Miracle-dz/UniFAR-FTUs huggingface dataset:
Greedy Based Single Path & Multi Path Protocol for Wireless Sensor Networks

- Many latest equipments uses Wireless Sensor Networks (WSNs) to monitor physical or environmental conditions but there is huge scope for node failures due to energy constraints or due to tendency of a wireless networks which is as that of a vector where nodes can be added or removed from the network upon the request of application due to which network can’t stick to a specific topology. The scope of a wireless sensor node is very limited with respected to transmission range of wireless sensor nodes due to which multiple hops are needed for a node to exchange information with other nodes. A multipath routing scheme is used to distribute the network traffic across multiple paths instead of routing all the traffic towards a single path by which the consumption of energy will be splitted among multiple nodes within a network resulting in longer lifetime of a datagram and hence it provides better quality of service. In this paper, we propose a energy efficient multipath routing protocol that uses the 1) reusable energy that can be transmitted to other node while performing hopping, 2)the available buffer size of a node or a router, 3) handling of Signal to Noise Ratio that is used to predict the finest next hop through the paths construction phase, our proposed system examines two methods of traffic allocation where the first method uses a single path among all the possible available paths to transfer the data message when the path cost is below a specific threshold value based on which alternative path is selected then the datagram is transmitted and the second method is to split up the transmitted message into number of segments of equal size based on error correction codes and then transmit it across multiple paths simultaneously where a datagram is received at the destination node without sustaining excessive delay.
Text Based Synthesis and Generation of Images using Deep Learning

: There is a need to encourage children to learn the art of storytelling. Storytelling unleashes the creativity in young minds. What would be more interesting, would be to help them bring their figments of imagination to life. There is a human condition called synesthesia where a simulation of one cognitive pathway leads to experiences in a second cognitive pathway. The word literally means joined perception. There is something really intriguing about this idea of being able to express a thought or an idea or a concept in two entirely different mediums. Generative adversarial networks have demonstrated that it is possible to create fake but photorealistic looking images after training on an image data. Generative adversarial networks, specifically Deep Convolutional Generative Adversarial Networks, are used to create synthetic images from the given text sentence, such that for even the wildest story, an illustration will be possible. The implications of this ability to synthesize one data type to another will eventually allow us to create all sorts of entertainment with just detailed descriptions. The designers, engineers and scientists could all hash out theoretical concepts with real-time visual feedback on their thought processes. This project aims at synthesis of images from the given text using Deep Convolutional Generative Adversarial Networks.
Multimodal Biometric Systems: Study to Improve Accuracy and Performance

Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. In literature survey experimental studies show that Unimodal biometric systems had many disadvantages regarding performance and accuracy. Multimodal biometric systems perform better than unimodal biometric systems. In biometric system physical or behavioral traits are used. A multimodal biometric identification system aims to fuse two or more physical or behavioral traits. It is used in order to improve the accuracy. The fusion of multiple biometrics helps to minimize the system error rates. This paper presents an overview of multimodal biometrics. This includes block diagram of multimodal biometrics, module of multimodal biometric system, different levels of fusion in multimodal biometrics.
Energy-Efficient Unequal Clustering Routing Algorithm for WSNs

Wireless sensor networks(WSNs) are used to monitor the environment where the networks are deployed. The Lifetime of WSNs can be increased by energy-efficient or energy balancing algorithms. Balanced energy consumption among all nodes is the main issue. In this paper, a new energy-efficient unequal clustering routing protocol (EEUCR) has been presented. In this protocol, the area of the network is divided into the number of rings of unequal size and each ring is further divided into a number of clusters. Rings nearer to the base station(BS) have a smaller area and the area of rings keeps on increasing as the distance from BS increases. This helps to balance the energy consumption among the sensor nodes. The nodes with heterogeneous energy are deployed in the network. Nodes nearer to the base station have higher energy as compared to farther nodes. Static clustering is used but cluster heads(CHs) are not fixed and are elected on the basis of the remaining energy of the sensor node. Simulation results are compared with existing protocols and show improvement in energy consumption, which, in turn, increases the network lifetime of WSN and also balance the energy consumption of sensor node
A Neutral Point Clamped Based Inverter for Three-Phase UPS System Applications

Uninterrupted Power Supply (UPS) system supplies required power in case of utility power failures. UPS system has got significant importance in the field of industrial electronics due to increase in technology of power electronic devices.This paper proposes a Neutral Point Clamped (NPC) inverter with simple Sinusoidal Pulse Width Modulation Technique (SPWM), which is well suitable for inverter control. The proposed system would be subjected to fast transient response, small steady state error and low harmonic distortion test under different load conditions. Finally, the comparative results for the proposed scheme and the observer based voltage control scheme are presented to show that the proposed scheme achieves better performance. The results are tested and verified through MATLAB/Simulink. The proposed methodology reduces the total cost, complexity and THD.
Clinical Microbiology: Open Access

