Spatial Transformations and Urban Conservation of Religious-Historic Towns: A Case of Vrindavan, India

The spatial transformations can be observed at different religious-historic towns of India due to urbanization. Research is based upon fact that there is substantial change in the built environment because of spatial transformations at the religious-historic towns. The process of modernization in the functions and spatial layout is unavoidable at any historic town. The study attempts to focus on various urban historic conservation components, including the look of historic buildings, their earlier uses, and its immediate surroundings to improve the built environment of historic towns. A theoretical framework for the urban conservation of ancient towns is the main objective of study. How to modernize the historic conservation function while preserving the space’s texture and integrity. The research started with the investigation of the morphological growth of Mathura district, India through satellite images and in-depth study of the evolution process of street network in Vrindavan town, which is one of the main temple towns of Mathura district. There is a significant difference in the layout & architectural character of old part and the newly developed Vrindavan. Due to increased accessibility and movement, the spatial structure of traditional religious precincts, which were once local integration centres, has significantly changed. Increasing & changing mode of transportation and further increase in the religious tourism might be the cause or a big reason for the spatial transformations and correspondingly there is a challenge to conserve & preserve the religious precincts of historic towns. The study tries to analyze spatial transformations with the help of Historical GIS at different scales of urban form. Suggestive measures to conserve the environmental ambience of religious-historic towns are the outcome of the research.

Spatial restructuring and the logic of industrial land redevelopment in urban China: Ⅳ. A case study of jointly redevelopment by multi-actors

Within the inventory era, urban shrinkage characterized by economic decline and space decay has been widely witnessed in China. The modes and trajectories of urban redevelopment have thus become areas of major concern for both policymakers and scholars. Taking the multi-actor participation nature of redevelopment, this paper stemming from the game-theoretical approach demonstrates in empirical terms on two major fronts. First, the redevelopment of industrial land without the transfer of land use right is shown to be deadlocked by the incapacitation of the original land-user and unlocked with the participation of the new developer. Second, the preference of the original land-user to maximize its interests by operating by itself rather than continue to cooperate with the new developer is observed in the post-redevelopment stage. Therein, the entrepreneurial local government acts only as a “mediator” between the two market entities and tends not to directly intervene in their cooperation. Thus that, it is the reasonable distribution of potential benefits or the pricing of different rights in the land property right bundle rather than their definition that matters more for land redevelopment. Because the delineation of land property rights has never been a problem in a mature land market, particularly for stock industrial land.

Enhancing the Quality of Low-Light Printed Circuit Board Images through Hue, Saturation, and Value Channel Processing and Improved Multi-Scale Retinex

To address the issue of deteriorated PCB image quality in the quality inspection process due to insufficient or uneven lighting, we proposed an image enhancement fusion algorithm based on different color spaces. Firstly, an improved MSRCR method was employed for brightness enhancement of the original image. Next, the color space of the original image was transformed from RGB to HSV, followed by processing the S-channel image using bilateral filtering and contrast stretching algorithms. The V-channel image was subjected to brightness enhancement using adaptive Gamma and CLAHE algorithms. Subsequently, the processed image was transformed back to the RGB color space from HSV. Finally, the images processed by the two algorithms were fused to create a new RGB image, and color restoration was performed on the fused image. Comparative experiments with other methods indicated that the contrast of the image was optimized, texture features were more abundantly preserved, brightness levels were significantly improved, and color distortion was prevented effectively, thus enhancing the quality of low-lit PCB images.

Sound Source Localization Based on SRP-PHAT Spatial Spectrum and Deep Neural Network

Microphone array-based sound source localization(SSL)is a challenging task in adverse acoustic scenarios.To address this,a novel SSL algorithm based on deep neural network(DNN)using steered response power-phase transform(SRP-PHAT)spatial spectrum as input feature is presented in this paper.Since the SRP-PHAT spatial power spectrum contains spatial location information,it is adopted as the input feature for sound source localization.DNN is exploited to extract the efficient location information from SRP-PHAT spatial power spectrum due to its advantage on extracting high-level features.SRP-PHAT at each steering position within a frame is arranged into a vector,which is treated as DNN input.A DNN model which can map the SRP-PHAT spatial spectrum to the azimuth of sound source is learned from the training signals.The azimuth of sound source is estimated through trained DNN model from the testing signals.Experiment results demonstrate that the proposed algorithm significantly improves localization performance whether the training and testing condition setup are the same or not,and is more robust to noise and reverberation.

