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Expansion of immunization coverage is dependent in part on delivering potent vaccines in an equitable and timely manner to immunization outreach session sites from Cold Chain Points (CCPs). When duration of travel between the last CCP and the session site (Time-to-Supply) is too long, three consequences may arise: decreased potency due to exposure to heat and freezing, beneficiary dropouts due to delayed session starts, and, increased operational costs for the Health Facility (HF) conducting the outreach sessions. Guided by the Government of India’s recommendation on cold chain point expansion to ensure that all session sites are within a maximum of 60 min from the last CCP, CHAI and the State Routine Immunization Cell in the state of Madhya Pradesh collaborated to pilot a novel approach to cold chain network optimization and expansion in eight districts of Madhya Pradesh. Opportunities for realignment of remote sub-health centers (SHCs) and corresponding session sites to alternative existing CCPs or to HFs which could be converted to new CCPs were identified, and proposed using a greedy adding algorithm-based optimization which relied on health facility level geo-location data. Health facility geo-coordinates were collected through tele-calling and site visits, and a Microsoft Excel based optimization tool was developed. This exercise led to an estimated reduction in the number of remote SHCs falling beyond the permissible travel time from CCPs by 56.89 percent (132 remote sites), from 232 to 100. The 132 resolved sites include 73 sites realigned to existing CCPs, and 59 sites to be attached to 22 newly proposed CCPs. Both the network optimization approach and the institutional capacity built during this project will continue to be useful to India’s immunization program. The approach is replicable and may be leveraged by developing countries facing similar challenges due to geographical, institutional, and financial constraints.

Ground-based gravitational-wave detectors like Cosmic Explorer (CE) can be tuned to improve their sensitivity at high or low frequencies by tuning the response of the signal extraction cavity. Enhanced sensitivity above 2 kHz enables measurements of the post-merger gravitational-wave spectrum from binary neutron star mergers, which depends critically on the unknown equation of state of hot, ultra-dense matter. Improved sensitivity below 500 Hz favors precision tests of extreme gravity with black hole ringdown signals and improves the detection prospects while facilitating an improved measurement of source properties for compact binary inspirals at cosmological distances. At intermediate frequencies, a more sensitive detector can better measure the tidal properties of neutron stars. We present and characterize the performance of tuned CE configurations that are designed to optimize detections across different astrophysical source populations. These tuning options give CE the flexibility to target a diverse set of science goals with the same detector infrastructure. We find that a 40 km CE detector outperforms a 20 km in all key science goals other than access to post-merger physics. This suggests that CE should include at least one 40 km facility.

The paper proposes a CNN architecture in which features extracted using shape from eye-fundus images are fed as an input. Firstly, we preprocess the images extensively. These preprocessed i...

The charge carrier dynamics that radically affect the performance of solution-processed 2,4-bis[4-(N,N-diisobutylamino)-2,6-dihydroxyphenyl]squaraine (DIB SQ):[6,6]-phenyl-C70-butyric acid methyl ester (PC70BM) based organic solar cells is analyzed by means of steady-state, impedance and transient spectroscopic techniques. Analysis of current density–voltage characteristics using the modified Shockley diode equation and the light intensity-dependent measurements signifies the contribution of shunt and trap-mediated recombination losses in reducing the fill factor (FF) and efficiency of the SQ based device. Bias voltage-dependent impedance spectra modelled with an equivalent circuit reveal information on the physical processes of charge carrier transport and recombination in the device. Poor charge transport limits the FF of the device, evidenced by the dominance of the resistance corresponding to the charge transport processes over the recombination resistance. Transient photovoltage analysis further confirms the existence of bimolecular and monomolecular recombination in the device. Bias dependent transient photocurrent analysis is performed to probe the charge carrier dynamics at the operating voltages of the device, and it reveals that poor charge extraction due to charge trapping also affects the FF of the device. Furthermore, the morphology of the SQ:PCBM blend film is found to be unfavorable for the smooth extraction and transport of the charge carriers, complementing the findings from steady-state, impedance, and transient measurements.

