Even though meet-in-the-middle preimage attack framework has been successfully applied to attack most of narrow-pipe hash functions, it seems difficult to apply this framework to attack double-branch hash functions. Only few results have been published on this research. This paper proposes a refined strategy of applying meet-in-the-middle attack framework to double-branch hash functions. The main novelty is a new local-collision approach named one-message-word local collision. We have applied our strategy to two double-branch hash functions RIPEMD and RIPEMD-128, and obtain the following results.·On RIPEMD. We find a pseudo-preimage attack on 47-step compression function, where the full version has 48 steps, with a complexity of 2119. It can be converted to a second preimage attack on 47-step hash function with a complexity of 2124.5. Moreover, we also improve previous preimage attacks on (intermediate) 35-step RIPEMD, and reduce the complexity from 2113 to 296. ·On RIPEMD-128. We find a pseudo-preimage on (intermediate) 36-step compression function, where the full version has 64 steps, with a complexity of 2123. It canl be converted to a preimage attack on (intermediate) 36-step hash function with a complexity of 2126.5. Both RIPEMD and RIPEMD-128 produce 128-bit digests. Therefore our attacks are faster than the brute-force attack, which means that our attacks break the theoretical security bound of the above step-reduced variants of those two hash functions in the sense of (second) preimage resistance. The maximum number of the attacked steps on both those two hash functions is 35 among previous works based to our best knowledge. Therefore we have successfully increased the number of the attacked steps. We stress that our attacks does not break the security of full-version RIPEMD and RIPEMD-128. But the security mergin of RIPEMD becomes very narrow. On the other hand, RIPEMD-128 still has enough security margin.

Network function virtualization improves the flexibility of infrastructure resource allocation but the application of commodity facilities arouses new challenges for systematic reliability. To meet the carrier-class reliability demanded from the 5G mobile core, several studies have tackled backup schemes for the virtual network function deployment. However, the existing backup schemes usually sacrifice the efficiency of resource allocation and prevent the sharing of infrastructure resources. To solve the dilemma of balancing the high level demands of reliability and resource allocation in mobile networks, this paper proposes an approach for the problem of pooling deployment of virtualized network functions in virtual EPC network. First, taking pooling of VNFs into account, we design a virtual network topology for virtual EPC. Second, a node-splitting algorithm is proposed to make best use of substrate network resources. Finally, we realize the dynamic adjustment of pooling across different domains. Compared to the conventional virtual topology design and mapping method (JTDM), this approach can achieve fine-grained management and overall scheduling of node resources; guarantee systematic reliability and optimize global view of network. It is proven by a network topology instance provided by SNDlib that the approach can reduce total resource cost of the virtual network and increase the ratio of request acceptance while satisfy the high-demand reliability of the system.

A narrowband active noise control (NANC) system is very effective for controlling low-frequency periodic noise. A frequency mismatch (FM) with the reference signal will degrade the performance or even cause the system to diverge. To deal with an FM and obtain an accurate reference signal, NANC systems often employ a frequency estimator. Combining an autoregressive predictive filter with a variable step size (VSS) all-pass-based lattice adaptive notch filter (ANF), a new frequency estimation method is proposed that does not require prior information of the primary signal, and the convergence characteristics are much improved. Simulation results show that the designed frequency estimator has a higher accuracy than the conventional algorithm. Finally, hardware experiments are carried out to verify the noise reduction effect.

In many real application scenarios of image segmentation problems involving limited and low-quality data, employing prior information can significantly improve the segmentation result. For example, the shape of the object is a kind of common prior information. In this paper, we introduced a new kind of prior information, which is named by prior distribution. On the basis of nonparametric statistical active contour model, we proposed a novel distribution prior model. Unlike traditional shape prior model, our model is not sensitive to the shapes of object boundary. Using the intensity distribution of objects and backgrounds as prior information can simplify the process of establishing and solving the model. The idea of constructing our energy function is as follows. During the contour curve convergence, while maximizing distribution difference between the inside and outside of the active contour, the distribution difference between the inside/outside of contour and the prior object/background is minimized. We present experimental results on a variety of synthetic and natural images. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.

