This paper provides a prototype neural prosthesis system dedicated to restoring continence and micturition function for patients with lower urinary tract diseases, such as detrusor hyperreflexia and detrusor-sphincter dyssynergia. This system consists of an ultra low-noise electroneurogram (ENG) signal recording module, a bi-phasic electrical stimulator module and a control unit for closed-loop bladder monitoring and controlling. In order to record extremely weak ENG signal from extradural sacral nerve roots, the system provides a programmable gain from 80 dB to 117 dB. By combining of advantages of commercial-off-the-shelf (COTS) electronics and custom designed IC, the recording front-end acquires a fairly low input-referred noise (IRN) of 0.69 μVrms under 300 Hz to 3 kHz and high area-efficiency. An on-chip multi-steps single slope analog-to-digital converter (ADC) is used to digitize the ENG signals at sampling rate of 10 kSPS and achieves an effective number of bits (ENOB) of 12.5. A bi-phasic current stimulus generator with wide voltage supply range (±0.9 V to ±12.5 V) and variable output current amplitude (0-500 μA) is introduced to overcome patient-depended impedance between electrode and tissue electrolyte. The total power consumption of the entire system is 5.61 mW. Recording and stimulation function of this system is switched by control unit with time division multiplexing strategy. The functionality of this proposed prototype system has been successfully verified through in-vivo experiments from dogs extradural sacral nerve roots.

With IC design entering the nanometer scale integration, the reliability of VLSI has declined due to small-delay defects, which are hard to detect by traditional delay fault testing. To detect small-delay defects, on-chip delay measurement, which measures the delay time of paths in the circuit under test (CUT), was proposed. However, our pre-simulation results show that when using on-chip delay measurement method to detect small-delay defects, test generation under the single-path sensitization is required. This constraint makes the fault coverage very low. To improve fault coverage, this paper introduces techniques which use segmented scan and test point insertion (TPI). Evaluation results indicate that we can get an acceptable fault coverage, by combining these techniques for launch off shift (LOS) testing under the single-path sensitization condition. Specifically, fault coverage is improved 27.02∼47.74% with 6.33∼12.35% of hardware overhead.

We present a low-complexity complementary pair affine projection (CP-AP) adaptive filter which employs the intermittent update of the filter coefficients. To achieve both a fast convergence rate and a small residual error, we use a scheme combining fast and slow AP filters, while significantly reducing the computational complexity. By employing an evolutionary method which automatically determines the update intervals, the update frequencies of the two constituent filters are significantly decreased. Experimental results show that the proposed CP-AP adaptive filter has an advantage over conventional adaptive filters with a parallel structure in that it has a similar convergence performance with a substantial reduction in the total number of updates.

Strongly deterministic multithreading provides determinism for multithreaded programs even in the presence of data races. A common way to guarantee determinism for data races is to isolate threads by buffering shared memory accesses. Unfortunately, buffering all shared accesses is prohibitively costly. We propose an approach called DRDet to efficiently make data races deterministic. DRDet leverages the insight that, instead of buffering all shared memory accesses, it is sufficient to only buffer memory accesses involving data races. DRDet uses a sound data-race detector to detect all potential data races. These potential data races, along with all accesses which may access the same set of memory objects, are flagged as data-race-involved accesses. Unsurprisingly, the imprecision of static analyses makes a large fraction of shared accesses to be data-race-involved. DRDet employs two optimizations which aim at reducing the number of accesses to be sent to query alias analysis. We implement DRDet on CoreDet, a state-of-the-art deterministic multithreading system. Our empirical evaluation shows that DRDet reduces the overhead of CoreDet by an average of 1.6X, without weakening determinism and scalability.

A simple, low reflection, and highly-efficient pilot-plant scale microwave irradiation reactor for woody biomass pretreatment was fabricated. Pretreatment is an essential process for effective bioethanol production. The fabricated reactor consists of 8 microwave irradiators which are attached to a metal pipe. The woody biomass mixture which contains water and organic acid flows through the metal pipe and is heated by microwaves at a total power of 12,kW. To design the microwave irradiators, we used a 3D Finite Element Method (FEM) simulator, which was based on the measured complex permittivity data of the woody biomass mixture. The simulation results showed that the reflection coefficient $|S_{11}|$ from the reactor was less than -30,dB when the woody biomass mixture temperature was between 30$^{circ}$C and 90$^{circ}$C. Finally, we experimentally confirmed that the fabricated irradiation reactor yielded a microwave absorption efficiency of 79%.

