RevSCA

In this paper, a comprehensive review is presented which is classified as analysis, design and optimization of current mode logic. Literature review reveals that amongst available logic design techniques used for mixed signal applications; current mode logic (CML) is most efficient logic design technique. Current-Mode Logic (CML) is most promising in reducing power consumption, switching/transient noise and also provides analog friendly environment. Thus, CML is a natural candidate for researchers to explore and implementing digital blocks in mixed-signal circuits. Further, operation of inverter/buffer in MCML and PFSCL style, basic terminologies, static and delay parameters related to MCML and PFSCL circuits are also presented in this paper. Realization approaches to implement various logic functions is described for both MCML and PFSCL styles. Static and dynamic variant of MCML and PFSCL circuits are simulated for performance comparison and their suitability in high performance logic designs.

The performance of two popular approaches for off-line signature verification in terms of signature legibility and signature type is studied. We investigate experimentally if the knowledge of letters, syllables or name instances can help in the process of imitating a signature. Experimental results are given on a sub-corpus of the MCYT signature database for random and skilled forgeries. We use for our experiments two machine experts, one based on global image analysis and statistical distance measures, and the second based on local image analysis and Hidden Markov Models. Verification results are reported in terms of Equal Error Rate (EER), False Acceptance Rate (FAR) and False Rejection Rate (FRR).

SUMMARYUser‐interactive undo is a recovery facility that enables users to correct mistakes easily by canceling or re‐executing operations that have already been executed. This paper presents an interesting common structural property that has been discovered in programs that implementing user‐interactive undo features. The property shows that there is a one‐to‐one correspondence between program statements that raise erroneous effects and those statements that can undo these effects. Statistical validation has been conducted which gives evidences to show that this property holds for 99 per cent of all the cases. An approach for automated verification of user‐interactive undo features in database applications through the use of this empirical property is further proposed. Based on the verification results, test cases are automatically generated to confirm the correctness of these features. A case study has been conducted to evaluate the performance of the proposed verification and testing approach in terms of fault detection capability. Copyright © 2010 John Wiley & Sons, Ltd.

Cyber-Physical Systems (CPS) intrinsically combine hardware and physical systems with software and network, which are together creating complex and correlated interactions. CPS applications often experience uncertainty in interacting with environment through unreliable sensors. They can be faulty and exhibit runtime errors if developers have not considered environmental interaction uncertainty adequately. Existing work in verifying CPS applications ignores interaction uncertainty and thus may overlook uncertainty-related faults. To improve verification accuracy, in this article we propose a novel approach to verifying CPS applications with explicit modeling of uncertainty arisen in the interaction between them and the environment. Our approach builds an Interactive State Machine network for a CPS application and models interaction uncertainty by error ranges and distributions. Then it encodes both the application and uncertainty models to Satisfiability Modulo Theories (SMT) formula to leverage SMT solvers searching for counterexamples that represent application failures. The precision of uncertainty model can affect the verification results. However, it may be difficult to model interaction uncertainty precisely enough at the beginning, because of the uncontrollable noise of sensors and insufficient data sample size. To further improve the accuracy of the verification results, we propose an approach to identifying and calibrating imprecise uncertainty models. We exploit the inconsistency between the counterexamples’ estimate and actual occurrence probabilities to identify possible imprecision in uncertainty models, and the calibration of imprecise models is to minimize the inconsistency, which is reduced to a Search-Based Software Engineering problem. We experimentally evaluated our verification and calibration approaches with real-world CPS applications, and the experimental results confirmed their effectiveness and efficiency.

This work presents an innovative deep neural network-based speaker verification (SV) model. SV is an authentication process that confirms whether a speaker is authorized. Text Independent Speaker Verification identifies who is speaking, not what is being said. India’s diverse linguistic and cultural landscape introduces challenges such as dialect and accent variations, multilingualism, multi-speaker scenarios, and varying quality of voice data recorded. Literature hints that the EER of the speaker verification model shoots up if tested with multi-language and multi-channel data; we have used the IITG-MV dataset, which has these variations captured specially for Indian conditions. Currently, convolutional neural networks (CNNs) have shown encouraging results in speaker verification. Our work presents CNN combined with the LSTMs, where CNN is a feed-forward neural network, and LSTMs are a recurrent neural network. The pipeline extracts the acoustic characteristics from the input speech signal using MFCC and fed into models based on deep neural networks like VGG-16. However, the raw speech signal is fed into the SincNet model. The CNN-LSTMs model demonstrates significantly superior accuracy to SincNet and VGG16, making it the most effective approach for text-independent speaker verification in diverse Indian languages and channels. Despite using a basic deep neural network, the results demonstrate the efficiency of our proposed architecture. CNN-LSTM’s state-of-the-art performance is closer to that of the ECAPA-TDNN model.

