To implement the parallel acceleration of convolution operation of Convolutional Neural Networks (CNNs) on field programmable gate array (FPGA), large quantities of the logic resources will be consumed, expecially DSP cores. Many previous researches fail to make a well balance between DSP and LUT6. For better resource efficiency, a typical convolution structure is implemented with LUT6s in this paper. Besides, a novel convolution structure is proposed to further reduce the LUT6 resource consumption by modifying the typical convolution structure. The equations to evaluate the LUT6 resource consumptions of both structures are presented and validated. The theoretical evaluation and experimental results show that the novel structure can save 3.5-8% of LUT6s compared with the typical structure.

Liquid crystal director distributions between strong and weak polar anchoring surfaces in hybrid aligned cells are numerically analyzed. When the anchoring is a critical one, homogeneously or homeotropicly liquid crystal alignment can be obtained. Such cells have no threshold voltage and a driving voltage can be reduced less than 0.5 volt.

We propose a rootkit installation method inside a GPU kernel execution process which works through GPU context manipulation. In GPU-based applications such as deep learning computations and cryptographic operations, the proposed method uses the feature by which the execution flow of the GPU kernel obeys the GPU context information in GPU memory. The proposed method consists of two key ideas. The first is GPU code manipulation, which is able to hijack the execution flow of the original GPU kernel to execute an injected payload without affecting the original GPU computation result. The second is a self-page-table update execution during which the GPU kernel updates its page table to access any location in system memory. After the installation, the malicious payload is executed only in the GPU kernel, and any no evidence remains in system memory. Thus, it cannot be detected by conventional rootkit detection methods.

In this letter, we investigate a design of efficient antenna allocation at the full duplex receiver (FDR) in a multi-input multi-output multi-eavesdropper (MIMOME) wiretap channel for physical layer security improvement. Specifically, we propose the allocation which are feasible for the practical scenario with self-interference (SI) taken into account, because the jamming signals from FDR not only confuse the eavesdropper but also inevitably cause SI at the FDR. Due to the nolinear and coupling of the antenna allocation optimization problem, we transform the original problem into an integer programming problem. Then, we derive the optimal solution and the corresponding beamforming matrices in closed-form by means of combining spatial alignment and null-space projection method. Furthermore, we present the feasibility condition and full-protection condition, which offer insight into principles that enable more efficient and effective use of FDR in the wiretap channel for security improvement. From the simulation results, we validate the theoretical analysis and demonstrate the outstanding performance of the proposed antennas allocation at FDR.

This paper proposes a novel Depth From Defocus (DFD) technique based on the property that two images having different focus settings coincide if they are reblurred with the opposite focus setting, which is referred to as the “cross reblurring” property in this paper. Based on the property, the proposed technique estimates the block-wise depth profile for a target object by minimizing the mean squared error between the cross-reblurred images. Unlike existing DFD techniques, the proposed technique is free of lens parameters and independent of point spread function models. A compensation technique for a possible pixel disalignment between images is also proposed to improve the depth estimation accuracy. The experimental results and comparisons with the other DFD techniques show the advantages of our technique.

MDS transformation plays an important role in resisting against differential cryptanalysis (DC) and linear cryptanalysis (LC). Recently, M. Sajadieh, et al.[15] designed an efficient recursive diffusion layer with Feistel-like structures. Moreover, they obtained an MDS transformation which is related to a linear function and the inverse is as lightweight as itself. Based on this work, we consider one specific form of linear functions to get the diffusion layer with low XOR gates for the hardware implementation by using temporary registers. We give two criteria to reduce the construction space and obtain six new classes of lightweight MDS transformations. Some of our constructions with one bundle-based LFSRs have as low XOR gates as previous best known results. We expect that these results may supply more choices for the design of MDS transformations in the (lightweight) block cipher algorithm.

Sound event detection is intended to identify the sound events in audio recordings, which has widespread applications in real life. Recently, convolutional recurrent neural network (CRNN) models have achieved state-of-the-art performance in this task due to their capabilities in learning the representative features. However, the CRNN models are of high complexities with millions of parameters to be trained, which limits their usage for the mobile and embedded devices with limited computation resource. Model distillation is effective to distill the knowledge of a complex model to a smaller one, which can be deployed on the devices with limited computational power. In this letter, we propose a novel multi model-based distillation approach for sound event detection by making use of the knowledge from models of multiple teachers which are complementary in detecting sound events. Extensive experimental results demonstrated that our approach achieves a compression ratio about 50 times. In addition, better performance is obtained for the sound event detection task.

In this paper, a new transceiver system for the in-vehicle communication system is proposed to enhance data transmission rate and timing accuracy in TDM-based application. The proposed system utilizes point-to-point (P2P) channel, a closed-loop clock forwarding path, and a transceiver with a repeater and clock delay adjuster. The proposed system with 4 ECU (Electronic Computing Unit) nodes is implemented in 180nm CMOS technology and, when compared with conventional bus-based system, achieved more than 125 times faster data transmission. The maximum data rate was 2.5Gbps at 1.8V power supply and the worst peak-to-peak jitter for the data and clock signals over 5000 data symbols were about 49.6ps and 9.8ps respectively.

