A robust saliency detection approach for images with a complex background is proposed. The absorbing Markov chain integrating low-level, mid-level and high-level cues dynamically evolves by using the similarity between pixels to detect saliency objects. The experimental results show that the proposed algorithm has advantages in saliency detection, especially for images with a chaotic background or low contrast.

In order to improve the noise robustness of automatic speaker recognition, many techniques on speech/feature enhancement have been explored by using deep neural networks (DNN). In this work, a DNN multi-level enhancement (DNN-ME), which consists of the stages of signal enhancement, cepstrum enhancement and i-vector enhancement, is proposed for text-independent speaker recognition. Given the fact that these enhancement methods are applied in different stages of the speaker recognition pipeline, it is worth exploring the complementary role of these methods, which benefits the understanding of the pros and cons of the enhancements of different stages. In order to use the capabilities of DNN-ME as much as possible, two kinds of methods called Cascaded DNN-ME and joint input of DNNs are studied. Weighted Gaussian mixture models (WGMMs) proposed in our previous work is also applied to further improve the model's performance. Experiments conducted on the Speakers in the Wild (SITW) database have shown that DNN-ME demonstrated significant superiority over the systems with only a single enhancement for noise robust speaker recognition. Compared with the i-vector baseline, the equal error rate (EER) was reduced from 5.75 to 4.01.

Person Re-Identification has received extensive study in the past few years and achieves impressive progress. Recent outstanding methods extract discriminative features by slicing feature maps of deep neural network into several stripes. Still there have improvement on feature fusion and metric learning strategy which can help promote slice-based methods. In this paper, we propose a novel framework that is end-to-end trainable, called Multi-level Slice-based Network (MSN), to capture features both in different levels and body parts. Our model consists of a dual-branch network architecture, one branch for global feature extraction and the other branch for local ones. Both branches process multi-level features using pyramid feature alike module. By concatenating the global and local features, distinctive features are exploited and properly compared. Also, our proposed method creatively introduces a triplet-center loss to elaborate combined loss function, which helps train the joint-learning network. By demonstrating the comprehensive experiments on the mainstream evaluation datasets including Market-1501, DukeMTMC, CUHK03, it indicates that our proposed model robustly achieves excellent performance and outperforms many of existing approaches. For example, on DukeMTMC dataset in single-query mode, we obtain a great result of Rank-1/mAP =85.9%(+1.0%)/74.2%(+4.7%).

Low-density parity-check (LDPC) codes can be used to improve the storage reliability of multi-level cell (MLC) flash memories because of their strong error-correcting capability. In order to improve the weighted bit-flipping (WBF) decoding of LDPC codes in MLC flash memories with cell-to-cell interference (CCI), we propose two strategies of normalizing weights and adjusting log-likelihood ratio (LLR) values. Simulation results show that the WBF decoding under the proposed strategies is much advantageous in both error and convergence performances over existing WBF decoding algorithms. Based on complexity analysis, the strategies provide the WBF decoding with a good tradeoff between performance and complexity.

It is generally believed that jamming signals similar to communication signals tend to demonstrate better jamming effects. We believe that the above conclusion only works in certain situations. To select the correct jamming scheme for a multi-level quadrature amplitude modulation (MQAM) signal in a complex environment, an optimal jamming method based on orthogonal decomposition (OD) is proposed. The method solves the jamming problem from the perspective of the in-phase dimension and quadrature dimension and exhibits a better jamming effect than normal methods. The method can construct various unconventional jamming schemes to cope with a complex environment and verify the existing jamming schemes. The Experimental results demonstrate that when the jammer ideally knows the received power at the receiver, the proposed method will always have the optimal jamming effects, and the constructed unconventional jamming scheme has an excellent jamming effect compared with normal schemes in the case of a constellation distortion.

Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease prevention. Most of the existing event detection methods are based on shallow machine learning methods which mainly rely on domain knowledge and elaborately designed features. Another challenge is that some crucial information as well as the interactions among words or arguments may be ignored since most works treat words and sentences equally. Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction. Furthermore, we propose a multi-level attention mechanism, including word level attention which determines the importance of words in a sentence, and the sentence level attention which determines the importance of relevant arguments. Finally, we train dependency word embeddings and add sentence vectors to enrich semantic information. The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms other state-of-the-art methods.

This paper proposes an enhanced BLE scanner with user-level channel awareness and simultaneous channel scanning to increase theoretical scanning capability by up to three times. With better scanning capability, channel analysis quality also has been improved by considering channel-specific signal characteristics, without the need of beacon-side changes.