A wide use of indexing algorithms for bio-medical images is done by the researchers. This paper proposes a novel feature based algorithm for indexing in brain tumor MR images which helps to compare and analyze the extent of disease amongst a database. After the detection of cancer by using various filters and operations, we use features like centroid, length of the parameter, number of connected components and Laplacian of Gaussian for indexing. This enables us to extract similar images in the database as well as to analyze the impact of disease in the unknown sample.

Free space optics (FSO) is a high-speed underwater, terrestrial and satellite communication technology. There is no necessity to get a government license for operating these communication links. FSO system is always prone to some atmospheric challenges. The light beam propagates through an open atmosphere; the refractive index fluctuation due to atmospheric turbulence impacts its properties, thus, results in a fluctuating bit error rate (BER). We obtain the closed-form expressions to investigate the average BER, outage performance of an FSO system employing binary phase-shift keying (BPSK) with subcarrier intensity modulation (SIM) and wavelength diversity. The system performance is analyzed using closed-form expressions at various atmospheric turbulence levels, attenuation, and misalignment error. The results show that the wavelength diversity based BPSK-SIM FSO system offers enhanced performance than the single input single output system.

Content Based Image Retrieval (CBIR) techniques based on texture have gained a lot of popularity in recent times. In the proposed work, a feature vector is obtained by concatenation of features extracted from local mesh peak valley edge pattern (LMePVEP) technique; a dynamic threshold based local mesh ternary pattern technique and texture of the image in five different directions. The concatenated feature vector is then used to classify images of two datasets viz. Emphysema dataset and Early Lung Cancer Action Program (ELCAP) lung database. The proposed framework has improved the accuracy by 12.56%, 9.71% and 7.01% in average for data set 1 and 9.37%, 8.99% and 7.63% in average for dataset 2 over three popular algorithms used for image retrieval.

The detectors of the laser interferometer gravitational-wave observatory (LIGO) are broadly limited by the quantum noise and rely on the injection of squeezed states of light to achieve their full sensitivity. Squeezing improvement is limited by mode mismatch between the elements of the squeezer and the interferometer. In the current LIGO detectors, there is no way to actively mitigate this mode mismatch. This paper presents a new deformable mirror for wavefront control that meets the active mode matching requirements of advanced LIGO. The active element is a piezo-electric transducer, which actuates on the radius of curvature of a 5 mm thick mirror via an axisymmetric flexure. The operating range of the deformable mirror is 120±8 mD in vacuum and an additional 200 mD adjustment range accessible out of vacuum. Combining the operating range and the adjustable static offset, it is possible to deform a flat mirror from −65 mD to −385 mD. The measured bandwidth of the actuator and driver electronics is 6.8 Hz. The scattering into higher-order modes is measured to be

Calculations in Ising model can be cumbersome and non-intuitive. Here we provide a formulation that addresses these issues for 1-D scenario. We represent the microstates of spin interactions as a diagonal matrix. This is done using two operations: Kronecker sum and Kronecker product. The calculations thus become simple matter of manipulating diagonal matrices. We address the following problems in this work: spins in the magnetic field, open-chain 1-D Ising model, closed-chain 1-D Ising model, 1-D Ising model in an external magnetic field. We believe that this representation will help provide students as well as experts with a simple yet powerful technique to carry out calculations in this model., 15 pages, 4 figures, abstract and overall text modified, references added

Free space optical (FSO) communication refers to a line of sight technology, which comprises optical source and detector to create a link without the use of physical connections. Similar to other wireless communication links, these are severely affected by losses that emerged due to atmospheric turbulence and lead to deteriorated intensity of the optical signal at the receiver. This impairment can be compensated easily by enhancing the transmitter power. However, increasing the transmitter power has some limitations as per radiation regulations. The requirement of high transmit power can be reduced by employing diversity methods. This paper presents, a wavelength-based diversity method with equal gain combining receiver, an effective technique to provide matching performance to single input single output at a comparatively low transmit power.

FSO communication systems have received huge attention in recent years due to low cost, huge bandwidth and secure alternative to current technologies. However, the performance is critically impaired by atmospheric turbulence. This paper presents the wavelength diversity as a mitigating technique, based on the fact that refractive index variation is different at different wavelengths. The strong atmospheric conditions are modeled with negative exponential distribution. We derive the closed-form expressions for outage probability and average BER. Numerical results for outage probability and average BER at different diversity orders have been presented to demonstrate our suggestions for improving the system performance.

