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Underwater Sensor Network (USN) is the wireless network infrastructure applicable in deep ocean to detect, collect, and transmit information to a remote data acquisition location on shore. Underwater routing protocols are used to provide route information in addition to ensuring efficient data and information collection and transmission. Some routing strategies for USNs are linked to high energy consumption, high end-to-end delay and shorter network lifetime. These issues are due to unnecessary packet flooding especially during route establishment, improper selection of next hop neighbor and frequent communication for route maintenance. Considering these challenges, this paper presents a localization free energy efficient and link reliable (E2LR) routing protocol that controls unnecessary flooding of hello packets to reduce energy consumption in the information distribution phase. Further, E2LR calculates the link quality using a composite metric for next hop selection which ultimately reduces end-to-end delay and regularly updates the energy status in order to achieve optimization during routing operations. Simulation results show that E2LR exhibit higher performance than both H2-DAB and R-ERP2R in terms of energy efficiency during information distribution phase and data forwarding phase. Moreover, E2LR also shows superior performance regarding end-to-end delay, network lifetime and packet delivery ratio than compared protocols.

The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods.

During the processing of the fishery resources, the significant portion is either discarded or used to produce low-value fish meal and oil. However, the discarded portion is the rich source of valuable proteins such as collagen, vitamins, minerals, and other bioactive compounds. Collagen is a vital protein in the living body as a component of a fibrous structural protein in the extracellular matrix, connective tissue and building block of bones, tendons, skin, hair, nails, cartilage and joints. In recent years, the use of fish collagen as an increasingly valuable biomaterial has drawn considerable attention from biomedical researchers, owing to its enhanced physicochemical properties, stability and mechanical strength, biocompatibility and biodegradability. This review focuses on summarizing the growing role of fish collagen for biomedical applications. Similarly, the recent advances in various biomedical applications of fish collagen, including wound healing, tissue engineering and regeneration, drug delivery, cell culture and other therapeutic applications, are discussed in detail. These applications signify the commercial importance of fish collagen for the fishing industry, food processors and biomedical sector.

The demand for applications of intelligent computing for human life is increasing, as the load of work, tension and hectic routine always challenge individual life. It is really getting difficult to manage an adequate amount of time. Especially for planning balance diet, proper usage of drugs and medications, sufficient sleep time for an active mind, steadiness in exercise and others that eventually create imbalance to live healthy life. A very little focus is conducted on such important considerations. This, in order to support such important planning and decisions, we introduce a Sleep Well Live Fit (SWeLF) android based software computational tool that can assist to record our daily routine activities including the intake of meals, drugs and medications, working hours, physical exercise (via sensors) and others. The prime objective of this tool is to inform and suggest the number of calories consumed on the basis of his routine and about the amount of sleep essentially required for healthy life. In this paper, we examine the core features of the SWeLF tool to support the computing in a human life in comparison of existing methodologies. A detail experiment methodology is conducted to investigate the practicality, and demonstrated the usability of SWeLF tool in E-Health domain that eventually could help individuals to live fit.

The recent state of the art innovations in technology enables the development of low-cost sensor nodes with processing and communication capabilities. The unique characteristics of these low-cost sensor nodes such as limited resources in terms of processing, memory, battery, and lack of tamper resistance hardware make them susceptible to clone node or node replication attack. The deployment of WSNs in the remote and harsh environment helps the adversary to capture the legitimate node and extract the stored credential information such as ID which can be easily re-programmed and replicated. Thus, the adversary would be able to control the whole network internally and carry out the same functions as that of the legitimate nodes. This is the main motivation of researchers to design enhanced detection protocols for clone attacks. Hence, in this paper, we have presented a systematic literature review of existing clone node detection schemes. We have also provided the theoretical and analytical survey of the existing centralized and distributed schemes for the detection of clone nodes in static WSNs with their drawbacks and challenges.

