3D ear shape reconstruction and recognition for biometric applications

This paper presents a new method based on a generalized neural reflectance (GNR) model for enhancing ear recognition under variations in illumination. It is based on training a number of synthesis images of each ear taken at single lighting direction with a single view. The way of synthesizing images can be used to build training cases for each ear under different known illumination conditions from which ear recognition can be significantly improved. Our training algorithm assigns to recognize the ear by similarity measure on ear features extracting firstly by the principal component analysis method and then further processing by the Fisher’s discriminant analysis to acquire lower-dimensional patterns. Experimental results conducted on our collected ear database show that lower error rates of individual and symmetry are achieved under different variations in lighting. The recognition performance of using our proposed GRN model significantly outperforms the performance that without using the proposed GNR model

Self-organizing adaptation for facial emotion mapping

This paper presents an emotion mapping system that attempts to emulate human brain reference model. The system first locates the human face in an image, and then identifies the localized face emotion. The understanding of cognitive system is presented in the paper. It highlights how individual module is mapped to the proposed system. Then, single- and multi-layer self-organizing emotion maps are described. The system is evaluated through various test sets. The experimental results show encouraging hit rates for identifying emotions of unknown subjects

A blockchain based autonomous decentralized online social network

Online social networks (OSN) are becoming more important in people's daily life, however, all popular OSNs are centralized, and this raises a series of security, privacy and management issues. A decentralized architecture based on blockchain technology provides the ability to solve above issues. In this paper, an OSN service is developed based on blockchain technology in order to make it operate decentralized. Large volume of data normally required low-security requirements can be stored in Interplanetary Filesystem (IPFS) to make data decentralized. A decentralized autonomous organization is developed for user autonomy, users can self-manage the OSN in a democratic way

Emotional advisor to help children with autism in social communication

The deficit or impairment in the ability to rationalize emotional states is known as mind-blindness. This condition is seen to be the key inhibitor of social and emotional intelligence for autistic people. Autism is a spectrum of neuro-developmental conditions which affects one’s social functioning, communication and is often accompanied with repetitive behaviours and obsessive interests. Inabilities resulting from mind-blindness include gauging the interest of other parties during conversations, withdrawal from social contact, oblivion to social cues, in difference to people’s opinions and incomprehensible non-verbal communication. The existing assistive devices and tools mostly serve as remedial tools that provide a learning environment for autistic children to learn about the norms of social behaviour. However, these tools lack the capability to operate in conjunction with real-world situations. An idea is proposed that aims to fulfill this need. We propose a portable device which is able to assist autistic people in communication in real-life situations. We believe that this portable device can help to narrow the gap between us and the world of autism through assisted communication. In this paper, we present one part of this device which is called Emotional Advisor to assist autistic children in engaging in meaningful conversations where people are able to ascertain how they are feeling during communication

Blood pressure estimation with complexity features from electrocardiogram and photoplethysmogram signals

A novel method for the continual, cuff-less estimation of the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values based on signal complexity analysis of the photoplethysmogram (PPG) and the electrocardiogram (ECG) is reported. The proposed framework estimates the blood pressure (BP) values obtained from signals generated from 14 volunteers subjected to a series of exercise routines. Herein, the physiological signals were first pre-processed, followed by the extraction of complexity features from both the PPG and ECG. Subsequently the complexity features were used in regression models (artificial neural network (ANN), support vector machine (SVM) and LASSO) to predict the BP. The performance of the approach was evaluated by calculating the mean absolute error and the standard deviation of the predicted results and compared with the recommendations made by the British Hypertension Society (BHS) and Association for the Advancement of Medical Instrumentation. Complexity features from the ECG and PPG were investigated independently, along with the combined dataset. It was observed that the complexity features obtained from the combination of ECG and PPG signals resulted to an improved estimation accuracy for the BP. The most accurate DBP result of 5.15 ± 6.46 mmHg was obtained from ANN model, and SVM generated the most accurate prediction for the SBP which was estimated as 7.33 ± 9.53 mmHg. Results for DBP fall within recommended performance of the BHS but SBP is outside the range. Although initial results are promising, further improvements are required before the potential of this approach is fully realised

Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

Conventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively

A novel framework for making dominant point detection methods non-parametric

Most dominant point detection methods require heuristically chosen control parameters. One of the commonly used control parameter is maximum deviation. This paper uses a theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment for constructing a general framework to make most dominant point detection methods non-parametric. The derived analytical bound of the maximum deviation can be used as a natural bench mark for the line fitting algorithms and thus dominant point detection methods can be made parameter-independent and non-heuristic. Most methods can easily incorporate the bound. This is demonstrated using three categorically different dominant point detection methods. Such non-parametric approach retains the characteristics of the digital curve while providing good fitting performance and compression ratio for all the three methods using a variety of digital, non-digital, and noisy curves

Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): a double-blind, randomised, placebo-controlled trial

Background: Short-term treatment for people with type 2 diabetes using a low dose of the selective endothelin A receptor antagonist atrasentan reduces albuminuria without causing significant sodium retention. We report the long-term effects of treatment with atrasentan on major renal outcomes. Methods: We did this double-blind, randomised, placebo-controlled trial at 689 sites in 41 countries. We enrolled adults aged 18–85 years with type 2 diabetes, estimated glomerular filtration rate (eGFR)25–75 mL/min per 1·73 m 2 of body surface area, and a urine albumin-to-creatinine ratio (UACR)of 300–5000 mg/g who had received maximum labelled or tolerated renin–angiotensin system inhibition for at least 4 weeks. Participants were given atrasentan 0·75 mg orally daily during an enrichment period before random group assignment. Those with a UACR decrease of at least 30% with no substantial fluid retention during the enrichment period (responders)were included in the double-blind treatment period. Responders were randomly assigned to receive either atrasentan 0·75 mg orally daily or placebo. All patients and investigators were masked to treatment assignment. The primary endpoint was a composite of doubling of serum creatinine (sustained for ≥30 days)or end-stage kidney disease (eGFR <15 mL/min per 1·73 m 2 sustained for ≥90 days, chronic dialysis for ≥90 days, kidney transplantation, or death from kidney failure)in the intention-to-treat population of all responders. Safety was assessed in all patients who received at least one dose of their assigned study treatment. The study is registered with ClinicalTrials.gov, number NCT01858532. Findings: Between May 17, 2013, and July 13, 2017, 11 087 patients were screened; 5117 entered the enrichment period, and 4711 completed the enrichment period. Of these, 2648 patients were responders and were randomly assigned to the atrasentan group (n=1325)or placebo group (n=1323). Median follow-up was 2·2 years (IQR 1·4–2·9). 79 (6·0%)of 1325 patients in the atrasentan group and 105 (7·9%)of 1323 in the placebo group had a primary composite renal endpoint event (hazard ratio [HR]0·65 [95% CI 0·49–0·88]; p=0·0047). Fluid retention and anaemia adverse events, which have been previously attributed to endothelin receptor antagonists, were more frequent in the atrasentan group than in the placebo group. Hospital admission for heart failure occurred in 47 (3·5%)of 1325 patients in the atrasentan group and 34 (2·6%)of 1323 patients in the placebo group (HR 1·33 [95% CI 0·85–2·07]; p=0·208). 58 (4·4%)patients in the atrasentan group and 52 (3·9%)in the placebo group died (HR 1·09 [95% CI 0·75–1·59]; p=0·65). Interpretation: Atrasentan reduced the risk of renal events in patients with diabetes and chronic kidney disease who were selected to optimise efficacy and safety. These data support a potential role for selective endothelin receptor antagonists in protecting renal function in patients with type 2 diabetes at high risk of developing end-stage kidney disease. Funding: AbbVie

Adaptive processing of data structures for image content classification, indexing and retrieval of flowers

Computer-aided flower identification is a very useful tool for plant species identification aspect. In this research, a study was made on a development of machine learning system to characterize flower images efficiently. One of the most popular frameworks for the adaptive processing of data structures to date, was proposed by Frasconi et al., who used a Backpropagation Through Structures (BPTS) algorithm to carry out supervised learning. This supervised model has been successfully applied to a number of learning tasks that involve complex symbolic structural patterns, such as image semantic structures, internet behavior, and chemical compounds. In this project, we extend this model, using probabilistic estimates to acquire discriminative information from the learning patterns. Using this probabilistic estimation, smooth discriminant boundaries can be obtained through a process of clustering onto the observed input attributes. This approach enhances the ability of class discrimination techniques to recognize structural patterns. The capabilities of the proposed model are evaluated by the flowers image classification. The obtained results significantly support the capabilities of our proposed approach to classify and recognize flowers in terms of generalization and noise robustness.SUG 5/0