Efficient image copy detection using multi-scale fingerprints

Inspired by multi-resolution histogram, we propose a multi-scale SIFT descriptor to improve the discriminability. A series of SIFT descriptions with different scale are first acquired by varying the actual size of each spatial bin. Then principle component analysis (PCA) is employed to reduce them to low dimensional vectors, which are further combined into one 128-dimension multi-scale SIFT description. Next, an entropy maximization based binarization is employed to encode the descriptions into binary codes called fingerprints for indexing the local features. Furthermore, an efficient search architecture consisting of lookup tables and inverted image ID list is designed to improve the query speed. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. In addition, the multi-scale fingerprints are very discriminative such that the copies can be effectively distinguished from similar objects, which leads to an improved performance in the detection of copies. The experimental evaluation shows that our approach outperforms the state of the art methods.Inspired by multi-resolution histogram, we propose a multi-scale SIFT descriptor to improve the discriminability. A series of SIFT descriptions with different scale are first acquired by varying the actual size of each spatial bin. Then principle component analysis (PCA) is employed to reduce them to low dimensional vectors, which are further combined into one 128-dimension multi-scale SIFT description. Next, an entropy maximization based binarization is employed to encode the descriptions into binary codes called fingerprints for indexing the local features. Furthermore, an efficient search architecture consisting of lookup tables and inverted image ID list is designed to improve the query speed. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. In addition, the multi-scale fingerprints are very discriminative such that the copies can be effectively distinguished from similar objects, which leads to an improved performance in the detection of copies. The experimental evaluation shows that our approach outperforms the state of the art methods

Accuracy-Complexity Tradeoff Analysis and Complexity Reduction Methods for Non-Stationary IMT-A MIMO Channel Models

open access journalHigh-mobility wireless communication systems have attracted growing interests in recent years. For the deployment of these systems, one fundamental work is to build accurate and efficient channel models. In high-mobility scenarios, it has been shown that the standardized channel models, e.g., IMT-Advanced (IMT-A) multiple-input multiple-output (MIMO) channel model, provide noticeable longer stationary intervals than measured results and the wide-sense stationary (WSS) assumption may be violated. Thus, the non-stationarity should be introduced to the IMT-A MIMO channel model to mimic the channel characteristics more accurately without losing too much efficiency. In this paper, we analyze and compare the computational complexity of the original WSS and non-stationary IMT-A MIMO channel models. Both the number of real operations and simulation time are used as complexity metrics. Since introducing the nonstationarity to the IMT-A MIMO channel model causes extra computational complexity, some computation reduction methods are proposed to simplify the non-stationary IMT-A MIMO channel model while retaining an acceptable accuracy. Statistical properties including the temporal autocorrelation function, spatial cross-correlation function, and stationary interval are chosen as the accuracy metrics for verifications. It is shown that the tradeoff between the computational complexity and modeling accuracy can be achieved by using these proposed complexity reduction methods

A Non-Stationary IMT-Advanced MIMO Channel Model for High-Mobility Wireless Communication Systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.With the recent developments of high-mobility wireless communication systems, e.g., high-speed train (HST) and vehicle-to-vehicle (V2V) communication systems, the ability of conventional stationary channel models to mimic the underlying channel characteristics has widely been challenged. Measurements have demonstrated that the current standardized channel models, like IMT-Advanced (IMT-A) and WINNER II channel models, offer stationary intervals that are noticeably longer than those in measured HST channels. In this paper, we propose a non-stationary channel model with time-varying parameters including the number of clusters, the powers and the delays of the clusters, the angles of departure (AoDs), and the angles of arrival (AoAs). Based on the proposed non-stationary IMT-A channel model, important statistical properties, i.e., the local spatial cross-correlation function (CCF) and local temporal autocorrelation function (ACF) are derived and analyzed. Simulation results demonstrate that the statistical properties vary with time due to the non-stationarity of the proposed channel model. An excellent agreement is achieved between the stationary interval of the developed non-stationary IMT-A channel model and that of relevant HST measurement data, demonstrating the utility of the proposed channel model

A longitudinal study of summertime occupant behaviour and thermal comfort in office buildings in northern China

The adaptive behaviour and thermal responses of building occupants can be responsible for significant uncertainties when comparing monitored and modelled building energy performance. A better understanding of the interaction of occupants and their buildings is necessary for managing this uncertainty and reducing discrepancies between predicted and actual energy use (commonly known as ‘the performance gap’). This paper presents the results from a longitudinal study during a summer season of ten mixed-mode offices located in Harbin, a city in northern China, which experiences severe winters and warm summers. The study collected data from on-line daily surveys, field measurements of the local environment, occupants' experiences and adaptive control behaviours. Occupant-building interactions were analysed through observing adaptive behaviour, perceived thermal sensations in the physical environment, architectural geometric variables and personnel characteristics. The driving mechanisms for behaviours and feelings were also studied. The results showed a high probability of window opening for both day and night, and a high frequency of the use of a mix of cooling options, including fans and air conditioning, accompanied by natural ventilation in the summer season. The active interaction of the offices' internal environments with the outdoor environment motivated more connections of occupant thermal feelings with the outdoor physical variables. Relative humidity levels were potential key predictors for window opening, and the geometric parameters of offices, occupants' fan use and perceived thermal feelings also showed a level of predictive ability. Evaluating the nature of occupant feelings and behaviours interactions may inform and improve results from building performance-based design