Nano-drug delivery systems (NDDS) for colon-targeted drug delivery are an active area of research on local diseases affecting the colon, such as ulcerative colitis, Crohn's disease, colon cancer, and for the delivery of peptide or protein drugs and vaccinations. In particular, targeted nano-drug delivery to the colon is advantageous for colon-specific diseases because nanoparticles can accumulate in diseased parts, improve the efficacies of therapeutics, and enable localized treatments, which reduces systemic toxicity. However, there are many hurdles, such as burst drug release, enzyme and acidic degradation of drug and carrier in the stomach, pH variations, mucus entrapment, and systemic uptake in the upper small intestine, which could challenge and compromise the successful delivery of NDDS to the colon. With advancements in NDDS, it may be possible to overcome these challenges leading to efficient drug delivery for colon-specific disorders. This review describes a few of the potential colon-specific drug delivery areas and the challenges faced by colon-targeted orally administered delivery systems, and provides an updated summary of recent advances in the development of orally administered NDDS for colon targeting, and the future advances in this research.

Due to recent advances in wireless gadgets and mobile computing, the location-based services have attracted the attention of computing and telecommunication industries to launch location-based fast and accurate localization systems for tracking, monitoring and navigation. Traditional lateration-based techniques have limitations, such as localization error, and modeling of distance estimates from received signals. Fingerprinting based tracking solutions are also environment dependent. On the other side, machine learning-based techniques are currently attracting industries for developing tracking applications. In this paper we have modeled a machine learning method known as Linear Discriminant Analysis (LDA) for real time dynamic object localization. The experimental results are based on real time trajectories, which validated the effectiveness of our proposed system in terms of accuracy compared to naive Bayes, k-nearest neighbors, a support vector machine and a decision tree.

Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification., 30 pages, 13 tables, 1 figure

The coronavirus disease 19 (COVID-19) is a global pandemic of the twenty-first century and currently fourthwave is creating fear and panic worldwide. It is caused by severe acute respiratory syndrome coronavirus 2(SARS-COV-2), a highly contagious viral infection of humans. The COVID-19 can be spread mainly throughrespiratory droplet particles and in contact with a COVID-19 infected person. Clinical manifestation of COVID-19patients includes cough, fever, diarrhea, loss of taste and smell. In critical cases of COVID-19, the developmentof pneumonia and dyspnea leads to acute respiratory distress syndrome that may cause the death of thepatient. It is well established that Angiotensin-Converting Enzyme 2 (ACE2) receptors on alveolar cells act as anentry gate for the SARS-COV-2. However, ACE2 is also highly expressed in multiple extrapulmonary vital organssuch as the gastrointestinal system, cardiovascular system, kidney, etc. Therefore, the direct viral entry in theseorgans can be a likely pathway of injury. In addition, decoupling of immune responses leads to the cytokinesstorm, which might contribute to the injury of extrapulmonary organs. In this review, we report the multipleorgan pathogenesis and clinical manifestations of COVID-19 patients, which could aid clinicians and researchersin prioritizing therapeutics remedies and developing research for all vital body systems involved.

Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naive Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used. For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naive Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naive Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naive Bayes Tree and 88.12% (with forgeries).

Limited resources and network dynamicity are challenging issues in Bluetooth networks. Therefore, scatternet formation improvement and efficient routing algorithms are required that can efficiently construct a short route between a source and destination. As of today, many protocols have been proposed, mostly researchers focusing on simplicity and reliability, but only few of them fulfill the Bluetooth scatternet scenario. Therefore, it opens new doors for scatternet formation and inter-piconet routing for the Bluetooth scatternet. This paper presents a review of Bluetooth routing and scatternet formation protocols for inter-piconet communication. First, Bluetooth operation is explained followed by important applications and topologies are discussed. Then, inter-piconet routing and network formation protocols are critically analyzed. Finally, Bluetooth problems and open research issues are highlighted.

