Benchmarking GEANT4 simulation of mini-Iron Calorimeter for cosmic ray muon studies

A Prototype of the Iron Calorimeter for the India-based Neutrino Observatory is currently running at Madurai, India. This consists of twenty large area single gap Resistive Plate Chambers (RPCs) of size $\sim$ 185\,cm $\times$ 175\,cm sandwiched between 11 layers of iron plates of thickness 5.6\,cm. The total size of the mini-Iron Calorimeter(mini-ICAL) is 4\,m$\times$4\,m$\times$1.2\,m and having a weight of 85\,tons is a (1/600) scaled-down version of the ICAL detector of INO~\cite{apthesis}. The detector is magnetized using iron plates and two sets of copper coils, each coil has 18 turns. The central region of the mini-ICAL, where the RPCs are placed, has a nearly uniform magnetic field of 1.4\,T, similar to the magnetic field in the final ICAL detector. This prototype serves as the basis for the design of the demonstrators, which closely mimics the characteristics of a future ICAL. This detector is built to test the final electronics in the fringe field of the magnet and also to develop the experience to construct the ICAL detector. The mini-ICAL has been operational since 2018 and collects cosmic muon data with different configurations of RPC, and electronics, which are continuously changing for various R\&D efforts. Dedicated efforts are made in parallel to measure the momentum and azimuthal angle of $\mu^+$ and $\mu^-$ independently, which could be an important input to the cosmic neutrino event generators. To improve the precision of those measurements a dedicated simulation effort was made, particularly in the digitisation of the RPC signal for a real detector simulation, where efficiency, noise rate, strip multiplicity, etc. were matched with the real data collected during runs at mini-ICAL.Comment: 14 pages, 18 figure

Adaptive Resource Allocation Strategies for Dynamic Heterogeneous Traffic in Td-cdma/Tdd Systems

The purpose of this study was to investigate the co-channel interference present in TD-CDMA/TDD systems and TDMA/TDD systems and propose methods to avoid the co-channel interference. Time Slot Opposing algorithm which avoids co-channel interference in TD-CDMA/D-TDD system is reviewed as part of background study. The interference scenarios in TDMA/D-TDD systems are then studied and methods to avoid co-channel interference are proposed. The algorithms are then tested using real Internet data traffic to obtain a realistic analysis. Based on the background research, an extended Max {SIR} algorithm is proposed to avoid co-channel interference in TDMA/D-TDD systems. This algorithm is a centralized dynamic channel allocation algorithm that uses information from all the cells in the system to avoid co-channel interference and increase the signal power-to-interference power outage probability ratio. The proposed algorithm is then applied to a TDMA/D-TDD system that have subscribers grouped based on priority. As a last step of the research, traffic in TDMA/D-TDD systems is modeled using the ON-OFF traffic modeling and the Max {SIR} algorithm is applied. The results obtained using ON-OFF traffic modeling matched with the results obtained using analytical simulations.School of Electrical & Computer Engineerin

ATM PNNI Interfacing Issues with MPLS Networking

School of Electrical and Computer Engineerin

Improving Time and Position Resolution of RPC detectors using Time Over Threshold Information

INO-ICAL is a proposed underground particle physics experiment to study the neutrino oscillation parameters by detecting neutrinos produced in the atmospheric air showers. Iron CALorimeter (ICAL) is to have 151 layers of iron stacked vertically, with active detector elements in between the iron layers. The iron layers will be magnetized to enable the measurement of momentum and charge of the $\mu^-$ (or $\mu^+$) produced by $\nu_\mu$ (or $\bar{\nu}_\mu$) interactions. Resistive Plate Chambers (RPCs) have been chosen as the active detector elements due to their large area coverage, uncompromised sensitivity, consistent performance for decades, as well as cost effectiveness. The major factors that decide the physics potential of the ICAL experiment are efficiency, position resolution and time resolution of the large area RPCs. A prototype detector called miniICAL (with 11 iron layers) was commissioned to understand the engineering challenges in building the large scale magnet and its ancillary systems, and also to study the performance of the RPC detectors and readout electronics developed by the INO collaboration. As part of the performance study of the RPC detectors, an attempt is made to improve the position and time resolution of them. Even a small improvement in the position and time resolution will help to improve the measurements of momentum and directionality of the neutrinos in ICAL. The Time-over-Threshold (ToT) of the RPC pulses (signals) is recorded by the readout electronics. ToT is a measure of the pulse width and consequently the amplitude. This information is used to improve the time and position resolution of the RPCs and consequently INO physics potential