Almost all surgeries require anesthetist which increases the price of surgery. So, there is a requirement to automate the process of anesthesia. The proper dose of anesthesia is very critical. In this paper, we have proposed the application of fractional-order PID (FOPID) controllers for the automatic control of anesthesia during the surgical procedure. The FOPID controller provides more adjustable time and frequency responses of the control system. It has more degree of freedom as compared to a conventional controller. It gives the designer more flexibility to design the controller. The simulations were done to estimate the performance of the FOPID controllers to control anesthesia delivery rates for maintaining required physiological set points, i.e., muscle relaxation and mean atrial pressure (MAP). The FOPID controllers were tuned using grey wolf optimization (GWO) algorithm to obtain the observations. The results show that the FOPID controller with a proper tuning gives better results than other designed controllers producing minimum performance parameter values. The robustness analysis is also carried out to check the performance of the controller for under parameter uncertainty and Gaussian noise.

The research in the field of kinship verification is currently very active and many new avenues are being explored. The conventional methods used for the feature extraction and classification purposes in kinship verification often fail to live up to our expected results. The proposed work aims at extraction of facial-based features and integrates them with a Siamese neural network to achieve kinship verification. The given algorithm is found to achieve better results in terms of accuracy over other recent CNN-based algorithms for kinship verification. The accuracy has improved by 1.4 and 1.5% over CNN-points algorithm.

In this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape-based features, viz. solidity, circularity, discrete Fourier transform of radial length (RL) function, histogram of oriented gradient (HOG), moment, and histogram of active contour image, have also been identified and embedded into the proposed convolutional neural network. The performance is measured in terms of average recall and average precision values and compared with six similar methods for biomedical image classification. The average precision obtained for the proposed system is found to be 95.26% and the average recall value is found to be 69.56% in average for the two databases.

Free space optical (FSO) communication has gained significant attention among researchers for high bandwidth and secured data transfer applications. However, its performance is limited by the local atmospheric conditions. This paper focusses on an FSO system employing a diversity method to compensate signal loss occurred due to atmospheric turbulence conditions varying from moderate to strong. We derive a closed-form expression for two important parameters, viz.: average bit error rate (BER) and outage probability over gamma–gamma distribution channel. The numerical results for different combining schemes are presented in this paper. Enhancement in reliability and performance of FSO communication system with wavelength diversity is investigated. Effect of different wavelengths 850 nm, 1310 nm and 1550 nm on average BER is analysed and minimum BER is achieved for optimal combining at 1550 nm at diversity order $$R=3$$ .

Speech produced by human vocal apparatus conveys substantial non-semantic information including the gender of the speaker, voice quality, affective state, abnormalities in the vocal apparatus etc. Such information is attributed to the properties of the voice source signal, which is usually estimated from the speech signal. However, most of the source estimation techniques depend heavily on the goodness of the model assumptions and are prone to noise. A popular alternative is to indirectly obtain the source information through the Electroglottographic (EGG) signal that measures the electrical admittance around the vocal folds using dedicated hardware. In this paper, we address the problem of estimating the EGG signal directly from the speech signal, devoid of any hardware. Sampling from the intractable conditional distribution of the EGG signal given the speech signal is accomplished through optimization of an evidence lower bound. This is constructed via minimization of the KL-divergence between the true and the approximated posteriors of a latent variable learned using a deep neural auto-encoder that serves an informative prior. We demonstrate the efficacy of the method at generating the EGG signal by conducting several experiments on datasets comprising multiple speakers, voice qualities, noise settings and speech pathologies. The proposed method is evaluated on many benchmark metrics and is found to agree with the gold standard while proving better than the state-of-the-art algorithms on a few tasks such as epoch extraction., Submitted to IEEE/ACM Transactions on Audio, Speech and Language Processing

