (MC) algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC algorithm considers a group of voxels simultaneously for the application of the MC algorithm to explore interesting features of the original MC algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC algorithm is 3 to more than 10 times faster than the algorithm using a hash table. Another significant advantage of GMC is its compatibility with other algorithms that accelerate the MC algorithm. Together, the overall performance of the original MC algorithm is promoted even further.

Digital RF solutions have been shown to be advantageous in various design aspects, such as accurate modeling, design reuse, and scaling when migrating to the next CMOS process node. Consequently, the majority of new low-cost and feature cell phones are now based on this approach. However, another equally important aspect of this approach to wireless transceiver SoC design, which is instrumental in allowing fast and low-cost productization, is in creating the inherent capability to assess performance and allow for low-cost built-in calibration and compensation, as well as characterization and final-testing. These internal capabilities can often rely solely on the SoCs existing processing resources, representing a zero cost adder, requiring only the development of the appropriate algorithms. This paper presents various examples of built-in measurements that have been demonstrated in wireless transceivers offered by Texas Instruments in recent years, based on the digital-RF processor (DRPTM) technology, and highlights the importance of the various types presented; built-in self-calibration and compensation, built-in self-characterization, and built-in self-testing (BiST). The accompanying statistical approach to the design and productization of such products is also discussed, and fundamental terms related with these, such as 'soft specifications', are defined.

Based on the reiterative maximum signal minus interference level (MSMIL) criterion and adaptive beamforming, a novel interference suppression algorithm is proposed for shared-spectrum multistatic radar that must contend with clutter. In this algorithm, two-dimensional adaptive beamformers are designed for azimuths and range cells. Numerical results show advantages of the proposed method.

We propose a polarization mode dispersion (PMD) design for high-speed wavelength-division multiplexing (WDM) backbone network systems based on field PMD measurements on installed optical fibers for long-term commercial use. Implementing a high-speed network system on an installed fiber requires measuring PMD, because the PMD characteristics of most installed fibers are unknown. For enhanced practicality, we must be able to precisely evaluate PMD characteristics precisely with just one measurement. To understand the statistical properties of measured PMD values, we use the Jones Matrix Eigenanalysis (JME) method to conduct long-term (12 months) PMD measurements on installed fibers. We statistically analyze the measurement results and confirm that the measured values match the theory that considers the accuracy of the measurement instrument. This enables a PMD design of desired outage probability based on PMD measurements of installed fibers. We also carry out a 43-Gb/s return-to-zero differential quadrature phase shift keying (RZ-DQPSK) signal transmission with high PMD fibers in order to confirm the effectiveness of our PMD design. The PMD values of the in-line amplifier transmission line are settled so as to meet the worst value of the design. We confirm that 43-Gb/s RZ-DQPSK signals are stably transmitted at the design value.

This letter proposes a novel decision fusion algorithm for cooperative spectrum sensing in cognitive radio sensor networks where a reinforcement learning algorithm is utilized at the fusion center to estimate the sensing performance of local spectrum sensing nodes. The estimates are then used to determine the weights of local decisions for the final decision making process that is based on the Chair-Vashney optimal decision fusion rule. Simulation results show that the sensing accuracy of the proposed scheme is comparable to that of the Chair-Vashney optimal decision fusion based scheme even though it does not require any knowledge of prior probabilities and local sensing performance of spectrum sensing nodes.

This paper presents a strategy together with tool support for the translation of state machines from equational theories into rewrite theories, aiming at automatically generating rewrite theory specifications. Duplicate effort can be saved on specifying state machines both in equational theories and rewrite theories, when we incorporate the theorem proving facilities of CafeOBJ with the model checking facilities of Maude. Experimental results show that efficiencies of the generated specifications by the proposed strategy are significantly improved, compared with those that are generated by three other existing translation strategies.

In the face of constant malicious attacks to network-connected software systems, software vulnerabilities need to be discovered early in the development phase. In this paper, we present AspFuzz, a state-aware protocol fuzzer based on the specifications of application-layer protocols. AspFuzz automatically generates anomalous messages that exploit possible vulnerabilities. The key observation behind AspFuzz is that most attack messages violate the strict specifications of application-layer protocols. For example, they do not conform to the rigid format or syntax required of each message. In addition, some attack messages ignore the protocol states and have incorrect orders of messages. AspFuzz automatically generates a large number of anomalous messages that deliberately violate the specifications of application-layer protocols. To demonstrate the effectiveness of AspFuzz, we conducted experiments with POP3 and HTTP servers. With AspFuzz, we can discover 20 reported and 1 previously unknown vulnerabilities for POP3 servers and 25 reported vulnerabilities for HTTP servers. Two vulnerabilities among these can be discovered by the state-awareness of AspFuzz. It can also find a SIP state-related vulnerability.

