A robust feature extraction with optimized DBN-SMO for facial expression recognition

Multimedia Tools and Applications - Facial expression is the most common technique is used to convey the expressions of human beings. Due to different ethnicity and age, faces differ from one...     

Mechanical State Estimation for Overhead Transmission Lines With Level Spans

Electric power transmission lines face increased threats from malicious attacks and natural disasters. This underscores the need to develop new techniques to ensure safe and reliable transmission of electric power. This paper deals with the development of an online monitoring technique based on mechanical state estimation to determine the sag levels of overhead transmission lines in real time and hence determine if these lines are in normal physical condition or have been damaged or downed. A computational algorithm based on least squares state estimation is applied to the physical transmission line equations to determine the conductor sag levels from measurements of tension, temperature, and other transmission line conductor parameters. The estimated conductor sag levels are used to generate warning signals of vertical clearance violations in the energy management system. These warning signals are displayed to the operator to make appropriate decisions to maintain the line within the prescribed clearance limits and prevent potential cascading failures.      

High extinction, broadband polarisers using long-period fibre gratings in few-mode fibres

Long-period fibre gratings in specially designed birefringent few-mode fibres are used to demonstrate spectrally flat polarisers with bandwidths of 10 nm and polarisation extinction levels of 20 dB. This is enabled by the special dispersive properties of few-mode fibres, yielding fibre polarisers with performance approaching that of bulk-optic devices.     

Dynamic provisioning in multi-tenant service clouds

Cloud-based systems promise an on-demand service provisioning system along with a ??pay-as-you-use?? policy. In the case of multi-tenant systems this would mean dynamic creation of a tenant by integrating existing cloud-based services on the fly. Presently, dynamic creation of a tenant is handled by building the required components from scratch. Although multi-tenant systems help providers save cost by allocating multiple tenants to the same instance of an application, they incur huge reconfiguration costs. Cost and time spent on these reconfiguration activities can be reduced by re-constructing tenants from existing tenant configurations supported by service providers. Multi-tenant cloud-based systems also lack the facility of allowing clients to specify their requirements. Giving clients the flexibility to specify requirements helps them avoid spending an excessive amount of time and effort looking through a list of services, many of which might not be relevant to them. Moreover, dynamic provisioning in the cloud requires an integrated solution across the technology stack (software, platform and infrastructure) combining functional, non-functional and resource allocation requirements. Existing research works in the area of web service matching, although numerous, still fall short, since they usually consider each requirement type in isolation and cannot provide an integrated solution. To that end, in this paper we investigate the features needed for dynamic service provisioning on the cloud. We propose a novel User Interface-Tenant Selector-Customizer (UTC) model and approach, which enables cloud-based services to be systematically modeled and provisioned as variants of existing service tenants in the cloud. Our approach considers functional, non-functional and resource allocation requirements, which are explicitly specified by the client via the user interface component of the model. To the best of our knowledge, ours is the first such integrated approach. We illustrate our ideas using a realistic running example, and also present a proof-of-concept prototype built using IBM??s Rational Software Architect modeling tool. We also present experimental results demonstrating the applicability of our matching algorithm. Our results show significant reduction in matching time with the help of an elimination process that reduces the search space needed for performing matching.     

Influence of triethanolamine on the hydration characteristics of tricalcium silicate

The hydration characteristics of 3CaO.SiO₂ or β2CaO.SiO₂ are studied by an addition of 0.0, 0.1, 0.5 or 1.0% triethanolamine. The amount of Ca(OH)₂ found at 1, 3, 7 or 28 days was in the order C₃S + 0% TEA > C₃S +0.1% TEA > C₃S + 0.5% TEA > C₃S+1.0% TEA, irrespective of whether lime was estimated by X-ray, DTA, TGA or chemical analysis. The rate of hydration, in terms of the disappearance of 3CaO.SiO₂, showed that hydration proceeded faster in the presence of TEA after 1 day. Additions of TEA increase the induction period, promote the formation of a C-S-H with higher CaO/SiO₂ ratio, increase the formation of non-crystalline Ca(OH)₂ and enhance the surface area of the hydrated silicate product.     

