An autonomous navigational system using GPS and computer vision for futuristic road traffic

Navigational service is one of the most essential dependency towards any transport system and at present, there are various revolutionary approaches that has contributed towards its improvement. This paper has reviewed the global positioning system (GPS) and computer vision based navigational system and found that there is a large gap between the actual demand of navigation and what currently exists. Therefore, the proposed study discusses about a novel framework of an autonomous navigation system that uses GPS as well as computer vision considering the case study of futuristic road traffic system. An analytical model is built up where the geo-referenced data from GPS is integrated with the signals captured from the visual sensors are considered to implement this concept. The simulated outcome of the study shows that proposed study offers enhanced accuracy as well as faster processing in contrast to existing approaches

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Reliability in the utilization of the Global Positioning System (GPS) data demands a higher degree of accuracy with respect to time and positional information required by the user. However, various extrinsic and intrinsic parameters disrupt the data transmission phenomenon from GPS satellite to GPS receiver which always questions the trustworthiness of such data. Therefore, this manuscript offers a comprehensive insight into the data preprocessing methodologies evolved and adopted by present-day researchers. The discussion is carried out with respect to standard methods of data cleaning as well as diversified existing research-based approaches. The review finds that irrespective of a good number of work carried out to address the problem of data cleaning, there are critical loopholes in almost all the existing studies. The paper extracts open end research problems as well as it also offers an evidential insight using use-cases where it is found that still there is a critical need to investigate data cleaning methods

Phase I, Single-Dose, Dose-Escalating Study of Inhaled Dry Powder Capreomycin: a New Approach to Therapy of Drug-Resistant Tuberculosis

ABSTRACT Multidrug-resistant tuberculosis (MDR-TB) threatens global TB control. The lengthy treatment includes one of the injectable drugs kanamycin, amikacin, and capreomycin, usually for the first 6 months. These drugs have potentially serious toxicities, and when given as intramuscular injections, dosing can be painful. Advances in particulate drug delivery have led to the formulation of capreomycin as the first antituberculosis drug available as a microparticle dry powder for inhalation and clinical study. Delivery by aerosol may result in successful treatment with lower doses. Here we report a phase I, single-dose, dose-escalating study aimed at demonstrating safety and tolerability in healthy subjects and measuring pharmacokinetic (PK) parameters. Twenty healthy adults ( n = 5 per group) were recruited to self-administer a single dose of inhaled dry powder capreomycin (25-mg, 75-mg, 150-mg, or 300-mg nominal dose) using a simple, handheld delivery device. Inhalations were well tolerated by all subjects. The most common adverse event was mild to moderate transient cough, in five subjects. There were no changes in lung function, audiometry, or laboratory parameters. Capreomycin was rapidly absorbed after inhalation. Systemic concentrations were detected in each dose group within 20 min. Peak and mean plasma concentrations of capreomycin were dose proportional. Serum concentrations exceeded 2 μg/ml (MIC for Mycobacterium tuberculosis ) following the highest dose; the half-life ( t 1/2 ) was 4.8 ± 1.0 h. A novel inhaled microparticle dry powder formulation of capreomycin was well tolerated. A single 300-mg dose rapidly achieved serum drug concentrations above the MIC for Mycobacterium tuberculosis , suggesting the potential of inhaled therapy as part of an MDR-TB treatment regimen

Pharmacokinetics of Sequential Doses of Capreomycin Powder for Inhalation in Guinea Pigs

ABSTRACT The global control of tuberculosis (TB) is at risk by the spread of multidrug-resistant TB (MDR TB). Treatment of MDR TB is lengthy and involves injected drugs, such as capreomycin, that have severe side effects. It was previously reported that a single daily dose of inhaled capreomycin had a positive effect on the bacterial burden of TB-infected guinea pigs. The modest effect observed was possibly due to a dose that resulted in insufficient time of exposure to therapeutic systemic and local levels of the drug. In order to determine the length of time that systemic and local drug concentrations are above therapeutic levels during the treatment period, the present study investigated the disposition of capreomycin powders after sequential pulmonary administration of doses of 20 mg/kg of body weight. Capreomycin concentrations in bronchoalveolar lavage fluid and lung tissue of animals receiving a series of one, two, or three doses of capreomycin inhalable powder were significantly higher (50- to 100-fold) at all time points than plasma concentrations at the same time points or those observed in animals receiving capreomycin solution by intramuscular (i.m.) injection (10- to 100-fold higher). Notably, at the end of each dosing period, capreomycin concentrations in the lungs were approximately 100-fold higher than those in plasma and severalfold higher than the MIC, suggesting that sufficient capreomycin remains in the lung environment to kill Mycobacterium tuberculosis . No accumulation of capreomycin powder was detected in the lungs after 3 pulmonary doses. These results indicate that the systemic disposition of capreomycin after inhalation is the same as when injected i.m. with the advantage that higher drug concentrations are present at all times in the lungs, the primary site of infection