This paper analyzes the transmission performances of visible light communication (VLC) based on unipolar orthogonal frequency division multiplexing (OFDM), which is compatible with intensity modulation and direct detection (IM/DD). Three existing unipolar OFDM schemes, namely DC biased optical OFDM (DCO-OFDM), asymmetrically clipped optical OFDM (ACO-OFDM), and flip-OFDM are investigated and compared. While these three schemes have been analyzed for indoor optical wireless communication (OWC) subject to the limitation on the transmit optical power, they have not been carefully investigated and compared for VLC when a large transmit power is available due to the illumination requirement, and the signal dynamic range (DR) becomes the main limitation. For the analysis, DR expressions of DCO-OFDM, ACO-OFDM, and flip-OFDM signals are first derived. Then, the bit error rate (BER) expression of each unipolar OFDM scheme is derived in terms of the DR. For data rates in the range of 1-10Mbps, under the system parameters based on typical indoor environments, DCO-OFDM is observed to outperform the other two schemes. This superiority of DCO-OFDM is in contrast with previously reported results that indicate the attractiveness of ACO-OFDM and flip-OFDM over DCO-OFDM when the transmit optical power is the main limitation. Finally, light dimming is considered to identify the illumination level below which DCO-OFDM loses this superiority.

This paper aims to achieve a high-quality exposure level quantification of whole-body average-specific absorption rates (WBA-SARs) for small animals in a medium-size reverberation chamber (RC). A two-step method, which incorporates the finite-difference time-domain (FDTD) numerical solutions with electric field measurements in an RC-type exposure system, has been used as an evaluation method to determine the whole-body exposure level in small animals. However, there is little data that quantitatively demonstrate the validity and accuracy of this method in an RC up to now. In order to clarify the validity of the two-step method, we compare the physical quantities in terms of electric field strength and WBA-SARs by using a direct numerical assessment method known as the method of moments (MoM) with ten homogenous gel phantoms placed in an RC with 2GHz exposure. The comparison results show that the relative errors between the two-step method and the MoM approach are approximately below 10%, which reveals the validity and usefulness of the two-step technique. Finally, we perform a dosimetric analysis of the WBA-SARs for anatomical mouse models with the two-step method and determine the input power related to our developed RC-exposure system to achieve a target exposure level in small animals.

To secure a wireless sensor and actuator network (WSAN) in cyber-physical systems, trust management framework copes with misbehavior problem of nodes and stimulate nodes to cooperate with each other. The existing trust management frameworks can be classified into reputation-based framework and trust establishment framework. There, however, are still many problems with these existing trust management frameworks, which remain unsolved, such as frangibility under possible attacks. To design a robust trust management framework, we identify the attacks to the existing frameworks, present the countermeasures to them, and propose a hybrid trust management framework (HTMF) to construct trust environment for WSANs in the paper. HTMF includes second-hand information and confidence value into trustworthiness evaluation and integrates the countermeasures into the trust formation. We preform extensive performance evaluations, which show that the proposed HTMF is more robust and reliable than the existing frameworks.

In this paper, we propose a modified-error adaptive feedback active noise control (ANC) system using a linear prediction filter. The proposed ANC system is advantageous in terms of the rate of convergence, while maintaining stability, because it can reduce narrowband noise while suppressing disturbance, including wideband components. The estimation accuracy of the noise control filter in the conventional system is degraded because the disturbance corrupts the input signal to the noise control filter. A solution of this problem is to utilize a linear prediction filter. The linear prediction filter is utilized for the modified-error feedback ANC system to suppress the wideband disturbance because the linear prediction filter can separate narrowband and wideband noise. Suppressing wideband noise is important for the head-mounted ANC system we have already proposed for reducing the noise from a magnetic resonance imaging (MRI) device because the error microphones are located near the user's ears and the user's voice consequently corrupts the input signal to the noise control filter. Some simulation and experimental results obtained using a digital signal processor (DSP) demonstrate that the proposed feedback ANC system is superior to a conventional feedback ANC system in terms of the estimation accuracy and the rate of convergence of the noise control filter.

