Technological developments in direction control of axonal outgrowth are a must for advances in regenerative medicine of the nervous system. In order to solve the problem, we fabricate a new neural cell culture sheet by applying the soft lithography technique to micro-patterning of the extracellular matrix and using thin-film biodegradable polymer for the scaffold. Micro-trenches were coated with Dulbecco's phosphate-buffered saline (-) containing laminin, using micro-molding in capillaries (MIMIC), a soft lithography technique. Biodegradable thin films with micro-trenches were fabricated by UV-curing a polyanhydride solution covering the negative SU-8 mold through thiol-ene polymerization. Both approaches were performed conveniently, rapidly, and accurately. It is thought that these techniques are excellent in terms of convenience and high speed, and can contribute greatly to regenerative medicine.

This paper proposes a method to absorb flash crowd in P2P video streaming systems. The idea of the proposed method is to reduce the time before a newly arrived node becoming an uploader by explicitly constructing a group of newly arrived nodes called flash crowd absorber (FCA). FCA grows continuously while serving a video stream to the members of the group, and it is explicitly controlled so that the upload capacity of the nodes is fully utilized and it attains a nearly optimal latency of the stream during a flash crowd. A numerical comparison with a naive tree-based scheme is also given.

The long short-term memory recurrent neural network (LSTM) has achieved tremendous success for automatic speech recognition (ASR). However, the complicated gating mechanism of LSTM introduces a massive computational cost and limits the application of LSTM in some scenarios. In this paper, we describe our work on accelerating the decoding speed and improving the decoding accuracy. First, we propose an architecture, which is called Projected Gated Recurrent Unit (PGRU), for ASR tasks, and show that the PGRU can consistently outperform the standard GRU. Second, to improve the PGRU generalization, particularly on large-scale ASR tasks, we propose the Output-gate PGRU (OPGRU). In addition, the time delay neural network (TDNN) and normalization methods are found beneficial for OPGRU. In this paper, we apply the OPGRU for both the acoustic model and recurrent neural network language model (RNN-LM). Finally, we evaluate the PGRU on the total Eval2000 / RT03 test sets, and the proposed OPGRU single ASR system achieves 0.9% / 0.9% absolute (8.2% / 8.6% relative) reduction in word error rate (WER) compared to our previous best LSTM single ASR system. Furthermore, the OPGRU ASR system achieves significant speed-up on both acoustic model and language model rescoring.

Traffic engineering refers to techniques to accommodate traffic efficiently by dynamically configuring traffic routes so as to adjust to changes in traffic. If traffic changes frequently and drastically, the interval of route reconfiguration should be short. However, with shorter intervals, obtaining traffic information is problematic. To calculate a suitable route, accurate traffic information of the whole network must be gathered. This is difficult in short intervals, owing to the overhead incurred to monitor and collect traffic information. In this paper, we propose a framework for traffic engineering in cases where only partial traffic information can be obtained in each time slot. The proposed framework is inspired by the human brain, and uses conditional probability to make decisions. In this framework, a controller is deployed to (1) obtain a limited amount of traffic information, (2) estimate and predict the probability distribution of the traffic, (3) configure routes considering the probability distribution of future predicted traffic, and (4) select traffic that should be monitored during the next period considering the system performance yielded by route reconfiguration. We evaluate our framework with a simulation. The results demonstrate that our framework improves the efficiency of traffic accommodation even when only partial traffic information is monitored during each time slot.

This paper presents a design for the perpendicular-corporate feed in a four-layer circularly-polarized parallel-plate slot array antenna. We place a dielectric layer with adequate permittivity in the region between the coupling-aperture and the radiating-slot layers to remove x-shaped cavity walls completely in the radiating part of a conventional planar corporate-feed waveguide slot array antenna. To address fabrication constraints, the dielectric layer consists of PTFE and air. It excites a strong standing wave in the region and so provides 2×2-element subarrays with uniform excitation. None of the slot layers are in electrical contact due to air gaps between the slot layers. The four-layer structure with apertures for circular polarization contributes to wideband design for axial ratios because of the eigenmodes in the desired band. We realize an 11.9% bandwidth for axial ratios of less than 3.0dB as confirmed by measurements in the 60GHz band. At the design frequency, the measured realized gain is 32.7dBi with an antenna efficiency of 75.5%.

The separating redundancy is an important property in the analysis of the error-and-erasure decoding of a linear block code. In this work, we investigate the separating redundancy of the duals of first-order generalized Reed-Muller (GRM) codes, a class of nonbinary linear block codes that have nice algebraic properties. The dual of a first-order GRM code can be specified by two positive integers m and q and denoted by R(m,q), where q is the power of a prime number and q≠2. We determine the first separating redundancy value of R(m,q) for any m and q. We also determine the second separating redundancy values of R(m,q) for any q and m=1 and 2. For m≥3, we set up a binary integer linear programming problem, the optimum of which gives a lower bound on the second separating redundancy of R(m,q).

