Search

Chronic pain, such as neuropathic pain, causes anxiety and other negative emotions, which aggravates the pain sensation and increases the risk of chronic pain over time. Dopamine receptor D1 (DRD1) and dopamine receptor D2 (DRD2) in the basolateral amygdala (BLA) have been implicated in mediating anxiety-related behaviors, but their potential roles in the BLA in neuropathic pain-induced anxiety have not been examined. Electroacupuncture (EA) is commonly used to treat chronic pain and emotional disorders, but it is still unclear whether EA plays a role in analgesia and anxiety relief through DRD1 and DRD2 in the BLA. Here, we used western blotting to examine the expression of DRD1 and DRD2 and pharmacological regulation combined with behavioral testing to detect anxiety-like behaviors. We observed that injection of the DRD1 antagonist SCH23390 or the DRD2 agonist quinpirole into the BLA contributed to anxiety-like behaviors in naive mice. EA also activated DRD1 or inhibited DRD2 in the BLA to alleviate anxiety-like behaviors. To further demonstrate the role of DRD1 and DRD2 in the BLA in spared nerve injury (SNI) model-induced anxiety-like behaviors, we injected the DRD1 agonist SKF38393 or the DRD2 antagonist sulpiride into the BLA. We found that both activation of DRD1 and inhibition of DRD2 could alleviate SNI-induced anxiety-like behaviors, and EA had a similar effect of alleviating anxiety. Additionally, neither DRD1 nor DRD2 in the BLA affected SNI-induced mechanical allodynia, but EA did. Overall, our work provides new insights into the mechanisms of neuropathic pain-induced anxiety and a possible explanation for the effect of EA treatment on anxiety caused by chronic pain.

The increasing demand for portable and flexible energy storage devices drives the development of flexible electrodes and electrolytes. The aim of this work is to fabricate the flexible free-standing polyaniline/poly (vinyl alcohol) (PANI/PVA) composite electrode with good capacitance performance and shape memory behavior. The electrodes were fabricated by chemical oxidation polymerization of aniline in porous PVA (P-PVA) films. The morphology, electrochemical and mechanical properties of PANI/P-PVA electrodes were studied by scanning electron microscope, cyclic voltammetry, galvanostatic charge-discharge, and tensile test etc. The results revealed that the flexible PANI/P-PVA-1 electrode had good specific capacitance of 173.86 ​mF ​cm−2 at 1 ​mA ​cm−2, with the capacitance retention of 70.16% after 4000 charge-discharge cycles. Besides, it had excellent heat-induced shape memory effect. The fixed shape could completely recover to its original shape within 10 ​s at 80 ​°C, which is above the glass transition temperature (75.89 ​°C) of PANI/P-PVA-1. The comparatively tensile strength (2.86 ​MPa) and high elongation at break (315.72%) indicated its outstanding flexibility. Up to 200 times folding had no effect on the electrochemical properties. The free-standing polymer electrodes with excellent comprehensive performance provide potential applications in flexible energy-storage devices, electronic encapsulation and high stretchable electric devices etc.

Electroacupuncture (EA) can effectively alleviate anxiety disorders and memory impairments caused by various neurodegenerative diseases; however, the molecular mechanisms underlying its neuroprotective effects are unclear. Previous studies have shown that the renin-angiotensin system (RAS) comprises of two axes with mutual antagonism: the classical angiotensin converting enzyme/angiotensin II/angiotensin II type 1 receptor (ACE/Ang II/AT1R) axis and the protective angiotensin converting enzyme 2/angiotensin-(1-7)/Mas receptor (ACE2/Ang-(1-7)/MasR) axis. In this study, we observed that chronic cerebral hypoperfusion (CCH) mediated anxiety-like behavior and memory impairments in spontaneously hypertensive rats (SHR) via upregulation of the hippocampal classical axis (ACE/Ang II/AT1R) and the partial hippocampal protective axis (ACE2/Ang-(1-7)). However, Ang II levels were much higher than those of Ang-(1–7), indicating that the ACE/Ang II/AT1R axis plays a dominant role in the comorbidity of CCH and hypertension. Moreover, candesartan cilexetil (Canc) and perindopril (Peril) were used as positive control drugs. We found that EA, Canc, and Peril attenuated CCH-induced anxiety-like behavior and memory impairments in SHR, potentially via downregulation of the hippocampal classical axis (ACE/Ang II/AT1R) and upregulation of the whole hippocampal protective axis (ACE2/Ang-(1-7)/MasR). These results suggest that EA therapy for CCH with hypertension may be mediated by two hippocampal RAS axes.

