Search results

1 – 10 of over 12000
Article
Publication date: 22 October 2010

Ning Li

This study investigates the evolutionary pattern of China's electronics industry and China's industrial integration into the Northeast Asian region from a historical perspective…

Abstract

Purpose

This study investigates the evolutionary pattern of China's electronics industry and China's industrial integration into the Northeast Asian region from a historical perspective. The purpose is to shed some light on the catch‐up path of China's technological capabilities using some empirical evidence covering the period of 1974‐2000.

Design/methodology/approach

Market share and the Finger‐Kreinin similarity index (FKSI) are used as measures to trace the path of catch‐up from both quantitative and structural perspectives and evidence is provided at the sectoral level. The Standard International Trade Classification (SITC) classification systems is adopted and FKSI values are derived from international trade data at both four‐digit SITC and sectoral levels.

Findings

First, the take‐off points toward rapid progress of China's technological capability in different sectors happened not concurrently but in a sequentially manner. Second, as to structural evolution, the process of China's integration into the world market and the Northeast Asian region started in 1978 and the extent of integration has become higher and higher ever since. Until late 1990s, gaps between China and Japan and between China and Korea have been successfully narrowed in terms of comprehensiveness of export structure in electronics.

Originality/value

The period of 1974‐2000 saw the tremendous transition in China from a centralized and planned system into a market‐driven economy. It also saw several noteworthy shifts of China's industrial policy in order to build up its innovative capacity and to catch‐up with Japan and Korea. Unlike many other studies that deeply root in macroeconomic approach, this study traces the evolution of China's performance at the sectoral level by focusing on electronics industry. The findings of this paper are explained in terms of national industrial policy, location effects, and low‐cost sourcing.

Details

Journal of Science and Technology Policy in China, vol. 1 no. 3
Type: Research Article
ISSN: 1758-552X

Keywords

Article
Publication date: 12 May 2022

Naresh Kattekola and Shubhankar Majumdar

This paper aims to implement a novel design of approximate comparator which can be suitable for image processing applications.

Abstract

Purpose

This paper aims to implement a novel design of approximate comparator which can be suitable for image processing applications.

Design/methodology/approach

Here, the N-bit approximate comparator is implemented by taking reference of N as 2-, 4- and 8-bit. The design analyses the fractional change in error to bit in several bit formats. The final implementation of approximate comparator design application compares the structural similarity index, colour test and extraction of an image to the results.

Findings

The novel approximate comparator was designed using 2-, 4- and 8-bit to explore N-bit comparator expressions. The implementation, computations, evaluation of errors, applications and the design constraints were executed using Python and Synopsys, respectively. The computations, evaluation of errors, applications and the design constraints were executed using Python and Synopsys, respectively.

Originality/value

This paper presents the N-bit accurate and approximate comparator which is novel over the existing design of comparators.

Details

Circuit World, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0305-6120

Keywords

Article
Publication date: 6 August 2021

A. Valli Bhasha and B.D. Venkatramana Reddy

The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating…

Abstract

Purpose

The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem.

Design/methodology/approach

This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models.

Findings

From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images.

Originality/value

This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.

Article
Publication date: 17 March 2016

suryanarayana gunnam and Ravindra Dhuli

The purpose of this paper is to present an improved wavelet based approach in single image super resolution (SISR). The proposed method generates high resolution (HR) images by…

Abstract

Purpose

The purpose of this paper is to present an improved wavelet based approach in single image super resolution (SISR). The proposed method generates high resolution (HR) images by preserving the image contrast and edges simultaneously.

Design/methodology/approach

Covariance based interpolation algorithm is employed to obtain an initial estimate of the unknown HR image. The proposed method preserves the image contrast, by applying singular value decomposition (SVD) based correction on the dual-tree complex wavelet transform (DT-CWT) coefficients. In addition, the dual operating mode diffusion based shock filter (DBSF) ensures noise mitigation and edge preservation.

Findings

Experimental results on various test images reveal superiority of the proposed method over the existing SISR techniques in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and visual quality.