This letter reveals that whole link reciprocity does not exist in general amplify-and-forward (AF) time division duplex (TDD) relay systems due to the gain matrix. To resolve this problem, a novel gain matrix design method is proposed. Any existing gain matrix design criterion can be adopted in the downlink (uplink) to ensure optimal performance, and the proposed scheme is used in the uplink (downlink), with small adjustment, to keep whole link reciprocity. Simulation results show that, the proposed method can maintain whole link reciprocity without performance loss.

A novel virtual sensors-based positioning method has been presented in this paper, which can make use of both direct paths and indirect paths. By integrating the virtual sensor idea and Bayesian state and observation framework, this method models the indirect paths corresponding to persistent virtual sensors as virtual direct paths and further reformulates the wireless positioning problem as the maximum likelihood estimation of both the mobile terminal's positions and the persistent virtual sensors' positions. Then the method adopts the EM (Expectation Maximization) and the particle filtering schemes to estimate the virtual sensors' positions and finally exploits not only the direct paths' measurements but also the indirect paths' measurements to realize the mobile terminal's positions estimation, thus achieving better positioning performance. Simulation results demonstrate the effectiveness of the proposed method.

The availability is an important issue of software-defined networking (SDN). In this paper, the experiments based on a SDN testbed showed that the resource utilization of the data plane and control plane changed drastically when DDoS attacks happened. This is mainly because the DDoS attacks send a large number of fake flows to network in a short time. Based on the observation and analysis, a DDoS defense mechanism based on legitimate source and destination IP address database is proposed in this paper. Firstly, each flow is abstracted as a source-destination IP address pair and a legitimate source-destination IP address pair database (LSDIAD) is established by historical normal traffic trace. Then the proportion of new source-destination IP address pair in the traffic per unit time is cumulated by non-parametric cumulative sum (CUSUM) algorithm to detect the DDoS attacks quickly and accurately. Based on the alarm from the non-parametric CUSUM, the attack flows will be filtered and redirected to a middle box network for deep analysis via south-bound API of SDN. An on-line updating policy is adopted to keep the LSDIAD timely and accurate. This mechanism is mainly implemented in the controller and the simulation results show that this mechanism can achieve a good performance in protecting SDN from DDoS attacks.

The current paper presents an integral cryptanalysis in the single-key setting against light-weight block-cipher LBlock reduced to 22 rounds. Our attack uses the same 15-round integral distinguisher as the previous attacks, but many techniques are taken into consideration in order to achieve comprehensive understanding of the attack; choosing the best balanced-byte position, meet-in-the-middle technique to identify right key candidates, partial-sum technique, relations among subkeys, and combination of the exhaustive search with the integral analysis. Our results indicate that the integral cryptanalysis is particularly useful for LBlock like structures. At the end of this paper, which factor makes the LBlock structure weak against the integral cryptanalysis is discussed. Because designing light-weight cryptographic primitives is an actively discussed topic, we believe that this paper returns some useful feedback to future designs.

The Nyquist folding receiver (NYFR) is a novel analog-to-information architecture, which can achieve wideband receiving with a small amount of system resource. The NYFR uses a radio frequency (RF) non-uniform sampling to realize wideband receiving, and the practical RF non-uniform sample pulse train usually contains an aperture. Therefore, it is necessary to investigate the aperture impact on the NYFR output. In this letter, based on the NYFR output signal to noise ratio (SNR), the aperture impact on the NYFR is analyzed. Focusing on the aperture impact, the corresponding NYFR output signal power and noise power are given firstly. Then, the relation between the aperture and the output SNR is analyzed. In addition, the output SNR distribution containing the aperture is investigated. Finally, combing with a parameter estimation method, several simulations are conducted to prove the theoretical aperture impact.

To cope with complicated interference scenarios in realistic acoustic environment, supervised deep neural networks (DNNs) are investigated to estimate different user-defined targets. Such techniques can be broadly categorized into magnitude estimation and time-frequency mask estimation techniques. Further, the mask such as the Wiener gain can be estimated directly or derived by the estimated interference power spectral density (PSD) or the estimated signal-to-interference ratio (SIR). In this paper, we propose to incorporate the multi-task learning in DNN-based single-channel speech enhancement by using the speech presence probability (SPP) as a secondary target to assist the target estimation in the main task. The domain-specific information is shared between two tasks to learn a more generalizable representation. Since the performance of multi-task network is sensitive to the weight parameters of loss function, the homoscedastic uncertainty is introduced to adaptively learn the weights, which is proven to outperform the fixed weighting method. Simulation results show the proposed multi-task scheme improves the speech enhancement performance overall compared to the conventional single-task methods. And the joint direct mask and SPP estimation yields the best performance among all the considered techniques.