Novel dihydrobenzo[h]quinolines (DHBQs), the products of an efficient catalyst-free three-component reaction (3CR) recently developed by us, possess useful and strong aggregation-induced emission (AIE) characteristic. Here, a series of new dihydrobenzo[h]quinolines (h-DHBQs 4-1-34) and dihydrobenzo[f]quinolines (f-DHBQs 5a-e) were designed and synthesized by the 3CR to study their bioactivities as novel inhibitors against the influenza A (H1N1) virus. The structure-activity relationship (SAR) indicates that the antiviral activities of DHBQs depend on the combination of substituents and three of h-DHBQs (4-12, 4-25 and 4-27) show potent antiviral activity with IC

Electroacupuncture has shown protective effects on cognitive decline. However, the underlying molecular mechanisms are not clear. The present study was conducted to determine whether the cognitive function was ameliorated in cerebral hypoperfusion rats following electroacupuncture and to investigate the role of miR-137/NOX4 axis. In this study, chronic cerebral hypoperfusion (CCH) model was established by bilateral common carotid artery occlusion. Electroacupuncture treatment attenuated brain injury in CCH model group via regulating miR-137/NOX4 axis. Furthermore, the data of neuronal apoptosis and oxidative stress were observed. Our findings indicated that (1) neuronal apoptosis and oxidative stress in CCH rats were significantly increased compared with control group; (2) the animal cognitive performance was evaluated using the Morris water maze (MWM). The results showed that electroacupuncture therapy ameliorated spatial learning and memory impairment in cerebral hypoperfusion rats; and (3) electroacupuncture therapy reduces neuronal apoptosis and oxidative stress by activating miR-137/NOX4 axis. These results suggest that electroacupuncture therapy for CCH may be mediated by miR-137/NOX4 axis. Electroacupuncture therapy may act as a potential therapeutic approach for chronic cerebral hypoperfusion.

Anxiety is a common comorbidity associated with chronic pain, which results in chronic pain complexification and difficulty in treatment. Electroacupuncture (EA) is commonly used to treat chronic pain and anxiety. However, the underlying mechanisms of the EA effect are largely unknown. Here, we showed that a circuitry underlying chronic pain induces anxiety disorders, and EA can treat them by regulating such circuitry. Using chemogenetic methods, we found that chemogenetic activation of the rostral anterior cingulate cortex (rACC) glutamatergic output to the thalamus induced anxiety disorders in control rats. Then, chemogenetic inhibition of the rACC-thalamus circuitry reduced anxiety-like behavior produced by intraplantar injection of the complete Freund’s adjuvant (CFA). In this study, we examined the effects of EA on a rat model of CFA-mediated anxiety-like behaviors and the related mechanisms. We found that chemogenetic activation of the rACC-thalamus circuitry effectively blocked the effects of EA on chronic pain-induced anxiety-like behaviors in CFA rats. These results demonstrate an underlying rACC-thalamus glutamatergic circuitry that regulates CFA-mediated anxiety-like behaviors. This study also provides a potential mechanistic explanation for EA treatment of anxiety caused by chronic pain.