Due to the depletion of the public IPv4 address pool, the transition to IPv6 became inevitable. However, this ongoing transition is taking a long time, and the two incompatible versions of the Internet Protocol must coexist. Different IPv6 transition technologies were developed, which can be used to enable communication in various scenarios, but they also involve additional security issues. In this paper, first, we introduce our methodology for analyzing the security of IPv6 transition technologies in a nutshell. Then, we develop a priority classification method for the ranking of different IPv6 transition technologies and their most important implementations, so that the vulnerabilities of the most crucial ones may be examined first. Next, we conduct a comprehensive survey of the existing IPv6 transition technologies by describing their application scenarios and the basics of their operation and we also determine the priorities of their security analysis according to our ranking system. Finally, we show that those IPv6 transition technologies that we gave high priorities, cover the most relevant scenarios.

The Cuckoo Search (CS) is apt to be trapped in local optimum relating to complex target functions. This drawback has been recognized as the bottleneck of its widespread use. This paper, with the purpose of improving CS, puts forward a Cuckoo Search algorithm featuring Multi-Learning Strategies (LSCS). In LSCS, the Converted Learning Module, which features the Comprehensive Learning Strategy and Optimal Learning Strategy, tries to make a coordinated cooperation between exploration and exploitation, and the switching in this part is decided by the transition probability Pc. When the nest fails to be renewed after m iterations, the Elite Learning Perturbation Module provides extra diversity for the current nest, and it can avoid stagnation. The Boundary Handling Approach adjusted by Gauss map is utilized to reset the location of nest beyond the boundary. The proposed algorithm is evaluated by two different tests: Test Group A(ten simple unimodal and multimodal functions) and Test Group B(the CEC2013 test suite). Experiments results show that LSCS demonstrates significant advantages in terms of convergence speed and optimization capability in solving complex problems.

Access control and surveillance applications like walking-through security gates and immigration control points have a great demand for convenient and accurate biometric recognition in unconstrained scenarios with low user cooperation. The periocular region, which is a relatively new biometric trait, has been attracting much attention for recognition of an individual in such scenarios. This paper proposes a periocular recognition method that combines Phase-Based Correspondence Matching (PB-CM) with a texture enhancement technique. PB-CM has demonstrated high recognition performance in other biometric traits, e.g., face, palmprint and finger-knuckle-print. However, a major limitation for periocular region is that the performance of PB-CM degrades when the periocular skin has poor texture. We address this problem by applying texture enhancement and found out that variance normalization of texture significantly improves the performance of periocular recognition using PB-CM. Experimental evaluation using three public databases demonstrates the advantage of the proposed method compared with conventional methods.

In millimeter wave (mmWave) vehicular communications, multi-hop relay disconnection by line-of-sight (LOS) blockage is a critical problem, particularly in the early diffusion phase of mmWave-available vehicles, where not all vehicles have mmWave communication devices. This paper proposes a distributed position control method to establish long relay paths through road side units (RSUs). This is realized by a scheme via which autonomous vehicles change their relative positions to communicate with each other via LOS paths. Even though vehicles with the proposed method do not use all the information of the environment and do not cooperate with each other, they can decide their action (e.g., lane change and overtaking) and form long relays only using information of their surroundings (e.g., surrounding vehicle positions). The decision-making problem is formulated as a Markov decision process such that autonomous vehicles can learn a practical movement strategy for making long relays by a reinforcement learning (RL) algorithm. This paper designs a learning algorithm based on a sophisticated deep reinforcement learning algorithm, asynchronous advantage actor-critic (A3C), which enables vehicles to learn a complex movement strategy quickly through its deep-neural-network architecture and multi-agent-learning mechanism. Once the strategy is well trained, vehicles can move independently to establish long relays and connect to the RSUs via the relays. Simulation results confirm that the proposed method can increase the relay length and coverage even if the traffic conditions and penetration ratio of mmWave communication devices in the learning and operation phases are different.

This study designs a low-cost portable device that functions as a coaching assistant system which can support table tennis practice. Although deep learning technology is a promising solution to realizing human activity recognition, we propose using cosine similarity in making inferences. Our experiments show that the cosine similarity based inference can be a good alternative to the deep learning based inference for the assistant system when resources are limited.