A viral disease such as COVID-19 (Corona Virus Disease-2019) is a major health problem and worsens the complications of various diseases including cardiovascular diseases, hypertension, diabetes, and cancers. The current study summarises the therapeutic progress and efficacy of curcumin for the cure and prevention of severe acute respiratory syndrome (SARS) and SARS-related COVID-19. Curcumin is extracted from the herb Curcuma longa L., known for its various therapeutic functions such as anti-inflammatory, anti-obesity and anti- cancer activities. Very little is known about its therapeutic importance for the prevention and treatment of COVID-19 infections and complications. Curcumin has the ability to interact with spike protein or ACE2 protein in COVID-19-induced signal transduction pathway. Curcumin also suppresses several important signaling pathways in viral infection such as the well-known transcription factors including NF-kB, STAT-3, Wnt/β-catenin, Nrf2, p38/MAPK. Curcumin inhibit virus-induced inflammation by modulating the manifestation of various factors such as IL-10, IL-8, IL-6, TNF-α/β, and COX-2 in COVID-19 diseases. The modulation of multiple molecular targets by treatment with curcumin and anti-inflammatory effects of curcumin make it an ideal candidate phytochemical for the treatment of SARS and SARS-related COVID-19.

Our planet earth has seen many viral pandemics The most recent pandemic was Severe Acute Respiratory Syndrome (SARS-CoV-2) commonly called Corona Virus disease (COVID-19). It was first reported inWuhan Peoples Republic of China, in December 2019. COVID-19 is a positive-sense single-strand enveloped RNA virus mainly found in mammals. To date, a total of six species of coronavirus have been reported that affected humans. These mainly cause respiratory, hepatic, enteric, and neurological complications. Since it is a novel virus, different therapies were used for the treatment These measures comprised of mostly repurposing of already available drugs, more specifically antiviral drugs. In this review article, we have summarized the virus- host relation and chemical structure and also discussed in detail the proposed mechanism of these repurposed antivirals drugs.

This paper presents a new method for Forensic Speaker Recognition (FSR). The new method is based on extracting accent and language information from short utterances. Accent Classification (AC) and Language Identification (LI) play important role in the identification of people of different groups, communities and origins due to different speaking styles and native languages. In a multilingual society, the forensic experts use AC and LI to reduce search space for suspect recognition to regional and ethnic groups. In this paper, we use different baseline and deep learning methods to automate this process. The baseline methods used are Gaussian Mixture Model-Universal Background Model (GMM-UBM), i-vector and Gaussian Mixture Model-Support Vector Machine (GMM-SVM). The Mel-Frequency Cepstral Coefficients (MFCC) are used as speech features in the baseline methods. The deep learning methods used are Convolutional Neural Network (CNN) and Deep Neural Network (DNN). The recently proposed CNN based methods like VGGVox and GMM-CNN are used. VGGVox and GMM-CNN use speech spectrograms. In case of DNN, x-vectors method is used, which is based on DNN embedding. The experimental results show that GMM-SVM demonstrates better FSR performance compared to GMM-UBM and i-vector methods. Whereas, x-vectors method performs better than GMM-CNN and VGGVox methods. It also performs better than GMM-SVM method. The experimental results show that x-vectors method demonstrates 80.4% FSR accuracy. With AC, it achieves 85.4% accuracy. With LI, its accuracy is 90.2%. Whereas by combining AC and LI it obtains 95.1% accuracy. This shows that the proposed method based on AC and LI gives promising results.

