A review of convolutional neural network architectures and their optimizations

In this paper, a meminductor emulator and an active memristor emulator are designed to construct a new chaotic circuit. The initial-condition-triggered amplitude, frequency and parameter space boosting are investigated. The system owns homogenous, heterogeneous and extreme multistabilities at the same time. Various coexisting attractors with different offsets, amplitudes and frequencies are observed and analyzed. Furthermore, the presented circuit is implemented by analog circuit and DSP platform. The mentioned unique dynamic features are confirmed in the experiments. Experimental results indicate the presented system and its initial-condition-triggered features can be realized in DSP digital system. Since the system owns variable amplitude, frequency and parameter space, it has great potential value in encryption engineering fields.

The amplitude, frequency and parameter space boosting in a memristor–meminductor-based circuit

Deep learning (DL) is emerging as a new tool to model spectral data acquired in analytical experiments. Although applications are flourishing, there is also much interest currently observed in the scientific community on the use of DL for spectral data modelling. This paper provides a critical and comprehensive review of the major benefits, and potential pitfalls, of current DL tecnhiques used for spectral data modelling. Although this work focuses on DL for the modelling of near-infrared (NIR) spectral data in chemometric tasks, many of the findings can be expanded to cover other spectral techniques. Finally, empirical guidelines on the best practice for the use of DL for the modelling of spectral data are provided. 

Deep learning for near-infrared spectral data modelling: Hypes and benefits

Monitoring the growth conditions and behavior of fish will enable scientific management, reduce the threat of losses caused by disease and stress. Traditional monitoring methods are time-consuming, laborious, and untimely monitoring readily leads to aquaculture accidents. As a non-invasive, objective, and repeatable tool, machine vision systems have been widely used in various aspects of aquaculture monitoring. Nevertheless, the complex underwater environment makes it difficult to obtain ideal data processing results only using traditional image processing methods. Due to their powerful feature extraction capabilities, deep learning (DL) algorithms have been widely used in underwater image processing. Hence, the combination of DL algorithms and machine vision for the automated monitoring of aquaculture is of great importance. As evidence for the multidisciplinary aspects of DL applications, attention is focused on the latest DL methods applied to five fields of research: classification, detection, counting, behavior recognition, and biomass estimation. Meanwhile, due to the low training efficiency of DL models caused by insufficient dataset, transfer learning and GAN have also put into spotlight of this filed to pursue high performance of DL models. We also present the challenges and benchmarks in terms of the advantages and disadvantages of the selected method in each field. In addition, we review the sources of image acquisition and pre-processing methods in aquaculture. Finally, the challenges and prospects of DL in aquaculture machine vision systems are discussed. The literature review shows that the deep neural networks such as AlexNet, LSTM, VGG, and GoogLeNet, have been used for aquaculture machine vision systems.

Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish

A memristor with coexisting pinched hysteresis loops and twin local activity domains is presented and analyzed, with an emulator being designed and applied to the classic Chua’s circuit to replace ...

Extreme Multistability and Complex Dynamics of a Memristor-Based Chaotic System

Hyperspectral imaging is a non-destructive, nonpolluting, and fast technology, which can capture up to several hundred images of different wavelengths and offer relevant spectral signatures. Hyperspectral imaging technology has achieved breakthroughs in the acquisition of agricultural information and the detection of external or internal quality attributes of the agricultural product. Deep learning techniques have boosted the performance of hyperspectral image analysis. Compared with traditional machine learning, deep learning architectures exploit both spatial and spectral information of hyperspectral image analysis. To scrutinize thoroughly the current efforts, provide insights, and identify potential research directions on deep learning for hyperspectral image analysis in agriculture, this paper presents a systematic and comprehensive review. Firstly, its applications in agriculture are summarized, include ripeness and component prediction, different classification themes, and plant disease detection. Then, the recent achievements are reviewed in hyperspectral image analysis from the aspects of the deep learning models and the feature networks. Finally, the existing challenges of hyperspectral image analysis based on deep learning are summarized and the prospects of future works are put forward.

A review of deep learning used in the hyperspectral image analysis for agriculture

This paper proposed a novel switching scroll hyperchaotic system based on a memristor device and explored its application to secure communication. The new system could be switched between the double-scroll chaotic system and multiscroll one by switch and switch . We gave the construction process of the novel system, its numerical simulations, and dynamical properties, firstly. Moreover, the memristive circuit implementation of the new switching system was presented and the results were also in agreement with those of numerical simulation. Finally, the new switching memristive system was applied to secure communication by means of the drive-response synchronization with chaotic masking. When the voice signal is a rising waveform, it is encrypted by the double-scroll memristive system. When the voice signal is a falling waveform, the multiscroll memristive system works. The voice signal is completely submerged in the chaotic signal and could not be distinguished at all. Security analyses show that it is a successful application to secure communication.

/pdf/analysis-and-implementation-of-a-new-switching-memristor-1bnib8c5ir.pdf

Analysis and Implementation of a New Switching Memristor Scroll Hyperchaotic System and Application in Secure Communication

The invention discloses a field wheat high-throughput phenotype information obtaining device and a control method thereof. The device comprises a car frame, a measuring device and a control system, wherein the measuring device and the control system are fixedly arranged on the car frame, the car frame comprises a car frame body, a stretchable rod, a supporting frame, a first wheel and a second wheel, the measuring device comprises a network camera, an ultrasonic sensor, an NDVI sensor, a digital camera, a portable high spectrograph and an infrared distance measuring sensor, the control systemcomprises a computer, a controller and a stepping motor, the car frame body can be driven by the first wheel and the second wheel to move in spaces between lines of wheat in a wheat land, wheat phenotype information can be collected, the collected information can be sent to the computer, due to the fact that the device can freely move in the what land through the car body, information collection can be performed close to wheat, and compared with a traditional information collecting platform, information collecting accuracy can be achieved while the cost is lowered.

Field wheat high-throughput phenotype information obtaining device and control method thereof

The utility model discloses a field wheat high-throughput phenotypic information acquisition device The device comprises a frame, a measuring device and a control system, the measuring device and thecontrol system are fixedly arranged on the frame; wherein the frame comprises a frame body, a telescopic rod, a support frame, a first wheel and a second wheel; the measuring device includes: a measuring unit; the system comprises a network camera, an ultrasonic sensor, an NDVI sensor, a digital camera, a portable hyper-spectrometer and an infrared distance measuring sensor The control system includes: a controller; the device comprises a computer, a controller and a stepping motor, the frame body can be driven by the first wheels and the second wheels to move in a blank space between rows of wheat in a wheat field The wheat phenotype information can be collected, the collected information can be sent to the computer, the vehicle body can freely move on a wheat field and can be close towheat for information collection, and compared with a traditional information collection platform, the cost is reduced, and the accuracy of information collection can be achieved

Field wheat high-throughput phenotypic information acquisition device

The generation of new second Julia sets from the complex Gumowski–Mira map is given. We discuss the effect of the second Julia set by taking different parameters and analyse their characteristics. ...

Synchronization and coupling of new second julia sets from the complex Gumowski–Mira map

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