Electroacupuncture (EA) can effectively modulate pain perception and pain-related negative affect; however, we do not know whether the effect of EA on sensation and affect is parallel, or dissociated, interactional. In this study, we observed the effects of the anterior cingulate cortex (ACC) lesion and the primary somatosensory cortex (S1) activation on pain perception, pain-related affection, and neural oscillation in S1. ACC lesions did not affect pain perception but relieved pain-paired aversion. S1 activation increased pain perception and anxious behavior. EA can mitigate pain perception regardless of whether there is an ACC lesion. Chronic pain may increase the delta and theta band oscillatory activity in the S1 brain region and decrease the oscillatory activity in the alpha, beta, and gamma bands. EA intervention may inhibit the oscillatory activity of the alpha and beta bands. These results suggest that EA may mitigate chronic pain by relieving pain perception and reducing pain-related affection through different mechanisms. This evidence builds upon findings from previous studies of chronic pain and EA treatment.

Decorrelation reduction and search techniques play important roles in solving integer ambiguities of global navigation satellite system (GNSS) positioning. This contribution focuses on these two issues and can mainly be divided into two parts. (1) An efficient strategy to realize the popular vertical Bell Labs layered space–time (V-BLAST) algorithm is studied. Then, a cycled efficient V-BLAST (ceV-BLAST) reduction algorithm is constructed. The decorrelation outcome of ceV-BLAST reduction is demonstrated by analysis to be stronger than LAMBDA and LLL. (2) A search complexity estimator which can give a priori estimation of the search complexity is proposed. This estimator evaluates the search nod number directly and turns out to follow the real search complexity better than other estimators of the same type. At the same time, it is very time efficient. Utilizing two sets of GNSS observation data, we compare ceV-BLAST and the new search complexity estimator with existing decorrelation algorithms and estimators, respectively. The results have confirmed our arguments.

Objective. To investigate the effect of EA on regional cerebral blood flow, cognitive deficits, inflammation, and its probable mechanisms in chronic cerebral ischemia (CCI) rats.Methods. Rats were assigned randomly into sham operation group (sham group) and operation group. For operation group, CCI model was performed using the permanent bilateral common carotid artery occlusion (BCCAO) method, and then rats were further randomly divided into model group and electroacupuncture (EA) group. 2/15 Hz low-frequency pulse electric intervention was applied at “Baihui” and “Dazhui” acupoints in EA group. Four weeks later, Morris water maze test was adopted to assess the cognitive function, using laser Doppler flowmetry to test changes of regional cerebral blood flow (rCBF); double antibody sandwich enzyme-linked immunosorbent assay (DAS-ELISA) to measure proinflammatory cytokines (IL-6, TNF-α, and IL-1β); western blot to test the protein expression quantities of proinflammatory cytokines, JAK2, and STAT3; and RT-PCR to test JAK2 mRNA and STAT3 mRNA in the hippocampus in each group.Results. Compared with the model group, learning and memory abilities and rCBF and IL-6 expression of the EA group enhanced markedly; IL-1βand JAK2 significantly decreased; TNF-αand STAT3 also declined, but the difference was not apparent.Conclusion. Our research suggests that EA can improve cognitive deficits which may be induced by increasing rCBF and anti-inflammatory effect.

Our previous studies have confirmed that electroacupuncture (EA) can effectively intervene in pain memory, but the neural mechanism involved remains unclear. In this study, we observed the effects of EA in regulating pain memory-related behaviors and synchronous neural oscillations in the rostral anterior cingulate cortex (rACC). During nociceptive behavioral testing, pain memory induced a nonpain stimulus that spurred a neural oscillatory reaction similar to that caused by pain stimuli in the rACC. After EA, nonpain stimuli did not induce decreased neural oscillatory activity in the rACC until the presentation of pain stimuli. During aversive behavioral testing, EA, through the downregulation of theta power, inhibited the retrieval of aversive memory and relieved pain memory-induced aversive behaviors. These changes of oscillatory activity may be the hallmarks of EA therapy for pain memory.