In this paper, a real-time medium access control (MAC) protocol based on a coding-black-burst mechanism with low latency and high energy efficiency is proposed for wireless sensor networks. The Black-Burst (BB) mechanism is used to provide real-time access. However, when the traffic load is heavy, BB will cause a lot of energy loss and latency due to its large length. A binary coding mechanism is applied to BB in our coding-black-burst-based protocol to reduce the energy consumption and latency by at least (L-2(log2 L+1)) for L-length BB. The new mechanism also gives priority to the real-time traffic with longer waiting delays to access the channel. The theoretical analysis and simulation results indicate that our protocol provides low end-to-end delay and high energy efficiency for real-time communication.

For the RF power amplifier, its exposed input and output are susceptible to damage from Electrostatic (ESD) damage. The bi-direction protection is required at the input in push-pull operating mode. In this paper, considering the process compatibility to the power amplifier, cascaded Grounded-gate NMOS (ggNMOS) and Polysilicon diodes (PDIO) are stacked together to form an ESD clamp with forward and reverse protection. Through Transmission line pulse (TLP) and CV measurements, the clamp is demonstrated as latch-up immune and low parasitic capacitance bi-direction ESD protection, with 18.67/17.34V holding voltage (Vhold), 4.6/3.2kV ESD protection voltage (VESD), 0.401/0.415pF parasitic capacitance (CESD) on forward and reverse direction, respectively.

Free probability theory, which has become a main branch of random matrix theory, is a valuable tool for describing the asymptotic behavior of multiple systems, especially for large matrices. In this paper, using asymptotic free probability theory, a new cooperative scheme for spectrum sensing is proposed, which shows how the asymptotic free behavior of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for cognitive radio. Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance than the energy detection techniques and the Maximum-minimum eigenvalue (MME) scheme even for the case of a small sample of observations.

Radio Frequency based Device-Free Localization (RFDFL) is an emerging localization technique without requirements of attaching any electronic device to a target. The target can be localized by means of measuring the shadowing of received signal strength caused by the target. However, the accuracy of RFDFL deteriorates seriously in environment with WiFi interference. State-of-the-art methods do not efficiently solve this problem. In this paper, we propose a dual-band method to improve the accuracy of RFDFL in environment without/with severe WiFi interference. We introduce an algorithm of fusing dual-band images in order to obtain an enhanced image inferring more precise location and propose a timestamp-based synchronization method to associate the dual-band images to ensure their one-one correspondence. With real-world experiments, we show that our method outperforms traditional single-band localization methods and improves the localization accuracy by up to 40.4% in real indoor environment with high WiFi interference.

In 2005, collision resistance of several hash functions was broken by Wang et al. The strategy of determining message differences is the most important part of collision attacks against hash functions. So far, many researchers have tried to analyze Wang et al.'s method and proposed improved collision attacks. Although several researches proposed improved attacks, all improved results so far were based on the same message differences proposed by Wang et al. In this paper, we propose new message differences for collision attacks on MD4 and MD5. Our message differences of MD4 can generate a collision with complexity of less than two MD4 computations, which is faster than the original Wang et al.'s attack, and moreover, than the all previous attacks. This is the first result that improves the complexity of collision attack by using different message differences from Wang et al.'s. Regarding MD5, so far, no other message difference from Wang et al.'s is known. Therefore, study for constructing method of other message differences on MD5 should be interesting. Our message differences of MD5 generates a collision with complexity of 242 MD5 computations, which is slower than the latest best attack. However, since our attack needs only 1 bit difference, it has some advantages in terms of message freedom of collision messages.