Oseltamivir is recommended for treatment and prophylaxis of Influenza in persons at higher risk for Influenza complications such as individuals with diabetes, neuropsychiatric illnesses, and respiratory, cardiac, renal, hepatic or hematological diseases. However, a recent systematic review reported that reduction of antibody production, renal disorders, hyperglycemia, psychiatric disorders, and QT prolongation may be related to Oseltamivir use. The underlying mechanisms of these effects are reviewed. There is decisive evidence that administration of a clinically compatible dose of Oseltamivir in mice challenged by a respiratory syncytial virus (RSV) that lacks a neuraminidase gene showed symptom-relieving effects and inhibition of viral clearance. These effects were accompanied by decreased level of T cell surface sialoglycosphingolipid (ganglioside) GM1 that is regulated by the endogenous neuraminidase in response to viral challenge. Clinical and non-clinical evidence supports the view that the usual dose of Oseltamivir suppresses pro-inflammatory cytokines such as interferon-gamma, interleukin-6, and tumor necrosis factor-alpha almost completely in experimentally infected Influenza viruses in humans with partial suppression of viral shedding. Animal toxicity tests support the clinical evidence with regard to renal and cardiac disorders (bradycardia and QT prolongation) and do not disprove the metabolic effect. Reduction of antibody production and cytokine induction and renal, metabolic, cardiac, and prolonged psychiatric disorders after Oseltamivir use may be related to inhibition of the host’s endogenous neuraminidase. While the usual clinical dose of Zanamivir may not have this effect, a higher dose or prolonged administration of Zanamivir and other neuraminidase inhibitors may induce similar delayed reactions, including reduction of antibody and/or cytokine production.
Analysis of adaptive channel estimation techniques in MIMO-OFDM system

Multiple input multiple output (MIMO) can be used to improve information carrying capacity in orthogonal frequency division multiplexing (OFDM). In MIMO ‐ OFDM system channel estimation task becomes more difficult due to the change in channel parameter. Hence, to find appropriate channel estimation method which is able to follow changes at moment is matter of high significance. In this paper types of adaptive algorithm for channel estimation in MIMO ‐ OFDM system is discussed. The adap ‐ tive filter can be least mean square (LMS), recursive least square (RLS) or KALMAN which does not require prior knowledge of second order statistic (SOS) of channel and noise. The adaptive RLS channel estimation with adaptive forgetting factor outper ‐ forms in frequency selective fading. The LMS adaptive estimation can be used to improve the channel performance.But the low convergence speed and serious estimation error led to the combination of LMS with KALMAN filter for more accurate estima ‐ tion. However, computational complexity is increased since the result is transferred to the KALMAN filter. To improve bit error rate (BER) performance the KALMAN filter for different guard interval (GI) is studied. The adaptive filter based algorithm mostly uses least square (LS) or Minimum mean square error (MMSE) as initial channel estimation to reduce computational complexity. Finally, the mean square error of the various adaptive algorithm is compared, LMS+KALMAN outperforms the others.
A SURVEY ON KEYWORD-BASED SEARCH MECHANISM FOR DATA STORED IN CLOUD

— Now-a-days, most of the users store their personal and professional data on the cloud. Due to the massive increase in the storage and computing requirements of users, every time, data is getting transferred to the remote server in larger chunks, without an analysis whether the server on which the data is outsourced, is a trusted server. But the fact is, after outsourcing, users are with a great security risk factor that tends to lose the local possession of their large size of data. To maintain the privacy of personal documents stored in cloud environment, it should get encrypted before outsourcing to the cloud. After placing the data on the cloud, retrieving the same is also a quiet tedious job. In order to retrieve the data, several approaches are available in which keyword enabled search of the encrypted data is one of the outstanding techniques. The majority of these approaches are limited to handle a single keyword search with its own limitation. To enhance searching in terms of efficiency and fastness, a multi-key word search technique can be adopted to retrieve a corresponding document from cloud. This paper proposes a survey on a secure search scheme supporting single-keyword or multi-keyword ranked search over encrypted cloud data.
VARIOUS DATA MINING TECHNIQUES ANALYSIS TO PREDICT DIABETES MELLITUS

- Data mining approach helps to diagnose patient’s diseases. Diabetes Mellitus is a chronic disease to affect various organs of the human body. Early prediction can save human life and can take control over the diseases. This paper explores the early prediction of diabetes using various data mining techniques. The dataset has taken 768 instances from PIMA Indian Dataset to determine the accuracy of the data mining techniques in prediction. The analysis proves that Modified J48 Classifier provide the highest accuracy than other techniques.
Interference Alignment Technique to Improve Energy Efficiency in MIMO-IFBC

