Enhancing biometric recognition with spatio-temporal reasoning in smart environments

We discuss smart environments that identify and track their occupants using unobtrusive recognition modalities such as face, gait, and voice. In order to alleviate the inherent limitations of recognition, we propose spatio-temporal reasoning techniques based upon an analysis of the occupant tracks. The key idea underlying our approach is to determine the identity of a person based upon information from a track of events rather than a single event. We abstract a smart environment by a probabilistic state transition system in which each state records a set of individuals who are present in various zones of the smart environment. An event abstracts a recognition step, and the transition function defines the mapping between states upon the occurrence of an event. We express two forms of spatio-temporal reasoning in the form of transition functions: a track-based transition function and an error-correcting transition function. We also define the concepts of ‘precision’ and ‘recall’ to quantify the performance of the smart environment and provide experimental results to clarify the performance improvements from spatio-temporal reasoning. Our conclusion is that the state transition system is an effective abstraction of a smart environment and the application of spatial-temporal reasoning enhances the overall performance of a biometric recognition system.     

In vivo Targeting of DNA Vaccines to Dendritic Cells via the Mannose Receptor Induces Long-Lasting Immunity against Melanoma

Herein, we report effective, C-type lectin mannose receptor (MR)-selective, in vivo dendritic cell (DC)-targeting lipid nanoparticles (LNPs) of a novel lipid-containing mannose-mimicking di-shikimoyl- and guanidine head group and two n-hexadecyl hydrophobic tails (DSG). Subcutaneous administration of LNPs of the DSG/p-CMV-GFP complex showed a significant expression of green fluorescence protein in the CD11c⁺ DCs of the neighboring lymph nodes compared to the control LNPs of the BBG/p-CMV-GFP complex. Mannose receptor-facilitated in vivo DC-targeted vaccination (s.c.) with the electrostatic complex of LNPs of DSG/pCMV-MART1 stimulated long-lasting (270 days post B16F10 tumor challenge) antimelanoma immunity under prophylactic conditions. Remarkably, under therapeutic settings, vaccination (s.c.) with LNPs of the DSG/pCMV-MART1 complex significantly delayed melanoma growth and improved the survival of mice with melanoma. These findings demonstrate that this nonviral delivery system offers a resilient and potential approach to deliver DNA vaccines encoding tumor antigens to DCs in vivo with high efficacy.     

Hydrogen evolution reaction: The role of arsenene nanosheet and dopant

The state-of-the-art density functional theory (DFT) is employed to study the catalytic activity of arsenene for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). We have included dispersion correction to get accurate adsorption energy on the individual catalytic surface (top site). Using binding energy calculation, arsenene is shown to be a potential candidate for HER. Here we investigate the stability and electronic properties of the honeycomb structure of the arsenene system using first-principles calculation to find the effect of different dopants on the fundamental band gap, which is one of the primary parameters in the photocatalytic water splitting. Further, we sieved the dopant for better HER catalytic activity by substituting one of the arsenene (As) atoms by B, N, O, Ge, Ga and Se atoms to make arsenene a better candidate for HER. Our studies depict that HER activity is increased by 82% for O-doped arsenene and OER activity by 87% for B-doped arsenene as compared to pristine arsenene.     

Influence of Acid Modification on Selective Phenol Hydrogenation Over Pd/Activated Carbon Catalysts

The influence of the acid treatment on cyclohexanone selectivity of phenol hydrogenation over Pd on active carbon was studied in liquid phase reaction and by temperature-programmed desorption. Acid treatment of activated carbon led to an increased cyclohexanone/cyclohexanol ratio. Acid modification of the carbon support enriched the electron density of Pd, and enhanced the desorption of the phenoxy species, which resulted in improved cyclohexanone selectivity in phenol hydrogenation.     

Optimal machining conditions for turning Ti-6Al-4V using response surface methodology

Machining titanium is one of ever-increasing magnitude problems due to its characteristics such as low thermal conductivity, modulus of elasticity and work
hardening. The efficient titanium alloy machining involves a proper selection of process parameters to minimize the tangential force (F_z) and surface roughness (R_a). In the present work, the performance of PVD/TiAlN coated carbide inserts was investigated using response surface methodology (RSM) for turning Ti-6Al-4V. The effects of process parameters such as speed (v), feed (f), depth of cut (d) and back rake angle (γ_y) on F_z and R_a were investigated.
The experimental plan used for four factors and three levels was designed based on face centered, central composite design (CCD). The experimental results indicated that F_z increased with the increase in d, f and decreased with the increase in v and γ_y, whereas Ra decreased with the increase in v and γ_y, and increased with d and v. The goodness of fit of the regression equations and model fits (R ²) for F_z and R_a were found to be 0.968 and 0.970, which demonstrated that it was an effective model. A confirmation test was also conducted in order to verify the correctness of the model.     

