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Sanjeev Sharma

Bio: Sanjeev Sharma is an academic researcher from Rajiv Gandhi Proudyogiki Vishwavidyalaya. The author has contributed to research in topics: Medicine & Wireless network. The author has an hindex of 24, co-authored 186 publications receiving 1722 citations. Previous affiliations of Sanjeev Sharma include Punjab Agricultural University & Bhabha Atomic Research Centre.


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TL;DR: This paper has attempted to present an overview of the routing protocols, the known routing attacks and the proposed countermeasures to these attacks in various works.
Abstract: Mobile ad hoc networks (MANETs) are a set of mobile nodes which are self-configuring and connected by wireless links automatically as per the defined routing protocol. The absence of a central management agency or a fixed infrastructure is a key feature of MANETs. These nodes communicate with each other by interchange of packets, which for those nodes not in wireless range goes hop by hop. Due to lack of a defined central authority, securitizing the routing process becomes a challenging task thereby leaving MANETs vulnerable to attacks, which results in deterioration in the performance characteristics as well as raises a serious question mark about the reliability of such networks. In this paper we have attempted to present an overview of the routing protocols, the known routing attacks and the proposed countermeasures to these attacks in various works. ——————————  ——————————

97 citations

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TL;DR: In this paper, the sunflower stalks were pretreated by steam explosion (at 1.05 kg / cm 2 for 0.5-1.5 h ) using solid:liquid ratio of 0.05 g / ml and subsequently saccharified enzymatically.
Abstract: The sunflower stalks were pretreated by steam explosion (at 1.05 kg / cm 2 for 0.5– 1.5 h ) and sodium hydroxide (0.25– 1.5% w / v NaOH at 1.05 kg / cm 2 for 0.5– 1.5 h ) using solid:liquid ratio of 0.05 g / ml and subsequently saccharified enzymatically. Steam explosion at 1.05 kg / cm 2 pressure for 1.5 h was found to be the optimum pretreatment. Maximum enzymatic saccharification of 57.8% was observed by treating 5% ( w / v ) pretreated sunflower stalks with T. reesei Rut-C 30 cellulase ( 25 FPU / g ) at 50°C, pH 5.0 for 72 h .

97 citations

Journal ArticleDOI
TL;DR: In this paper, sunflower hulls were hydrolyzed with Trichoderma reesei Rut C 30 cellulase and showed 59.8% saccharification, achieving a maximum ethanol yield of 0.454 g/g.
Abstract: Pretreated sunflower hulls hydrolyzed with Trichoderma reesei Rut C 30 cellulase showed 59.8% saccharification. Enzymatic hydrolysate concentrated to 40 g/l reducing sugars was fermented with Saccharomyces cerevisiae var. ellipsoideus under optimum conditions of time (24 h ) , pH (5.0), temperature (30°C) and inoculum size, and it showed a maximum ethanol yield of 0.454 g/g . Ethanol production scaled up in 1 and 15 l fermentors under optimum conditions revealed maximum ethanol yields of 0.449 and 0.446 g/g , respectively.

82 citations

Journal ArticleDOI
TL;DR: This paper provides review of different popular histogram equalization techniques and experimental study based on the absolute mean brightness error (AMBE), peak signal to noise ratio (PSNR), Structure similarity index (SSI) and Entropy.
Abstract: Histogram Equalization is a contrast enhancement technique in the image processing which uses the histogram of image. However histogram equalization is not the best method for contrast enhancement because the mean brightness of the output image is significantly different from the input image. There are several extensions of histogram equalization has been proposed to overcome the brightness preservation challenge. Contrast enhancement using brightness preserving bi-histogram equalization (BBHE) and Dualistic sub image histogram equalization (DSIHE) which divides the image histogram into two parts based on the input mean and median respectively then equalizes each sub histogram independently. This paper provides review of different popular histogram equalization techniques and experimental study based on the absolute mean brightness error (AMBE), peak signal to noise ratio (PSNR), Structure similarity index (SSI) and Entropy.

76 citations

Journal ArticleDOI
TL;DR: A review of different popular histogram equalization techniques and experimental study based on the absolute mean brightness error (AMBE), peak signal to noise ratio (PSNR), Structure similarity index (SSI) and Entropy as mentioned in this paper.
Abstract: Histogram Equalization is a contrast enhancement technique in the image processing which uses the histogram of image. However histogram equalization is not the best method for contrast enhancement because the mean brightness of the output image is significantly different from the input image. There are several extensions of histogram equalization has been proposed to overcome the brightness preservation challenge. Contrast enhancement using brightness preserving bi-histogram equalization (BBHE) and Dualistic sub image histogram equalization (DSIHE) which divides the image histogram into two parts based on the input mean and median respectively then equalizes each sub histogram independently. This paper provides review of different popular histogram equalization techniques and experimental study based on the absolute mean brightness error (AMBE), peak signal to noise ratio (PSNR), Structure similarity index (SSI) and Entropy.

65 citations


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01 Jan 2014
TL;DR: These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care.
Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

9,618 citations

01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: This paper reviews process parameters and their fundamental modes of action for promising pretreatment methods and concludes that pretreatment processing conditions must be tailored to the specific chemical and structural composition of the various, and variable, sources of lignocellulosic biomass.

6,110 citations

Journal Article
TL;DR: A diagnosis of gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) or chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)
Abstract: 1. Type 1 diabetes (due to b-cell destruction, usually leading to absolute insulin deficiency) 2. Type 2 diabetes (due to a progressive insulin secretory defect on the background of insulin resistance) 3. Gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) 4. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young [MODY]), diseases of the exocrine pancreas (such as cystic fibrosis), and drugor chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)

2,339 citations