Pakistan is considered to be one of the most severely iodine deficient countries in the region. A decade earlier, 70% of the population was estimated to be at risk of iodine deficiency. However, the recent use of iodized salt has reduced the intensity of the problem. Earlier studies regarding iodine deficiency in Pakistan were restricted to the northern mountainous regions, but have now been extended to the sub-Himalaya areas and the plains of Punjab. The aim of the present study was to investigate the prevalence of goiter and iodine status among school children in district Kohat, Pakistan. The 30 cluster approach was adopted for the study. From each cluster, 40 samples were collected from school going children. The palpation method was used to measure goiter grade, whereas, for urinary iodine estimation, the wet digestion method was used. The prevalence of goiter in the district was found to be 35% (37.16% in boys and 33% in girls). The median urinary iodine concentration was found to be 56±31.19?g/L. Estimation of iodine content in salt illustrated that 56.8% people were consuming non-iodized salt and 43.5% were using iodized salt. The current study suggests a severe iodine deficiency in school children of district Kohat and regular iodine supplementation is required on an instant basis.South East Asia Journal of Public Health Vol.4(2) 2014: 42-46

In wireless communication, received signal strength indicator (RSSI) is a measurement of power in the received signal. This parameter is mainly used in radio frequency (RF) based distance estimation, position estimation, and handover mechanisms. In real-time applications such as object tracking, a handover mechanism, distance estimations require continuous reception of RSSI measurements in order to accurately estimate the position of the object. However, there are some constraints such as signal attenuation, signal loss, multi-path effects. These parameters cause variations in RSSI, and hence it becomes weak, which produces communication holes. Communication holes refer to the phenomenon when the signal drops to zero, and there is a communication blockage, which is practically negligible and can be considered as disconnection. This communication hole occurs due to various factors such as multi-path effects, noise, moving out of the coverage zone, device manufacturing faults, and inquiry procedure as well. The main focus of this paper is to address the issue of communication holes in Bluetooth networks. In Bluetooth networks communication holes also occur due to the delay in the inquiry procedure. Therefore, an existing gradient filter is modified based on the most recent measurements received. The proposed extended gradient filter is tested using simulated and real time RSSI measurements and compared with Kalman Filter and Kalman Smoother. The results show better performance in case of communication holes occurrence.

Epidemiological studies have shown that inflammation plays a critical role in the development and progression of various chronic diseases, including cancers, neurological diseases, hepatic fibrosis, diabetic retinopathy, and vascular diseases. Decursin and decursinol angelate (DA) are pyranocoumarin compounds obtained from the roots of Angelica gigas. Several studies have described the anti-inflammatory effects of decursin and DA. Decursin and DA have shown potential anti-inflammatory activity by modulating growth factors such as vascular endothelial growth factor, transcription factors such as signal transducer and activator of transcription 3 and nuclear factor kappa-light-chain-enhancer of activated B cells, cellular enzymes including matrix metalloproteinases cyclooxygenase, and protein kinases such as extracellular receptor kinase, phosphatidylinositol-3-kinase, and protein kinase C. These compounds have the ability to induce apoptosis by activating pro-apoptotic proteins and the caspase cascade, and reduced the expression of anti-apoptotic proteins such as B-cell lymphoma 2 and B-cell lymphoma-extra-large. Interaction with multiple molecular targets and cytotoxic effects, these two compounds are favorable candidates for treating various chronic inflammatory diseases such as cancers (prostate, breast, leukemia, cervical, and myeloma), rheumatoid arthritis, diabetic retinopathy, hepatic fibrosis, osteoclastogenesis, allergy, and Alzheimer's disease. We have summarized the preliminary studies regarding the biological effects of decursin and DA. In this review, we will also highlight the functions of coumarin compounds that can be translated to a clinical practice for the treatment and prevention of various inflammatory ailments.

The development of the location based services and rapid advancement in communication services has extensively increased the researcher's interest in an area of the localization systems. In the context-aware computing, one of the key capability is location determination. At present, indoor location-based systems use various techniques like Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA) and Received Signal Strength Indicator (RSSI). This work focus on the review of different active and passive indoor localization techniques developed over the period of time. The common localization techniques include the active localization systems as the person being detected and tracked carries the tag or any device attached. The other class called passive localization stand for the locating the person without the use of any tag or device. The analysis of the localization systems is based on the wireless technology applied, accuracy and precision positioning algorithm, scalability, complexity and costs. This paper compares different localization systems by analyzing pros and cons.