Effectiveness of Drainless versus Drained Onlay Mesh Hernioplasty in Patients Undergoing Elective Open Ventral Hernia Repair: A Prospective Interventional Study

Introduction: Ventral Hernia Repair (VHR) is one of the most frequently performed surgical procedures worldwide. The two commonly used surgical techniques for ventral hernia are onlay and sublay repairs. The use of drains in hernioplasty is controversial, as some studies suggest an increased incidence of surgical site infections. Aim: This study aims to compare the postoperative outcomes between patients who had drains placed and those who did not, undergoing elective open VHR. Materials and Methods: A prospective interventional study was conducted at the Department of General Surgery, Chettinad Hospital and Research Institute, Kelambakkam, Tamil Nadu, India. The study duration was one year, from September 2020 to September 2021. A total of 50 hernia patients participated, with 25 undergoing drainless and 25 undergoing drain onlay mesh hernioplasty. Surgical complications such as surgical site infection, seroma formation, and duration of hospital stay were observed and compared between the two groups. Independent t-tests and Chi-square tests were used to compare continuous and categorical variables, respectively. Results: Out of 50 patients, 15 (60%) in the drain group were aged between 11 to 60 years, while 11 (44%) in the drainless group were aged between 18 to 40 years. Postoperative seroma was present in 6 (12%) patients, with an equal distribution in both groups (p-value >0.05). Surgical site infection was present in 3 (60%) and 2 (40%) patients in group A and group B, respectively (p-value >0.05). The mean duration of hospital stay was 6.36±1.89 and 4.92±1.91 days in group A and group B, respectively (p-value=0.010). Conclusion: The presence or absence of a drain did not significantly affect the formation of seroma among the participants. The incidence of infection did not vary significantly with or without the use of a drain

Automatic Lung Cancer Detection From Ct Image Using Improved Deep Neural Network And Ensemble Classifier

The development of the computer-aided detection system placed an important role in the clinical analysis for making the decision about the human disease. Among the various disease examination processes, lung cancer needs more attention because it affects both men and women, which leads to increase the mortality rate. Traditional lung cancer prediction techniques failed to manage the accuracy because of low-quality image that affects the segmentation process. So, in this paper new optimized image processing and machine learning technique is introduced to predict the lung cancer. For recognizing lung cancer, non-small cell lung cancer CT scan dataset images are collected. The gathered images are examined by applying the multilevel brightness-preserving approach which effectively examines each pixel, eliminates the noise and also increase the quality of the lung image. From the noise-removed lung CT image, affected region is segmented by using improved deep neural network that segments region in terms of using layers of network and various features are extracted. Then the effective features are selected with the help of hybrid spiral optimization intelligent-generalized rough set approach, and those features are classified using ensemble classifier. The discussed method increases the lung cancer prediction rate which is examined using MATLAB-based results such as logarithmic loss, mean absolute error, precision, recall and F-scor

Detecting lung cancer region from CT image using meta-heuristic optimized segmentation approach

Lung tumor detection using computer-aided modeling improves the accuracy of detection and clinical recommendation precision. An optimal tumor detection requires noise reduced computed tomography (CT) images for pixel classification. In this paper, the butterfly optimization algorithm-based K-means clustering (BOAKMC) method is introduced for reducing CT image segmentation uncertainty. The introduced method detects the overlapping features for optimal edge classification. The best-fit features are used to trained and verified for their similarity. The clustering process recurrently groups the feature matched pixels into clusters and updates the centroid based on further classifications. In this classification process, the uncertain pixels are identified and mitigated in the tumor detection analysis. The best-¯t features are used to train local search instances in the BOA process, which influences the similar pixel grouping in the uncertainty detection process. The proposed BOAKMC improves accuracy and precision by 10.2% and 13.39% and reduces classification failure and time by 11.29% and 11.52%, respectively