A Randomized Linear-Work EREW PRAM Algorithm to Find a Minimum Spanning Forest

We present a randomized EREW PRAM algorithm to find a minimum spanning forest in a weighted undirected graph. On an n -vertex graph the algorithm runs in o(( log n) ^{1+ ɛ} ) expected time for any ɛ >0 and performs linear expected work. This is the first linear-work, polylog-time algorithm on the EREW PRAM for this problem. This also gives parallel algorithms that perform expected linear work on two general-purpose models of parallel computation—the QSM and the BSP.     

Anosognosia in parietal lobe syndrome

Patients with right parietal lesions often deny their paralysis (anosognosia), but do they have "tacit" knowledge of their paralysis? I devised three novel tests to explore this. First, the patients were given a choice between a bimanual task (e.g., tying shoe laces) vs a unimanual one (e.g., threading a bolt). They chose the former on 17 of 18 trials and, surprisingly, showed no frustration or learning despite repeated failed attempts. I conclude that they have no tacit knowledge of paralysis (or, if such knowledge exists, it is not available for this particular task). Second, I used a "virtual reality box" to convey the optical illusion to the patient that she was moving her paralyzed left hand up and down to the rhythm of a metronome, and yet she showed no sign of surprise. Third, I irrigated patient BM's left ear canal with cold water, a procedure that is known to shift that patient's spatial frame of reference by stimulating the vestibular system. Surprisingly, this allowed her "repressed" memory of the paralysis to come to the surface; she said she had been paralyzed continuously for several days. I suggest that the vestibular stimulation produces these remarkable effects by mimicking REM sleep. These patients also employ a whole arsenal of grossly exaggerated Freudian "defense mechanisms" to account for their paralysis. To explain this, I propose that in normal individuals the left hemisphere ordinarily deals with small, local anomalies by trying to impose consistency but, when the anomaly exceeds threshold, an interaction with the right hemisphere forces a "paradigm shift." A failure of this process, in patients with right hemisphere damage, might partially account for anosognosia. Finally, I present a new conceptual framework that may help link several psychological and neurological phenomena such as Freudian defense mechanisms, vestibular stimulation, anosognosia, memory repression, visual illusions, anterograde amnesia, REM sleep, dreaming, and humor.     

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Feature extraction is the most critical step in classification of multispectral image. The classification accuracy is mainly influenced by the feature sets that are selected to classify the image. In the past, handcrafted feature sets are used which are not adaptive for different image domains. To overcome this, an evolutionary learning method is developed to automatically learn the spatial-spectral features for classification. A modified Firefly Algorithm (FA) which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose. For extracting the most efficient features from the data set, we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions. For selecting spatial and spectral features we have studied three different approaches namely overlapping window (OW-3DFS), non-overlapping window (NW-3DFS) adaptive window cube (AW-3DFS) and Pixel based technique. Fivefold Multiclass Support Vector Machine (MSVM) is used for classification purpose. Experiments conducted on Madurai LISS IV multispectral image exploited that the adaptive window approach is used to increase the classification accuracy.     

ArRaNER: A novel named entity recognition model for biomedical literature documents

Developments in advanced innovations have prompted the generation of an immense amount of digital information. The data deluge contains hidden information that is difficult to extract. In the biomedical domain, the development of technology has caused the production of voluminous data. Processing these voluminous textual data is referred to as ‘biomedical content mining’. Emerging artificial intelligence (AI) models play a major role in the automation of Pharma 4.0. In AI, natural language processing (NLP) plays a dynamic role in extracting knowledge from biomedical documents. Research articles published by scientists and researchers contain an enormous amount of hidden information. Most of the original and peer-reviewed articles are indexed in PubMed. Extracting meaningful information from a large number of literature documents is very difficult for human beings. This research aims to extract the named entities of literature documents available in the life science domain. A high-level architecture is proposed along with a novel named entity recognition (NER) model. The model is built using rule-based machine learning (ML). The proposed ArRaNER model produced better accuracy and was also able to identify more entities. The NER model was tested on two different datasets: a PubMed dataset and a Wikipedia talk dataset. The ArRaNER model obtains an accuracy of 83.42% on the PubMed articles and 77.65% on the Wikipedia articles.