Zhixiao Wang

Optimal Scheme of Retinal Image Enhancement using Curvelet Transform and Quantum Genetic Algorithm

A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.

Linear (k, n) Secret Sharing Scheme with Cheating Detection

Linear (k,n) secret sharing scheme is a class of (k,n) secret sharing, where all the n shares of a secret satisfy a linear relationship. It plays an important role in other cryptographic systems, such as multi-party computation and function sharing schemes. On the other hand, cheating problem in (k,n) secret sharing is an important issue, such that cheaters (dishonest players) submit forged shares during secret reconstruction to fool honest players. During decades of research on cheating prevention, vast (k,n) secret sharing schemes against cheating have been proposed. However, most of these schemes are not linear schemes since it contains redundant information in their shares to achieve cheating detection. Since linear (k,n) secret sharing is an important primitive in threshold cryptography, linear (k,n) secret sharing scheme with the capability of cheating detection is also worthwhile to be discussed. In this paper, we propose a linear (k,n) secret sharing scheme against cheating based on Shamirs original scheme, which possesses the following merits: (1) Our scheme is just combination of two Shamirs schemes. Therefore, our scheme can be used in other threshold cryptographic systems which are based on Shamirs scheme. (2) The size of share in proposed scheme almost reaches its theoretic lower bound in (k,n) secret sharing with cheating detection. (3) In the phase of cheating detection, only one honest player can detect the cheating from other k-1 cheaters, which achieves a stronger detection effective than the previous linear secret sharing schemes against cheating.

Feature fusion of palmprint and face via tensor analysis and curvelet transform

In order to improve the recognition accuracy of the unimodal biometric system and to address the problem of the small samples recognition, a multimodal biometric recognition approach based on feature fusion level and curve tensor is proposed in this paper. The curve tensor approach is an extension of the tensor analysis method based on curvelet coefficients space. We use two kinds of biometrics: palmprint recognition and face recognition. All image features are extracted by using the curve tensor algorithm and then the normalized features are combined at the feature fusion level by using several fusion strategies. The k-nearest neighbour (KNN) classifier is used to determine the final biometric classification. The experimental results demonstrate that the proposed approach outperforms the unimodal solution and the proposed nearly Gaussian fusion (NGF) strategy has a better performance than other fusion rules.

Efficient geographical 3D routing for Wireless Sensor Networks in smart spaces

Wireless Sensor Networks (WSNs) is one of the basic components in smart spaces. These kinds of networks are facing many challenges mainly due to limited resources. Indeed, routing protocols are one of the greatest challenges in this domain. Several 3D (Three Dimensions) routing protocols in real environment have been proposed for wireless sensor networks. Some are based on partial or full flooding schemes which consume more energy and incur higher communication overhead, or counted on construction or partition of cubes, convex hulls or Delaunay triangulations where their constructor are relatively complicated and additional cost could be required. In this paper, we present a simple efficient geographical 3D routing algorithm, efficient subminimal Ellipsoid geographical Greedy-Face 3D Routing (EGF3D). The algorithm is based on Greedy and Face routing strategies which are restricted in one subminimal ellipsoid fixed by three points: the forwarding endpoint node, the destination endpoint node and the subminimal angle neighbor in the favorable forwarding direction. We evaluate our approach and compare with the current routing algorithms in this domain. The simulation results show the competitive improvement delivery ratio, end-to-end delay and the communication overhead compared with greedy-random-greedy (GRG) and energy efficiency localized 3D greedy routing algorithm (ERGrd).

Hopfield-Association: Establishing a Shared Key in the Wireless Sensor Networks

Wireless sensor networks (WSNs) are often deployed in hostile environments, thus being subjected to great security risks. Establishing secure communication between two sensors nodes is of typical defense against eavesdropping, therefore in-volveing the key agreement. In this paper, we present a key agreement scheme without the trusted third parties (TTP) by exploiting the special characteristics of Hop field neural network: the two nodes converge in a steady state from their respective initial states after iterating finite times, while maintaining the confidentiality of the key by quantifying the key to i-bit strings. Compared to existing solution, the proposed method requires less memory and has lower communication overhead to key agreement.

