scholarly journals Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

2015 ◽  
Vol 12 (104) ◽  
pp. 20141225 ◽  
Author(s):  
Shre Kumar Chatterjee ◽  
Saptarshi Das ◽  
Koushik Maharatna ◽  
Elisa Masi ◽  
Luisa Santopolo ◽  
...  
Keyword(s):  
Electrical Response ◽  
Electrical Signal ◽  
Statistical Features ◽  
Electrical Signals ◽  
Physiological Processes ◽  
Online Classification ◽  
External Stimuli ◽  
Signal Time ◽  
Stationary Plant

Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli—sodium chloride (NaCl), sulfuric acid (H 2 SO 4 ) and ozone (O 3 ). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.

scholarly journals Electrical Signaling of Plants under Abiotic Stressors: Transmission of Stimulus-Specific Information

2021 ◽  
Vol 22 (19) ◽  
pp. 10715
Author(s):  
Maxim Mudrilov ◽  
Maria Ladeynova ◽  
Marina Grinberg ◽  
Irina Balalaeva ◽  
Vladimir Vodeneev
Keyword(s):  
Abiotic Factors ◽  
Electrical Signal ◽  
Specific Information ◽  
Systemic Response ◽  
Long Distance ◽  
Variation Potential ◽  
Electrical Signals ◽  
Physiological Processes ◽  
Abiotic Stressors ◽  
Electrical Signaling

Plants have developed complex systems of perception and signaling to adapt to changing environmental conditions. Electrical signaling is one of the most promising candidates for the regulatory mechanisms of the systemic functional response under the local action of various stimuli. Long-distance electrical signals of plants, such as action potential (AP), variation potential (VP), and systemic potential (SP), show specificities to types of inducing stimuli. The systemic response induced by a long-distance electrical signal, representing a change in the activity of a complex of molecular-physiological processes, includes a nonspecific component and a stimulus-specific component. This review discusses possible mechanisms for transmitting information about the nature of the stimulus and the formation of a specific systemic response with the participation of electrical signals induced by various abiotic factors.

scholarly journals Chemical Sensing Employing Plant Electrical Signal Response-Classification of Stimuli Using Curve Fitting Coefficients as Features

Biosensors ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 83 ◽  
Author(s):  
Shre Chatterjee ◽  
Obaid Malik ◽  
Siddharth Gupta
Keyword(s):  
Discriminant Analysis ◽  
Curve Fitting ◽  
Electrical Signal ◽  
Quality Of Data ◽  
Linear Discriminant ◽  
Curve Fit ◽  
Signal Response ◽  
External Stimuli ◽  
Response Classification

In order to exploit plants as environmental biosensors, previous researches have been focused on the electrical signal response of the plants to different environmental stimuli. One of the important outcomes of those researches has been the extraction of meaningful features from the electrical signals and the use of such features for the classification of the stimuli which affected the plants. The classification results are dependent on the classifier algorithm used, features extracted and the quality of data. This paper presents an innovative way of extracting features from raw plant electrical signal response to classify the external stimuli which caused the plant to produce such a signal. A curve fitting approach in extracting features from the raw signal for classification of the applied stimuli has been adopted in this work, thereby evaluating whether the shape of the raw signal is dependent on the stimuli applied. Four types of curve fitting models—Polynomial, Gaussian, Fourier and Exponential, have been explored. The fitting accuracy (i.e., fitting of curve to the actual raw signal) depicted through R-squared values has allowed exploration of which curve fitting model performs best. The coefficients of the curve fit models were then used as features. Thereafter, using simple classification algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) etc. within the curve fit coefficient space, we have verified that within the available data, above 90% classification accuracy can be achieved. The successful hypothesis taken in this work will allow further research in implementing plants as environmental biosensors.

