The New Food Safety Regime in the US: How Will it Affect Canadian Competitiveness

The FSMA appears to be a major undertaking with a very large responsibility placed on the FDA. It would seem that bottlenecks to exporting are bound to appear which will be very frustrating for Canadian firms. It is important for Canadian firms and Canadian policy makers to work hard to ensure that temporary bottlenecks do not become permanent inhibitors of trade. The Canadian government needs to understand industry concerns and use any mechanisms – including those in the NAFTA – to initiate consultations with the US. Given the likely lags in implementation, North American food markets are likely to exhibit considerable disequilibrium over the near term. Trade flows will be affected. As the implementation programs of the FSMA become more transparent, more sophisticated analysis into its effect on Canadian competitiveness in the US market can be undertaken.food, safety, competitiveness, Agribusiness, Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety,

The New Food Safety Regime in the US: How Will it Affect Canadian Competitiveness

The Food Safety Modernization Act (FSMA) which was signed into law in January, 2011 represents a major initiative to improve food safety in the US. The legislation mandates the US Food and Drug Administration with developing a regulatory system to implement the Act. As yet, the full effect of the Act cannot be evaluated because the regulatory requirements are yet to be developed. There is little doubt, however, that those firms, both domestic and foreign, that wish to supply US consumers with food will face a considerable increase in regulatory costs. This paper outlines the major requirements of the FSMA and suggests how the regulatory burden may fall on foreign versus US domestic suppliers. Areas where Canadian firms may be disadvantaged relative to US firms are outlined. Opportunities that may arise from the FSMA for Canadian agri-food firms are discussed, as are the areas where the FSMA may not conform with the international trade commitments of the United States.competitiveness, food safety, regulatory burden, SPS, Agribusiness, Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety, International Relations/Trade,

Multiple convolutional neural network training for Bangla handwritten numeral recognition

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high-dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, three different CNNs with same architecture are trained with different training sets and combined their decisions for Bangla handwritten numeral recognition. One CNN is trained with ordinary training set prepared from handwritten scan images; and training sets for other two CNNs are prepared with fixed (positive and negative, respectively) rotational angles of original images. The proposed multiple CNN based approach is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset

Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, a CNN based method has been investigated for Bangla handwritten numeral recognition. A moderated pre-processing has been adopted to produce patterns from handwritten scan images. On the other hand, CNN has been trained with the patterns plus a number of artificial patterns. A simple rotation based approach is employed to generate artificial patterns. The proposed CNN with artificial pattern is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset

Hot-spot traffic pattern on hierarchical 3D mesh network

A Hierarchical 3D-Mesh (H3DM) Network s a 2D-mesh network of multiple basic modules (BMs), in which the basic modules are 3D-torus networks that are hierarchically interconnected for higher-level networks. In this paper, we evaluate the dynamic communication performance of a H3DM network under hot-spot traffic pattern using a deadlock-free dimension order routing algorithm with minimum number of virtual channels. We have also evaluated the dynamic communication performance of the mesh and torus networks. It is shown that under most imbalance hot-spot traffic pattern H3DM network yields high throughput and low average transfer time than that of mesh and torus networks, providing better dynamic communication performance compared to those networks

Bangla handwritten numeral recognition using convolutional neural network

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Although Bangla is a major language in Indian subcontinent and is the first language of Bangladesh study regarding Bangla handwritten numeral recognition (BHNR) is very few with respect to other major languages such Roman. The existing BHNR methods uses distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. It also automatically provides some degree of translation invariance. In this paper, a CNN based BHNR is investigated. The proposed BHNR-CNN normalizes the written numeral images and then employ CNN to classify individual numerals. It does not employ any feature extraction method like other related works. 17000 hand written numerals with different shapes, sizes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent exiting methods

Architecture and network-on-chip implementation of a new hierarchical interconnection network

