Sayyid Samir Al-Busaidi, Afaq Ahmad* and Medhat Awadalla
This paper proposes a novel design for Binary to Gray code encoders and/or counters using multiplexers and flip-flops. The proposed design are modular based, whereby other stages can be added as per the requirement of the desired applications. Moreover, the external clock timing signal drives only the first stage, while all remaining stages are linked to the outputs from preceding stages. The successive stages transitions at half the rate of the preceding stage thereby, makes the design power efficient since the dissipated power is quadratic frequency dependent. The proposed design can be modified to increase the counters duration or increase the counters resolution according to the applications need. Increasing the Gray counters time span by powers of two simply necessitates augmenting the design by more stages, while maintaining a constant clock rate. On the other hand, doubling the time resolution of the Gray counter over a constant time span can be achieved by adding another stage while subsequently doubling the clock rate.
Surabhi Singh*, Santosh Ahlawat and Sarita Sanwal
Agriculture is a gigantic sector of the Indian economy as its share to gross domestic product (GDP) is almost 17 per cent. Over 60 per cent of the population adopts agriculture as main occupation. In spite of a large of Indian economy, agriculture is lagging behind many aspects and characterised by poor connectivity and disintegration of market, unreliable and delayed information to the farmers, small land holdings, non adoption or less adoption of improved technology and so on. It has become indispensable to explore various ways to keep our farmers updated about modern technologies and relevant information. The development and timely dissemination of better personalized technologies specific to different agro-climatic conditions, size of land holding, soil type, type of crops and related pests/diseases is the real issue to brazen out ahead for the agricultural scientists/experts. The timely availability of right information and its proper utilisation is indispensable for agriculture. ICT based initiatives can be taken for propagation of information, transfer of technology, procurement of inputs and selling of outputs in a way so that farmers can be benefitted. The timely information and practical solutions of the agricultural problems helps the farmers to adopt good agricultural practices, make better choices of inputs and to plan the cultivation properly.
M. A. H. Akhand, Tanzima Sultana, M. I. R. Shuvo and Al-Mahmud
Vehicle Routing Problem (VRP) is a real life constraint satisfaction problem to find minimal travel distances of vehicles to serve customers. Capacitated VRP (CVRP) is the simplest form of VRP considering vehicle capacity constraint. Constructive and clustering are the two popular approaches to solve CVRP. A constructive approach creates routes and attempts to minimize the cost at the same time. Clarke and Wright’s Savings algorithm is a popular constructive method based on savings heuristic. On the other hand, a clustering based method first assigns nodes into vehicle wise cluster and then generates route for each vehicle. Sweep algorithm and its variants and Fisher and Jaikumar algorithm are popular among clustering methods. Route generation is a traveling salesman problem (TSP) and any TSP optimization method is useful for this purpose. In this study, popular constructive and clustering methods are studied, implemented and compared outcomes in solving a suite of benchmark CVRPs. For route optimization, Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Velocity Tentative Particle Swarm Optimization (VTPSO) are employed in this study which are popular nature inspired optimization techniques for solving TSP. Experimental results revealed that parallel Savings is better than series Savings in constructive method. On the other hand, Sweep Reference Point using every stop (SRE) is the best among clustering based techniques.
Kamalpreet Kaur* and O.P. Gupta
Maturity checking has become mandatory for the food industries as well as for the farmers so as to ensure that the fruits and vegetables are not diseased and are ripe. However, manual inspection leads to human error, unripe fruits and vegetables may decrease the production . Thus, this study proposes a Tomato Classification system for determining maturity stages of tomato through Machine Learning which involves training of different algorithms like Decision Tree, Logistic Regression, Gradient Boosting, Random Forest, Support Vector Machine, K-NN and XG Boost. This system consists of image collection, feature extraction and training the classifiers on 80% of the total data. Rest 20% of the total data is used for the testing purpose. It is concluded from the results that the performance of the classifier depends on the size and kind of features extracted from the data set. The results are obtained in the form of Learning Curve, Confusion Matrix and Accuracy Score. It is observed that out of seven classifiers, Random Forest is successful with 92.49% accuracy due to its high capability of handling large set of data. Support Vector Machine has shown the least accuracy due to its inability to train large data set.
Chetan R. Dudhagara and Hasamukh B. Patel
In a recent era of modern technology, there are many problems for storage, retrieval and transmission of data. Data compression is necessary due to rapid growth of digital media and the subsequent need for reduce storage size and transmit the data in an effective and efficient manner over the networks. It reduces the transmission traffic on internet also. Data compression try to reduce the number of bits required to store digitally. The various data and image compression algorithms are widely use to reduce the original data bits into lesser number of bits. Lossless data and image compression is a special class of data compression. This algorithm involves in reducing numbers of bits by identifying and eliminating statistical data redundancy in input data. It is very simple and effective method. It provides good lossless compression of input data. This is useful on data that contains many consecutive runs of the same values. This paper presents the implementation of Run Length Encoding for data compression.