In order to upgrade the refresh rate about High-Resolution (1280×1024) OLED-on-Silicon (OLEDoS) microdisplay, this paper discusses one compression scan strategy by reducing scan time redundancy. This scan strategy firstly compresses the low-bit gray level scan serial as one unit; second, the scan unit is embedded into the high-bit gray level serial and new scan sequence is generated. Furthermore, micro-display platform is designed to verify the scan strategy performance. The experiment shows that this scan strategy can deal with 144Hz refresh rate, which is obviously faster than the traditional scan strategy.

In this paper we investigate a low complexity channel estimation and data transmission scheme for bi-directional relaying networks. We also propose a semi-orthogonal pilot structure for channel estimation to increase the efficiency of data transmission between the Base Station (BS) and Mobile Station (MS) via a fixed Relay Node (RN).

In the localized Discrete Fourier Transform-Spread-Orthogonal Frequency Division Multiplexing (DFT-S-OFDM) uplink cooperative system, multiple carrier frequency offsets (MCFO), arising from the nodes' separate oscillators and Doppler spreads, drastically degrade the performance of the receiver. To solve the problem, this letter proposes an efficient MCFO compensation method which fully exploits the diversity gain of space frequency block coded (SFBC) and the characteristic of inter-carrier interference (ICI). Moreover, the bit error ratio (BER) lower bound of the proposed algorithm is theoretically derived. Simulation results validate the theoretical analysis and demonstrate that the proposed MCFO compensation method can achieve robust BER performance in a wide range of MCFO in the multipath Rayleigh fading channel.

In this paper, the problem on secure communication based on chaos synchronization is investigated. The dual channel information transmitting technology is proposed to increase the security of secure communication system. Based on chaos synchronization, a new digital secure communication scheme is presented for a class of master-slave systems. Finally some numerical simulation examples are given to demonstrate the effectiveness of the given results.

Tor's hidden services provide both sender privacy and recipient privacy to users. A hot topic in security of Tor is how to deanonymize its hidden services. Existing works proved that the recipient privacy could be revealed, namely a hidden server's real IP address could be located. However, the hidden service's circuit is bi-directionally anonymous, and the sender privacy can also be revealed. In this letter, we propose a novel approach that can transparently discover the client of the hidden service. Based on extensive analysis on the hidden service protocol, we find a combination of cells which can be used to generate a special traffic feature with the cell-padding mechanism of Tor. A user can implement some onion routers in Tor networks and monitor traffic passing through them. Once the traffic feature is discovered, the user confirms one of the controlled routers is chosen as the entry router, and the adjacent node is the client. Compared with the existing works, our approach does not disturb the normal communication of the hidden service. Simulations have demonstrated the effectiveness of our method.

The objective of this letter is to present a family of q-ary codes with parameters $[rac{q^m-1}{q-1},rac{q^m-1}{q-1}-2m,d]$, where m is a positive integer, q is a power of an odd prime and 4≤d≤5. The parameters are proved to be optimal or almost optimal with respect to an upper bound on linear codes.

Recent studies in protein structure prediction such as AlphaFold have enabled deep learning to achieve great attention on the Drug-Target Affinity (DTA) task. Most works are dedicated to embed single molecular property and homogeneous information, ignoring the diverse heterogeneous information gains that are contained in the molecules and interactions. Motivated by this, we propose an end-to-end deep learning framework to perform Molecular Heterogeneous features Fusion (MolHF) for DTA prediction on heterogeneity. To address the challenges that biochemical attributes locates in different heterogeneous spaces, we design a Molecular Heterogeneous Information Learning module with multi-strategy learning. Especially, Molecular Heterogeneous Attention Fusion module is present to obtain the gains of molecular heterogeneous features. With these, the diversity of molecular structure information for drugs can be extracted. Extensive experiments on two benchmark datasets show that our method outperforms the baselines in all four metrics. Ablation studies validate the effect of attentive fusion and multi-group of drug heterogeneous features. Visual presentations demonstrate the impact of protein embedding level and the model ability of fitting data. In summary, the diverse gains brought by heterogeneous information contribute to drug-target affinity prediction.

Reducing the energy consumption of wireless communication systems with new technologies and solutions continues to be an important concern in developing future standards. In this paper, we study the routing strategies in multi-hop relaying networks. For a 2-way assignment routing method, an efficient feedback scheme is presented to minimize the power consumption over the whole system. Compared with the full channel information in traditional feedback scheme, only the backward accumulated feedback metrics are required. If the proposed routing calculation is used, there is no performance loss. When the number of the hops and the relays is large, the new scheme achieves a significant feedback overhead reduction. Moreover, we show a proof for the optimality of the presented routing strategy based on mathematical induction.

