A computer vision system for the classification of moving object

The aim of this research is to produce a system that can detect the moving object and classify it into three classes: “Humans, Vehicle and Animals”. Using fixed video camera in outdoors environment, the system will capture the images and digitize them using (Piccolo Pro II) frame grabber at a rate of 25 frames per second. The Background Subtraction technique has been employed in the work as it is able to provide the most complete feature for data. However, it is extremely sensitive to dynamic changes like changing of illumination. Background Subtraction is done by taking the differenc e between any frame and the background in detecting the Moving Object. In order to reduce the effect of noise pixels resulting from the Background Subtraction operation, a number of pre-processing methods have been applied on the detected moving object. These preprocessing operations involve the use of median filter as well as morphological filters. Then the outline of the object will be extracted using border extraction technique. The classification makes use of both the shape and the dynamic features of the objects. In increasing the performance of the classification, all features are sequentially arranged so that the goal of this research is to be achieved. In this work, the performance achieved is 93% for class human, 93% for class vehicle and 64% for class animal

Critical Obstacles to Adopt the Organic Farming in Jordan: From Marketing Perspective

Bringing the demand and supply of food produce has been a great challenge for the professionals and policy makers all around the globe.  The ever increasing attention towards organic farming has motivated the researchers to conduct the present study so as to understand the obstacles in adopting organic farming (OF), in developing countries. Keeping in view the existing gap in the literature of organic farming an exploratory qualitative approach has been used so as to get insights of the organic farming and to explore the new fact so as to contribute in the existing body of knowledge. The study found that: the absence of organization to assess and certify organic products, high cost, lack of financing sources, low yield, high price, specific market of organic food, low environmental awareness of farmers, unsuccessful agricultural reforms, lack of coordination among stakeholders and institutional changes have been the main obstacles needed to be resolved so as to increase the organic farming in developing countries

Literature survey about elements of manufacturing shop floor operation key performance indicators

In the era of globalisation, manufacturing industries are compelled to continuously monitor their manufacturing operations to maintain competitiveness. As a result, manufacturers have integrated several measurement models to inspect their manufacturing operations. These models comprise of a set of Key Performance Indicators (KPIs), which are capable to enumerate the effectiveness, competence, efficiency and proficiency of manufacturing operations. This paper presents a review of manufacturing shop floor operation KPIs that has been studied in the recent literature. Based on the reviewed literature author proposes various KPI elements such as: description, category, scope, formula, unit of measure, range, trend, mode of display, viewers and manufacturing approach. These elements can help manufacturers to better describe, classify, analyze and measure the appropriate KPIs for their shop floor operations. Thus, enabling manufacturers to accomplish and uphold great quality, increased productivity and throughput

Enhancing pharmaceutical packaging through a technology ecosystem to facilitate the reuse of medicines and reduce medicinal waste

The idea of reusing dispensed medicines is appealing to the general public provided its benefits are illustrated, its risks minimized, and the logistics resolved. For example, medicine reuse could help reduce medicinal waste, protect the environment and improve public health. However, the associated technologies and legislation facilitating medicine reuse are generally not available. The availability of suitable technologies could arguably help shape stakeholders’ beliefs and in turn, uptake of a future medicine reuse scheme by tackling the risks and facilitating the practicalities. A literature survey is undertaken to lay down the groundwork for implementing technologies on and around pharmaceutical packaging in order to meet stakeholders’ previously expressed misgivings about medicine reuse (’stakeholder requirements’), and propose a novel ecosystem for, in effect, reusing returned medicines. Methods: A structured literature search examining the application of existing technologies on pharmaceutical packaging to enable medicine reuse was conducted and presented as a narrative review. Results: Reviewed technologies are classified according to different stakeholders’ requirements, and a novel ecosystem from a technology perspective is suggested as a solution to reusing medicines. Conclusion: Active sensing technologies applying to pharmaceutical packaging using printed electronics enlist medicines to be part of the Internet of Things network. Validating the quality and safety of returned medicines through this network seems to be the most effective way for reusing medicines and the correct application of technologies may be the key enabler