Device-free indoor localization (DFIL) system can locate the position of human body in the indoor environment by observing the changes in the received signal strength indicator (RSSI) of the wireless local area network (WLAN). The accuracy of a DFIL system is depreciated, as the change in the indoor environment due to furniture and other infrastructure movement. This paper investigates the development of testbed of the Wi-Fi network for measuring the RSSI in various indoor environment, as the initial step for designing the fingerprinting-based algorithms for Wi-fi network. The database of RSSI fingerprint is created initially and then a fingerprint-based algorithm is developed for locating the position of a human body in the indoor environment. The localization algorithm tests the minimum distance in the RSSI values related to the different test points in the indoor environment. This work further demonstrates that how the fingerprints of RSSI are collected and which network configurations generate the most reliable RSSI measurement. For the first phase of designing the testbed, the configurations of different equipment and various tools are elaborated in the indoor environment. For the second phase the RSSI is measured in different propagation indoor environment. The extensive experiments were performed that allow quantification of how changes in an environment affect accuracy. Thus, it is demonstrated that each link offers a viable approach to developing a more robust system for device-free localization that is less susceptible to changes in the environment.

Cadmium is considered one of the most toxic, non biodegradable heavy metal for the human and animals. The purpose of the present study was to investigate the changes in biochemical parameters of blood and accumulation of cadmium in various tissue caused by various levels of dietary cadmium chloride (CdCl2) in broiler chicks. CdCl2 was administered through drinking water to broiler chicks. In spectral analysis, CdCl2 treatment caused a significant increase in Glutamate pyruvate transaminase (GPT), creatinine and uric acid levels in all treated groups. Intriguingly, the GPT, creatinine, and uric acid levels were significantly higher at 75 mg/kg as compared to the groups treated with high doses (100, 125 and 150 mg/kg) of CdCl2. Atomic Absorption Spectrophotometer (AAS) was used for the determination of Cd accumulation in kidney, liver and Breast muscles. AAS analysis revealed that Cd accumulation is increased in breast muscles as compared to liver and kidney at higher doses of Cd than 75 mg/kg.

Based on wireless sensor networks, device-free indoor localization technique has received much attenuation in the field of indoor localization systems. Due to different indoor environmental parameters, multi-path fading, human presences and different system configurations, the WiFi-based received signal strength indicator (RSSI) values are effected. This paper elaborates on the development of a wireless network test-bed to measure the Received Signal Strength Indicator (RSSI) in different environments, as the first step for the application of fingerprinting-type localization algorithms of wireless LAN devices. This paper shows how the environment for RSSI measurement is built and what network configurations yield the most reliable measurements. In the first phase of building a test-bed, configurations of off-the-shelf-equipment and the corresponding applications are explained. The second phase is to measure the RSSI in different propagation and physical environments. In this phase, different environments that have already been built in the first phase are examined. The signal strength is the only information we get from the APs that is usable for the localization process. An “RF sensor” network can monitor RSS values on links in the network and perform device-free localization, i.e., locating a person or object moving in the area in which the network is deployed. Experimental tests reported here and in past literature are shown to validate the analysis.

Based on wireless sensor networks, device-free indoor localization technique has received much attention in the field of indoor localization system Due to different indoor environmental parameters, multipath fading, human presences and different system configurations, the WiFi-based received signal strength indicator values are affected. This problem leads the researchers to choose any filter or smoothing algorithm to get proper values of received signal strength in the indoor environment. The aim of using the filter or smoothing algorithm is to get a signal value which can clearly identify the changes in the signal values due to interferences caused by various factors in the indoor environment. Wi-Fi based device-free indoor localization technique has become more attractive because its simplicity, as well as it does not require extra hardware. However the implementation of this technique in the indoor environment becomes complex, sometimes because of imprecise and unreliable readings caused by multipath fading. There are various filters designed and tested to remove the inconsistencies occurring in RSSI readings due to the various environmental effects. This paper presents various types of filters which are used for smoothing the raw data received signal strength indicator values and also improving the strength of these values by mitigating multipath fading effects in an indoor environment.

