Two-Thickness-Method (TTM) based on an open-ended coaxial probe was investigated with an emphasis on uncertainty analysis to perfect this technique. Uncertainty equations in differential forms are established for the simultaneous measurement of complex electromagnetic (EM) parameters in the systematical consideration of various error factors in measurement. Worst-case differential uncertainty equations were defined while the implicit partial derivation techniques were used to find the coefficients in formulation. The relations between the uncertainties and test sample's thicknesses were depicted via 3D figures, while the influence of the coaxial line's dimension on the measurement accuracy is also included based on the same analysis method. The comparisons between the measured errors and theoretical uncertainty prediction are given for several samples, which validate the effectiveness of our analysis.

Improving the overall performance of reliable speech communication in ultrashort wave radios over very noisy channels is of great importance and practical use. An iterative joint source-channel (de-)coding and (de-)modulation (JSCCM) algorithm is proposed for ITU-T Rec.G.729EV by both exploiting the residual redundancy and passing soft information throughout the receiver while introducing a systematic global iteration process. Being fully compatible with existing transmitter structure, the proposed algorithm does not introduce additional bandwidth expansion and transmission delay. Simulations show substantial error correcting performance and synthesized speech quality improvement over conventional separate designed systems in delay and bandwidth constraint channels by using the JSCCM algorithm.

Handoff plays an important role in vehicular networks due to high movement of vehicles. To provide seamless connectivity under Access Points (AP), this paper proposes an adaptive handoff triggering method to minimize communication time for a vehicle with an AP switch (i.e., whether and when to trigger a handoff process). In the proposed method, combined with an improved data transmission rate based trigger, handoff triggering decision is executed based on three different communication methods (called C-Dire, C-Relay and C-ALLRelay) to minimize the transmission delay when a vehicle moves from an AP to another. Transmission delay is derived through considering vehicle mobility and transmission rate diversity. The simulation results show that the proposed method is proven to be adaptive to vehicular networks.

Cloud computing enables computational resource-limited devices to economically outsource much computations to the cloud. Modular exponentiation is one of the most expensive operations in public key cryptographic protocols, and such operation may be a heavy burden for the resource-constraint devices. Previous works for secure outsourcing modular exponentiation which use one or two untrusted cloud server model or have a relatively large computational overhead, or do not support the 100% possibility for the checkability. In this letter, we propose a new efficient and verifiable algorithm for securely outsourcing modular exponentiation in the two untrusted cloud server model. The algorithm improves efficiency by generating random pairs based on EBPV generators, and the algorithm has 100% probability for the checkability while preserving the data privacy.

Frequency-variation method (FVM), reported in [1], was further studied for simultaneously measuring the both complex permittivity and complex permeability by intentionally changing the test frequency to obtain different reflections. An enhanced coaxial-probe-based in-situ measurement system has been established. The spectral domain full-wave model is derived to take place of the quasi-static one. A novel coaxial probe is designed so that the one-port calibration could be performed with Agilent-supplied precise cal-kit instead of the liquid standard. Criterions for a right order of the interpolation polynomial used to approximate the frequency-dependent EM parameters; measures to reduce the residual mismatch errors and random error in reflection measurements and to suppress the ambiguities in solving the transcendent equation system were experimentally studied to resolve the problems and improve the accuracy in dispersive absorbing materials' test. Several typical dispersive absorbing coatings have been tested via FVM. The good comparison between the measured results and reference ones validate the feasibility of the proposed improved technique.

In this paper, we will propose a robust watermarking system for digital audio sound to protect the copyright of publication and claim of ownership. The proposed watermarking scheme uses the frequency extent between 1 Hz and 20 Hz, which cannot be heard by the unaided human ear, to embed the watermark. Thus, the original audio quality will not be influenced by the watermark. Currently, the techniques of Perceptual Audio Coder contain MPEG-1, -2, -2.5, MPEG-2 AAC, MPEG-4 AAC and Window Media Audio. From experimental results, the proposed watermarking system can resist attacks of previous audio coders and low bit-rate compression. The watermark is extracted with 100% correction after previous encoder attacks. Furthermore, to authenticate the audio signal, the system can quickly extract the watermark without the knowledge of original audio signals.

Glaucoma is a common high-incidence eye disease. The detection of the optic cup and optic disc in fundus images is one of the important steps in the clinical diagnosis of glaucoma. However, the fundus images are generally intensity inhomogeneity, and complex organizational structure, and are disturbed by blood vessels and lesions. In order to extract the optic disc and optic cup regions more accurately, we propose a segmentation method of the optic disc and optic cup in fundus image based on distance regularized two-layer level with sparse shape prior constraint. The experimental results show that our method can segment the optic disc and optic cup region more accurately and obtain satisfactory results.