SPECIALIZED OBJECT-ORIENTED TOOLS FOR THE DEVELOPMENT OF INFORMATIONCALCULATING APPLICATIONS

The paper presents an approach to building specialized objectoriented software tools for the development of so-called informationcalculating applications including computer aided accounting, business correspondence, statistics etc . These software tools form an integrated development environment allowing the computer assisted development of information-calculating applications. This development environment consists of a formula interpreter, a screen form generator and a specialized library of classes. The implementation of all these components was carried out using the Visual FoxPro database system and has been practically tested on a series of commercial applications concerning computer aided accountancy and business correspondence

Manufacturing enhancement through reduction of cycle time using time-study statistical techniques in automotive industry

Within the complex and competitive automotive manufacturing industry, manufacturing Cycle Time (CT) remains one of the Key Performance Indicators (KPIs). Its reduction is of strategic importance as it contributes to time-to-market shortening, faster bottleneck detection, achieving throughput targets and improving production-resource scheduling. This paper presents a case study on CT analysis for early stage identification of the bottleneck stations and the processes in a manual assembly line that is responsible for increased manufacturing CT. The case study is conducted on an automotive seat manufacturing plant in the UK. For detailed CT analysis, CT of each station is recorded. Results of the case study shows that bottlenecks identification at an early stage can significantly enhance the overall performance of the production line

A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images

The increasing use of smartphones and social media apps for communication results in a massive number of screenshot images. These images enrich the written language through text and emojis. In this regard, several studies in the image analysis field have considered text. However, they ignored the use of emojis. In this study, a robust two-stage algorithm for detecting emojis in screenshot images is proposed. The first stage localizes the regions of candidate emojis by using the proposed RGB-channel analysis method followed by a connected component method with a set of proposed rules. In the second verification stage, each of the emojis and non-emojis are classified by using proposed features with a decision tree classifier. Experiments were conducted to evaluate each stage independently and assess the performance of the proposed algorithm completely by using a self-collected dataset. The results showed that the proposed RGB-channel analysis method achieved better performance than the Niblack and Sauvola methods. Moreover, the proposed feature extraction method with decision tree classifier achieved more satisfactory performance than the LBP feature extraction method with all Bayesian network, perceptron neural network, and decision table rules. Overall, the proposed algorithm exhibited high efficiency in detecting emojis in screenshot images

The impact of geometric and motion features on sign language translators

Malaysian Sign Language (MSL) recognition system is a choice of augmenting communication between the hearing-impaired and hearing communities in Malaysia. Automatic translators can play an important role as alternative communication method for the hearing people to understand the hearing impaired ones. Automatic Translation using bare hands with natural gesture signing is a challenge in the field of machine learning. Researchers have used electronic and coloured gloves to solve mainly three issues during the preprocessing steps before the singings’ recognition stage. First issue is to differentiate the two hands from other objects. This is referred to as hand detection. The second issue is to describe the detected hand and its motion trajectory in very descriptive details which is referred to as feature extraction stage. The third issue is to find the starting and ending duration of the sign (transitions between signs). This paper focuses on the second issue, the feature extraction by studying the impact of the vector dimensions of the features. At the same time, signs with similar attributes have been chosen to highlight the importance of features’ extraction stage. The study also includes Hidden Markov Model (HMM) capability to differentiate between signs which have similar attributes

Feature extraction: hand shape, hand position and hand trajectory path

Vision-based hand posture detection and tracking is an important issue for Human to Computer Interaction applications. The performance of recognition system fIrst depends on the process of getting effIcient features to represent pattern characteristics [1]. There is no algorithm which shows how to select the representation or choose the features [2] so the selection of features will depend on the application. There are many different methods to represent 2-D images such as boundary, topological, shape grammar, description of similarity etc. [2-4]. Features should be chosen so that they are intensive to noise-like variation in pattern and keep the number of feature small for easy computation [5]. Hand posture shape features, motion trajectory feature and hand position with respect to other human upper body parts play an important role within the preparation stage of the gesture before recognition. In this chapter, features have been extracted from hand posture closed contours, hand posture trajectory and hand position has been identifIed. Algorithms have been developed for extracting these features after segmenting the head and the two hands. These extracted features can be attached to a recognizer such as Support Vector machine, Hidden Markov Model, etc. for hand gesture recognition