To study the forklift that is more suitable for handling goods in narrow spaces, the modeling and simulation of four wheel steering (4WS) forklift is studied. Firstly taking the type of TFC35 electric forklift as an example, the principle and the main parts of 4WS system based on steer by wire (SBW) technology are analyzed and researched. Secondly according to the Newton vector mechanics system, a dynamic model of the two degree of freedom (2DOF) is established for 4WS forklift, and then the dynamic model of the three degree of freedom (3DOF) of the forklift is established by Lagrange method to consider the effect of roll motion on the forklift. Finally using MATLAB software to build the simulation models, and under the same simulation parameters, the simulation results show that the two kinds of dynamic models have similar steady-state response, but due to the 3DOF dynamics model considering the roll factor, it can reflect the steering characteristics of actual forklift better than the 2DOF dynamics model. The forklift studied in this paper is flexible and more suitable for working in small space compared with the traditional rear-wheel steering (RWS) forklift.

Abstract\n\n \nIn biodiversity management and conservation, the identification and classification of natural vegetation are considered as a major issue. In this paper, natural vegetation and its formations are identified using a Worldview-2 spectral imagery. The classification of the Worldview-2 image and ancillary thematic data was performed by using an Improved Relevance Vector Machine with Mosquito Flying behaviour based swarm intelligence Optimization (IRVM-MFO) algorithm. Here the perceptive strength of the spectral signature and the Local features of spectral bands are considered in each pixel. In addition texture features such as Fourier spectrum and GLCM features are exploited to make the system more robust. In IRVM, the MFO approach used to optimize the kernel functions of parameters to improve the training process. The proposed IRVM-MFO shows improved classification performance in terms of parameters like overall sensitivity, specificity and accuracy compared with simple RVM and SVM methods. The proposed method has results in a high accuracy of 92.3% and the Kappa index varying between 0.92 and 0.78 at vegetation formation levels.

In an automotive electronics applications there are approximately 230 electronic control units ECU’s are used to provide intelligent driving assistance. So, there is an effective multiple objective real time task scheduling techniques are required to provide better solution in this domain. This paper describes novel multiobjective evolutionary algorithmic techniques such as Multi - Objective Genetic Algorithm (MOGA), Non-dominated Sorting Genetic Algorithm (NSGA) and Multi - Objective Messy Genetic Algorithm (MOMGA) for scheduling real time tasks to a multicore processor based ECU. These techniques improve the performance upon earlier reported of an ECU’s by considering multiple objectives such as, low power consumption (P), maximizing core utilization (U) and minimizing deadline missrate (δ). This work also analysis the schedulability of realtime tasks by computing the converging value of a series of task parameters such as execution time, release time, workload and arrival time. Finally, we investigated the performance parameters such as power consumption (P), deadline missrate (δ), and core utilization for the given architecture. The evaluation results show that the power consumption is reduced to about 5 - 8%, utilization of the core is increased about 10 % to 40% and deadline missrate is comparatively minimized with other scheduling approaches.

The ECG (Electrocardiogram) signal represents electrical behavior of\nheart over time and is measured by placing electrodes on specific locations of\nlimb. These signals are useful for monitoring and diagnosis of heart related issues.\nECG signals are often corrupted by artifacts during acquisition and transmission\npredominantly by high frequency power line interference, electromyography\nnoise and low frequency noise caused by motion of electrodes (baseline changes).\nAddition of these artifacts changes morphology of the ECG signal which affect\naccurate analysis and hence need to be reduced for better clinical evaluation.\nECG signals also generate massive volume of digital data, so they need to be\nsuitably compressed for efficient transmission and storage. Hence, for efficient\ncompression/approximation, in this paper, the ECG signal taken from MITBIH\ndatabase is preprocessed using Total Variation Denoising approach and\nthe preprocessed signals are then characterized using Bottom-Up approach.\nThe individual sections are then approximated using Chebyshev polynomials\nof suitable order. The performance of the approximation technique is measured\nby computing the Maximum Absolute Error, the Compression Ratio, Root\nMean Square Error, Percent Root Mean Square Difference and Percent Root\nMean Square Difference Normalized. The results are also compared with other\ntechniques reported in the literature, where significant improvements in all the\nperformance metrics are observed by the proposed method.

Abstract\n\n As VLSI device is concern low power operation of CMOS devices architecture are significant one. Here in the proposed work, image sensor based on the concept of Pulse width modulation are designed with the operation voltage of less than 300 mV and to operate in low signal to noise ratio. CMOS based image sensor are modelled for the wireless sensors in security purposes. On comparing to the previous sensors, transducer work this proposed work gives the high gain, reduced area structure, good throughput. Here, designed a circuit were CMOS image sensor operates in 0.4V with the output of 0.38V as output voltage, 54dB of dynamic range in sensor pixels.

With the rapid increase in internet users and customer reviews playing the major role in social media gave rise to sentiment analysis. Pre-processing of input text during sentiment analysis eliminates the incomplete and noisy data. Typically, sentiment is manifested separately and applying a pre-processing model for optimizing the cross-domain sentiment classification is highly required. In this paper, a method called Hidden Markova Continual Progression Cosine Similar (HM-CPCS) is proposed to explore the impact of pre-processing and optimize sentiment analysis. First, a measure of subsequent and antecedent probabilities of tags is made using Hidden Markova POS Tagger for the given input dataset. Subsequent and antecedent probabilities of tags are obtained by measuring the transition probabilities between states (i.e. domains) and observations (i.e. review statements) ensuring feature extraction accuracy. Next, the Continual Progression Stemmer continuously stems the text by adding prefix and suffix to form structured words for the given shortcuts and therefore reduce Error Rate Relative to Truncation (ERRT). Finally a Cosine Similarity function is applied to remove stop word for cross-domain sentiment analysis and classification. The performance evaluation of HM-CPCS method is done with standard benchmark data sets of consumer product and services reviews extracted from Sentiwordnet. The parameters used in evaluation are number of customer review words, execution time, accuracy and error rate. Experimental analysis shows that HM-CPCS method is able to reduce the time to extract the opinions from reviewers by 46% and improve the accuracy by 9% compared to the state-of-the-art works.