Tor is the most popular and well-researched low-latency anonymous communication network provides sender privacy to Internet users. It also provides recipient privacy by making TCP services available through “hidden service”, which allowing users not only to access information anonymously but also to publish information anonymously. However, based on our analysis of the hidden service protocol, we found a special combination of cells, which is the basic transmission unit over Tor, transmitted during the circuit creation procedure that could be used to degrade the anonymity. In this paper, we investigate a novel protocol-feature based attack against Tor's hidden service. The main idea resides in fact that an attacker could monitor traffic and manipulate cells at the client side entry router, and an adversary at the hidden server side could cooperate to reveal the communication relationship. Compared with other existing attacks, our attack reveals the client of a hidden service and does not rely on traffic analysis or watermarking techniques. We manipulate Tor cells at the entry router to generate the protocol-feature. Once our controlled entry onion routers detect such a feature, we can confirm the IP address of the client. We implemented this attack against hidden service and conducted extensive theoretical analysis and experiments over Tor network. The experiment results validate that our attack can achieve high rate of detection rate with low false positive rate.

The Monte Carlo simulation is applied to fault tree analyses of the sequential failure logic. In order to make the validity of the technique clear, case studies for estimation of the statistically expected numbers of system failures during (0, t] are conducted for two types of systems using the multiple integration method as well as the Monte Carlo simulation. Results from these two methods are compared. This validates the Monte Carlo simulation in solving the sequential failure logic with respectably small deviation rates for those cases.

As advances in networking technology help to connect industrial control networks with the Internet, the threat from spammers, attackers and criminal enterprises has also grown accordingly. However, traditional Network Intrusion Detection System makes significant use of pattern matching to identify malicious behaviors and have bad performance on detecting zero-day exploits in which a new attack is employed. In this paper, a novel method of anomaly detection in industrial control network is proposed based on RNN-GBRBM feature decoder. The method employ network packets and extract high-quality features from raw features which is selected manually. A modified RNN-RBM is trained using the normal traffic in order to learn feature patterns of the normal network behaviors. Then the test traffic is analyzed against the learned normal feature pattern by using osPCA to measure the extent to which the test traffic resembles the learned feature pattern. Moreover, we design a semi-supervised incremental updating algorithm in order to improve the performance of the model continuously. Experiments show that our method is more efficient in anomaly detection than other traditional approaches for industrial control network.

We propose a model for constructing a multilayered boundary in an information system to defend against intrusive anomalies by correlating a number of parametric anomaly detectors. The model formulation is based on two observations. First, anomaly detectors differ in their detection coverage or blind spots. Second, operating environments of the anomaly detectors reveal different information about system anomalies. The correlation among observation-specific anomaly detectors is first formulated as a Partially Observable Markov Decision Process, and then a policy-gradient reinforcement learning algorithm is developed for an optimal cooperation search, with the practical objectives being broader overall detection coverage and fewer false alerts. A host-based experimental scenario is developed to illustrate the principle of the model and to demonstrate its performance.

Most existing works on resource allocation in cooperative OFDMA systems have focused on homogeneous users with same service and demand. In this paper, we focus on resource allocation in a service differentiated cooperative OFDMA system where each user has a different QoS requirement. We investigate joint power allocation, relay selection and subcarrier assignment to maximize overall system rates with consideration of QoS guarantees and service support. By introducing QoS price, this combinatorial problem with exponential complexity is converted into a convex one, and a two-level dual-primal decomposition based QoS-aware resource allocation (QARA) algorithm is proposed to tackle the problem. Simulation results reveal that our proposed algorithm significantly outperforms previous works in terms of both services support and QoS satisfaction.

Relay, which promises to enhance the performance of future communication networks, is one of the most promising techniques for IMT-Advanced systems. In this paper, multiple-input multiple-output (MIMO) relay channels based on outdoor measurements are investigated. We focus on the link between the base station (BS) and the relay station (RS) as well as the link between the RS and the mobile station (MS). First of all, the channels were measured employing a real-time channel sounder in IMT-Advanced frequency band (2.35 GHz with 50 MHz bandwidth). Then, the parameters of multipath components (MPCs) are extracted utilizing space-alternating generalized expectation algorithm. MPC parameters of the two links are statistically analyzed and compared. The polarization and spatial statistics are gotten. The trends of power azimuth spectrum (PAS) and cross-polarization discrimination (XPD) with the separation between the RS and the MS are investigated. Based on the PAS, the propagation mechanisms of line-of-sight and non-line-of-sight scenarios are analyzed. Furthermore, an approximate closed-form expression of channel correlation is derived. The impacts of PAS and XPD on the channel correlation are studied. Finally, some guidelines for the antenna configurations of the BS, the RS and the MS are presented. The results reveal the different characteristics of relay channels and provide the basis for the practical deployment of relay systems.

