On-die transient event sensors and system-level ESD testing

System level electrostatic discharge (ESD) testing of electronic products is a critical part of product certification. Test methods were investigated to develop system level ESD simulation models to predict soft-failures in a system with multiple sensors. These methods rely completely on measurements. The model developed was valid only for the linear operation range of devices within the system. These methods were applied to a commercial product and used to rapidly determine when a soft failure would occur. Attaching cables and probes to determine stress voltages and currents within a system, as in the previous study, is time-consuming and can alter the test results. On-chip sensors have been developed which allow the user to avoid using cables and probes and can detect an event along with the level, polarity, and location of a transient event seen at the I/O pad. The sensors were implemented with minimum area consumption and can be implemented within the spacer cell of an I/O pad. Some of the proposed sensors were implemented in a commercial test microcontroller and have been tested to successfully record the event occurrence, location, level, and polarity on that test microcontroller. System level tests were then performed on a pseudo-wearable device using the on-chip sensors. The measurements were successful in capturing the peak disturbance and counting the number of ESD events without the addition of any external measurement equipment. A modification of the sensors was also designed to measure the peak voltage on a trace or pin inside a complex electronic product. The peak current can also be found when the sensor is placed across a transient voltage suppressor with a known I-V curve. The peak level is transmitted wirelessly to a receiver outside the system using frequency-modulated magnetic or electric fields, thus allowing multiple measurements to be made without opening the enclosure or otherwise modifying the system. Simulations demonstrate the sensors can accurately detect the peak transient voltage and transmit the level to an external receiver --Abstract, page iv

EMI analysis of DVI link connectors

EMI problems are not uncommon in high speed communication systems. As the system clock frequencies increases, so does the challenges in controlling the EMI in such systems. A connector is a very important part of a high speed communication system. Electromagnetic interference (EMI) is found to be tightly correlated to mode conversion: from differential-mode (DM) signals to common-mode (CM) currents and further to antenna-mode (AM) currents on the outside of cables or enclosures. Moreover, in such high speed systems, coupling to an adjacent cable-connector system is not uncommon. It is essential to understand and quantify this coupling path in order to mitigate the coupling. Though simulation based methods are widely used, such an approach is generally very time consuming and computationally resource hungry and an effort is made to quantify the coupling paths using measurement-simulation combinations with minimal simulation aid. This thesis presents a systematic approach to isolate and identify the different coupling paths in a high speed interface (in this case we show DVI), as well as identify which discontinuity (and hence the coupling path) is most critical to mitigate EMI. A transfer function based method is implemented to quantify the coupling in the connector cable system. The method developed in this study can be used for any high-speed interface in modern communication systems. --Abstract, page iii

Ir_urfs_vf: Image Recommendation with User Relevance Feedback Session and Visual Features in Vertical Image Search

In recent years, online shopping has grown exponentially and huge number of images are available online. Hence, it is necessary to recommend various product images to aid the user in effortless and efficient access to the desired products. In this paper, we present image recommendation framework with user relevance feedback session and visual features (IR_URFS_VF) to extract relevant images based on user inputs. User feedback is retrieved from image search history with clicked and un-clicked images. Image features are computed off-line and later used to find relevance between images. The relevance between images is determined by cosine similarity and are ranked based on clicked frequency and similarity score between images. Experiments results show that IR_URFS_VF outperforms CBIR method by providing more relevant ranked images to the user input query

Multimodal biometric authentication using ECG and fingerprint

Biometric system is a very important recognition system which is used for individual verification and identification. Various types of biometric traits are used in today's world, in which some are used for commercial purpose and few used for verification purpose. Existing authentication techniques are suffer from different errors like mismatch image, spoofing, falsification in the data, to solve this errors the combination of Electrocardiography(ECG) and fingerprint multimodal is introduced. This proposed modal produces effective recognition system when compared to individual recognition system. The proposed multimodal recognition system provides optimum results compared to the individual recognition system which yields better results for authentication compared to the Existing system