A Hierarchical Authentication Scheme for the Different Radio Ranges Sensor Networks

Current secure authentication scheme is typically assumed to be the same communication ranges of all nodes in WSN.Howerver, due to the influence of environment and dynamic topology, the communication radius of all nodes are no strictly consistent, which may cause different neighbor number and redundant neighbors for one central node. As a result, energy consumption of each node is unbalanced and inefficient. In this paper, we propose a secure Muti-hop authentication scheme, neighbor nodes are grouped according to their communication range using torus topology. Subsequently, every node is verified with keys derived from multiple one-way hash sub-chains corresponding to the level, which is picked with low probability between two or more sensors during key pre-distribution phase, the proposed scheme prevents malicious node from man-in-middle attacks and resilience against node compromised. Moreover, the distributed strategy and with fewer neighbors to their central node greatly alleviates communication overhead and extends the network lifetime resulting from equilibrium energy consumption.

Indoor localization based on subarea division with fuzzy C-means

One of the most significant researches in location-based services is the development of effective indoor localization. In this work, we propose a novel model of fingerprint localization, which divides location area into different subareas by fuzzy C-means and calculates location via relative distance fuzzy localization. In offline training stage, fuzzy C-means algorithm is used in localization model to divide localization area into different subareas and then to select the useful access points in subareas to reduce the dimensions of fingerprint. In online location stage, we use the nearest neighbor algorithm to select the subareas and to calculate the coordinate of the target point according to relative distance fuzzy localization algorithm, which converts traditional fingerprint of reference points into distance fingerprint and calculates the coordinate of the target point by fuzzy C-means algorithm. The noise and non-linear attenuation between the wireless signal and distance are taken into full consideration in relative distance fuzzy localization algorithm, which eliminates the random environmental noise. Experiments show that our proposed model is able to save the calculation time and improve the localization accuracy.

A Localization and Tracking Approach with Sparse Reference Tags

In traditional localization systems, it is required that moving object carries a device to transmit or receive signals, and then localization system is able to locate an object based on signal strength it received. In this paper, we propose a new passive localization and tracking approach based on RFID with sparse reference tags, which can estimate the location of moving objects by detecting and analyzing signal strength distribution of target area. We firstly construct a signal fluctuation ellipse model between RFID reader and tag through the experiments, and then present a localization method based on this model. Then a tracking method based on Hidden Markov Model (HMM) is proposed to predict the trajectory of an object in a passive localization system with sparse reference tag. The experimental results show that our method not only reduces the computation complexity and cost but also ensures the accuracy of localization and tracking.

An Advanced RFID Localization Algorithm Based on Region Division and Error Compensation

In RSSI-based RFID(Radio Frequency IDentification) indoor localization system, the signal path loss model of each sub-region is different from others in the whole localization area due to the influence of the multi-path phenomenon and other environmental factors. Therefore, this paper divides the localization area into many sub-regions and constructs separately the signal path loss model of each sub-region. Then an improved LANDMARC method is proposed. Firstly, the deployment principle of RFID readers and tags is presented for constructing localization sub-region. Secondly, the virtual reference tags are introduced to create a virtual signal strength space with RFID readers and real reference tags in every sub-region. Lastly, k nearest neighbor (KNN) algorithm is used to locate the target object and an error compensating algorithm is proposed for correcting localization result. The results in real application show that the new method enhances the positioning accuracy to 18.2% and reduces the time cost to 30% of the original LANDMARC method without additional tags and readers.

An experimental analysis of packet reception rate in RFID localization network

Indoor localization systems based on received signal strength indication (RSSI) techniques are usually used in office buildings. However, RSSI is susceptible to environmental influences, which may make localization performances unstable. Packet reception rate (PRR) is a good indicator which can reflect the changes in signal intensity with distance changing. This paper establishes a relationship between PRR and RSSI according to the classic radio propagation model in localization field, and theoretically analyzes the normal probability distribution of PRR. Based on communication experiments between Radio Frequency Identification (RFID) readers and tags, this paper discusses how PRR changes with respect to power and distances. Moreover, the distribution of received packets (RP) is described by performing curve fitting on frequency histogram. Its normality and other parameters (e.g. expectation, variation, kurtosis, and skewness) are also analyzed. The results show that this model is valid and proper for the description of relationship between PRR and RSSI. This research provides a novel perspective and theoretical support for the further study and application of RFID indoor localization.