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The potential application of infrared and Raman spectroscopies was explored as rapid and nondestructive tools for the identification of juvenile black seabream samples intoxicated by heavy metals (Zn, Cu, and Cd). Discrimination models were established on the basis of the infrared and Raman spectral data using three calibration methods, namely, partial least-squares discriminant analysis, least-squares support vector machines, and random forest. The combination of two spectroscopies was studied, in which three combination strategies were proposed and compared. Discrimination models achieved overall correct discriminations of 100% for identifying the fish intoxicated by one heavy metal or the heavy metal mixture. When samples intoxicated by different heavy metals were analyzed together, the discrimination accuracy remained >90%. Results confirmed the possibility of developing fast and reliable systems for the identification of juvenile black seabream intoxicated by heavy metals based on infrared and Raman spectroscopies.

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Purpose Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope. Design/methodology/approach In this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system. Findings This design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft. Originality/value This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.

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The great development of robot vision represented by deep learning places urgent demands on embedded vision implementation. This article introduces a hardware framework for implementation of embedded vision based on digital signal processor, which can be widely used in robot vision applications. Firstly, the article discusses implementation of a pretrained typical convolutional neural network on the digital signal processor embedded system for real-time handwritten digit recognition. Then, the article introduces the migration of OpenCV software packages to digital signal processor embedded system and the implementation flow of face detection algorithms with OpenCV on digital signal processor. The experimental results are remarkable with convolutional neural networks for handwritten digit recognition. This article provides a convenient and feasible design scheme of digital signal processor system for implementation of embedded vision.

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In this paper, we propose a logistic regression classification method based on the integration of a statistical learning model with linearized kernel preprocessing. The single Gaussian kernel and fusion of Gaussian and cosine kernels are adopted for linearized kernel pre-processing respectively. The adopted statistical learning models are the generalized linear model and the generalized additive model. Using a generalized linear model, the elastic net regularization is adopted to explore the grouping effect of the linearized kernel feature space. Using a generalized additive model, an overlap group-lasso penalty is used to fit the sparse generalized additive functions within the linearized kernel feature space. Experiment results on the Extended Yale-B face database and AR face database demonstrate the effectiveness of the proposed method. The improved solution is also efficiently obtained using our method on the classification of spectra data.