A new chip-thickness model for performance assessment of silicon carbide grinding

The chip-thickness models, used to assess the performance of grinding processes, play a major role in predicting the surface quality. In the present paper, an attempt has been made to develop a new chip-thickness model for the performance assessment of silicon carbide grinding by incorporating the modulus of elasticities of the grinding wheel and the workpiece in the existing basic chip-thickness model to account for elastic deformation. The new model has been validated by conducting experiments, taking the surface roughness as a parameter of evaluation .     

Automatic recognition of handwritten medical forms for search engines

A new paradigm, which models the relationships between handwriting and topic categories, in the context of medical forms, is presented. The ultimate goals are: (1) a robust method which categorizes medical forms into specified categories, and (2) the use of such information for practical applications such as an improved recognition of medical handwriting or retrieval of medical forms as in a search engine. Medical forms have diverse, complex and large lexicons consisting of English, Medical and Pharmacology corpus. Our technique shows that a few recognized characters, returned by handwriting recognition, can be used to construct a linguistic model capable of representing a medical topic category. This allows (1) a reduced lexicon to be constructed, thereby improving handwriting recognition performance, and (2) PCR (Pre-Hospital Care Report) forms to be tagged with a topic category and subsequently searched by information retrieval systems. We present an improvement of over 7% in raw recognition rate and a mean average precision of 0.28 over a set of 1,175 queries on a data set of unconstrained handwritten medical forms filled in emergency environments. This work was supported by the National Science Foundation.     

uWave: Accelerometer-based personalized gesture recognition and its applications

The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures. We evaluate uWave using a large gesture library with over 4000 samples for eight gesture patterns collected from eight users over one month. uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. We also present applications of uWave in gesture-based user authentication and interaction with 3D mobile user interfaces. In particular, we report a series of user studies that evaluates the feasibility and usability of lightweight user authentication. Our evaluation shows both the strength and limitations of gesture-based user authentication.     

The optimisation of the grinding of silicon carbide with diamond wheels using genetic algorithms

Modelling and optimisation are necessary for the control of any process to achieve improved product quality, high productivity and low cost. The grinding of silicon carbide is difficult because of its low fracture toughness, making it very sensitive to cracking. The efficient grinding of high performance ceramics involves the selection of operating parameters to maximise the MRR while maintaining the required surface finish and limiting surface damage. In the present work, experimental studies have been carried out to obtain optimum conditions for silicon carbide grinding. The effect of wheel grit size and grinding parameters such as wheel depth of cut and work feed rate on the surface roughness and damage are investigated. The significance of these parameters, on the surface roughness and the number of flaws, has been established using the analysis of variance. Mathematical models have also been developed for estimating the surface roughness and the number of flaws on the basis of experimental results. The optimisation of silicon carbide grinding has been carried out using genetic algorithms to obtain a maximum MRR with reference to surface finish and damage.Nomenclature C constant in mathematical model - C₁ constant in surface roughness model - C₂ constant in the number of flaws model - d depth of cut, m - dof degrees of freedom - f table feed rate, mm/min - M grit size (mesh) - MRR material removal rate, mm³/mm width-min - N_c number of flaws measured - R_a surface roughness measured, m - Y machining response - depth of cut exponent in mathematical model - ₁ depth of cut exponent in surface roughness model - ₂ depth of cut exponent in number of flaws model - feed rate exponent in mathematical model - ₁ feed rate exponent in surface roughness model - ₂ feed rate exponent in number of flaws model - grit size exponent in mathematical model - ₁ grit size exponent in surface roughness model - ₂ grit size exponent in number of flaws model     

Effect of nano additives (titanium and zirconium oxides) and diethyl ether on biodiesel-ethanol fuelled CI engine

The present work is dedicated to the comparative experimental study of biodiesel-ethanol blends in a compression ignition engine using TiO₂ (Titanium oxide) nanoparticle, ZrO₂ (Zirconium oxide) nanoparticle and DEE (Diethyl ether) additives. The test fuels used are a blend of biodiesel (80%) -ethanol (20%) (denoted as BE), a blend of BE with 25 ppm Titanium oxide nanoparticle (denoted as BE-Ti), a blend of BE with 25 ppm Zirconium oxide nanoparticle (denoted as BE-Zr) and a blend of BE with 50 ml Diethyl ether (denoted as BE-DEE). Addition of nanoparticles increases the oxidation rate, reduces the light-off temperature and creates large contact surface area with the base fuel thereby enhancing the combustion with minimal emissions. Experimental results shown that addition of Titanium nanoparticles increased NOx, HC and smoke with lowered BSFC and CO. Whereas addition of Zirconium nanoparticles increases BSFC and HC emissions with lowered CO, CO₂ and smoke emissions in comparison with BE blends. DEE addition to BE blends improved the heat release rate and increased HC, CO emissions were observed with lowered BSFC, NOx and smoke. Simultaneous reduction of NOx and smoke indicates the effect of DEE on Low temperature combustion (LTC).