A Low Cost Structurally Optimized Design for Diverse Filter Types.

A wide range of image processing applications deploys two dimensional (2D)-filters for performing diversified tasks such as image enhancement, edge detection, noise suppression, multi scale decomposition and compression etc. All of these tasks require multiple type of 2D-filters simultaneously to acquire the desired results. The resource hungry conventional approach is not a viable option for implementing these computationally intensive 2D-filters especially in a resource constraint environment. Thus it calls for optimized solutions. Mostly the optimization of these filters are based on exploiting structural properties. A common shortcoming of all previously reported optimized approaches is their restricted applicability only for a specific filter type. These narrow scoped solutions completely disregard the versatility attribute of advanced image processing applications and in turn offset their effectiveness while implementing a complete application. This paper presents an efficient framework which exploits the structural properties of 2D-filters for effectually reducing its computational cost along with an added advantage of versatility for supporting diverse filter types. A composite symmetric filter structure is introduced which exploits the identities of quadrant and circular T-symmetries in two distinct filter regions simultaneously. These T-symmetries effectually reduce the number of filter coefficients and consequently its multipliers count. The proposed framework at the same time empowers this composite filter structure with additional capabilities of realizing all of its Ψ-symmetry based subtypes and also its special asymmetric filters case. The two-fold optimized framework thus reduces filter computational cost up to 75% as compared to the conventional approach as well as its versatility attribute not only supports diverse filter types but also offers further cost reduction via resource sharing for sequential implementation of diversified image processing applications especially in a constraint environment

Resource-Efficient Image Buffer Architecture for Neighborhood Processors

Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps

Machine-Vision-Based Plastic Bottle Inspection for Quality Assurance

With the increasing utilization of plastic bottles in the fast-moving consumer goods industry, the efficiency and accuracy of the bottle defect inspection process becomes very important for quality assurance. Deep-learning-based inspection is accurate and robust, but at the same time data hogging and computationally expensive. Thus, it is not feasible for fast inspection. Therefore, this paper presents an efficient and fast machine-vision-based system for inspecting plastic bottle defects. The system is composed of a chamber which has a camera and illuminators to capture high-resolution images in controlled lighting conditions. Captured images are processed by using simple image processing techniques to identify multiple defects such as seated cap, dents on the body and label alignment on the plastic. The experimental results show that the proposed system is 95% accurate in determining a range of bottle defects. It is highly feasible for fast inspection and does not require high computation power and a large amount of training data

A Deep Learning-Based Framework for Visual Inspection of Plastic Bottles

This paper presents a deep learning-based framework for automating the visual inspection of plastic bottles in an Industry 4.0 context, detecting surface defects to enhance product quality. Our contributions include the acceleration of model development through knowledge transfer learning, an inventive data generation strategy that combines physical samples with synthetic data augmentation techniques, an extensive evaluation of pre-trained deep convolutional neural networks, and a user-friendly interface for real-time quality inspection reporting and making the information easily accessible and actionable. In comparison to existing methods, our proposed method outperforms with a higher Accuracy to Size Ratio of 7.0. This characteristic underscores its capacity to efficiently and accurately classify and detect defects across multiple classes while maintaining a low area utilization. This feature not only demonstrates its exceptional performance but also positions it as a practical solution for real-world scenarios with resource constraints

Towards a Carbon Neutral and Sustainable Campus: Case Study of NED University of Engineering and Technology

Globally, universities are evaluating and targeting to reduce their carbon emissions and operate on a sustainable basis. The overall aim of this study revolves in addressing the following three questions: (1) How to calculate carbon footprint, including indicators selection, criteria, and measurement, for higher education institutions? (2) How to evaluate impact and effectiveness of various mitigation strategies in context of a higher education institution? (3) What are the possible limitations of approach selected for carbon footprint calculation. This paper presents estimation of the carbon footprint of NED University using a carbon calculator along with the identification of sources with maximum contribution to its carbon footprint. The carbon footprint of the NED University main campus for 2017 was calculated to be approximately 21,500 metric tons of equivalent CO2 and carbon footprint per student was 1.79 metric tons of equivalent CO2. Scope 1 and Scope 2 emissions each contributed nearly 7% of the carbon footprint, while Scope 3 emissions accounted for 85.6% of the carbon footprint. Major interventions such as switching to renewables, usage of energy efficient appliances, electric vehicles, and massive tree plantation inside and outside the campus were identified as the most effective mitigation strategies

Internal architecture of processing element (PE_2,2).

<p>Internal architecture of processing element (PE_2,2).</p

Total multipliers for parallel and sequential architectures of chosen biomedical application.

<p>Total multipliers for parallel and sequential architectures of chosen biomedical application.</p

Selection logic for decomposing C into M and S.

<p>Selection logic for decomposing C into M and S.</p

Realization of distinct filter types by using proposed framework.

<p>(a) 3x3 Emboss filter. (b) 5x5 Laplacian of Gaussian filter (c) 3x3 Sobel-X filter.</p

Towards a Carbon Neutral and Sustainable Campus: Case Study of NED University of Engineering and Technology

Globally, universities are evaluating and targeting to reduce their carbon emissions and operate on a sustainable basis. The overall aim of this study revolves in addressing the following three questions: (1) How to calculate carbon footprint, including indicators selection, criteria, and measurement, for higher education institutions? (2) How to evaluate impact and effectiveness of various mitigation strategies in context of a higher education institution? (3) What are the possible limitations of approach selected for carbon footprint calculation. This paper presents estimation of the carbon footprint of NED University using a carbon calculator along with the identification of sources with maximum contribution to its carbon footprint. The carbon footprint of the NED University main campus for 2017 was calculated to be approximately 21,500 metric tons of equivalent CO2 and carbon footprint per student was 1.79 metric tons of equivalent CO2. Scope 1 and Scope 2 emissions each contributed nearly 7% of the carbon footprint, while Scope 3 emissions accounted for 85.6% of the carbon footprint. Major interventions such as switching to renewables, usage of energy efficient appliances, electric vehicles, and massive tree plantation inside and outside the campus were identified as the most effective mitigation strategies