Solar cells based on hybrid organic-inorganic perovskite materials, with fascinating properties like broad optical absorption spectrum, low exciton binding energy, long charge diffusion length, and high charge mobility, have emerged as one of the promising candidates for third generation photovoltaics. Till date, power conversion efficiency (PCE) achieved in these solar cells is more than 22% by employing low-cost production techniques. The performance of high-efficiency perovskite solar cells is extremely dependent on the quality of the absorber perovskite layer like grain size, roughness and presence of defects. Here, we have done a comparative study of the performance of mixed halide CH3NH3PbIxCl3-x, single halide CH3NH3PbI3 and bulk heterojunction CH3NH3PbI3-PC71BM (BHJ) perovskite solar cells (PSCs) with p-i-n architecture. The morphology of the different perovskite layers is analyzed using scanning electron microscopy (SEM) and atomic force microscopy (AFM) to understand its influence on the device performance. Defects density of states (DOS) arising from morphological variations can critically influence the overall performance of the perovskite solar cells. The PCE of the mixed and single halide, and bulk heterojunction perovskite solar cells is correlated with the defect density of states studied by means of capacitance spectroscopy.

Submicron scale structures with high index contrast are key to compact structures for realizing photonic integrated structures. Ultra-compact optical devices in silicon-on-insulator (SOI) substrates serve compatibility with semiconductor fabrication technology leading to reduction of cost and mass production. Photonic crystal structures possess immense potential for realizing various compact optical devices. However, coupling light to photonic crystal waveguide structures is crucial in order to achieve strong transmission and wider bandwidth of signal. Widening of bandwidth will increase potential for various applications and high transmission will make easy signal detection at the output. In this paper, the techniques reported so far for coupling light in photonic crystal waveguides have been reviewed and analyzed so that a comprehensive guide for an efficient coupling to photonic crystal waveguides can be made possible.

Glottal Closure Instants (GCIs) correspond to the temporal locations of significant excitation to the vocal tract occurring during the production of voiced speech. GCI detection from speech signals is a well-studied problem given its importance in speech processing. Most of the existing approaches for GCI detection adopt a two-stage approach (i) Transformation of speech signal into a representative signal where GCIs are localized better, (ii) extraction of GCIs using the representative signal obtained in first stage. The former stage is accomplished using signal processing techniques based on the principles of speech production and the latter with heuristic-algorithms such as dynamic-programming and peak-picking. These methods are thus task-specific and rely on the methods used for representative signal extraction. However, in this paper, we formulate the GCI detection problem from a representation learning perspective where appropriate representation is implicitly learned from the raw-speech data samples. Specifically, GCI detection is cast as a supervised multi-task learning problem solved using a deep convolutional neural network jointly optimizing a classification and regression cost. The learning capability is demonstrated with several experiments on standard datasets. The results compare well with the state-of-the-art algorithms while performing better in the case of presence of real-world non-stationary noise., Updated submission. Figures Added. Accepted in Interspeech 2019

Various texture based approaches have been proposed for image indexing in bio-medical image processing and a precise description of image for indexing in bio-medical image database has always been a challenging task. In this paper, an extension of local mesh peak valley edge pattern (LMePVEP) has been proposed and its effectiveness is experimentally justified. The proposed algorithm explores the relationship of center pixel with the surrounding ones along with the relationship of pixels amongst each other in five different directions. It is then compared with the original LMePVEP as well as a directional local ternary quantized extrema pattern (DLTerQEP) based approach using two bench mark databases viz. ELCAP database for lungs and Wiki cancer data set for thyroid cancer. Further a live dataset for brain tumor is also used for experimental evaluation. The experimental results show that an average improvement of 11.16% in terms of average retrieval rate (ARR) and 5.37% in terms of average retrieval precision (ARP) is observed for proposed enhanced LMePVEP over conventional LMePVEP.

Cancer is a lethal disease if not detected in an early stage. This paper presents an outline of different types of cancers and recent advancement in soft computing techniques for their detection. It focuses on how different image processing techniques are optimized using neural networks, fuzzy logic, and genetic algorithms to detect cancers.

Histopathology refers to the examination of biopsy samples by a pathologist using a microscope for analysing and classifying diseases. In order to study the manifestations of a disease, the analysis of histopathological image is done manually by a pathologist and therefore the diagnosis is subjective and greatly dependent on the level of expertise of the professional. In order to overcome this problem of a possible erroneous diagnosis and for early stage detection, an automated computerized image processing system is needed for quantitative diagnosis of biopsy samples. In this paper we have proposed a model for early stage cancerous cell detection and have reviewed and summarized the phases involved, namely, Image Pre-processing, Image Segmentation, Feature Extraction and Classification.