Because accurate position information plays an important role in wireless sensor networks (WSNs), target localization has attracted considerable attention in recent years. In this paper, based on target spatial domain discretion, the target localization problem is formulated as a sparsity-seeking problem that can be solved by the compressed sensing (CS) technique. To satisfy the robust recovery condition called restricted isometry property (RIP) for CS theory requirement, an orthogonalization preprocessing method named LU (lower triangular matrix, unitary matrix) decomposition is utilized to ensure the observation matrix obeys the RIP. In addition, from the viewpoint of the positioning systems, taking advantage of the joint posterior distribution of model parameters that approximate the sparse prior knowledge of target, the sparse Bayesian learning (SBL) approach is utilized to improve the positioning performance. Simulation results illustrate that the proposed algorithm has higher positioning accuracy in multi-target scenarios than existing algorithms.

The use of cross-polarized antennas for multiple-input multiple-output (MIMO) systems is receiving attention as they are able to double the number of antenna for half antenna spacing needs. This paper presents the channel correlation property of the 3rd Generation Partner Project (3GPP)/3GPP2 spatial channel model (SCM) with the polarization propagation. The statistical average of the per path polarization correlation given random cross-polarization discrimination (XPD) with co-located ideal tilted dipole antennas is derived. The impact on the random behavior of the polarization correlation due to the slant offset angle, the per path angular spread (AS), and the random XPD is analyzed. The simulation results show that the variation of polarization correlation caused by the random XPD is maximized with a 58 slant offset angle under the assumptions of all predefined scenarios in SCM. The per path AS has minor impact on the statistics of the polarization correlations. The randomness of polarization correlation is negligible for an XPD with small standard deviation.

The construction of photo-realistic 3D scenes from video data is an active and competitive area of research in the fields of computer vision, image processing and computer graphics. In this paper we address our recent work in this area. Unlike most methods of 3D scene construction, we consider the generation of virtual environments from video sequence with a video-cam's forward motion. Each frame is decomposed into sub-images, which are registered correspondingly using the Levenberg-Marquardt iterative algorithm to estimate motion parameters. The registered sub-images are correspondingly pasted together to form a pseudo-3D space. By controlling the position and direction, the virtual camera can walk through this virtual space to generate novel 2D views to acquire an immersive impression. Even if the virtual camera goes deep into this virtual environment, it can still obtain a novel view while maintaining relatively high resolution.

Performance analysis of a dual-hop semi-blind amplify-and-forward (AF) relay system in mixed Nakagami-m and Rician fading channels, is proposed. We derived the closed-form expression for the cumulative distribution function (CDF) of the equivalent end-to-end signal to noise ratio (SNR), based on which the exact outage probability and symbol error probability (SEP) are investigated. The theoretical analysis is validated by Monte Carlo simulation results.

We propose a new method for generating visual stereo images from the common two dimensional images without 3D reconstruction. The major novel contributions of this report are in two aspects. First, we address the detection of dominant motion presented in the given scenes, for doing so we borrow phase shift theorem and calculate the inverse Fourier transform of cross-power spectrum to find the maximum peak value whose position can be used to decide motion parameters. Secondly, unlike most of researchers study the stereo vision to recover 3D information for modeling and rendering, we address the visual stereo image generation without 3D reconstruction by applying the computed motion parameters to make decision of selecting two given images to form a stereo pair for left eye and right eye respectively. The proposed approaches can be employed for applications such as navigation in a virtual environment.

Cognitive Radios (CR) can recognize the communication environment and switch its communication scheme to more efficiently and flexibly utilize the radio spectrum. The performance of ultra wideband (UWB) degrades if interference is not suppressed properly. We propose here a series of adaptive chirp waveforms in UWB systems. By designing waveform shaping of both linear chirp and non-linear cases, we avoid the estimated spectrum of the on-going applications without the necessity of notch filters, and thus reduce the system complexity. We evaluate system performance of the proposed scheme by simulations and verify that the proposed scheme is a candidate for cognitive UWB systems.