Slow relaxation in spin glasses

The remarkable remanence effects observed in spin glasses are discussed. Some theoretical approaches and results are reviewed. Invited talk given at the Indian Academy of Sciences, Bangalore, Academy discussion meeting on phase transitions, June 21st, 1978.     

Discrete-Time Analysis of Integrated Voice/Data Multiplexers With and Without Speech Activity Detectors

Discrete-time analysis of two schemes for multiplexing voice and data is presented. In each scheme voice and data are multiplexed using the movable boundary frame allocation scheme. In the first scheme, speech activity detectors (SAD's) are not used, and hence, the variations in the voice traffic are only due to the on/off characteristics of voice. In the second scheme, SAD's are employed so that talker silences can he utilized for transmission of additional voice and/or data. In this scheme, the multiplexer performs digital speech interpolation as well as movable boundary frame allocation. The performance measures considered are probability of loss for voice calls, probability of speech clipping, speech packet rejection ratio, and the expected data message delay. In the case of the multiplexer with SAD, a tradeoff exists between data message delay and speech interpolation advantage. Some numerical examples are presented which illustrate the performance of the two multiplexers.     

Dynamics of flexible-link mechanisms

Dynamic response of flexible-link mechanism (structure) when subjected to external dynamic load is analyzed. The non-linear equations of motion are discretized using the finite element method (FEM) and Newmark time integration method combined with Newton-Raphson iterative technique is used for solution. From the dynamic response of the structure, the required kinematic characteristics are derived. Typical examples are worked out for illustration.     

Miniaturization of monopole antenna using high refractive index metamaterial loading

In this article, miniaturization of the wire monopole antenna with the help of high refractive index (HRI) metamaterial is presented. For the first time, HRI medium is realized by using the array of single ring split ring resonator. By surrounding the wire monopole with the array of SR‐SRRs, the effective wavelength is squeezed in the vicinity of the near field dominance. By the loading of the monopole with such an HRI medium, the size of the antenna is reduced from 29.5 to 16 mm, without any deformation in the current distribution and radiation pattern corresponding to the fundamental resonance. The simulated and measured results are agreed to the same.     

Coaxial‐fed high‐gain triple‐band zeroth‐order circularly polarized antenna using pseudo‐open termination and asymmetric unit cells

A coaxial‐fed tri‐band zeroth‐order resonance (ZOR) circularly polarized antenna with higher gains for all the excited ZORs is designed and analyzed in this paper. Epsilon negative transmission line (ENG TL) and pseudo‐open termination (P‐OT) unit cells with different series capacitances (C_S and C_S1 ) resonate shunt ZOR (f_sh ) and two series ZORs (f_se and f_se1 ), respectively. Asymmetric unit cell concept is applied to ENG‐TL and P‐OT unit cells to create vertical and horizontal components, and the 90° phase shift is provided by the ZOR, resulting in circular polarization (CP). Left‐hand CP (LHCP) is achieved by creating two 90° right bends to the extended stubs in ENG TL and P‐OT unit cells. Higher gains for all the excited ZORs are achieved by shifting the shorting pins of ENG TL and P‐OT unit cells far away from the center position. After fabrication, the measured resonances occur at 4.64 GHz (f_sh ), 4.04 GHz (f_se ), and 3.86 GHz (f_se1 ) with fractional bandwidths of 1.62%, 1.73%, and 1.6%, respectively. The measured LHCP peak gains are 4.05 dBic (f_sh ), 3.85 dBic (f_se ), and 3.94 dBic (f_se1 ). The average axial ratio obtained is less than 3‐dB in the 10‐dB fractional bandwidth of the proposed antenna.     