A novel method is proposed to estimate the 3D relative positions of an articulated body from point correspondences in an uncalibrated monocular image sequence. It is based on a camera perspective model. Unlike previous approaches, our proposed method does not require camera parameters or a manual specification of the 3D pose at the first frame, nor does it require the assumption that at least one predefined segment in every frame is parallel to the image plane. Our work assumes a simpler assumption, for example, the actor stands vertically parallel to the image plane and not all of his/her joints lie on a plane parallel to the image plane in the first frame. Input into our algorithm consists of a topological skeleton model and 2D position data on the joints of a human actor. By geometric constraint of body parts in the skeleton model, 3D relative coordinates of the model are obtained. This reconstruction from 2D to 3D is an ill-posed problem due to non-uniqueness of solutions. Therefore, we introduced a technique based on the concept of multiple hypothesis tracking (MHT) with a motion-smoothness function between consecutive frames to automatically find the optimal solution for this ill-posed problem. Since reconstruction configurations are obtained from our closed-form equation, our technique is very efficient. Very accurate results were attained for both synthesized and real-world image sequences. We also compared our technique with both scaled-orthographic and existing perspective approaches. Our proposed method outperformed other approaches, especially in scenes with strong perspective effects and difficult poses.

Multiple antenna is introduced into spectrum sensing in cognitive radios recently. However, conventional multiple antenna spectrum sensing schemes exploited only space diversity. In this paper, we propose a new multiple antenna sensing scheme based on space and time diversity (MASS-BSTD). First, the primary user signal to be sensed is over-sampled at each antenna, and signal samples collected at the same time instant from different antennas are stacked into a column vector. Second, each column vector is utilized to estimate space correlation matrix that exploits space diversity, and two consecutive column vectors are utilized to estimate time correlation matrix that exploits time diversity. Third, the estimated space correlation matrix and time correlation matrix are combined and analyzed using eigenvalue decomposition to reduce information redundancy of signals from multiple antennas. Lastly, the derived eigenvalues are utilized to construct the test statistic and sense the presence of the primary user signal. Since the proposed MASS-BSTD exploits both space diversity and time diversity, it achieves performance gain over the counterparts that only exploit space diversity. Furthermore, the proposed MASS-BSTD requires no prior information on the primary user, the channel between primary user transmitter and secondary user receiver, and is robust to noise uncertainty. Theoretical analysis and simulation results show that the proposed MASS-BSTD can sense the presence of primary user signal reliably.

Power-aware X-filling is a preferable approach to avoiding IR-drop-induced yield loss in at-speed scan testing. However, the ability of previous X-filling methods to reduce launch switching activity may be unsatisfactory, due to low effect (insufficient and global-only reduction) and/or low scalability (long CPU time). This paper addresses this reduction quality problem with a novel GA (Genetic Algorithm) based X-filling method, called GA-fill. Its goals are (1) to achieve both effectiveness and scalability in a more balanced manner and (2) to make the reduction effect of launch switching activity more concentrated on critical areas that have higher impact on IR-drop-induced yield loss. Evaluation experiments are being conducted on both benchmark and industrial circuits, and the results have demonstrated the usefulness of GA-fill.

The performance of spectrum sensing in cognitive radio can be improved by employing multiple antennas. In this letter, the effect of antenna correlation on the performance improvement by deploying multiple antennas in the sensing node of the secondary system is investigated. It is proved mathematically that in the regime of low SNR, with antenna correlation, the secondary sensing node can achieve almost the same performance improvement as that without correlation. Simulation results verify the conclusions.

This paper presents a high speed single-stage latched comparator which is scheduled in time for both amplification and latch operations. Small active area and simple switching strategy besides desired power consumption at high comparison rates qualifies the proposed comparator to be repeatedly employed in high speed flash A/D converters. A strategy of kickback noise elimination besides gain enhancement is also introduced. A low power holding read-out circuit is presented. Post-Layout simulation results confirm 500 MS/s comparison rate with 5 mv resolution for a 1.6 v peak-to-peak input signal range and 600 µw power consumption from a 3.3 v power supply by using TSMC model of 0.35 µm CMOS technology. Total active area of proposed comparator and read-out circuit is about 300 µm2.