This paper presents the design of an Extended Gradient Predictor and Filter for smoothing Received Signal Strength Indicator (RSSI). In wireless communication RSSI is a measurement of power in received signal, which is a parameter used for handover and position estimation purpose. In real time applications such as handover and position estimation requires continuous reception of RSSI measurements. Due to the effect of noise, variations occur in the received signal, which sometimes produce communication holes. In that case, RSSI measurements become zero, which produces distance estimation error. In order to minimize the effect communication, an existing Gradient filter is modified based on most recent RSSI measurement received. The advantage of the proposed extended Gradient RSSI predictor and filter is that it predicts and filters RSSI values in the presence of communication holes for real time object tracking. The performance of proposed extended Gradient RSSI predictor and filter is tested using simulated and real-time RSSI measurements using Bluetooth technology, which shows better performance in case of communication holes occur.

This paper presents an extended Kalman filter-based hybrid indoor position estimation technique which is based on integration of fingerprinting and trilateration approach. In this paper, Euclidian distance formula is used for the first time instead of radio propagation model to convert the received signal to distance estimates. This technique combines the features of fingerprinting and trilateration approach in a more simple and robust way. The proposed hybrid technique works in two stages. In the first stage, it uses an online phase of fingerprinting and calculates nearest neighbors (NN) of the target node, while in the second stage it uses trilateration approach to estimate the coordinate without the use of radio propagation model. The distance between calculated NN and detective access points (AP) is estimated using Euclidian distance formula. Thus, distance between NN and APs provides radii for trilateration approach. Therefore, the position estimation accuracy compared to the lateration approach is better. Kalman filter is used to further enhance the accuracy of the estimated position. Simulation and experimental results validate the performance of proposed hybrid technique and improve the accuracy up to 53.64% and 25.58% compared to lateration and fingerprinting approaches, respectively.

Bluetooth is a low power, short range and promising wireless technology but has limited resources. Bluetooth devices are connected in an ad hoc fashion in a piconet and their communication approach is driven by a master-slave configuration. The Bluetooth basic communication unit is called a piconet. A larger Bluetooth network can support multiple piconets, where different piconets are connected through a common node called a relay. A relay node provides connection among different piconets. Relay nodes use different hopping patterns for different piconets and forward data from one piconet to another. Multiple connected piconets are called a scatternet; a scatternet may have multiple relays. Unnecessary inflexible relay selection may decrease network performance and increase scheduling overhead. Moreover, position of a relay node has also impact on Bluetooth scatternet performance. Bluetooth current specification describes how to build a piconet, but the construction of a scatternet and inter-piconet communication has been left out. This paper presents a Flexible Relay Selection Technique (FRST), which reduces unnecessary relays by reducing relay reduction control overhead. Empirically, through simulations, the proposed FRST has reduced the control overhead, transmission delay and network stability. The simulation results have demonstrated an improvement on networks. All these results are evidence that the FRST has outperformed existing protocols for the same issue. Hence, the inefficiency of the inter-piconet scheduling in a scatternet of a Bluetooth network can be resolved by implementing the proposed FRST.

Bluetooth is one of mobile ad hoc network technologies, which allows communication through piconet. A piconet consists of at most eight active devices at the same time. A multiple piconets network is called a scatternet, which is connected through a common node known as a relay. The limited energy available in a Bluetooth-enabled device is a critical issue for the mobile devices to participate effectively in the scatternet. Therefore, to improve the network performance, energy capability of a Bluetooth device should be analyzed periodically, and subsequently to determine its role in the network based on this energy analysis. It is expected that the more energy capable device shall perform more demanding role, i.e. the role of a high performance device shall be selected based on its energy level. In this paper, we discussed the design issues of an efficient Dynamic Energy-Aware Network Maintenance (DENM) technique. The proposed technique dynamically adapts changes in the network and maintains a constructed scatternet, as well as it ensures network stability and reduces overhead. The simulation results showed that the proposed technique outperformed the existing protocols in term of message overhead, transmission delay, and reconstruction time.