This paper aims to explore the appropriate investment strategy for Chinese power enterprises with the effect of the nationwide carbon emissions trading (NCET) market. Based on the system dynamics (SD) theory and the analysis of investment strategies, the SD model for the investment analysis of power enterprises is proposed. The simulation experiments based on three different investment policy scenarios (i.e., conservative, neutral and active) are conducted. According to the simulation results, the reasonable short-term investment for enterprises should be increased. If enterprises choose to invest more resources in the installation of green powers (hydropower, wind power and photovoltaic), their carbon emissions and profits may be more difficult to achieve qualitative changes in the short term. It is suggested that before the establishment of NCET market, enterprises should give priority to investing in clean technology instead of large-scale green energy installation. In the long run, increasing the investment of green power generation will help enterprises resist the rising cost of carbon trading. In addition, even in the conservative policy environment, the enterprise can still achieve its carbon discharges peak value before 2030, so the government may consider adopting a loose policy standard to support the economic interests of power enterprises.

In this work, solid-state polymer supercapacitor (SSC) was assembled using poly(3,4-ethylenedioxythiophene/carbon paper (PEDOT/CP) as an electrode and ionic liquid (1-butyl-3-methylimidazole tetrafluoroborate)/polyvinyl alcohol/sulfuric acid (IL/PVA/H2SO4) as a gel polymer electrolyte (GPE). The GPE was treated through freezing&ndash, thawing (F/T) cycles to improve the electrochemical properties of PEDOT SSC. Cyclic voltammetry (CV), galvanostatic charge&ndash, discharge measurements (GCD) and electrochemical impedance spectroscopy (EIS) techniques and conductivity were carried out to study the electrochemical performance. The results showed that the SSC based on ionic liquid GPE (SSC-IL/PVA/H2SO4) has a higher specific capacitance (with the value of 86.81 F/g at 1 mA/cm2) than the SSC-PVA/H2SO4.The number of F/T cycles has a great effect on the electrochemical performance of the device. The energy density of the SSC treated with 3 F/T cycles was significantly improved, reaching 176.90 Wh/kg. Compared with the traditional electrolytes, IL GPE has the advantages of high ionic conductivity, less volatility, non-flammability and wider potential window. Moreover, the IL GPE has excellent elastic recovery and self-healing performance, leading to its great potential applications in flexible or smart energy storage equipment.

Feature learning and metric learning are two important components in person re-identification (re-id). In this paper, we utilize both aspects to refresh the current State-Of-The-Arts (SOTA). Our solution is based on a classification network with label smoothing regularization (LSR) and multi-branch tree structure. The insight is that some middle network layers are found surprisingly better than the last layers on the re-id task. A Hierarchical Deep Learning Feature (HDLF) is thus proposed by combining such useful middle layers. To learn the best metric for the high-dimensional HDLF, an efficient eXQDA metric is proposed to deal with the large-scale big-data scenarios. The proposed HDLF and eXQDA are evaluated with current SOTA methods on five benchmark datasets. Our methods achieve very high re-id results, which are far beyond state-of-the-art solutions. For example, our approach reaches 81.6%, 96.1% and 95.6% Rank-1 accuracies on the ILIDS-VID, PRID2011 and Market-1501 datasets. Besides, the code and related materials (lists of over 1800 re-id papers and 170 top conference re-id papers) are released for research purposes.

The contamination of soil and plants with heavy metals, which has detrimental influences on plant growth, water purification, and food safety, has emerged as a serious global issue. To better understand the spatial variations of contamination of heavy metals associated city development and land use types, we collected soil samples and Magnolia grandiflora branches to quantify lead (Pb) and cadmium (Cd) contents of the roadside, industrial, residential, and park greenbelts in Hefei City, China. We found that Pb content in soil was the highest in roadside greenbelts and the lowest in parks with industrial and residential greenbelts being intermediate, while Cd in soil was the highest in greenbelts close to city center and decreased with the distance to city center. Pb in M. grandiflora, however, did not differ among greenbelt types but decreased with distance to the city center. Cd in M. grandiflora was the highest in roadside and lowest in parks and also decreased with the distance to the city center. Across all greenbelt types and the distances to the city center, Pb and Cd contents were positively correlated in soil and plants. Our findings suggest that vehicle traffic, population density, and age of urbanization collectively contribute to soil and plant contamination of Pb and Cd.