Originality/value

With SVD based correction, the proposed method preserves the image contrast and also the DBSF operation helps to achieve sharper edges.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 35 no. 3
Type: Research Article
ISSN: 0332-1649

Article
Publication date: 16 July 2021

Dure Jabeen, S.M. Ghazanfar Monir, Shaheena Noor, Muhammad Rafiullah and Munsif Ali Jatoi

Watermarking technique is one of the significant methods in which carrier signal hides digital information in the form of watermark to prevent the authenticity of the stakeholders…

Abstract

Purpose

Watermarking technique is one of the significant methods in which carrier signal hides digital information in the form of watermark to prevent the authenticity of the stakeholders by manipulating different coefficients as watermark in time and frequency domain to sustain trade-off in performance parameters. One challenging component among others is to maintain the robustness, to limit perceptibility with embedding information. Transform domain is more popular to achieve the required results in color image watermarking. Variants of complex Hadamard transform (CHT) have been applied for gray image watermarking, and it has been proved that it has better performance than other orthogonal transforms. This paper is aimed at analyzing the performance of spatio-chromatic complex Hadamard transform (Sp-CHT) that is proposed as an application of color image watermarking in sequency domain (SD).

Design/methodology/approach

In this paper, color image watermarking technique is designed and implemented in SD using spatio-chromatic – conjugate symmetric sequency – ordered CHT. The color of a pixel is represented as complex number a*+jb*, where a* and b* are chromatic components of International Commission on Illumination (CIE) La*b* color space. The embedded watermark is almost transparent to human eye although robust against common signal processing attacks.

Findings

Based on the results, bit error rate (BER) and peak signal to noise ratio are measured and discussed in comparison of CIE La*b* and hue, saturation and value color model with spatio-chromatic discrete Fourier transform (Sp-DFT), and results are also analyzed with other discrete orthogonal transforms. It is observed from BER that Sp-CHT has 8%–12% better performance than Sp-DFT. Structural similarity index has been measured at different watermark strength and it is observed that presented transform performs better than other transforms.

Originality/value

This work presents the details and comparative analysis of two orthogonal transforms as color image watermarking application using MATLAB software. A finding from this study demonstrates that the Complex Hadamard transform is the competent candidate that can be replaced with DFT in many signal processing applications.

Article
Publication date: 31 December 2021

Praveen Kumar Lendale and N.M. Nandhitha

Speckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many…

Abstract

Purpose

Speckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches.

Design/methodology/approach

The work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information.

Findings

The proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators.

Originality/value

Fuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.

Details

International Journal of Intelligent Unmanned Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2049-6427

Keywords

Article
Publication date: 7 December 2022

Fatemeh Mostafavi, Mohammad Tahsildoost, Zahra Sadat Zomorodian and Seyed Shayan Shahrestani

In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the…

Abstract

Purpose

In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design.

Design/methodology/approach

A methodology using an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout. The proposed methodology is then evaluated by being applied to 300 sample apartment units in Tehran, Iran. Four pix2pix models were trained to predict illuminance, spatial daylight autonomy (sDA), primary energy intensity and ventilation maps. The simulation results were considered ground truth.

Findings

The results showed an average structural similarity index measure (SSIM) of 0.86 and 0.81 for the predicted illuminance and sDA maps, respectively, and an average score of 88% for the predicted primary energy intensity and ventilation representative maps, each of which is outputted within three seconds.

Originality/value

The proposed framework in this study helps upskilling the design professionals involved with the architecture, engineering and construction (AEC) industry through engaging artificial intelligence in human–computer interactions. The specific novelties of this research are: first, evaluating indoor environmental metrics (daylight and ventilation) alongside the energy performance of space layouts using pix2pix model, second, widening the assessment scope to a group of spaces forming an apartment layout at five different floors and third, incorporating the impact of building context on the intended objectives.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

Article
Publication date: 15 October 2021

Rangayya, Virupakshappa and Nagabhushan Patil

One of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades…

Abstract

Purpose

One of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades, but it has few classification issues in terms of poor performances. Hence, the authors proposed a novel model for face recognition.