We propose a T-junction OMT consisting of an offset stepped post. The offset stepped post contributes to the matching of two rectangular ports at the short circuit, situated at the opposite side walls. The structure without conventional ridges is simple and makes it possible to achieve robust performance. We fabricated a proposed T-junction OMT in a single piece of an aluminum alloy, using a commercial metal 3D-printer. The simple and compact structure with robust performance is proposed to overcome the disadvantages of a 3D-printer, such as fabrication tolerance and surface roughness. The measured results demonstrated a return loss of 22dB and an insertion loss of 0.3dB, with a bandwidth of 8% in the K-band.

360-degree video is an important component of the emerging Virtual Reality. In this paper, we propose a new adaptation method for tiling-based viewport adaptive streaming of 360-degree video. The proposed method is able to dynamically select the best tiling scheme given the network conditions and user status. Experiments show that our proposed method can improve the viewport quality by up to 2.3 dB compared to a conventional fixed tiling method.

We explore ways to optimize online, permutation-based authenticated encryption (AE) schemes for lightweight applications. The lightweight applications demand that AE schemes operate in resource-constrained environments, which raise two issues: 1) implementation costs must be low, and 2) ensuring proper use of a nonce is difficult due to its small size and lack of randomness. Regarding the implementation costs, recently it has been recognized that permutation-based (rather than block-cipher-based) schemes frequently show advantages. However, regarding the security under nonce misuse, the standard permutation-based duplex construction cannot ensure confidentiality. There exists one permutation-based scheme named APE which offers certain robustness against nonce misuse. Unfortunately, the APE construction has several drawbacks such as ciphertext expansion and bidirectional permutation circuits. The ciphertext expansion would require more bandwidth, and the bidirectional circuits would require a larger hardware footprint. In this paper, we propose new constructions of online permutation-based AE that require less bandwidth, a smaller hardware footprint and lower computational costs. We provide security proofs for the new constructions, demonstrating that they are as secure as the APE construction.

We propose new key recovery attacks on the two-round single-key n-bit Even-Mansour ciphers (2SEM) that are secure up to 22n/3 queries against distinguishing attacks proved by Chen et al. Our attacks are based on the meet-in-the-middle technique which can significantly reduce the data complexity. In particular, we introduce novel matching techniques which enable us to compute one of the two permutations without knowing a part of the key information. Moreover, we present two improvements of the proposed attack: one significantly reduces the data complexity and the other reduces the time complexity. Compared with the previously known attacks, our attack first breaks the birthday barrier on the data complexity although it requires chosen plaintexts. When the block size is 64 bits, our attack reduces the required data from 245 known plaintexts to 226 chosen plaintexts with keeping the time complexity required by the previous attacks. Furthermore, by increasing the time complexity up to 262, the required data is further reduced to 28, and DT=270, where DT is the product of data and time complexities. We show that our data-optimized attack requires DT=2n+6 in general cases. Since the proved lower bound on DT for the single-key one-round n-bit Even-Mansour ciphers is 2n, our results imply that adding one round to one-round constructions does not sufficiently improve the security against key recovery attacks. Finally, we propose a time-optimized attacks on 2SEM in which, we aim to minimize the number of the invocations of internal permutations.

Dynamic linear feedback shift registers (DLFSRs) are a scheme to transfer from one LFSR to another. In cryptography each LFSR included in a DLFSR should generate maximal-length sequences, and the number of switches transferring LFSRs should be small for efficient performance. This corresponding addresses on searching such conditioned DLFSRs. An efficient probabilistic algorithm is given to find such DLFSRs with two or four switches, and it is proved to succeed with nonnegligible probability.

In this paper, we propose a new asymptotically stabilizing control law for a four-wheeled vehicle with a steering limitation. We adopt a locally semiconcave control Lyapunov function (LS-CLF) for the system. To overcome the nonconvexity of the input-constraint set, we utilize a saturation function and a signum function in the control law. The signum function makes the vehicle velocity nonzero except at the origin so that the angular velocity can be manipulated within the input constraint. However, the signum function may cause a chattering phenomenon at certain points of the state far from the origin. Thus, we integrate a lazy-switching mechanism for the vehicle velocity into the control law. The mechanism makes a sign of the vehicle velocity maintain, and the new control input also decreases the value of the LS-CLF. We confirm the effectiveness of our method by a computer simulation and experiments.

Dual-rotor machine (DRM) is a multiple input and output electromechanical device with two electrical and two mechanical ports which make it an optimal transmission system for hybrid electric vehicles. In attempt to boost its performance and efficiency, this work presents a dual-rotor permanent magnet (DR-PM) machine system used for continuously variable transmission (CVT) in HEVs. The proposed DR-PM machine is analyzed, and modeled in consideration of vehicle driving requirements. Considering energy conversion modes and torque transfer modes, operation conditions of the DR-PM machine system used for CVT are illustrated in detail. Integrated control model of the system is carried out, besides, intelligent speed ratio control strategy is designed by analyzing the dynamic coupling modes upon the integrated models to satisfy the performance requirements, reasonable energy-split between machine and engine, and optimal fuel economy. Experimental results confirm the validity of the mathematical model of the DR-PM machine system in the application of CVT, and the effectiveness of the intelligent speed ratio control strategy.