Due to its ability to model image corruptions explicitly, sparse representation (SR) attracts much attention from the community of visual tracking in recent years. However, the existing tracking approaches generally employ the l 1 norm regularization method to achieve sparse recovery. As a result, they have to set the regularization parameter to an appropriate value, which is actually a crucial but difficult task in practice. To avoid this difficulty, we develop a new algorithm to solve the resultant SR problem under the framework of variational Bayesian inference. The algorithm can simultaneously estimate the sparse coefficients and other unknown parameters in an automatic manner. Consequently, using this algorithm to achieve SR involves little user intervention. In addition, we employ a new observation likelihood function to allow for the incorporation of a particle screening mechanism into the tracking process. Based on the above modifications, we develop a new algorithm for visual tracking based on sparse prototypes. Experimental results over various video sequences demonstrate the effectiveness and robustness of the algorithm.

As a necessary electrical equipment for the construction of power grid, the safety and reliability of the transformer is the focus that people have always paid attention to. In recent years, the DC bias of the transformer has a serious impact on the safe operation of the power system, which has attracted wide attention of scholars at home and abroad. In view of this phenomenon, combined with the basic principle of transformer excitation, the simulation of the DC bias phenomenon is carried out by using Simulink software in this paper. According to the simulation results, the distortion characteristics of excitation current under DC bias are summarized. Then, based on the variation characteristics of excitation current under over excitation and DC bias, the law of transformer core vibration is deduced, and the cause of transformer abnormal vibration under DC bias is revealed. Finally, by analyzing the data of a transformer substation running under DC bias, it is found that the measured data are basically the same with our simulation results, which provides a basis and reference for further research on the phenomenon of transformer DC bias.

Feature and metric designing are two vital aspects in person re-identification. In this letter, we firstly propose a novel spatiogram based person descriptor. Such spatiograms of different image regions from several color channels are calculated and accumulated to create a histogram vector and two distinctive spatial statistical vectors. Secondly, through further investigating the multi-shot set-based metric based on the recent collaborative representation model, we propose an effective and efficient multi-shot metric, which fuses the residual and coding coefficients after collaboratively coding samples on all person classes. Finally, we evaluate the proposed descriptor and metric with other published methods on benchmark datasets. Our methods not only achieve state-of-the-art results but also are novel, straightforward and computationally efficient, which will facilitate the real-time surveillance applications such as pedestrian tracking.

The electrophilic aromatic substitution for nitration, halogenation, sulfonation, and acylation is a vastly important category of chemical transformation. Its reactivity and regioselectivity is predominantly determined by nucleophilicity of carbon atoms on the aromatic ring, which in return is immensely influenced by the group that is attached to the aromatic ring a priori. In this work, taking advantage of recent developments in quantifying nucleophilicity (electrophilicity) with descriptors from the information-theoretic approach in density functional reactivity theory, we examine the reactivity properties of this reaction system from three perspectives. These include scaling patterns of information-theoretic quantities such as Shannon entropy, Fisher information, Ghosh-Berkowitz-Parr entropy and information gain at both molecular and atomic levels, quantitative predictions of the barrier height with both Hirshfeld charge and information gain, and energetic decomposition analyses of the barrier height for the reactions. To that end, we focused in this work on the identity reaction of the monosubstituted-benzene molecule reacting with hydrogen fluoride using boron trifluoride as the catalyst in the gas phase. We also considered 19 substituting groups, 9 of which are ortho/para directing and the other 9 meta directing, besides the case of R = -H. Similar scaling patterns for these information-theoretic quantities found for stable species elsewhere were disclosed for these reactions systems. We also unveiled novel scaling patterns for information gain at the atomic level. The barrier height of the reactions can reliably be predicted by using both the Hirshfeld charge and information gain at the regioselective carbon atom. The energy decomposition analysis ensued yields an unambiguous picture about the origin of the barrier height, where we showed that it is the electrostatic interaction that plays the dominant role, while the roles played by exchange-correlation and steric effects are minor but indispensable. Results obtained in this work should shed new light for better understanding of the factors governing the reactivity for this class of reactions and assisting ongoing efforts for the design of new and more efficient catalysts for such kind of transformations.