Design/methodology/approach

The proposed method consists of four major sections such as data acquisition, segmentation, feature extraction and recognition. Initially, the images are transferred into grayscale images, and they pose issues that are eliminated by resizing the input images. The contrast limited adaptive histogram equalization (CLAHE) utilizes the image preprocessing step, thereby eliminating unwanted noise and improving the image contrast level. Second, the active contour and level set-based segmentation (ALS) with neural network (NN) or ALS with NN algorithm is used for facial image segmentation. Next, the four major kinds of feature descriptors are dominant color structure descriptors, scale-invariant feature transform descriptors, improved center-symmetric local binary patterns (ICSLBP) and histograms of gradients (HOG) are based on clour and texture features. Finally, the support vector machine (SVM) with modified random forest (MRF) model for facial image recognition.

Findings

Experimentally, the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy, similarity index, dice similarity coefficient, precision, recall and F-score results. However, the proposed method offers superior recognition performances than other state-of-art methods. Further face recognition was analyzed with the metrics such as accuracy, precision, recall and F-score and attained 99.2, 96, 98 and 96%, respectively.

Originality/value

The good facial recognition method is proposed in this research work to overcome threat to privacy, violation of rights and provide better security of data.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 8 September 2022

Johnny Kwok Wai Wong, Mojtaba Maghrebi, Alireza Ahmadian Fard Fini, Mohammad Amin Alizadeh Golestani, Mahdi Ahmadnia and Michael Er

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes…

Abstract

Purpose

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

Design/methodology/approach

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

Findings

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

Originality/value

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

Details

Construction Innovation , vol. 24 no. 2
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 1 March 2021

Anil Kumar Uppugunduru and Syed Ershad Ahmed

Multipliers that form the basic building blocks in most of the error-resilient media processing applications are computationally intensive and power-hungry modules. Therefore…

Abstract

Purpose

Multipliers that form the basic building blocks in most of the error-resilient media processing applications are computationally intensive and power-hungry modules. Therefore, improving the multiplier’s performance in terms of area, critical path delay and power has become an important research area. This paper aims to propose two improved multiplier designs based on a new approximate compressor circuit to reduce the hardware complexity at the partial product reduction stage. The proposed approximate 4:2 compressor design significantly reduces the overall hardware cost of the multiplier. The error introduced by the approximate compressor is reduced using a new technique of assigning inputs to the compressors in the partial product reduction structure.

Design/methodology/approach

The multiplier designs implemented using the proposed approximate 4:2 compressor are targeted for error-resilient applications. For fair comparisons, various multiplier designs, including the proposed one, are implemented in MATLAB. The quality analysis is carried out using standard images, and metrics such as structural similarity index are computed to quantify the result of proposed designs with the existing architectures. Next, Verilog gate-level designs are synthesized to compute area, delay and power to prove the efficacy of the proposed designs.

Findings

Exhaustive error and hardware analysis have been carried out for the existing and proposed multiplier architectures. Error analysis carried out using MATLAB proves that the proposed designs achieve better quality metrics than existing designs. Hardware results show that area, the power consumed and critical path delay are reduced up to 39.8%, 51.7% and 15.9%, respectively, compared to the existing designs. Toward the end, the proposed designs impact is quantified and compared with existing designs on real-time image sharpening and image multiplication applications.

Originality/value

The area, delay and power metrics of the multiplier can be improved using an approximate compressor in an error-resilient application. Accordingly, in this work, a new compressor is proposed that reduces the hardware complexity in the multiplier architecture. However, the proposed approximate compressor, while reducing the computational complexity, tends to introduce error in the multiplier. The error introduced by the approximate compressor is reduced using a new technique of assigning inputs to the compressors in the partial product reduction structure. With the help of the approximate compressor and a technique of input realignment, hardware efficient and highly accurate multiplier designs are achieved.

Details

Circuit World, vol. 48 no. 2
Type: Research Article
ISSN: 0305-6120

Keywords

1 – 10 of over 12000