For wideband MIMO-OFDM systems, scheduling and link adaptation are key techniques to improve the throughput performance. However, in systems without reciprocity between the uplink and the downlink channels, these techniques require a high feedback overhead of the channel quality indication (CQI) information. In this paper, we propose a novel CQI feedback reduction method, which is based on the conventional compression techniques exploiting the discrete cosine transformation (DCT). The basic idea is to adaptively permute the CQI sequences of different MIMO streams according to one of the possible patterns before the DCT compression so that the amount of feedback bits is minimized. The possible patterns used are carefully designed according to our analysis of the two types of correlations (the inter-stream correlation and the inter-subband correlation) that exist in MIMO-OFDM transmission, as well as their impact on the compression efficiency. Simulation results verify that the proposed method can effectively reduce the CQI feedback overhead under varying channel conditions.

Accurate segmentation of fundus vessel structure can effectively assist doctors in diagnosing eye diseases. In this paper, we propose a fundus blood vessel segmentation network combined with cross-modal features and verify our method on the public data set OCTA-500. Experimental results show that our method has high accuracy and robustness.

In face super-resolution reconstruction, the interference caused by the texture and color of the hair region on the details and contours of the face region can negatively affect the reconstruction results. This paper proposes a semantic-based, dual-branch face super-resolution algorithm to address the issue of varying reconstruction complexities and mutual interference among different pixel semantics in face images. The algorithm clusters pixel semantic data to create a hierarchical representation, distinguishing between facial pixel regions and hair pixel regions. Subsequently, independent image enhancement is applied to these distinct pixel regions to mitigate their interference, resulting in a vivid, super-resolution face image.

To super-resolve low-resolution (LR) face image suffering from strong noise and fuzzy interference, we present a novel approach for noisy face super-resolution (SR) that is based on three-level information representation constraints. To begin with, we develop a feature distillation network that focuses on extracting pertinent face information, which incorporates both statistical anti-interference models and latent contrast algorithms. Subsequently, we incorporate a face identity embedding model and a discrete wavelet transform model, which serve as additional supervision mechanisms for the reconstruction process. The face identity embedding model ensures the reconstruction of identity information in hypersphere identity metric space, while the discrete wavelet transform model operates in the wavelet domain to supervise the restoration of spatial structures. The experimental results clearly demonstrate the efficacy of our proposed method, which is evident through the lower Learned Perceptual Image Patch Similarity (LPIPS) score and Fréchet Inception Distances (FID), and overall practicability of the reconstructed images.

Remote data possession checking for cloud storage is very important, since data owners can check the integrity of outsourced data without downloading a copy to their local computers. In a previous work, Chen proposed a remote data possession checking protocol using algebraic signature and showed that it can resist against various known attacks. In this paper, we find serious security flaws in Chen's protocol, and shows that it is vulnerable to replay attack by a malicious cloud server. Finally, we propose an improved version of the protocol to guarantee secure data storage for data owners.

An autoencoder is trained to generate the background from the surveillance image by setting the training label as the shuffled input, instead of the input itself in a traditional autoencoder. Then the multi-scale features are extracted by a sparse autoencoder from the surveillance image and the corresponding background to detect foreground.

A robust pedestrian detection approach in thermal infrared imageries for an all-day surveillance is proposed. Firstly, the candidate regions which are likely to contain pedestrians are extracted based on a saliency detection method. Then a deep convolutional network with a multi-task loss is constructed to recognize the pedestrians. The experimental results show the superiority of the proposed approach in pedestrian detection.

In a previous work [1], Wang et al. proposed a privacy-preserving outsourcing scheme for biometric identification in cloud computing, namely CloudBI. The author claimed that it can resist against various known attacks. However, there exist serious security flaws in their scheme, and it can be completely broken through a small number of constructed identification requests. In this letter, we modify the encryption scheme and propose an improved version of the privacy-preserving biometric identification design which can resist such attack and can provide a much higher level of security.

Recently, it has been shown in the literature that in a relaying network utilizing multiple relay precoding techniques, the signal-to-noise ratio (SNR) at each destination node will scale linearly with the number of relays K, which is referred to as the distributed array gain (DAG) K. In this paper, we focus on the performance of multiple relay precoding based on limited channel state information (CSI) feedback, which is different from the prior studies that assume perfect CSI at each of the relay nodes. Our analysis shows that the conventional limited feedback scheme fails to obtain the DAG K, which is a consequence of the phase ambiguity introduced by the channel quantization function. Based on the theoretical analysis, we propose a novel feedback and precoding procedure, and prove that the proposed procedure can obtain the DAG K with only one additional feedback bit for quantizing each relay-destination channel compared with the conventional scheme. Simulation results verify that with the proposed procedure, the SNR performance is effectively improved when the number of relays K is small, and scales linearly with K in relatively large K regime.