For amplify-and-forward (AF) relaying with imperfect channel estimation, we present the average symbol error rate (SER) and the diversity and multiplexing tradeoff (DMT) analysis for both opportunistic relaying (OPR) and all-participate relaying (APR) schemes. SER comparisons show that when the channel estimation quality order is no larger than 1, OPR will perform worse than APR in high SNR region. Moreover, small channel estimation quality orders will also lead to significant DMT loss.

Human action recognition (HAR) exhibits limited accuracy in video surveillance due to the 2D information captured with monocular cameras. To address the problem, a depth estimation-based human skeleton action recognition method (SARDE) is proposed in this study, with the aim of transforming 2D human action data into 3D format to dig hidden action clues in the 2D data. SARDE comprises two tasks, i.e., human skeleton action recognition and monocular depth estimation. The two tasks are integrated in a multi-task manner in end-to-end training to comprehensively utilize the correlation between action recognition and depth estimation by sharing parameters to learn the depth features effectively for human action recognition. In this study, graph-structured networks with inception blocks and skip connections are investigated for depth estimation. The experimental results verify the effectiveness and superiority of the proposed method in skeleton action recognition that the method reaches state-of-the-art on the datasets.

Noninvasive recognition is an important trend in diabetes recognition. Unfortunately, the accuracy obtained from the conventional noninvasive recognition methods is low. This paper proposes a novel Diabetes Noninvasive Recognition method via the plantar pressure image and improved Capsule Network (DNR-CapsNet). The input of the proposed method is a plantar pressure image, and the output is the recognition result: healthy or possibly diabetes. The ResNet18 is used as the backbone of the convolutional layers to convert pixel intensities to local features in the proposed DNR-CapsNet. Then, the PrimaryCaps layer, SecondaryCaps layer, and DiabetesCaps layer are developed to achieve the diabetes recognition. The semantic fusion and locality-constrained dynamic routing are also developed to further improve the recognition accuracy in our method. The experimental results indicate that the proposed method has a better performance on diabetes noninvasive recognition than the state-of-the-art methods.

Since the Merkle-Damgård hash function (denoted by MDFH) that uses a fixed input length random oracle as a compression function is not indifferentiable from a random oracle (denoted by RO) due to the extension attack, there is no guarantee for the security of cryptosystems, which are secure in the RO model, when RO is instantiated with MDHF. This fact motivates us to establish a criteria methodology for confirming cryptosystems security when RO is instantiated with MDHF. In this paper, we confirm cryptosystems security by using the following approach: 1.Find a weakened random oracle (denoted by WRO) which leaks values needed to realize the extension attack. 2.Prove that MDHF is indifferentiable from WRO. 3.Prove cryptosystems security in the WRO model. The indifferentiability framework of Maurer, Renner and Holenstein guarantees that we can securely use the cryptosystem when WRO is instantiated with MDHF. Thus we concentrate on such finding WRO. We propose Traceable Random Oracle (denoted by TRO) which leaks values enough to permit the extension attack. By using TRO, we can easily confirm the security of OAEP encryption scheme and variants of OAEP encryption scheme. However, there are several practical cryptosystems whose security cannot be confirmed by TRO (e.g. RSA-KEM). This is because TRO leaks values that are irrelevant to the extension attack. Therefore, we propose another WRO, Extension Attack Simulatable Random Oracle (denoted by ERO), which leaks just the value needed for the extension attack. Fortunately, ERO is necessary and sufficient to confirm the security of cryptosystems under MDHF. This means that the security of any cryptosystem under MDHF is equivalent to that under the ERO model. We prove that RSA-KEM is secure in the ERO model.

In this study, we address the problem of salient region detection. Recently, saliency detection with contrast based approaches has shown to give promising results. However, different individual features exhibit different performance. In this paper, we show that the combination of color uniqueness and color spatial distribution is an effective way to detect saliency. A Color Adaptive Thresholding Watershed Fusion Segmentation (CAT-WFS) method is first given to retain boundary information and delete unnecessary details. Based on the segmentation, color uniqueness and color spatial distribution are defined separately. The color uniqueness denotes the color rareness of salient object, while the color spatial distribution represents the color attribute of the background. Aiming at highlighting the salient object and downplaying the background, we combine the two characters to generate the final saliency map. Experimental results demonstrate that the proposed algorithm outperforms existing salient object detection methods.