This paper presents a series of improvements made in the Topside Sounders Model assisted by Digisonde (TaD), verification results on these improvements, and its validation at the topside part of the profile. The TaD is based on the simple empirical functions for the O + /H + transition height ( h T ), the topside electron density scale height ( H T ), and their ratio, based on the Alouette/ISIS database. From its first release, published some years ago, TaD offers analytical formulas for obtaining the shape of the vertical plasma distribution in the topside ionosphere and plasmasphere. This first version of the TaD model (TaDv1) is using Digisonde measured parameters of the F layer maximum density, its height, and its scale height to specify the profiler’s characteristics at its lower boundary. TaDv1 models separately the O + and H + density profiles, providing the H + scale height in the plasmasphere, extracted from ISIS-1 topside sounder data, as a function of geomagnetic latitude. The upgraded version of TaD (TaDv2) provides calculation of O + , H + , and He + density distributions in transition region between topside F region and plasmasphere, extracted from the analysis of the electron density profiles from ISIS-1, and in addition approximates the plasmaspheric scale height as a function of altitude, latitude, local time, and season using an optimization procedure to achieve best fit with the measured profiles. These improvements, which concern the part of the profile above the transition height, are presented in detail in the first part of the paper. In the second part we present statistical results for the verification of the model’s improvements that show that the optimization procedure contributes to a reduction of the model error of more than two times. The model validation for the topside part of the profile is presented in the last part of this paper, comparing Incoherent Scatter Radar (ISR) electron density profiles (EDP) with the model reconstructed profiles. Comparison with measured EDP from ISR at middle latitudes gives a model error of 3TECU which is close to the GNSS measurement error. Further improvements of TaD reconstruction model are attempted in a follow-on paper, mainly targeted to the reliable operational implementation of the algorithm.

Probabilistic fracture mechanics (PFM) has been recognized as a promising methodology in structural integrity assessments of aged pressure boundary components of nuclear power plants because it can rationally represent the influencing parameters in their inherent probabilistic distributions without over conservativeness. In Japan, a PFM analysis code PASCAL (PFM Analysis of Structural Components in Aging LWR) has been developed by the Japan Atomic Energy Agency (JAEA) to evaluate the through-wall cracking frequencies of Japanese reactor pressure vessels (RPVs) considering neutron irradiation embrittlement and pressurized thermal shock (PTS) transients. In addition, efforts have been made to strengthen the applicability of PASCAL to structural integrity assessments of domestic RPVs against non-ductile fracture. On the other hand, unlike deterministic analysis codes, the verification of PFM analysis codes is not easy. A series of activities has been performed to verify the applicability of PASCAL. In this study, as a part of the verification activities, a working group was established in Japan, with seven organizations from industry, universities and institutes voluntarily participating as members. Through one year activities, the applicability of PASCAL for structural integrity assessments of domestic RPVs was confirmed with great confidence. This paper presents the details of the verification activities of the working group including the verification plan, approaches and results.

This paper describes a semi-automatic system for road verification from high resolution orthophotos in an urban context. The system combines several road detection and road verification approaches from current literature to form a more general solution. Each road detection / verification approach is realized as an independent module representing a unique road model and thus a unique strategy. The object-wise verification result of each module is formulated as a binary decision between the classes "correct road" and "incorrect road". These individual decisions are combined by Dempster-Shafer fusion, which provides tools for dealing with uncertain and incomplete knowledge about the statistical properties of the data. For each road detection / verification module a function for confidence is introduced that reflects degree of correspondence of an actual test situation with an optimal situation according to the underlying road model of that module. Experimental results achieved with four different test sites in Japan demonstrate the potential and confirm the reliability of the new system.