Traditional carrier phase combinations are linear functions of the original carrier phases. We develop a new way of carrier phase combination that regards carrier phases of different frequencies as the basis of the carrier phase space. The combined carrier phase is a point of this space. Then, this point, i.e., the combined carrier phase, is mapped back to a single-dimensional carrier phase by a bidirectional mapping. The new single-dimensional carrier phase is called mapped carrier phase. The advantages of this combination approach are a long wavelength and small noise of the mapped carrier phase, which make ambiguity resolution easy. Unfortunately, the mapped carrier phase value is not well determined due to the noise in the observed phases. On the contrary, a set of possible mapped carrier phase values are attained; however, only one value is correct. To reduce the number of candidates and fix the correct value of the mapped carrier phase, the following steps are discussed: (1) The integer nature of the original carrier ambiguity is used to attain an initial set of possible mapped carrier phase values; (2) the distribution of the mapped carrier phase ambiguity is included to reduce the possible values; and (3) the Gaussian least-squares objective function is introduced to fix the correct value. As a result of these steps, a single-epoch positioning algorithm is established. Two experiments are carried out to preliminarily compare the new algorithm with LAMBDA. The results show that the new algorithm is slightly below LAMBDA in resolution success rate, but computationally more efficient than LAMBDA.

As a continuation of our recent efforts to quantify chemical reactivity with quantities from the information-theoretic approach within the framework of density functional reactivity theory, the effectiveness of applying these quantities to quantify electrophilicity for the bimolecular nucleophilic substitution (SN2) reactions in both gas phase and aqueous solvent is presented in this work. We examined a total of 21 self-exchange SN2 reactions for the compound with the general chemical formula of R1R2R3C-F, where R1, R2, and R3 represent substituting alkyl groups such as -H, -CH3, -C2H5, -C3H7, and -C4H9 in both gas and solvent phases. Our findings confirm that scaling properties for information-theoretic quantities found elsewhere are still valid. It has also been verified that the barrier height has the strongest correlation with the electrostatic interaction, but the contributions from the exchange-correlation and steric effects, though less significant, are indispensable. We additionally unveiled that the barrier height of these SN2 reactions can reliably be predicted not only by the Hirshfeld charge and information gain at the regioselective carbon atom, as previously reported by us for other systems, but also by other information-theoretic descriptors such as Shannon entropy, Fisher information, and Ghosh-Berkowitz-Parr entropy on the same atom. These new findings provide further insights for the better understanding of the factors impacting the chemical reactivity of this vastly important category of chemical transformations.

A novel and efficient method for person re-identification based on mahalabobis distance feature is proposed. This feature is built on the existing features but exhibits extraordinary discriminative power. With an additional lightweight PCA, it can be cascaded to greatly enhance the overall discrimination. Then Keep It Simple and Straightforward (KISS) metric learning is conducted on such enhanced features. The approach has been evaluated against current methods on a benchmark dataset and can reach outstanding performance.

This paper proposes a new spatio-temporal appearance feature named Phasic Maximal and Local Maximal Occurrence (PM-LOMO) representation for video-based person re-identification. To perform temporal alignment of the sequence, we selected the optimal period of walking cycle and divide frames into several phases based on the extreme points of the sequence's Flow Energy Profile (FEP). To describe the appearance of the video, we averaged the local maximal occurrence representations of all the frames in a same phase, and then we chose maximal features among the phases. Extensive experiments on public datasets demonstrate the better effectiveness of the proposed method compared with the state-of-art approaches.