A path loss model for low antenna heights below surrounding buildings in residential areas is presented to contribute to the construction of VHF band wireless systems. The model is constructed on the basis of measurement results at 167.65MHz, near center frequency at VHF band. Path loss characteristics in the middle VHF band are compared to those in bands above UHF. The dominant paths in bands above UHF include propagation paths below surrounding buildings, such as paths along roads. However, in the middle VHF band, these paths are instantly attenuated because their 1st Fresnel zone radius is larger than the average building height or road width. The dominant path in the middle VHF band is the over-roof propagation path, and the 1st Fresnel zone of the path is shielded by the buildings and the ground surface. The proposed path loss model has two features. First, it derives the effective height of the ground surface from the terrain profile of the buildings and the ground surface. Second, it uses formulas of a two-path model to take the shielding of the 1st Fresnel zone into account. Finally, it is shown that the proposed model is able to predict the path loss measurement results more accurately than the conventional model.

In this letter, we present a new method of alleviating the deterioration in the quality of real-time video service during vertical handover (VHO). The proposed method stochastically delays the starting time of the service disruption of VHO in order to reduce the number of lost frames caused by the inter-frame dependency of multi-layered video traffic. The results show that the proposed method significantly decreases the average frame loss time at the sacrifice of an increased handover execution time by one half of the group of picture (GOP) interval of the video traffic.

In this paper, we propose a texture descriptor based on amplitude distribution and phase distribution of the discrete Fourier transform (DFT) of an image. One dimensional DFT is applied to all the rows and columns of an image. Histograms of the amplitudes and gradients of the phases between adjacent rows/columns are computed as the feature descriptor, which is called aggregated DFT (ADFT). ADFT can be easily combined with completed local binary pattern (CLBP). The combined feature captures both global and local information of the texture. ADFT is designed for isotropic textures and demonstrated to be effective for roughness classification of castings. Experimental results show that the amplitude part of ADFT is also discriminative in describing anisotropic textures and it can be used as a complementary descriptor of local texture descriptors such as CLBP.

Oblivious RAM (ORAM) schemes, the concept introduced by Goldreich and Ostrovsky, are very useful technique for protecting users' privacy when storing data in remote untrusted servers and running software on untrusted systems. However they are usually considered impractical due to their huge overhead. In order to reduce overhead, many improvements have been presented. Thanks to these improvements, ORAM schemes can be considered practical on cloud environment where users can expect huge storage and high computational power. Especially for private information retrieval (PIR), some literatures demonstrated they are usable. Also dedicated PIRs have been proposed and shown that they are usable in practice. Yet, they are still impractical for protecting software running on untrusted systems. We first survey recent researches on ORAM and PIR. Then, we present a practical software-based memory protection scheme applicable to several environments. The main feature of our scheme is that it records the history of accesses and uses the history to hide the access pattern. We also address implementing issues of ORAM and propose practical solutions for these issues.

Presentation slide composition is an important job for knowledge workers. Instead of starting from scratch, users tend to make new presentation slides by reusing existing ones. A primary challenge in slide reuse is to select desired materials from a collection of existing slides. The state-of-the-art solution utilizes texts and images in slides as well as file names to help users to retrieve the materials they want. However, it only allows users to choose an entire slide as a query but does not support the search for a single element such as a few keywords, a sentence, an image, or a diagram. In this paper, we investigate content-based search for a variety of elements in presentation slides. Users may freely choose a slide element as a query. We propose different query processing methods to deal with various types of queries and improve the search efficiency. A system with a user-friendly interface is designed, based on which experiments are performed to evaluate the effectiveness and the efficiency of the proposed methods.