: Characterising the energy efficiency(EE) and spectral efficiency(SE) performance of multiple-input– multiple-output interfering broadcast channels (MIMO-IFBC) for the design of green wireless system.In this work, develop a new network architecture based on EE and SE maximization for Multi-Cell MIMO-IFBC within the context of interference alignment (IA). This work develop two methods to optimize energy efficiency for different signal-to-noise ratio regions. For high SNR operating regions, develop a grouping-based interference alignment scheme to jointly cancel intra-and inter-cell interferences and thus transform the MIMO-IFBC to a single-cell MIMO broadcast channel.A gradient-based power adaptation scheme is developed based on singular value decomposition and water filling power adaptation to maximize energy efficiency for each cell. For moderate SNR cases, employ an approach using dirty paper coding (DPC) based on the principle of multiple access channel and broadcast channel duality to perform interference alignment while maximizing EE in each cell. The algorithm in its dual form is solved by using a subgradient method and a bisection searching scheme. However,the extent to which interference can be aligned over a finitenumber of signalling dimensions remains unknown.The important concern for interference alignment scheme is the requirement of global channel knowledge.So this work can achieveinterference alignment with only local channel knowledge at each node.In the proposed work determining the sum rate based on channel coefficients and can calculate spectralefficiency at high signal to noise region. By using maximum SINR algorithm ,analyse sum rate Vs signal to noise ratio to get the maximum achievable spectral efficiency. Simulation results gives the performance of the proposed schemes over several existing approaches.
Event Detection in Video Using Saliency Value and Histogram of Optical Flow

: The range of applications is huge in the field of object/event detection, so abnormal event detection has gained marvelous research attention. In video analysis abnormal event detection is a challenging task. In this paper, we proposes an architecture for abnormal event detection in video using saliency value and Histogram of optical flow, for many video surveillance application event detection is most important. An event might be characterized as an event that deviates from the normal. The main difficulty in detecting such events is their unpredictability. The problems such as diversity of monitoring scene, different crowd density and mutual occlusion among crowds etc result in a low recognition rate for abnormal crowd behavior detection. In order to solve these problems, this project uses salience value for each frame, to describe the normal patterns on the scene, we are using a Histogram of optical flow orientation and magnitude (HOFM) feature descriptor based on optical flow information and KNN classifier is used for classification.
Surgical Apgar score predicts postoperative complications after surgery for gastric cancer

Aim: Complete surgical resection plays the most important role in the cure of gastric cancer, although surgery for gastric cancer remains a high-risk procedure. Prediction of postoperative complications thus plays an important role in gastric cancer treatment. The aim of this study was to evaluate whether the Surgical Apgar Score (SAS) can predict the risk of complications after gastrectomy. Method: We retrospectively compared clinical characteristics, surgical factors, and oncological factors between patients with and those without postoperative complications. Multivariate logistic-regression analysis was used to assess predictors of postoperative complications. Results: The study group comprised 190 patients who underwent gastrectomy. Grade 2 or higher complications developed in 20 patients (10.5%). Total gastrectomy and an SAS of ≤6 were significantly more frequent among patients with complications, and these factors were identified to be independent predictors of postoperative complications. The odds ratio of an SAS of ≤6 was 3.606 (95% confidence interval, 1.080-12.020; p=0.037). Conclusions: SAS is a useful predictor of complications after surgery for gastric cancer.
TITLE PAGE Title: Lean Mass Predicts Asthma Better than Fat Mass among Women

The obesity phenotype associated with asthma is not known. Our objective was to define the relative contribution of various distributions of fat and lean mass to asthma prevalence. Data were obtained from 2,525 participants (including 1,422 women) who underwent Dualenergy X-ray Absorptiometry (DEXA) at the year 20 examination in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort. Total, truncal, arm, and leg distributions of fat and lean mass were adjusted to the persons height. Self-reported asthma was the outcome. Asthma among women was associated with greater total fat mass, arm fat mass, and total lean mass, truncal lean mass, and arm lean mass. Among men, none of the above mass measures were significantly associated with asthma. Among women, the association with asthma was stronger for total lean mass than for total fat mass. Further, among various regional distributions of lean and fat mass in women, truncal lean mass was the strongest predictor. Total lean mass is more strongly associated than total fat mass with asthma among women. These findings are contrary to the popular perception that excess physiological fat drives the obesity-asthma association. Rather, we hypothesize that ectopic fat within the lean tissues drives this association among women.
CMOS BASED CURRENT FEEDBACK OP-AMP WITH IMPROVED BANDWIDTH