In order to improve the accuracy of pedestrian detection, we proposed an algorithm of multi-channel feature detection based on Discrete Cosine Transform (DCT). We use a two-layer convolution network for arranging the image information after DCT to build a new channel in the frequency domain. The channel can describe complex textures about pedestrian. Combined with the features of the histogram of gradient and the color space as well as the DCT frequency domain, a low cost multi-channel pedestrian detector has been trained based on the Adaboost algorithm. Experimental results on two datasets demonstrate that our model outperforms state-of-the-art methods and the effect is remarkable in low false positive per image.

In order to simulate this feature and detect the salient region rapidly, we propose the Spatial-Temporal Feature in Compress Domain (STFCD) model. By respectively using H.264 residual coding length and motion vector coding length, we simulate the salient stimulus intensity and then get video saliency features. Finally, we use the linear weighted fusion algorithm to get the final video saliency maps. Experimental results on three public datasets demonstrate that our model outperforms state-of-the-art methods.

The truncated singular value decomposition (TSVD) regularization applied in ill-posed problem is studied. Through mathematical analysis, a new method for truncated parameter selection which is applied in TSVD regularization is proposed. In the new method, all the local optimal truncated parameters are selected first by taking into account the interval estimation of the observation noises; then the optimal truncated parameter is selected from the local optimal ones. While comparing the new method with the traditional generalized cross-validation (GCV) andLcurve methods, a random ill-posed matrices simulation approach is developed in order to make the comparison as statistically meaningful as possible. Simulation experiments have shown that the solutions applied with the new method have the smallest mean square errors, and the computational cost of the new algorithm is the least.

Various kinds of models have been proposed for saliency detection, but each has its limitations in application. Fusing multiple complementary models is expected to improve the performance. However, most of the fusion methods treat individual model equally and are greatly degraded by poor saliency map. In this paper, we firstly present a no-reference metric to assess the quality of saliency map. Then, a fusion framework is constructed by weighted averaging good saliency maps and filtering out poor ones. In this framework, candidate models can be selected with preference to fast ones. Experimental results on two public datasets show that our method not only outperforms state-of-art unsupervised saliency model but also is more robust than present fusion algorithms.

Video object recognition is an importance topic in both defense and civilian applications. However, object recognition guarantee is not considered in current video coding standard H.264. For this issue, experiments based on H.264/AVC codec are designed to measure the affection of coding parameters on recognition performance. The quantitative functions of recognition performance with coding rate and resolution ratio are given. More practically, such functions can be used for optimal tradeoff between the channel bandwidth and video object recognition performance when efficient video data transfer is needed.

A novel spatiogram-based person descriptor is well constructed for person re-identification. The spatiograms are first accumulated on multiple image regions among multiple colour channels. Then the devised feature fuses multi-channel colour histograms with the first- and the second-order spatial information of the image's vertical axis. A simple but efficient metric is adopted to combine the feature's three distinctive components. The proposed method is compared with other feature-based methods on a benchmark dataset, achieving significant performance.

This paper proposes a bottom-up saliency detection algorithm based on multi-dictionary sparse recovery. Firstly, the SLIC algorithm is used to segment the image into superpixels in multilevel and atoms with a high background possibility are selected from the boundary superpixels to construct the multidictionary. Secondly, sparse recovery of the entire image is achieved using multi-dictionary to get subsaliency maps from the perspective of sparse recovery errors. The final saliency map is generated in a weighted fusion manner. Experimental results on three public datasets demonstrate the effectiveness of our model.

Researchers have done a great number of studies on the visual object tracking and the video encoding transmission respectively. However, there are still no public reports about the influence on the visual object tracking raised by the video coding rates. In this paper, for this issue, a typical tracker with the features of HOG and the most commonly-used video encoding methods-H.264/AVC, are chosen as the test objects. In order to study how the code rates affect the performance of visual object tracking, the tracking experiments under different code rates are designed and the function of tracking performance is caused by the code rates. The result shows that the compromise can be achieved between the channel bandwidth and the visual object tracking performance through selecting the appropriate code rates for the encoder which provides basis for encoding optimization object function of different video applications.