A reconfigurable broadband linear power amplifier (PA) for long-range WLAN applications fabricated in a 180nm RF CMOS process is presented here. The proposed reconfigurable in/output matching network provides the PA with broadband capability at the two center frequencies of 0.5GHz and 0.85GHz. The output matching network is realized by a switchable transformer which shows maximum peak passive efficiencies of 65.03% and 73.45% at 0.45GHz and 0.725GHz, respectively. With continuous wave sources, a 1-dB bandwidth (BW1-dB) according to saturated output power is 0.4-1.2GHz, where it shows a minimum output power with a power added efficiency (PAE) of 25.62dBm at 19.65%. Using an adaptive power cell configuration at the common gate transistor, the measured PA under LTE 16-QAM 20MHz (40MHz) signals shows an average output power with a PAE exceeding 20.22 (20.15) dBm with 7.42 (7.35)% at an ACLRE-UTRA of -30dBc, within the BW1-dB.

It is a hot issue that speeding up the network layers and decreasing the network parameters in convolutional neural networks (CNNs). In this paper, we propose a novel method, namely, symmetric decomposition of convolution kernels (SDKs). It symmetrically separates k×k convolution kernels into (k×1 and 1×k) or (1×k and k×1) kernels. We conduct the comparison experiments of the network models designed by SDKs on MNIST and CIFAR-10 datasets. Compared with the corresponding CNNs, we obtain good recognition performance, with 1.1×-1.5× speedup and more than 30% reduction of network parameters. The experimental results indicate our method is useful and effective for CNNs in practice, in terms of speedup performance and reduction of parameters.

Recently, image forensics community has paid attention to the research on the design of effective algorithms based on deep learning technique. And facts proved that combining the domain knowledge of image forensics and deep learning would achieve more robust and better performance than the traditional schemes. Instead of improving algorithm performance, in this paper, the safety of deep learning based methods in the field of image forensics is taken into account. To the best of our knowledge, this is the first work focusing on this topic. Specifically, we experimentally find that the method using deep learning would fail when adding the slight noise into the images (adversarial images). Furthermore, two kinds of strategies are proposed to enforce security of deep learning-based methods. Firstly, a penalty term to the loss function is added, which is the 2-norm of the gradient of the loss with respect to the input images, and then an novel training method is adopt to train the model by fusing the normal and adversarial images. Experimental results show that the proposed algorithm can achieve good performance even in the case of adversarial images and provide a security consideration for deep learning-based image forensics.

Learning semantic representation for translation context is beneficial to statistical machine translation (SMT). Previous efforts have focused on implicitly encoding syntactic and semantic knowledge in translation context by neural networks, which are weak in capturing explicit structural syntax information. In this paper, we propose a new neural network with a tree-based convolutional architecture to explicitly learn structural syntax information in translation context, thus improving translation prediction. Specifically, we first convert parallel sentences with source parse trees into syntax-based linear sequences based on a minimum syntax subtree algorithm, and then define a tree-based convolutional network over the linear sequences to learn syntax-based context representation and translation prediction jointly. To verify the effectiveness, the proposed model is integrated into phrase-based SMT. Experiments on large-scale Chinese-to-English and German-to-English translation tasks show that the proposed approach can achieve a substantial and significant improvement over several baseline systems.

The number of infected hosts on enterprise networks has been increased by drive-by download attacks. In these attacks, users of compromised popular websites are redirected toward websites that exploit vulnerabilities of a browser and its plugins. To prevent damage, detection of infected hosts on the basis of proxy logs rather than blacklist-based filtering has started to be researched. This is because blacklists have become difficult to create due to the short lifetime of malicious domains and concealment of exploit code. To detect accesses to malicious websites from proxy logs, we propose a system for detecting malicious URL sequences on the basis of three key ideas: focusing on sequences of URLs that include artifacts of malicious redirections, designing new features related to software other than browsers, and generating new training data with data augmentation. To find an effective approach for classifying URL sequences, we compared three approaches: an individual-based approach, a convolutional neural network (CNN), and our new event de-noising CNN (EDCNN). Our EDCNN reduces the negative effects of benign URLs redirected from compromised websites included in malicious URL sequences. Evaluation results show that only our EDCNN with proposed features and data augmentation achieved a practical classification performance: a true positive rate of 99.1%, and a false positive rate of 3.4%.

This letter proposes a novel speech enhancement system based on the ‘L’ shaped triple-microphone. The modified coherence-based algorithm and the first-order differential beamforming are combined to filter the spatial distributed noise. The experimental results reveal that the proposed algorithm achieves significant performance in spatial filtering under different noise scenarios.

A Lévy search strategy based chaotic artificial bee colony algorithm (LABC) is proposed in this paper. The chaotic sequence, global optimal mechanism and Lévy flight mechanism were introduced respectively into the initialization, the employed bee search and the onlooker bee search. The experiments show that the proposed algorithm performed better in convergence speed, global search ability and optimization accuracy than other improved ABC.