Multi-Fidelity High-Throughput Optimization of Electrical Conductivity in P3HT-CNT Composites

Combining high-throughput experiments with machine learning accelerates materials and process optimization toward user-specified target properties. In this study, a rapid machine learning-driven automated flow mixing setup with a high-throughput drop-casting system is introduced for thin film preparation, followed by fast characterization of proxy optical and target electrical properties that completes one cycle of learning with 160 unique samples in a single day, a > 10 ×  improvement relative to quantified, manual-controlled baseline. Regio-regular poly-3-hexylthiophene is combined with various types of carbon nanotubes, to identify the optimum composition and synthesis conditions to realize electrical conductivities as high as state-of-the-art 1000 S cm⁻¹. The results are subsequently verified and explained using offline high-fidelity experiments. Graph-based model selection strategies with classical regression that optimize among multi-fidelity noisy input-output measurements are introduced. These strategies present a robust machine-learning driven high-throughput experimental scheme that can be effectively applied to understand, optimize, and design new materials and composites.     

Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics

Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates provide a convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updating results and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool called Insights Feed for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploring a Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guiding computational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression.     

Effect of intake air oxygen enrichment for improving engine performance and emissions control in diesel engine

Stringent emission regulations and health awareness about air pollution have led researchers to find alternative means of minimising emissions in diesel engines. In this article, the influence of oxygen enrichment is discussed to determine the effect on diesel engine performance, emission characteristics and combustion characteristics. Normal diesel and oxygen-enriched diesel are used in this experiment. The increase in oxygen concentration led to complete combustion, producing higher thermal efficiency and low harmful emissions. From the results, it is noted that oxygen-enriched diesel fuel showed reduction of CO, HC and smoke emissions, while NOx emission increased.

Abbreviations/Nomenclature DI: direct injection; NOx: oxides of nitrogen; O₂: oxygen; HC: hydrocarbon; PM: particulate matters; CO: carbon monoxide; CO₂: carbon dioxide     

On-Line Learning Failure-Tolerant Neural-Aided Controller for Earthquake Excited Structures

This paper presents an on-line learning failure-tolerant neural controller capable of controlling buildings subjected to severe earthquake ground motions. In the proposed scheme the neural controller aids a conventional H∞ controller designed to reduce the response of buildings under earthquake excitations. The conventional H∞ controller is designed to reduce the structural responses for a suite of severe earthquake excitations using specially designed frequency domain weighting filters. The neural controller uses a sequential learning radial basis function neural network architecture called extended minimal resource allocating network. The parameters of the neural network are adapted on-line with no off-line training. The performance of the proposed neural-aided controller is illustrated using simulation studies for a two degree of freedom structure equipped with one actuator on each floor. Results are presented for the cases of no failure and failure of the actuator on each of the two floors under several earthquake excitations. The study indicates that the performance of the proposed neural-aided controller is superior to that of the H∞ controller under no actuator failure conditions. In the presence of actuator failures, the performance of the primary H∞ controller degrades considerably, since actuator failures have not been considered for the design. Under these circumstances, the neural-aided controller is capable of controlling the acceleration and displacement structural responses. In many cases, using the neural-aided controller, the response magnitudes under failure conditions are comparable to the performance of the H∞ controller under no-failure conditions.     

Symbolic modular deadlock analysis

Methods in object-oriented concurrent libraries often encapsulate internal synchronization details. As a result of information hiding, clients calling the library methods may cause thread safety violations by invoking methods in an unsafe manner. This is frequently a cause of deadlocks. Given a concurrent library, we present a technique for inferring interface contracts that specify permissible concurrent method calls and patterns of aliasing among method arguments. In this work, we focus on deriving contracts that guarantee deadlock-free execution for the methods in the library. The contracts also help client developers by documenting required assumptions about the library methods. Alternatively, the contracts can be statically enforced in the client code to detect potential deadlocks in the client. Our technique combines static analysis with a symbolic encoding scheme for tracking lock dependencies, allowing us to synthesize contracts using a SMT solver. Additionally, we investigate extensions of our technique to reason about deadlocks in libraries that employ signaling primitives such as wait-notify for cooperative synchronization. Our prototype tool analyzes over a million lines of code for some widely-used Java libraries within an hour, thus demonstrating its scalability and efficiency. Furthermore, the contracts inferred by our approach have been able to pinpoint real deadlocks in clients, i.e. deadlocks that have been a part of bug-reports filed by users and developers of client code.