A Midimew-connected Mesh Network (MMN) is a minimal distance mesh with wrap-around links network of multiple basic modules (BMs), in which the BMs are 2D-mesh networks that are hierarchically interconnected for higher-level networks. In this paper, we present the architecture of the MMN, addressing of node, routing of message, and evaluate the static network performance of MMN, TESH, mesh and torus networks. In addition, we propose the network-on-chip (NoC) implementation of MMN. With innovative combination of diagonal and hierarchical structure, the MMN possesses several attractive features, including constant degree, small diameter, low cost, small average distance, moderate bisection width and high fault tolerant performance than that of other conventional and hierarchical interconnection networks. The simple architecture of MMN is also highly suitable for NoC implementation. To implement all the links of level-3 MMN, only four layers are needed which is feasible with current and future VLSI technologies

Time-cost effective factor of a Midimew connected Mesh network

Hierarchical Interconnection Network (HIN) is indispensable for the practical implementation of future generation massively parallel computer systems which consists of hundred thousands nodes or even millions of nodes. Because it yields good performance with low cost due to reduction of communication links and by exploring the locality in the communication & traffic patterns. A Midimew connected Mesh Network (MMN) is an HIN comprised of numerous basic modules, where the basic modules are 2D-mesh networks and they are hierarchically interconnected using midimew network to construct the higher level networks. In this paper, we present the architecture of a MMN and evaluate the time-cost effective factor of MMN, TESH, mesh, and torus networks. It is found that the proposed MMN yields slightly high time-cost effectiveness factor with small diameter and average distance as compared to other networks. Overall, performance with respect to time-cost effectiveness factor with small diameter and average distance suggests that the proposed MMN will be a indispensable choice for the next generation massively parallel computer systems

Cost effective factor of a midimew connected mesh network

Background and Objective: Hierarchical Interconnection Network (HIN) is very much essential for the practical implementation of future generation Massively Parallel Computers (MPC) systems which consists of millions of nodes. It yields better performance with low cost due to reduction of wires and by exploring the locality in the communication\and traffic patterns. The main objective of this paper is to analyze the static cost effective factor of Midimew connected Mesh Network (MMN). Materials and Methods: A Midimew connected Mesh Network (MMN) is a HIN comprised of numerous basic modules, where the basic modules are 2D-mesh networks and they are hierarchically interconnected using midimew network to assemble the higher level networks. Results: This study, present the architecture of a MMN and evaluate the cost effective factor of MMN, TESH (Tori-connected Mesh), mesh and torus networks. The results shows that the cost effective factor of MMN was trivially higher than that of mesh and torus network. Conclusion: It was revealed that the proposed MMN yields a little bit high cost effectiveness factor with small diameter and average distance. Overall, performance with respect to cost effective factor with small diameter and average distance suggests that the MMN will be a promising choice for next generation MPC system

Adaptive interval type-2 fuzzy logic controller for autonomous mobile robot

A Type-2 Fuzzy logic controller adapted with genetic algorithm, called type-2 genetic fuzzy logic controller (T2GFLC), is presented in this paper to handle uncertainty with dynamic optimal learning. Genetic algorithm is employed to simultaneous design of type-2 membership functions and rule sets for type-2 fuzzy logic controllers. Traditional fuzzy logic controllers (FLCs), often termed as type-1 fuzzy logic systems using type-1 fuzzy sets, cannot handle large amount of uncertainties present in many real environments. Therefore, recently type-2 FLC has been proposed. The type-2 FLC can be considered as a collection of different embedded type-1 FLCs. However, the current design process of type-2 FLC is not automatic and relies on human experts. The purpose of our study is to make the design process automatic. Moreover, to reduce the computation time of T2GFLC an efficient type-reduction strategy for interval type-2 fuzzy set is also introduced. The evolved type-2 FLCs can deal with large amount of uncertainties and exhibit better performance for the mobile robot. Furthermore, it has outperformed their type-1 counterparts as well as the adaptive type-1 FLCs