Ecotourism experiences reported online: Classification of satisfaction attributes

2012 ◽  
Vol 33 (3) ◽  
pp. 702-712 ◽  
Author(s):  
Weilin Lu ◽  
Svetlana Stepchenkova
Keyword(s):  
Online Classification

scholarly journals Physiological Implications of Hydrogen Sulfide in Plants: Pleasant Exploration behind Its Unpleasant Odour

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Zhuping Jin ◽  
Yanxi Pei
Keyword(s):  
Hydrogen Sulfide ◽  
Growth And Development ◽  
Defense Responses ◽  
Plant Responses ◽  
Plant Growth And Development ◽  
Systematic Classification ◽  
Physiological Processes ◽  
Overwhelming Evidence

Recently, overwhelming evidence has proven that hydrogen sulfide (H2S), which was identified as a gasotransmitter in animals, plays important roles in diverse physiological processes in plants as well. With the discovery and systematic classification of the enzymes producing H2Sin vivo, a better understanding of the mechanisms by which H2S influences plant responses to various stimuli was reached. There are many functions of H2S, including the modulation of defense responses and plant growth and development, as well as the regulation of senescence and maturation. Additionally, mounting evidence indicates that H2S signaling interacts with plant hormones, hydrogen peroxide, nitric oxide, carbon monoxide, and other molecules in signaling pathways.

Online EEG Classification of Covert Speech for Brain–Computer Interfacing

2017 ◽  
Vol 27 (08) ◽  
pp. 1750033 ◽  
Author(s):  
Alborz Rezazadeh Sereshkeh ◽  
Robert Trott ◽  
Aurélien Bricout ◽  
Tom Chau
Keyword(s):  
Chance Level ◽  
Support Vector ◽  
Eeg Classification ◽  
Time Frequency ◽  
Computer Interfaces ◽  
Online Classification ◽  
Average Accuracy ◽  
Gamma Power ◽  
Brain Computer Interfacing

Brain–computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10[Formula: see text]s of mental repetitions of the word “no” and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10[Formula: see text]s each of covert repetition of the words “yes” and “no”. Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of [Formula: see text] was reached across participants in the online classification of no versus rest, with 10 out of 12 participants surpassing the chance level (60.0% for [Formula: see text]). The online classification of yes versus no yielded an average accuracy of [Formula: see text], with eight participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.

scholarly journals Classification of Serranidae Species Using Color Based Statistical Features

2017 ◽  
Vol 2 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Bilal İşçimen ◽  
Yakup Kutlu ◽  
Cemal Turan
Keyword(s):  
Statistical Features

scholarly journals Analyzing event related potentials using adaptive filter

2015 ◽  
Vol 18 (2) ◽  
pp. 123-130
Author(s):  
Huong Thi Minh Nguyen ◽  
Khai Quoc Le ◽  
Hai Chi Nguyen ◽  
Tri Minh Ngo ◽  
Linh Quang Huynh
Keyword(s):  
Electrical Response ◽  
Event Related Potentials ◽  
Lms Algorithm ◽  
Calculation Algorithm ◽  
Average Value ◽  
Heart Motion ◽  
Related Potentials ◽  
Adaptive Noise ◽  
External Stimuli ◽  
The Waves

ERPs (Event Related Potentials) are EEG signals which are directly measured from cortical electrical response to external stimuli such as feelings, sensual or cognitive events. The evaluation of the amplitude and latency of the ERP wave has important significance in evaluating neurological reflex. However, the ERP wave amplitude is small compared with the EEG wave, and considerably affected by the noise such as eyes, muscles, heart motion etc. In this paper, datasets are collected from ERPLAB and journals provided available datasets with the stimulus of sound and light. Using adaptive noise cancellation (ANC) combined with LMS algorithm the waves P300 of ERP were detected and separated. The algorithm was evaluated by the ratio SNR and average value. Results were compared with other published tools such as P300 calculation algorithm of ERPLAB softwar.

scholarly journals Classification Of Genres In Multimedia Journalism

2020 ◽  
Vol 02 (11) ◽  
pp. 112-118
Author(s):  
Madinabonu Nuriddinova ◽  
Keyword(s):  
Virtual Network ◽  
Online Data ◽  
Online Classification ◽  
Data Journalism ◽  
Multimedia Journalism ◽  
Online Format

Тhe article focuses on multimedia issues that are gaining popularity in journalism. It also includes analysis of increasingly popular multimedia articles online, classification of multimedia genres, and transformation issues. Online format of data journalism, journalistic skills, classification online data materials are also covered in it. The virtual network genres are covered with a basis of extensive examples.

Sign in / Sign up

Export Citation Format

Share Document