Mobile operators need to migrate from 2G to 3G networks in a cost-effective manner. Cognitive radio systems are currently being investigated as a promising solution to achieve spectrum efficiency by allowing coexistence of unlicensed (secondary) networks and licensed (primary) networks. However, conventional mechanisms to operate these systems incur additional complexity and fail to maximize network performance. In this paper, we propose a pilot sensing and frequency selection method with low complexity for OFDMA-based cognitive radio systems. Subject to the interference constraints imposed by the primary network, capacity maximization problems involving both up-link and down-link connections are considered for overall network performance improvement. The throughput and outage probability of the proposed method are evaluated by simulations. Our proposed method shows outstanding performance if the channel varies frequently in the primary network and the frequency reuse factor of the primary network is high.

Robust local image features have become crucial components of many state-of-the-art computer vision algorithms. Due to limited hardware resources, computing local features on embedded system is not an easy task. In this paper, we propose an efficient parallel computing framework for speeded-up robust features with an orientation towards multi-DSP based embedded system. We optimize modules in SURF to better utilize the capability of DSP chips. We also design a compact data layout to adapt to the limited memory resource and to increase data access bandwidth. A data-driven barrier and workload balance schemes are presented to synchronize parallel working chips and reduce overall cost. The experiment shows our implementation achieves competitive time efficiency compared with related works.

Power-aware scheduling of periodic tasks in real-time systems has been extensively studied to save energy while still meeting the performance requirement. Many previous studies use the probability information of tasks' execution cycles to assist the scheduling. However, most of these approaches adopt heuristic algorithms to cope with realistic CPU models with discrete frequencies and cannot achieve the globally optimal solution. Sometimes they even show worse results than non-stochastic DVS schemes. This paper presents an optimal DVS scheme for frame-based real-time systems under realistic power models in which the processor provides only a limited number of speeds and no assumption is made on power/frequency relation. A suboptimal DVS scheme is also presented in this paper to work out a solution near enough to the optimal one with only polynomial time expense. Experiment results show that the proposed algorithm can save at most 40% more energy compared with previous ones.

Internet group-based application layer services such as the overlay networks and P2P systems can benefit from end-to-end network status information. An efficient and accurate bandwidth measurement technique plays an important role in acquiring this information. We propose an end-to-end bottleneck link capacity measurement technique that utilizes path signatures combined with graphical analyses. This feature reduces the probe overhead and decreases the convergence time. We used ns-2 simulations and actual Internet measurements, which resulted in a high level of accuracy and a short probe time with low overhead.

The sensing scheme based on the generalized likelihood ratio test (GLRT) technique has attracted a lot of research interest in the field of cognitive radios (CR). Although its potential advantages in detecting correlated primary signal have been illustrated in prior work, no theoretical analysis of the positive effects of the correlation has appeared in the literature. In this letter, we derive the theoretical false-alarm and detection probabilities of GLRT detector. The theoretical analysis shows that, in the low signal-to-noise ratio (SNR) region, the detector's performance can be improved by exploiting the high correlations between the primary signal samples. The conclusions of the analysis are verified by numerical simulation results.

We propose a low-memory-bandwidth, high-efficiency VLSI architecture for 60-k word real-time continuous speech recognition. Our architecture includes a cache architecture using the locality of speech recognition, beam pruning using a dynamic threshold, two-stage language model searching, a parallel Gaussian Mixture Model (GMM) architecture based on the mixture level and frame level, a parallel Viterbi architecture, and pipeline operation between Viterbi transition and GMM processing. Results show that our architecture achieves 88.24% required frequency reduction (66.74 MHz) and 84.04% memory bandwidth reduction (549.91 MB/s) for real-time 60-k word continuous speech recognition.

A new type of modulation called continuous-phase parallel-combinatory high-compaction multicarrier modulation (CPPC/HC-MCM) is proposed. CPPC/HC-MCM employs the technique of continuous-phase modulation (CPM) and avoids the formation of amplitude gaps between two successive signals to enhance the spectral efficiency of conventional PC/HC-MCM. Results of simulations show that CPPC/HC-MCM is spectrally efficient and achieves a smaller bit error rate than conventional (unmodulated) PC/HC-MCM at a common spectral efficiency even if the peak-to-average power ratio is considered.

In this letter, we give a generalized construction for sets of frequency-hopping sequences (FHSs) based on power-residue sequences. Our construction encompasses a known optimal construction and can generate new optimal sets of FHSs which simultaneously achieve the Peng-Fan bound and the Lempel-Greenberger bound.