Deep Learning Based Forecasting of Indian Summer Monsoon Rainfall

Accurate short range weather forecasting has significant implications for various sectors. Machine learning based approaches, e.g., deep learning, have gained popularity in this domain where the existing numerical weather prediction (NWP) models still have modest skill after a few days. Here we use a ConvLSTM network to develop a deep learning model for precipitation forecasting. The crux of the idea is to develop a forecasting model which involves convolution based feature selection and uses long term memory in the meteorological fields in conjunction with gradient based learning algorithm. Prior to using the input data, we explore various techniques to overcome dataset difficulties. We follow a strategic approach to deal with missing values and discuss the models fidelity to capture realistic precipitation. The model resolution used is (25 km). A comparison between 5 years of predicted data and corresponding observational records for 2 days lead time forecast show correlation coefficients of 0.67 and 0.42 for lead day 1 and 2 respectively. The patterns indicate higher correlation over the Western Ghats and Monsoon trough region (0.8 and 0.6 for lead day 1 and 2 respectively). Further, the model performance is evaluated based on skill scores, Mean Square Error, correlation coefficient and ROC curves. This study demonstrates that the adopted deep learning approach based only on a single precipitation variable, has a reasonable skill in the short range. Incorporating multivariable based deep learning has the potential to match or even better the short range precipitation forecasts based on the state of the art NWP models.Comment: 14 pages, 14 figures. The manuscript is under review with journal 'Transactions on Geoscience and Remote Sensing

Mapping Concurrent Wasting and Stunting Among Children Under Five in India: A Multilevel Analysis

Objectives: The study aims to examine the coexisting forms, patterns, and predictors of concurrent wasting and stunting (WaSt) among children under five in India.Methods: We used data from the National Family Health Survey to understand the trend and association of WaSt among children under five-year-old in India. Univariate analysis and cross-tabulations were performed for WaSt cases. The association was determined using multilevel binary logistic regression and multilevel regression, and the results were provided as adjusted odds ratios (aOR) with 95% confidence intervals at the significance level of p < 0.05.Results: The prevalence of WaSt has decreased from 8.7% in 2005–06 to 5.2 percent in 2019–2020. The proportion of WaSt children grew rapidly from 6 to 18 months, peaked at 19 months (8%), then dropped after 24 months. The prevalence of concurrent wasting and stunting is higher among boys compared to girls. Compared to children of different birth orders, those in the higher birth order are 1.2 times more likely to be WaSt cases (aOR = 1.20, 95% CI = 1.09, 1.33). The education of the mother is strongly correlated with WaSt instances, and children of more educated mothers have a 47% lower chance of being WaSt cases (aOR = 0.63, 95% CI = 0.57, 0.71). Children from wealthy families are 52% less likely to be WaSt cases (aOR = 0.48, 95% CI = 0.43, 0.55).Conclusion: This study emphasizes the importance of concurrent wasting and stunting and its relationship with socioeconomic factors among children under five in India

Performance analysis of bee-hive routing in multi-radio networks

In recent years, wireless communication technology has reduced the distance between people and has hence become a significant part of our lives. Two such technologies are WiFi(IEEE 802.11) and WiMAX(IEEE 802.16) where the latter is a long range system covering many kilometers, whereas former is a synonym for WLAN providing a coverage of only short ranges. This work describes the implementation of a framework in which a multi-hop, ad-hoc network is deployed with hybrid nodes to enhance network throughput. The data traffic received is split between the WiFi and WiMAX radios on the basis of th e split coefficient value statically. The routing algorithm being implemented in this paper is the be e-hive algorithm. Bee-hive algorithm is a multi-path routing algorithm inspired by the social behavior of swarms of bees. It is dynamic, robust and flexible yet simple algorithm which can prove helpful for optimal

Oncogenic gene expression and epigenetic remodeling of cis-regulatory elements in ASXL1-mutant chronic myelomonocytic leukemia

Myeloid neoplasms are clonal hematopoietic stem cell disorders driven by the sequential acquisition of recurrent genetic lesions. Truncating mutations in the chromatin remodeler ASXL1 (ASXL1MT) are associated with a high-risk disease phenotype with increased proliferation, epigenetic therapeutic resistance, and poor survival outcomes. We performed a multi-omics interrogation to define gene expression and chromatin remodeling associated with ASXL1MT in chronic myelomonocytic leukemia (CMML). ASXL1MT are associated with a loss of repressive histone methylation and increase in permissive histone methylation and acetylation in promoter regions. ASXL1MT are further associated with de novo accessibility of distal enhancers binding ETS transcription factors, targeting important leukemogenic driver genes. Chromatin remodeling of promoters and enhancers is strongly associated with gene expression and heterogenous among overexpressed genes. These results provide a comprehensive map of the transcriptome and chromatin landscape of ASXL1MT CMML, forming an important framework for the development of novel therapeutic strategies targeting oncogenic cis interactions

Characterizing ESD Stress Currents in Human Wearable Devices

Currents induced on an I/O of a human wearable device IC are predicted using a test IC as a wearable device capable of transient event detection and level sensing. ESD on this pseudo wearable device using the test IC is characterized for different test scenarios and compared to the prediction