Positioning information plays a significant role in multi-unmanned aerial vehicles (UAVs) applications. Traditionally, the positioning information is widely provided by Global Navigation Satellite System (GNSS) due to its good performance and global coverage. However, owing to complicated flight environment or signal blockage, jamming and unintentional interference, the UAVs may fail to locate themselves by using GNSS alone. As a new method to resolve these problems, cooperative positioning, by incorporating peer-to-peer range measurements and assisted information, has attracted more and more attentions due to its ability to enhance the accuracy and availability of positioning. However, achieving good performance of cooperative positioning of multi-UAVs is challenging as their mobility, arbitrary nonlinear state-evolution, measurement models and limited computation and communication resources. In this paper, we present a factor graph (FG) representation and message passing methodology to solve cooperative positioning problem among UAVs in 3-dimensional environment where GNSS cannot provide services. Moreover, to deal with the nonlinear state-evolution and measurement models while decreasing the computation complexity and communication cost, we develop a distributed algorithm for dynamic and hybrid UAVs by means of Spherical-Radial Cubature Rules (CR) method with belief propagation (BP) and variational message passing (VMP) methods (CRBP-VMP) on the FG. The proposed CRBP deals with the highly non-linear state-evolution models and non-Gaussian distributions, the VMP method is employed for ranging message, gets the simpler message representation and can reduce communication cost in the joint estimation problem. Simulation results demonstrate that the higher positioning accuracy, the better convergence as well as low computational complexity and communication cost of the proposed CRBP-VMP algorithm, which can be achieved compared with sum-product algorithm over a wireless network (SPAWN) and traditional Cubature Kalman Filters (CKF) method.

Secure two-party comparison plays a crucial role in many privacy-preserving applications, such as privacy-preserving data mining and machine learning. In particular, the available comparison protocols with the appropriate input/output configuration have a significant impact on the performance of these applications. In this paper, we firstly describe a taxonomy of secure two-party comparison protocols which allows us to describe the different configurations used for these protocols in a systematic manner. This taxonomy leads to a total of 216 types of comparison protocols. We then describe conversions among these types. While these conversions are based on known techniques and have explicitly or implicitly been considered previously, we show that a combination of these conversion techniques can be used to convert a perhaps less-known two-party comparison protocol by Nergiz et al. (IEEE SocialCom 2010) into a very efficient protocol in a configuration where the two parties hold shares of the values being compared, and obtain a share of the comparison result. This setting is often used in multi-party computation protocols, and hence in many privacy-preserving applications as well. We furthermore implement the protocol and measure its performance. Our measurement suggests that the protocol outperforms the previously proposed protocols for this input/output configuration, when off-line pre-computation is not permitted.

The packet classification problem to determine the behavior of incoming packets at the network devices. The processing latency of packet classification by linear search is proportional to the number of classification rules. To limit the latency caused by classification to a certain level, we should develop a classification algorithm that classifies packets in a time independent of the number of classification rules. Arbitrary (including noncontiguous) bitmask rules are efficiently expressive for controlling higher layer communication, achiving access control lists, Quality of Service and so on. In this paper, we propose a classification algorithm based on run-based trie [1] according to arbitrary bitmask rules. The space complexity of proposed algorithm is in linear in the size of a rule list. The time complexity except for construction of that can be regarded as constant which is independent the number of rules. Experimental results using a packet classification algorithm benchmark [2] show that our method classifies packets in constant time independent of the number of rules.

Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness.

Along with the proliferation of IoT (Internet of Things) devices, cyberattacks towards them are on the rise. In this paper, aiming at efficient precaution and mitigation of emerging IoT cyberthreats, we present a multimodal study on applying machine learning methods to characterize malicious programs which target multiple IoT platforms. Experiments show that opcode sequences obtained from static analysis and API sequences obtained by dynamic analysis provide sufficient discriminant information such that IoT malware can be classified with near optimal accuracy. Automated and accelerated identification and mitigation of new IoT cyberthreats can be enabled based on the findings reported in this study.

The security of elliptic curve cryptography is closely related to the computational complexity of the elliptic curve discrete logarithm problem (ECDLP). Today, the best practical attacks against ECDLP are exponential-time generic discrete logarithm algorithms such as Pollard's rho method. A recent line of inquiry in index calculus for ECDLP started by Semaev, Gaudry, and Diem has shown that, under certain heuristic assumptions, such algorithms could lead to subexponential attacks to ECDLP. In this study, we investigate the computational complexity of ECDLP for elliptic curves in various forms — including Hessian, Montgomery, (twisted) Edwards, and Weierstrass representations — using index calculus. Using index calculus, we aim to determine whether there is any significant difference in the computational complexity of ECDLP for elliptic curves in various forms. We provide empirical evidence and insight showing an affirmative answer in this paper.

Image quality depends on the randomness of the k-space signal under-sampling in compressed sensing MRI (CS-MRI), especially for two-dimensional image acquisition. We investigate the feasibility of non-random signal under-sampling CS-MRI to stabilize the quality of reconstructed images and avoid arbitrariness in sampling point selection. Regular signal under-sampling for the phase-encoding direction is adopted, in which sampling points are chosen at equal intervals for the phase-encoding direction while varying the sampling density. Curvelet transform was adopted to remove the aliasing artifacts due to regular signal under-sampling. To increase the incoherence between the measurement matrix and the sparsifying transform function, the scale of the curvelet transform was varied in each iterative image reconstruction step. We evaluated the obtained images by the peak-signal-to-noise ratio and root mean squared error in localized 3×3 pixel regions. Simulation studies and experiments showed that the signal-to-noise ratio and the structural similarity index of reconstructed images were comparable to standard random under-sampling CS. This study demonstrated the feasibility of non-random under-sampling based CS by using the multi-scale curvelet transform as a sparsifying transform function. The technique may help to stabilize the obtained image quality in CS-MRI.