A discriminative reference-based method for scene image categorization is presented in this letter. Reference-based image classification approach combined with K-SVD is approved to be a simple, efficient, and effective method for scene image categorization. It learns a subspace as a means of randomly selecting a reference-set and uses it to represent images. A good reference-set should be both representative and discriminative. More specifically, the reference-set subspace should well span the data space while maintaining low redundancy. To automatically select reference images, we adapt affinity propagation algorithm based on data similarity to gather a reference-set that is both representative and discriminative. We apply the discriminative reference-based method to the task of scene categorization on some benchmark datasets. Extensive experiment results demonstrate that the proposed scene categorization method with selected reference set achieves better performance and higher efficiency compared to the state-of-the-art methods.

This paper proposes a novel method to segment the object of a specific class based on a rough detection window (such as Deformable Part Model (DPM) in this paper), which is robust to the positions of the bounding boxes. In our method, the DPM is first used to generate the root and part windows of the object. Then a set of object part candidates are generated by randomly sampling windows around the root window. Furthermore, an undirected graph (the minimum spanning tree) is constructed to describe the spatial relationships between the part windows. Finally, the object is segmented by grouping the part proposals on the undirected graph, which is formulated as an energy function minimization problem. A novel energy function consisting of the data term and the smoothness term is designed to characterize the combination of the part proposals, which is globally minimized by the dynamic programming on a tree. Our experimental results on challenging dataset demonstrate the effectiveness of the proposed method.

The goal of dimension reduction is to represent high-dimensional data in a lower-dimensional subspace, while intrinsic properties of the original data are kept as much as possible. An important challenge in unsupervised dimension reduction is the choice of tuning parameters, because no supervised information is available and thus parameter selection tends to be subjective and heuristic. In this paper, we propose an information-theoretic approach to unsupervised dimension reduction that allows objective tuning parameter selection. We employ quadratic mutual information (QMI) as our information measure, which is known to be less sensitive to outliers than ordinary mutual information, and QMI is estimated analytically by a least-squares method in a computationally efficient way. Then, we provide an eigenvector-based efficient implementation for performing unsupervised dimension reduction based on the QMI estimator. The usefulness of the proposed method is demonstrated through experiments.

The Hough voting framework is a popular approach to parts based pedestrian detection. It works by allowing image features to vote for the positions and scales of pedestrians within a test image. Each vote is cast independently from other votes, which allows for strong occlusion robustness. However this approach can produce false pedestrian detections by accumulating votes inconsistent with each other, especially in cluttered scenes such as typical street scenes. This work aims to reduce the sensibility to clutter in the Hough voting framework. Our idea is to use object segmentation and object pose parameters to enforce votes' consistency both at training and testing time. Specifically, we use segmentation and pose parameters to guide the learning of a pedestrian model able to cast mutually consistent votes. At test time, each candidate detection's support votes are looked upon from a segmentation and pose viewpoints to measure their level of agreement. We show that this measure provides an efficient way to discriminate between true and false detections. We tested our method on four challenging pedestrian datasets. Our method shows clear improvements over the original Hough based detectors and performs on par with recent enhanced Hough based detectors. In addition, our method can perform segmentation and pose estimation as byproducts of the detection process.

Internet traffic classification is an essential step for stable service provision. The payload signature classifier is considered a reliable method for Internet traffic classification but is prohibitively computationally expensive for real-time handling of large amounts of traffic on high-speed networks. In this paper, we describe several design techniques to minimize the search space of traffic classification and improve the processing speed of the payload signature classifier. Our suggestions are (1) selective matching algorithms based on signature type, (2) signature reorganization using hierarchical structure and traffic locality, and (3) early packet sampling in flow. Each can be applied individually, or in any combination in sequence. The feasibility of our selections is proved via experimental evaluation on traffic traces of our campus and a commercial ISP. We observe 2 to 5 times improvement in processing speed against the untuned classification system and Snort Engine, while maintaining the same level of accuracy.

Runtime analysis is to enhance the safety of critical systems by monitoring the change of corresponding external environments. In this paper, a modified FTA approach, making full utilization of the existing safety analysis result, is put forward to achieve runtime safety analysis. The procedures of the approach are given in detail. This approach could be widely used in safety engineering of critical systems.