Thermal comfort, occupant control behaviour and performance gap – a study of office buildings in north-east China using data mining

Simulation techniques have been increasingly applied to building performance evaluation and building environmental design. However, uncertain and random factors, such as occupant behaviour, can generate a performance gap between the results from computer simulations and real buildings. This study involved a longitudinal questionnaire survey conducted for one year, along with a continuous recording of environmental parameters and behaviour state changes, in ten offices located in the severe cold region of north-east China. The offices varied from private rooms to open-plan spaces. The thermal comfort experiences of the office workers and their environmental control behaviours were tracked and analysed during summer and winter seasons. The interaction of the thermal comfort experiences of the occupants and behaviour changes were analysed, and window-opening behaviour patterns were defined by applying data mining techniques. The results also generated window-opening behaviour working profiles to link to building performance simulation software. The aim was to apply these profiles to further study the discrepancies between simulation and monitored results that arise from real-world occupant behaviour patterns

Biosynthesis of depsipeptides with a 3-hydroxybenzoate moiety and selective anticancer activities involves a chorismatase.

Neoantimycins are anticancer compounds of 15-membered ring antimycin-type depsipeptides. They are biosynthesized by a hybrid multimodular protein complex of nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS), typically from the starting precursor 3-formamidosalicylate. Examining fermentation extracts of Streptomyces conglobatus, here we discovered four new neoantimycin analogs, unantimycins B-E, in which 3-formamidosalicylates are replaced by an unusual 3-hydroxybenzoate (3-HBA) moiety. Unantimycins B-E exhibited levels of anticancer activities similar to those of the chemotherapeutic drug cisplatin in human lung cancer, colorectal cancer, and melanoma cells. Notably, they mostly displayed no significant toxicity toward noncancerous cells, unlike the serious toxicities generally reported for antimycin-type natural products. Using site-directed mutagenesis and heterologous expression, we found that unantimycin productions are correlated with the activity of a chorismatase homolog, the nat-hyg5 gene, from a type I PKS gene cluster. Biochemical analysis confirmed that the catalytic activity of Nat-hyg5 generates 3-HBA from chorismate. Finally, we achieved selective production of unantimycins B and C by engineering a chassis host. On the basis of these findings, we propose that unantimycin biosynthesis is directed by the neoantimycin-producing NRPS-PKS complex and initiated with the starter unit of 3-HBA. The elucidation of the biosynthetic unantimycin pathway reported here paves the way to improve the yield of these compounds for evaluation in oncotherapeutic applications

Research on quantitative inversion of ion adsorption type rare earth ore based on convolutional neural network

Rare earth resource is a national strategic resource, which plays an essential role in the field of high technology research and development. In this paper, we aim to use remote sensing quantitative inversion prospecting technology, use surface-to-surface mode, and model inversion and evaluation through convolutional neural network model to achieve a new research method for large-scale, low-cost, rapid and efficient exploration of ion-adsorbed rare earth ore. The results show that the RE2O3 content of samples has significant negative correlation with the second, third and fourth band of GF-2 image, but has no significant correlation with the first band of GF-2 image; the convolution neural network model can be used to reconstruct the RE2O3 content. The content distribution map of RE2O3 obtained by inversion is similar to that of geochemical map, which indicates that the convolution neural network model can be used to invert the RE2O3 content in the sampling area. The quantitative inversion results show that the content distribution characteristics of ion adsorption rare earth ore in the study area are basically consistent with the actual situation; there are two main high anomaly areas in the study area. The high anomaly area I is a known mining area, and the high anomaly area II can be a prospective area of ion adsorption type rare earth deposit. It shows that the remote sensing quantitative inversion prospecting method of ion adsorption type rare earth deposit based on Convolutional Neural Networks (CNN) model is feasible

Manipulating chiral-spin transport with ferroelectric polarization

A collective excitation of the spin structure in a magnetic insulator can transmit spin-angular momentum with negligible dissipation. This quantum of a spin wave, introduced more than nine decades ago, has always been manipulated through magnetic dipoles, (i.e., timereversal symmetry). Here, we report the experimental observation of chiral-spin transport in multiferroic BiFeO3, where the spin transport is controlled by reversing the ferroelectric polarization (i.e., spatial inversion symmetry). The ferroelectrically controlled magnons produce an unprecedented ratio of up to 18% rectification at room temperature. The spin torque that the magnons in BiFeO3 carry can be used to efficiently switch the magnetization of adja-cent magnets, with a spin-torque efficiency being comparable to the spin Hall effect in heavy metals. Utilizing such a controllable magnon generation and transmission in BiFeO3, an alloxide, energy-scalable logic is demonstrated composed of spin-orbit injection, detection, and magnetoelectric control. This observation opens a new chapter of multiferroic magnons and paves an alternative pathway towards low-dissipation nanoelectronics