Phase I, single-dose, dose-escalating study of inhaled dry powder capreomycin : a new approach to therapy of drug-resistant tuberculosis

Multidrug-resistant tuberculosis (MDR-TB) threatens global TB control. The lengthy treatment includes one of the injectable drugs kanamycin, amikacin, and capreomycin, usually for the first 6 months. These drugs have potentially serious toxicities, and when given as intramuscular injections, dosing can be painful. Advances in particulate drug delivery have led to the formulation of capreomycin as the first antituberculosis drug available as a microparticle dry powder for inhalation and clinical study. Delivery by aerosol may result in successful treatment with lower doses. Here we report a phase I, single-dose, dose-escalating study aimed at demonstrating safety and tolerability in healthy subjects and measuring pharmacokinetic (PK) parameters. Twenty healthy adults (n = 5 per group) were recruited to self-administer a single dose of inhaled dry powder capreomycin (25-mg, 75-mg, 150-mg, or 300-mg nominal dose) using a simple, handheld delivery device. Inhalations were well tolerated by all subjects. The most common adverse event was mild to moderate transient cough, in five subjects. There were no changes in lung function, audiometry, or laboratory parameters. Capreomycin was rapidly absorbed after inhalation. Systemic concentrations were detected in each dose group within 20 min. Peak and mean plasma concentrations of capreomycin were dose proportional. Serum concentrations exceeded 2 μg/ml (MIC for Mycobacterium tuberculosis) following the highest dose; the half-life (t1/2) was 4.8 ± 1.0 h. A novel inhaled microparticle dry powder formulation of capreomycin was well tolerated. A single 300-mg dose rapidly achieved serum drug concentrations above the MIC for Mycobacterium tuberculosis, suggesting the potential of inhaled therapy as part of an MDR-TB treatment regimen.Gates Foundation.http://aac.asm.orghb2013ay201

A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network

Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and global market uncertainty. The volatility estimation of stock is one of the challenging tasks for traders. Accurate prediction of stock price helps investors to reduce the risk in portfolio or investment. Stock prices are nonlinear. To deal with nonlinearity in data, we propose a hybrid stock prediction model using the prediction rule ensembles (PRE) technique and deep neural network (DNN). First, stock technical indicators are considered to identify the uptrend in stock prices. We considered moving average technical indicators: moving average 20 days, moving average 50 days, and moving average 200 days. Second, using the PRE technique-computed different rules for stock prediction, we selected the rules with the lowest root mean square error (RMSE) score. Third, the three-layer DNN is considered for stock prediction. We have fine-tuned the hyperparameters of DNN, such as the number of layers, learning rate, neurons, and number of epochs in the model. Fourth, the average results of the PRE and DNN prediction model are combined. The hybrid stock prediction model results are computed using the mean absolute error (MAE) and RMSE metric. The performance of the hybrid stock prediction model is better than the single prediction model, namely DNN and ANN, with a 5% to 7% improvement in RMSE score. The Indian stock price data are considered for the work

A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network

Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and global market uncertainty. The volatility estimation of stock is one of the challenging tasks for traders. Accurate prediction of stock price helps investors to reduce the risk in portfolio or investment. Stock prices are nonlinear. To deal with nonlinearity in data, we propose a hybrid stock prediction model using the prediction rule ensembles (PRE) technique and deep neural network (DNN). First, stock technical indicators are considered to identify the uptrend in stock prices. We considered moving average technical indicators: moving average 20 days, moving average 50 days, and moving average 200 days. Second, using the PRE technique-computed different rules for stock prediction, we selected the rules with the lowest root mean square error (RMSE) score. Third, the three-layer DNN is considered for stock prediction. We have fine-tuned the hyperparameters of DNN, such as the number of layers, learning rate, neurons, and number of epochs in the model. Fourth, the average results of the PRE and DNN prediction model are combined. The hybrid stock prediction model results are computed using the mean absolute error (MAE) and RMSE metric. The performance of the hybrid stock prediction model is better than the single prediction model, namely DNN and ANN, with a 5% to 7% improvement in RMSE score. The Indian stock price data are considered for the work

Dead-block elimination in cache: A mechanism to reduce i-cache power consumption in high performance microprocessors

Abstract. Both power and performance are important design parameters of the present day processors. This paper explores an integrated software and circuit level technique to reduce leakage power in L1 instruction caches of high performance microprocessors, by eliminating basic blocks from the cache, as soon as they are dead. The effect of this dead block elimination in cache on both the power consumption of the I-cache and the performance of the processor is studied. Identification of basic blocks is done by the compiler from the control flow graph of the program. This information is conveyed to the processor, by annotating the first instruction of selected basic blocks. During execution, the blocks that are not needed further are traced and invalidated and the lines occupied by them are turned off. This mechanism yields an average of about 5 % to 16 % reduction, in the energy consumed for different sizes of I-cache, for a set of the SPEC CPU 2000 benchmarks [16], without any performance degradation.