This paper presents a two-dimensional wavelet based decomposition algorithm for classification of biomedical images. The two-dimensional wavelet decomposition is done up to five levels for the input images. Histograms of decomposed images are then used to form the feature set. This feature set is further reduced using probabilistic principal component analysis. The reduced set of features is then fed into either K nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. The proposed algorithm has been found better up to 3.3%, 12.75%, and 13.75% on average over the first, second, and third algorithm, respectively, using KNN and up to 6.22%, 13.9%, and 14.1% on average using ANN. The dataset used for comparison consisted of CT Scan images of lungs and MR images of heart as obtained from different sources.

Regression testing is a maintenance activity that is performed to ensure the validity of modified software. The activity takes a lot of time to run the entire test suite and is very expensive. Thus it becomes a necessity to choose the minimum set of test cases with the ability to cover all the faults in minimum time. The paper presents a new test case reduction hybrid technique based on Genetic algorithms(GA) and bee colony optimization (BCO) .GA is an evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. BCO is a swarm intelligence algorithm. The proposed approach adopts the behavior of bees to solve the given problem. It proves to be optimistic approach which provides optimum results in minimum time.

We investigate the effects of observatory locations on the probability of discovering optical/infrared counterparts of gravitational wave sources. We show that for the LIGO--Virgo network, the odds of discovering optical/infrared (OIR) counterparts show some latitude dependence, but weak or no longitudinal dependence. A stronger effect is seen to arise from the timing of LIGO/Virgo observing runs, with northern OIR observatories having better chances of finding the counterparts in northern winters. Assuming identical technical capabilities, the tentative mid-2017 three-detector network observing favors southern OIR observatories for discovery of EM counterparts., Comment: 14 pages, 10 figures, Submitted to ApJ

Even experienced developers struggle to implement security policies correctly. For example, despite 15 years of development, standard Java libraries still suffer from missing and incorrectly applied permission checks, which enable untrusted applications to execute native calls or modify private class variables without authorization. Previous techniques for static verification of authorization enforcement rely on manually specified policies or attempt to infer the policy by code-mining. Neither approach guarantees that the policy used for verification is correct. In this paper, we exploit the fact that many modern APIs have multiple, independent implementations. Our flow- and context-sensitive analysis takes as input an API, multiple implementations thereof, and the definitions of security checks and security-sensitive events. For each API entry point, the analysis computes the security policies enforced by the checks before security-sensitive events such as native method calls and API returns, compares these policies across implementations, and reports the differences. Unlike code-mining, this technique finds missing checks even if they are part of a rare pattern. Security-policy differencing has no intrinsic false positives: implementations of the same API must enforce the same policy, or at least one of them is wrong! Our analysis finds 20 new, confirmed security vulnerabilities and 11 interoperability bugs in the Sun, Harmony, and Classpath implementations of the Java Class Library, many of which were missed by prior analyses. These problems manifest in 499 entry points in these mature, well-studied libraries. Multiple API implementations are proliferating due to cloud-based software services and standardization of library interfaces. Comparing software implementations for consistency is a new approach to discovering "deep" bugs in them.

Software developers are increasingly choosing memory-safe languages. As a result, semantic vulnerabilities---omitted security checks, misconfigured security policies, and other software design errors---are supplanting memory-corruption exploits as the primary cause of security violations. Semantic attacks are difficult to detect because they violate program semantics, rather than language semantics. This paper presents Pecan, a new dynamic anomaly detector. Pecan identifies unusual program behavior using history sensitivity and depth-limited context sensitivity. Prior work on context-sensitive anomaly detection relied on stack-walking, which incurs overheads of 50% to over 200%. By contrast, the average overhead of Pecan is 5%, which is low enough for practical deployment. We evaluate Pecan on four representative real-world attacks from security vulnerability reports. These attacks exploit subtle bugs in Java applications and libraries, using legal program executions that nevertheless violate programmers' expectations. Anomaly detection must balance precision and sensitivity: high sensitivity leads to many benign behaviors appearing anomalous (false positives), while low sensitivity may miss attacks. With application-specific tuning, Pecan efficiently tracks depth-limited context and history and reports few false positives.