A mobile robot with a two-degree-of-freedom suspension for traversing uneven terrain with minimal slip: Modeling, simulation and experiments

It is known in literature that a wheeled mobile robot (WMR) with fixed length axle will slip on an uneven terrain. One way to avoid wheel slip is to use a torus-shaped wheel with lateral tilt capability which allows the distance between the wheel-ground contact points to change even with a fixed length axle. Such an arrangement needs a two degree-of-freedom (DOF) suspension for the vertical and lateral tilting motion of the wheel. In this paper modeling, simulation, design and experimentation with a three-wheeled mobile robot, with torus-shaped wheels and a novel two DOF suspension allowing independent lateral tilt and vertical motion, is presented. The suspension is based on a four-bar mechanism and is called the double four-bar (D4Bar) suspension. Numerical simulations show that the three-wheeled mobile robot can traverse uneven terrain with low wheel slip. Experiments with a prototype three-wheeled mobile robot moving on a constructed uneven terrain along a straight line, a circular arc and a path representing a lane change, also illustrate the low slip capability of the three-wheeled mobile robot with the D4Bar suspension. (C) 2015 Elsevier Ltd. All rights reserved.

An Alternating l(p) - l(2) Projections Algorithm (ALPA) for Speech Modeling using Sparsity Constraints

We address the problem of separating a speech signal into its excitation and vocal-tract filter components, which falls within the framework of blind deconvolution. Typically, the excitation in case of voiced speech is assumed to be sparse and the vocal-tract filter stable. We develop an alternating l(p) - l(2) projections algorithm (ALPA) to perform deconvolution taking into account these constraints. The algorithm is iterative, and alternates between two solution spaces. The initialization is based on the standard linear prediction decomposition of a speech signal into an autoregressive filter and prediction residue. In every iteration, a sparse excitation is estimated by optimizing an l(p)-norm-based cost and the vocal-tract filter is derived as a solution to a standard least-squares minimization problem. We validate the algorithm on voiced segments of natural speech signals and show applications to epoch estimation. We also present comparisons with state-of-the-art techniques and show that ALPA gives a sparser impulse-like excitation, where the impulses directly denote the epochs or instants of significant excitation.

A Cause and Effect Analysis of Motion Trajectories for Modeling Actions

An action is typically composed of different parts of the object moving in particular sequences. The presence of different motions (represented as a 1D histogram) has been used in the traditional bag-of-words (BoW) approach for recognizing actions. However the interactions among the motions also form a crucial part of an action. Different object-parts have varying degrees of interactions with the other parts during an action cycle. It is these interactions we want to quantify in order to bring in additional information about the actions. In this paper we propose a causality based approach for quantifying the interactions to aid action classification. Granger causality is used to compute the cause and effect relationships for pairs of motion trajectories of a video. A 2D histogram descriptor for the video is constructed using these pairwise measures. Our proposed method of obtaining pairwise measures for videos is also applicable for large datasets. We have conducted experiments on challenging action recognition databases such as HMDB51 and UCF50 and shown that our causality descriptor helps in encoding additional information regarding the actions and performs on par with the state-of-the art approaches. Due to the complementary nature, a further increase in performance can be observed by combining our approach with state-of-the-art approaches.

Thermal Modeling of Organic Light-Emitting Diode Display Panels

Power densities required to operate active-matrix organic-light-emitting diode (AMOLED) based displays for high luminance applications, lead to temperature rise due to self heating. Temperature rise leads to significant degradation and consequent reduction in life time. In this work numerical techniques based computational fluid dynamics (CFD) is used to determine the temperature rise and its distribution for an AMOLED based display for a given power density and size. Passive cooling option in form of protruded rectangular fins is implemented to reduce the display temperature.

Constraints based analysis of extended cybernetic models

The cybernetic modeling framework provides an interesting approach to model the regulatory phenomena occurring in microorganisms. In the present work, we adopt a constraints based approach to analyze the nonlinear behavior of the extended equations of the cybernetic model. We first show that the cybernetic model exhibits linear growth behavior under the constraint of no resource allocation for the induction of the key enzyme. We then quantify the maximum achievable specific growth rate of microorganisms on mixtures of substitutable substrates under various kinds of regulation and show its use in gaining an understanding of the regulatory strategies of microorganisms. Finally, we show that Saccharomyces cerevisiae exhibits suboptimal dynamic growth with a long diauxic lag phase when growing on a mixture of glucose and galactose and discuss on its potential to achieve optimal growth with a significantly reduced diauxic lag period. The analysis carried out in the present study illustrates the utility of adopting a constraints based approach to understand the dynamic growth strategies of microorganisms. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

Microkinetic Modeling of CO Oxidation on Ionic Palladium-Substituted Ceria

This study presents a plausible dual-site mechanism and microkinetic model for CO oxidation over palladium-substituted ceria incorporating the theoretical oxygen storage capacity of different-catalysts into the kinetic model. A rate expression without prior assumption of rate-determining steps has been developed for the proposed microkinetic model using reaction route analysis. Experiments were conducted using various percentages of palladium in ceria that were synthesized by solution combustion. Obtained catalysts were characterized by X-ray diffraction, X-ray photoelectron spectra, and Brunauer-Emmett-Teller surface area measurements. A detailed mechanism was, developed, and the kinetic parameters and rate expression were validated with the conversion data obtained in the presence of the catalysts. Furthermore, a reduced rate expression based on rate-determining step and most abundant reactive intermediate approximation was obtained and tested against the original rate expression for different experimental conditions. From the results obtained it was concluded that the simulated rate predictions matched the experimental trend with reasonable accuracy, validating the kinetic parameters proposed it this study.

Time-Instant Sampling Based Encoding of Time-Varying Acoustic Spectrum

The inner ear has been shown to characterize an acoustic stimuli by transducing fluid motion in the inner ear to mechanical bending of stereocilia on the inner hair cells (IHCs). The excitation motion/energy transferred to an IHC is dependent on the frequency spectrum of the acoustic stimuli, and the spatial location of the IHC along the length of the basilar membrane (BM). Subsequently, the afferent auditory nerve fiber (ANF) bundle samples the encoded waveform in the IHCs by synapsing with them. In this work we focus on sampling of information by afferent ANFs from the IHCs, and show computationally that sampling at specific time instants is sufficient for decoding of time-varying acoustic spectrum embedded in the acoustic stimuli. The approach is based on sampling the signal at its zero-crossings and higher-order derivative zero-crossings. We show results of the approach on time-varying acoustic spectrum estimation from cricket call signal recording. The framework gives a time-domain and non-spatial processing perspective to auditory signal processing. The approach works on the full band signal, and is devoid of modeling any bandpass filtering mimicking the BM action. Instead, we motivate the approach from the perspective of event-triggered sampling by afferent ANFs on the stimuli encoded in the IHCs. Though the approach gives acoustic spectrum estimation but it is shallow on its complete understanding for plausible bio-mechanical replication with current mammalian auditory mechanics insights.