Induction motor fault identification is essential to improve efficiency in industrial processes improving costs, production line and maintenance time. This paper presents a novel motor fault detection methodology based on Quaternion Signal Analysis (QSA). The proposed method establishes the quaternion coefficients as the value of motor current measurement and the variables x, y and z are the measurements from a triaxial-accelerometer mounted on the induction motor chassis. The method obtains the rotation of quaternions and applies quaternion rotation statistics such as mean, cluster shades and cluster prominence in order to get their features, and these are used to classify the motor state using the tree classification algorithm. This methodology is validated experimentally and compared to other methods to determine the efficiency of this method for feature detection and motor fault identification and classification.

Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson’s disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson’s disease by the Unified Parkinson’s Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands; the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size; however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs.

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.

In this work, we present the implementation in a reconfigurable architecture of a dense stereo vision algorithm based on census transform. Analyzing census transform algorithm we found that size and access memory could be reduced, which consequently also reduced the latency time. Furthermore, architecture resources are optimized and efficient thanks to binary operations and integer arithmetic used by census transform directly compatible with the FPGA. Final architecture is able to construct 130 dense disparity maps per second for each corresponding pair of stereo images. A performance analysis, among other three disparity map implementations and our architecture, shows that at the end, we propose a better trade off among performance, latency, logic elements and memory size. The optimization and the resource saving rend our architecture an interesting option to solve the problem of stereo vision in real time, quite used in autonomous navigation.