The aim of this work is to develop a method for the simultaneous analysis of multiple groups and their members based on hierarchical tensor manifold modeling. The method is particularly designed to analyze multiple teams, such as sports teams and business teams. The proposed method represents members' data using a nonlinear manifold for each team, and then these manifolds are further modeled using another nonlinear manifold in the model space. For this purpose, the method estimates the role of each member in the team, and discovers correspondences between members that play similar roles in different teams. The proposed method was applied to basketball league data, and it demonstrated the ability of knowledge discovery from players' statistics. We also demonstrated that the method could be used as a general tool for multi-level multi-group analysis by applying it to marketing data.

With the increase of data quantity, people have begun to attach importance to cloud storage. However, numerous security accidents occurred to cloud servers recently, thus triggering thought about the security of traditional single cloud. In other words, traditional single cloud can't ensure the privacy of users' data to a certain extent. To solve those security issues, multi-cloud systems which spread data over multiple cloud storage servers emerged. They employ a series of erasure codes and other keyless dispersal algorithms to achieve high-level security. But non-systematic codes like RS require relatively complex arithmetic, and systematic codes have relatively weaker security. In terms of keyless dispersal algorithms, they avoid key management issues but not suit to complete parallel optimization or deduplication which is important to limited cloud storage resources. So in this paper, we design a new kind of XOR-based non-systematic erasure codes - Privacy Protecting Codes (PPC) and a SIMD encoding algorithm for better performance. To achieve higher-level security, we put forward a novel deduplication-friendly dispersal algorithm called Hash Cyclic Encryption-PPC (HCE-PPC) which can achieve complete parallelization. With these new technologies, we present a multi-cloud storage system called CloudS. For better user experience and the tradeoffs between security and performance, CloudS provides multiple levels of security by various combinations of compression, encryption and coding schemes. We implement CloudS as a web application which doesn't require users to perform complicated operations on local.

We propose a novel multiple kernel learning (MKL) method using a collaborative representation constraint, called CR-MKL, for fusing the emotion information from multi-level features. To this end, the similarity and distinctiveness of multi-level features are learned in the kernels-induced space using the weighting distance measure. Our method achieves better performance than existing methods by using the voiced-level and unvoiced-level features.

A terahertz-wave communication system directly connected to an optical fiber network is promising for application to future mobile backhaul and fronthaul links. The possible broad bandwidth in the terahertz band is useful for high-speed signal transmission as well as radio-space encapsulation to the high-frequency carrier. In both cases, the low-latency feature becomes important to enhance the throughput in mobile communication and is realized by waveform transport technology without any digital-signal-processing-based media conversion. A highly precise optical frequency comb signal generated by optical modulation and the vector signal demodulation technology adopted from advanced optical fiber communication technologies help perform modulation and demodulation with impairment compensation at just the edges of the link. Terahertz wave, radio over fiber, waveform transport, coherent detection, multilevel modulation, radio on radio.

An accurate time-of-arrival (TOA) estimation method for isolated pulses positioning system is proposed in this paper. The method is based on a multi-level crossing timing (MCT) digitizer and least square (LS) criterion, namely LS-MCT method, in which TOA of the received signal is directly described as a parameterized combination of a set of MCT samples of the leading and trailing edges of the signal. The LS-MCT method performs a receiver training process, in which a GPS synchronized training pulse generator (TPG) is used to obtain training data and determine the parameters of the TOA combination. The LS method is then used to optimize the combination parameters with a minimization criterion. The proposed method is compared to the conventional TOA estimation methods such as leading edge level crossing discriminator (LCD), adaptive thresholding (ATH), and signal peak detection (PD) methods. Simulation results show that the proposed algorithm leads to lower sensitivity to signal-to-noise ratio (SNR) and attains better TOA estimation accuracy than available TOA methods.

In this paper, a novel charge coupled device matched filter (CCD-MF) for Electrical code division multiplexing (ECDM) decoder is proposed and experimentally demonstrated. Simulation results clarify the influence of low charge transfer efficiency (CTE) and the validity of a parallel CCD-MF we proposed. A 15-channel ECDM system using a 2 Gchip/s, 2-parallel CCD-MF is experimentally demonstrated.

We propose a new security model based on MLS Policy to achieve a better security performance on confidentiality, integrity and availability. First, it realizes a combination of BLP model and Biba model through a two-dimensional independent adjustment of integrity and confidentiality. And, the subject's access range is adjusted dynamically according to the security label of related objects and the subject's access history. Second, the security level of the trusted subject is extended to writing and reading privilege range respectively, following the principle of least privilege. Third, it adjusts the objects' security levels after adding confidential information to prevent the information disclosure. Fourth, it uses application-oriented logic to protect specific applications to avoid the degradation of security levels. Thus, it can ensure certain applications operate smoothly. Lastly, examples are presented to show the effectiveness and usability of the proposed model.