A current mode feedback operational amplifier designed using a complimentary MOS circuit technology has been discussed in this paper. A class AB amplifier has been used in the design and the design results in improvement of values of number of parameter when compared to the previously reported architecture. The circuit is designed using 0. 35μm technology. Bandwidth of the presented architecture was obtained, that was always greater than 50MHz. On the other hand , bandwidth in case of previously reported circuit was coming out to be always more than 2MHz. Thus in our proposed circuit, there is a considerable improvement in the bandwidth of the circuit. Also the Slew rate, which was improved from 5.2V/μsec to 8.1V/ μsec. Settling time of the proposed circuit has been reduced to 134 nsec which was of order of 212 nsec in case of the previous design.
A NEW EFFICIENT RESCHEDULING METHOD USING A-STAR ALGORITHM FOR TRAIN TRAVEL MANAGEMENT SYSTEM

This paper deal with rescheduling the process of train travel management system. In order to so reduce the delays of train when accidents, natural disasters or technical problems occur on railway lines. An algorithm for automatic train rescheduling with a train rescheduling pattern system. We purpose a rescheduling algorithm of train groups to reschedule trains, instead of previous just waiting. A Petri-net-based conflict resolution algorithm adapted from the A-star algorithm is designed to search for an optimal or a near-optimal realistic schedule. The algorithm takes into account the railway working principles when generating new characterizing, so that the new schedule has less train delays and respects the safety principles. An evaluation principle based on the extent of the interference towards the original timetable and in a particular time is designed to select the optimal path for each train. Show that the rescheduling algorithm is able to reduce the delays of train time and make the parallel processing, make a contact of the real-time response of the train-group rescheduling well.
A Diffeomorphic Demons based Deformable Thoracic CT Image Registration between Full Inhale and Full Exhale Frames

Deformable (elastic/non-rigid) image frames from sequences of the human thorax acquired for a subject during the process of breathing from three APs (Anatomical Positions) is registered. The images up for registration are modeled after tangible physically deformable objects. All the image elements are selected as Maxwell’s ‘demons’. The periphery of these images is considered to be deforming as an effect of the resultant forces of the demon elements. The paper presents an iterative non-parametric diffeomorphic demon registration process for thoracic CT (Computed Tomography) images. An alternate optimization approach concerning Thirion’s demons algorithm to provide a fast non-linear registration algorithm has been proposed. The credibility and performance of the above proposed method is demonstrated by its exemplary experimental results.
Logical Data Model For Cloud Computing

-Cloud computing creates virtual Computing Environment in typical network computing pattern for sharing data and computation. Data is huge and multiple data center need collaboration in deployment and implementation of data intensive application because it is inevitable to transmit data across the different data centers. This Paper introduce cloud computing concept and highlight the various data models supported by existing cloud base storage.Cloud Data Management has to address several challenges,especially when replicated data are concurrently updated at different sites or when the system workload and the resources requested by clients change dynamically.
A Review on Traffic Route Optimizing by Using Different Swarm Intelligence Algorithm

— Traffic network control includes complex traffic conditions connected to traffic flow dynamics, driver behaviour, and traffic demand. The traffic management centre can control desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. This paper discusses methods for clustering to improve energy efficiency of homogeneous Wireless Sensor Networks (WSN) . This paper proposes the analysis between Ant Colony Optimization ( ACO), Bacterial Foraging Optimization (BFO) and Particle swarm optimization ( PSO) which is used for optimize the traffic path. BFO and ACO as an algorithm for cluster head selection for WSN. BFO, PSO and ACO are chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO technology is for optimizing load by making Accident Warning System Model in vehicle to vehicle to avoid collision and efficient or optimal paths in traffic full area by distribution. Particle swarm optimization (PSO) is a probability based model which is used to optimize the traffic signals. An analysis of the literature allows one to conclude that ACO is a hugely viable approach to solve scheduling problems in VEHICULAR AD-HOC NETWORK.
Effects of Lower-Limb Dominance on Isokinetic and Functional Profiles in Healthy Young Active Men – A Pilot Study

The effects of limb dominance on isokinetic and functional performance tests are still unclear and this correct understanding is needed to improve the ability of the clinician to evaluate lower limb injuries and their progression, especially in the sports arena. Thus, this pilot study proposed to investigate the influence of limb dominance on knee isokinetic evaluation and performance in functional tests and in the limb symmetry in healthy young active men. Fifteen male subjects (22.1 ± 3.0 years) from a college community took part in this study. Measurements of knee concentric and eccentric isokinetic evaluations (peak torque, acceleration time and deceleration time – to the knee flexion and extension) were carried out using a Biodex Multi-Joint System III isokinetic dynamometer at an angular velocity of 60o/s. Knee function was assessed by two functional tests: the hop test for distance and the one-legged vertical jump. No significant difference (α = 5%) in the isokinetic and functional profiles was found between the dominant and non-dominant limbs. Therefore it seems that the limb dominance does not decisively influence the limb symmetry in healthy young active men, or their exposure to one risk factors for musculoskeletal injuries, related to the bilateral asymmetry.