Global Positioning System (GPS) is a well known navigation system for outdoor applications; however this technology does not work in indoor environments. In order to overcome this limitation, Bluetooth technology can be employed. Furthermore, Bluetooth technology provides an accurate and low cost solution for short range wireless communication. Most of the digital devices provide the Bluetooth functionality which also makes it a good candidate for indoor positioning. In this paper, we use Bluetooth devices for indoor positioning and use signal based parameters such as received power level for position estimation. The accuracy of indoor positioning system is greatly dependant on the parameters selected for estimation and the measurements obtained from the environment. However, the measurements are corrupted by various environmental conditions such as temperature, reflection, presence of obstacles, human body and other communication signals. Therefore, we need to filter the measurements. This paper presents an experimental relationship between the received power level and distance using the standard radio propagation model. The idea behind this study is to provide an accurate distance estimate for Trilateration approach. Based on the experiments performed, the average error is minimized from 5.87 meters to 2.67 meters using gradient filter. The use of gradient filter improves the accuracy by 45 %.

A mobile ad-hoc network (MANET) is a collection of wireless mobile nodes, which creates an infrastructure less temporary network. Bluetooth is a short range, low-cost, low and low power wireless technology. Bluetooth allows communication among electronic devices through its basic network know as piconet. In a piconet one device controls the local network (piconet) know as master and others act as slaves. Multiple piconets may exist in Bluetooth network known as scatternet. The connection among different piconets is provided through a common node known as relay. Relay is connected with more then one master with different hopping pattern relay node forwards data among different piconets. The degree (number of connection) of relay has a serious impact on the network performance. A larger degree of relay increases the packet loss rate and transmission delay. The large degree of relay has to switches among different piconets that increase the scheduling overhead. The paper considers the problem of designing an effective Dynamic Load Balancing (DLB) technique to share the traffic load through Backup Relay (BR). The simulation results show using UCBR and NS-2 the proposed technique avoids bottleneck that reduces the transmission delay, packet loss and increases the throughput plus network lifetime.

A metabolic abnormality such as obesity is a major obstacle in the maintenance of the human health system and causes various chronic diseases including type 2 diabetes, hypertension, cardiovascular diseases, as well as various cancers. This study was designed to summarize the recent scientific knowledge regarding the anti-obesity role of curcumin (diferuloylmethane), which is isolated from the herb curcuma longa, known to possess anti-inflammatory activities. However, little is known about its exact underlying molecular mechanisms in the treatment of obesity and metabolic diseases. Furthermore, cell cultures, animal models of obesity, and few human clinical and epidemiological studies have added the promise for future therapeutic interventions of this dietary compound.An electronic search was performed using Science finder, Medline, Scopus, Google scholar and collected English language articles from 2000 to 2010, relating to the role of curcumin in obesity and metabolic diseases.Obesity has been classified as a growing epidemic and its associated metabolic disorders are considered a major risk to the health system. Curcumin interacts with specific proteins in adipocytes, pancreatic cells, hepatic stellate cells, macrophages, and muscle cells, where it suppresses several cellular proteins such as transcription factor NF-kB, STAT-3, Wnt/β-catenin and activates PPAR-γ, Nrf2 cell signaling pathway. In addition, curcumin downregulates the inflammatory cytokines, resistin and leptin, and upregulates adiponectin as well as other associated proteins. The interactions of curcumin with several signal transduction pathways reverse insulin resistance, hyperglycemia, hyperlipidemia, and other inflammatory symptoms associated with obesity and metabolic diseases.The modulation of several cellular transduction pathways by curcumin has recently been extended to elucidate the molecular basis for obesity and obesity-related metabolic diseases. These findings might enable novel phytochemical treatment strategies as well as curcumin translation to the clinical practice for the treatment and prevention of obesity-related chronic diseases. Furthermore, the relatively low cost of curcumin, safety and proven efficacy make it advisable to include curcumin as part of healthy diet.