We propose frequency-domain optical code-division-multiplexing (CDM) employing quadrature-amplitude-modulation (QAM) using two of multi-level (M-ary) data generated based on electrical-domain spatial code spreading. Its spectral efficiency is enhanced compared to the conventional scheme with amplitude-shift-keying (ASK) using only one of M-ary data. Although it demands the recovery of amplitude and optical phase information, the practicality of the receiver is retained with self-homodyne detection using a phase-shift-keying (PSK) pilot light. Performance is theoretically evaluated and the optimal parameters are derived. Finally, the feasibility of the proposed technique is experimentally confirmed.

This paper describes ultra-high capacity wavelength-division multiplexed (WDM) transmission technologies for 100-Tbit/s-class optical transport networks (OTNs). First, we review recent advances in ultra-high capacity transmission technologies focusing on spectrally-efficient multi-level modulation techniques and ultra-wideband optical amplification techniques. Next, we describe an ultra-high capacity WDM transmission experiment, in which high speed polarization-division multiplexed (PDM) 16-ary quadrature amplitude modulation (16-QAM), generated by an optical synthesis technique, in combination with coherent detection based on digital signal processing with pilotless algorithms, realize the high spectral efficiency (SE) of 6.4 b/s/Hz. Furthermore, ultra-wideband hybrid optical amplification utilizing distributed Raman amplification (DRA) and C- and extended L-band erbium-doped fiber amplifiers (EDFAs) is shown to realize 10.8-THz total signal bandwidth. By using these techniques, 69.1-Tbit/s transmission is demonstrated over 240-km of pure silica-core fibers (PSCFs). Furthermore, we describe PDM 64-QAM transmission over 160 km of PSCFs with the SE of 9.0 b/s/Hz.

In renewable power generators, because of high initial cost and duty cycle of systems, efficiency parameter has an important place. For this reason, line frequency controlled multilevel inverters are one of most proper choices for renewable power converters. Among these, diode-clamped multilevel inverter structures are one of most important and best efficient inverters. In this paper, a simple diode-clamped equivalent circuit for exploring the efficiency under resistive loads is proposed, and based on this simple circuit, the on-state power dissipation in improved and original diode-clamped multilevel inverter under resistive loads is analyzed. Then, comparative efficiency equations are extracted for inverters that use metal oxide semiconductor field-effect transistors (MOSFETs) and other p-n junction as switches. These equations enable us to have a better idea of conducting power dissipation in diode-clamped and help us to choose appropriate switches for having a lower on-state power dissipation. Some cases are studied and in the end it is proven that the calculated efficiency under resistive load is a boundary for inductive load with the same impedance in diode-clamped inverter with p-n junction switches. This means that calculating the efficiency under resistive loads enables us to approximately predict efficiency under inductive loads.

In the symbiotic cognitive radio (CR) networks, the CR users (CUs) may assist the primary transmission in a cooperation time, and obtain an incentive time for their own data transmission. In this letter, we study the throughput maximization problem in the symbiotic CR networks. Under the symbiosis and transmit power constraints, we aim to find the optimal cooperation time to assist the primary transmission and power allocations among the CUs. Given the cooperation time, the optimal power allocations can be solved by multi-level water-filling (MWF) with individual volume limits. A theoretical analysis is presented on the cooperation time and a modified bisection algorithm with low complexity is proposed to find the sub-optimal cooperation time. Simulation results show that the spectrum usage efficiency can be significantly improved as the number of CUs increases.

An integrated multi-level simulation environment is developed for a highly collision-resistant RFID system. An analog/mixed-signal (AMS) simulator for a circuit-level description of analog front-end power/signal transmission through electro-magnetic coupling is concurrently connected to a tailored software simulator for system-level description of digital back-end processing of TH-CDMA based anti-collision communication. The feasibility of the RFID system in which more than 1,000 transponders can be identified by a single reader in 400 msec is successfuly explored, under a practical presence of field disturbances such as background noises in communication channels as well as variations of electro-magnetic coupling strengths for power transmission.

In wireless access system, we need to use a limited frequency and electric power efficiently. And so we propose the fixed wireless access network using 5 GHz frequency which band has a good propagation performance in line of sight (LOS). In the proposed network, the several multi-level modulation methods are combined and identical frequency is reused by considering on the antenna directivity. As constructing this network, we can efficiently use frequency in 5 GHz band and enlarge system capacity. In this paper, it is assumed that user terminals are distributed nonuniformly over the service area. We analyze accommodation number of user terminals and the optimum combination of modulation methods. Numerical results show that most effective method is the combination of 16QAM and 256QAM, which can accommodate up to about 1.4 times as many users as only QPSK modulation method.