Radiation Belt Storm Probes: A New Mission for Space Weather Forecasting

SummaryEmbedded control software that controls safety‐critical IoT devices requires systematic and comprehensive verification to ensure safe operation of the device. However, rigorous verification in this domain has not been feasible due to the high complexity of embedded control software, which is characterized by the frequent use of multi‐tasking, interrupts, and periodic alarms. Realizing that two major factors, scalability and exactness, are extremely difficult to achieve at the same time but critical for effective and efficient verification in this domain, this work introduces a domain‐specific compositional OS‐in‐the‐Loop (OiL) verification approach and sets out to push the boundary in achieving both factors. The suggested approach (1) models the behavior of the underlying operating system to limit the search space using the notion of controlled concurrency, (2) performs heterogeneous composition of controllers with the formal OS model to reduce verification complexity, and (3) utilizes state‐of‐the‐art verification techniques for the purpose of comprehensive verification up to a given search depth.

Model checking is an effective technique used to identify subtle problems in software safety. Its comprehensive search method on system state space provides high-level confidence regarding verification results, and its automated counterexample generation facility is a useful tool for tracing potential safety bugs. However, this comprehensiveness requires a large amount of resources and is often too expensive to be applied in practice. This work reports our experience with the software safety analysis of the Trampoline operating system using model checking. Trampoline OS is an open source operating system for automotive electronic/electrical devices based on the OSEK/VDX international standard. We present methods for converting the Trampoline kernel code into formal models and a series of experiments using an incremental verification approach. The conversion methods include functional modularization and treatment for hardware-dependent code, such as context-switching behavior. The incremental verification approach aims at increasing the level of confidence in the verification even when comprehensiveness cannot be provided due to the limitations of the hardware resource. We also report on a safety bug found in the Trampoline kernel during the experiments.

Electromagnetic inverse scattering problems are nonlinear and ill-posed. They are often transformed into optimization problems and solved by deterministic or stochastic algorithms. In this article, an inversion method combining a customized particle swarm optimization (PSO) algorithm and the fast Fourier transform (FFT) is proposed. Stochastic algorithms tackle optimization problems in a global manner, which is an important feature in reducing the risk of optimization falling into local extrema. Another feature utilized here is that constraints can be easily applied in the optimization process, yielding better results. Based on this consideration, a customized PSO algorithm incorporating the mutation operator is proposed to further enrich the swarm diversity. However, the large-scale search in the solution space also increases the computational burden, which is the major limitation of stochastic algorithms being applied in microwave imaging problems. In this article, a novel cost function of inversion optimization problems is derived based on the Born approximation so that the evaluation of individual fitness can be performed by the FFT. Reconstructions are performed using both synthetic and experimental data to illustrate the key features of the proposed approach, and good results have been obtained in terms of imaging accuracy and robustness.

Aiming at the large delay and large inertia in cone storage and irrigation, this paper adopts machine learning and predictive control, proposes a closed-loop N4SID (numerical subspace state system identification) algorithm, which is adjusted by IMC based on Smith predictor PI controller and parameter tuning are given, and the steps designed in cascade control are given. The experimental results verify the effectiveness of the proposed method.Compared with closed-loop SVA and open-loop methods, the method proposed in this paper has better verification results and pole assignment. According to this model, the PI controller is adjusted using IMC based on Smith Smith predictor. Compared with the traditional ZN adjustment method, this method shows good results in a conical system. In addition, the PI parameters can be adjusted to obtain robust control using the H∞-based mixed sensitivity algorithm. The contribution of this paper is the use of closed subspace system identification to identify the MIMO state space. The developed model validates the IMC tuned PI controller based on the Smith predictor in the factory.

Quantified Constraint Satisfaction Problems (QCSP) are a generalization of Constraint Satisfaction Problems (CSP) in which variables may be quantified existentially and universally. QCSP offers a natural framework to express PSPACE problems as finite two-player games or planning under uncertainty. State-of-the-art QCSP solvers have an important drawback: they explore much larger combinatorial spaces than the natural search space of the original problem since they are unable to recognize that some sub-problems are necessarily true. We introduce a new tool, inspired by the cut rule of Prolog as a tool under responsibility of the designer of the QCSP, to prune those parts of the search space which are by construction known to be useless. We use this new tool to restore on one hand the annihilator property of true for disjunction in QCSP solver and, on the other hand, to prune the search space in two-player games. It is a simple solution to use efficiently QCSP to design finite two-player games without restricting the QCSP language. This tool does not need to modify the QCSP solver but has only one requirement: be able to tell the QCSP solver that the current QCSP is solved. Our QCSP solver built over Ge Code, a CSP library, obtained very good results compared to state-of-the-art QCSP solvers.

A fuzzy transfer function based on the behavior of meta-heuristic algorithm and its application for high-dimensional feature selection problems