Bluetooth is a short range wireless communication protocol. However, the discovery process to establish connection in Bluetooth network is very time consuming process. This paper presents localization techniques for Bluetooth networks. There is an increasing interest in location identification, and communication with other Bluetooth devices within a local area network. The idea behind localization techniques is to address position estimation in Bluetooth networks in order to reduce the time taken for device discovery to establish connection process. In this paper Received Signal Strength Indicator (RSSI) is obtained using Inverse square law. The Received Power (Prx) is obtained indirectly by converting RSSI values to Prx using Friis equation. Positioning is based on Prx and converted to estimate the distance using simple radio propagation model based on Friis equation. The advantage of positioning is to know the object actual position with respect to the distance from the transmitter. The relationship obtained from positioning using Prx will be used to minimize the discovery time in connection establishment process in Bluetooth networks. This work is part of the design process of roaming protocol for Bluetooth networks.

Bluetooth is an established standard for low cost, low power, wireless personal area network. Currently, Bluetooth does not support any roaming protocol in which handoff occurs dynamically when a Bluetooth device is moving out of the piconet. If a device is losing its connection to the master device, no provision is made to transfer it to another master. Handoff is not possible in a piconet, as in order to stay within the network, a slave would have to keep the same master. So, by definition intra-handoff is not possible within a piconet. This research mainly focuses on Bluetooth technology and designing a roaming protocol for Bluetooth equipped multi agent systems. A mathematical model is derived for an agent. The idea behind the mathematical model is to know when to initiate the roaming process for an agent. A desired trajectory for the agent is calculated using its x and y coordinates system, and is simulated in SIMULINK. Various roaming techniques are also studied and discussed. The advantage of designing a roaming protocol is to ensure the Bluetooth enabled roaming devices can freely move inside the network coverage without losing its connection or break of service in case of changing the base stations.

Rapid advancement in remote sensing sensors has resulted in an enormous increase in the use of satellite imagery SI and images taken from unmanned aerial vehicles UAVs in a wide range of remote sensing applications. These applications include urban planning, environment monitoring, map updating, change detection, and precision agriculture. This paper focuses on an agricultural application of SI and UAV images. SI-UAV images possess high temporal, textural, and intensity differences due to rapid changes in agricultural crops with the passage of time. Feature points such as scale invariant feature transform SIFT , oriented FAST and rotated BRIEF ORB , and speeded-up robust features SURF are not invariant to such differences and underperform in SI-UAV image registration. To deal with this problem, we propose a new method that combines the strength of nearest neighbor NN and brute force BF descriptor matching strategies to register SI?UAV images. The proposed method is named NN-BF. For NN-BF first corresponding feature point descriptor matches are identified between SI-UAV images of the training set with overlap error. Then the corresponding descriptors are matched with the descriptors of SI images of the test set with NN strategy. The resulting descriptor matches are then further matched with the descriptors of UAV images of the test set using BF strategy. Finally, the descriptor matches obtained are processed with RANSAC to remove outliers and estimate a homography for image registration. Experiments are performed on an agricultural land image dataset. The experimental results show that the NN-BF method improves SIFT, SURF, and ORB feature point performances and also outperforms recently proposed feature matching strategies for remote sensing images. SIFT on average obtains 6.1% and 18.9% better precision scores than SURF and ORB with NN-BF, respectively. SIFT also obtains lower root mean square error than SURF and ORB with NN-BF.