Can Electrochemical Sensors Be Used for Identification and Phylogenetic Studies in Lamiaceae?

Electrochemical sensors have shown potential in recent years for plant species identification and phylogenetic studies. These works have been used to investigate the affinities of different species in many genera. However, the ability of electrochemical sensors to study relationships between different genera within a family has not been investigated. In this work, we selected 31 species in the Labiatae and 5 exotaxa as subjects to investigate the feasibility of electrochemical sensors at the genus level. The results show that electrochemical sensors are still very effective for the identification of these plants. Different pattern recognition techniques can make the identification more efficient. Also, the fingerprint profiles collected by the sensors can be used for phylogenetic studies of Labiatae. The phylogram divides all the species into five clusters, where the exotaxa are in one cluster. Species in the Labiatae are mainly distributed in four other clusters. Importantly, the different genera of species all showed close affinities, representing that electrochemical fingerprinting can well distinguish the affinities between the different genera. The results of this work demonstrate the great potential of electrochemical sensors in the study of plant phylogeny. Its application is not limited to the study at the species level, but can be extended to the genus level.

Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees

This study aims to evaluate different combinations of features and algorithms to be used in the control of a prosthetic hand wherein both the configuration of the fingers and the gripping forces can be controlled. This requires identifying machine learning algorithms and feature sets to detect both intended force variation and hand gestures in EMG signals recorded from upper-limb amputees. However, despite the decades of research into pattern recognition techniques, each new problem requires researchers to find a suitable classification algorithm, as there is no such thing as a universal ’best’ solution. Consideration of different techniques and data representation represents a fundamental practice in order to achieve maximally effective results. To this end, we employ a publicly-available database recorded from amputees to evaluate different combinations of features and classifiers. Analysis of data from 9 different individuals shows that both for classic features and for time-dependent power spectrum descriptors (TD-PSD) the proposed logarithmically scaled version of the current window plus previous window achieves the highest classification accuracy. Using linear discriminant analysis (LDA) as a classifier and applying a majority-voting strategy to stabilize the individual window classification, we obtain 88% accuracy with classic features and 89% with TD-PSD features.

Script Identification for Devanagari and Gurumukhi using OCR

The study of character research is an active area for research as it pertains a lot of challenges. Various pattern recognition techniques are being used every day. As there are so many writing styles available, development of OCR (Optical Character Recognition) for handwritten text is difficult. Therefore, several measures have to be taken to improve the recognition process so that the burden of computation can be decreased and the accuracy for pattern recognition can be increased. The main objective of this review was to recognize and analyze handwritten document images. In this paper, we present a scheme to identify different Indian scripts like Devanagari and Gurumukhi.

Recognizing Molecular Structural Features by Pattern Recognition Techniques

Recognition of molecular structural features is one of the most attractive fields in chemistry, especially when combining with machine learning techniques. Pattern recognition techniques are straightforward in recognizing graphic features, but little attention was given to recognize molecular structural features. In this work, we propose a new method taking advantage of pattern recognition techniques to analyze structural features and obtain novel chemical insights. Specifically, the cluster analysis is presented to recognize structural features, which provides an alternative to the most widely used root mean square deviation (RMSD) method and the recently proposed blob detection method. Based on this, the convex hull of the molecule is constructed. The convex hull of molecules is highly appealing in the sense that one can introduce established theorems and properties from other disciplines into chemistry. Novel molecular descriptors based on convex hulls can be defined and show encouraging results, especially in providing new insights in understanding non-covalent interactions, adsorption processes, etc.

Experimental Study on Wound Area Measurement with Mobile Devices

Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach’s feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals.

Facilitative Exercise for Surface Myoelectric Activity Using Robot Arm Control System – Training Scheme with Gradually Increasing Difficulty Level –

Various control systems for robot arms using surface myoelectric signals have been developed. Abundant pattern-recognition techniques have been proposed to predict human motion intent based on these signals. However, it is laborious for users to train the voluntary control of myoelectric signals using those systems. In this research, we aim to develop a rehabilitation support system for hemiplegic upper limbs with a robot arm controlled by surface myoelectric signals. In this study, we construct a simple one-link robot arm that is controlled by estimating the wrist motion from the surface myoelectric signals on the forearm. We propose a training scheme with gradually increasing difficulty level for robot arm manipulation to evoke surface myoelectric signals. Subsequently, we investigate the possibility of facilitative exercise for the voluntary surface myoelectric activity of the desired muscles through trial experiments.

Chemometric Approaches in Questioned Documents

Questioned documents comprehend analysis of identity theft, forged signatures or texts, documents alterations and falsification of security documents or banknotes. Questions involving inks or paper require chemical analysis, and multivariate analysis or chemometrics has been an emerging tool for data evaluation and interpretation after instrumental data collection in this area. The goal of this study is to identify previous articles that applied multivariate analysis within questioned documents for forensic purposes. The search for articles was performed in four databases (Google Scholar, Science Direct, Pubmed and Scopus). Sixty studies, published in the last ten years, were selected. Thirty-four articles described pen inks analysis; fourteen articles comprehended printed documents studies; eight articles evaluated paper analysis, and four articles included banknotes analysis. Spectroscopy, mass spectrometry, chromatography, thermo gravimetric analysis and multivariate image analysis were the analytical methods applied to collect chemical data. Chemometrics methods included mainly unsupervised pattern recognition techniques, regression methods, and supervised pattern recognition techniques, amongst other methods. This review summarized and discussed multivariate analysis techniques applied in different questioned documents sub-areas, highlighting the importance of this knowledge for forensic analysts. In addition, it shows new research topics such as different printing and pen inks, papers and security documents analysis herein not included.