End-to-end spatial transform face detection and recognition

Background Several face detection and recogni tion methods have been proposed in the past decades that have excellent performance.The conventional face recognition pipeline comprises the following:(1)face detection,(2)face alignment,(3)feature extraction,and(4)similarity,which are independent of each other.The separate facial analysis stages lead to redundant model calculations,and are difficult for use in end-to-end training.Methods In this paper,we propose a novel end-to-end trainable convolutional network framework for face detection and recognition,in which a geometric transformation matrix is directly learned to align the faces rather than predicting the facial landmarks.In the training stage,our single CNN model is supervised only by face bounding boxes and personal identities,which are publicly available from WIDER FACE and CASIA-WebFace datasets.Our model is tested on Face Detection Dataset and Benchmark(FDDB)and Labeled Face in the Wild(LFW)datasets.Results The results show 89.24%recall for face detection tasks and 98.63%accura cy for face recognition tasks.

Speech Separation Algorithm Using Gated Recurrent Network Based on Microphone Array

Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improve separation performance.However,speech separation in reverberant noisy environment is still a challenging task.To address this,a novel speech separation algorithm using gate recurrent unit(GRU)network based on microphone array has been proposed in this paper.The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost.The proposed algorithm extracts the sub-band steered response power-phase transform(SRP-PHAT)weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position in formation.Since the GRU net work has the advantage of processing time series data with faster training speed and fewer training parameters,the GRU model is adopted to process the separation featuresof several sequential frames in the same sub-band to estimate the ideal Ratio Masking(IRM).The proposed algorithm decomposes the mixture signals into time-frequency(TF)units using gammatone filter bank in the frequency domain,and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM.The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost.Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech sep-aration in noisy and reverberant environments,provides good performance in terms of speech quality and intelligibility,and has the generalization capacity to reverberate.

Spatial transformation of general sampling-aliasing frequency region for rotating-blade parameter identification with emphasis on single-probe blade tip-timing measurement

Blade-health monitoring is intensely required for turbomachinery because of the high failure risk of rotating blades.Blade-Tip Timing(BTT)is considered as the most promising technique for operational blade-vibration monitoring,which obtains the parameters that characterize the blade condition from recorded signals.However,its application is hindered by severe undersampling and stringent probe layouts.An inappropriate probe layout can make most of the existing methods invalid or inaccurate.Additionally,a general conflict arises between the allowed and required layouts because of arrangement restrictions.For the sake of economy and safety,parameter identification based on fewer probes has been preferred by users.In this work,a spatial-transformation-based method for parameter identification is proposed based on a single-probe BTT measurement.To present the general Sampling-Aliasing Frequency(SAFE)map definition,the traditional time-frequency analysis methods are extended to a time-sampling frequency.Then,a SAFE map is projected onto a parameter space using spatial transformation to extract the slope and intercept parameters,which can be physically interpreted as an engine order and a natural frequency using coordinate transformation.Finally,the effectiveness and robustness of the proposed method are verified by simulations and experiments under uniformly and nonuniformly variable speed conditions.

On the inverse scattering transform in two spatial and one temporal dimensions

Some results and developments on the extension of the inverse scattering transform to solve non-linear evolution equations in one time and two space dimensions are described.

国家行政区划调整下的城市多中心发展——城市空间转型的政策启示

Polycentric urban development has profound impacts on urban development worldwide.Most studies have identified its complex drivers of social economy and natural condition while ignoring the state-led policy factors.In recent years,China has undergone dramatic administrative division adjustment(ADA)during the process of unique state-led urbanization.However,as a crucial government strategy,the impacts of ADA on urban polycentricity remain unclear.This research investigates the relationship between ADA and urban polycentricity through spatial difference-in-differences models.The results reveal that ADA has contributed to the polycentric urban development in China.Specifically,boundary restructuring has more substantial impacts than hierarchy reorganization.In addition,ADA has significantly promoted urban polycentricity in local cities in central China and neighbouring cities in eastern China,while it has no significant effects in western China.Furthermore,ADA reshapes urban polycentricity mainly by the influencing mechanism of construction land and industrial structure.Policymakers should consider the various ADA’s impacts on urban polycentricity with socio-economic conditions.This research provides a deeper insight into urban spatial transformation with state-led drivers.