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Figure 4. Hamming Window applied to each frame-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Use of speech spectrum for modifying work domain on signals from time to frequency is made possible using Fourier coefficients. At such applications the rapid and practical way of estimating the spectrum is use of rapid Fourier changes.

Figure 3 Frame blocking of the speech signal-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

The next frame will begin M samples (i.e. 156 samples) after the first frame, and it will overlap the first frame by N-M samples (256 – 156 = 100 samples). Then the third frame will start at 2M samples after the first frame and it will overlap first frame by N-2M. The fourth frame will start at 3M samples after the first, and it will overlap it by N-3M. The process will continue until all input signal is accounted for. The result of this step plotted using MATLAB plot command and displayed in Figure 3. Figure 3 Frame blocking of the speech signal The next step in the processing is to window each individual frame so as to minimize the signal discontinuities at the beginning and end of each frame. The concept here is to minimize the spectral distortion by using the window to taper the signal to zero at the beginning and end of each frame. If we define the window as w(n), 0 ≤ n ≤ N −1, where N is the number of samples in each frame, then the result of windowing is the signal y (n) = x (n)w(n), 0 ≤ n ≤ N −1 l l

opencc-by-4.0Dec 2010View details →

Figure 1. The Structure of the Automatic Translate Voice to Sign Language Animation System-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

All technologies of voice recognition, speaker identification and verification, each has its own advantages and disadvantages and may requires different treatments and techniques. The choice of which technology to use is application-specific. At the highest level, all voice recognition systems contain two main modules: feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the voice signal that can later be used to represent each word. Feature matching involves the actual procedure to identify the unknown word by comparing extracted features from his/her voice input with the ones from a set of known words. A wide range of possibilities exist for parametrically representing the speech signal for the voice recognition task, such as Linear Prediction Coding (LPC), RASTA-PLP and Mel-Frequency Cepstrum Coefficients (MFCC).

opencc-by-4.0Dec 2010View details →

Figure 1 Scatter Plot of Standardized Residual by Standardized Predicted Value-Potential Predictability of ZPD of Children's Cognitive Development

One of the essential assumptions that should be met in regression analysis is the linearity of the data. The result of the analysis of variance (ANOVA) shows that regression model is linear; F (1, 39) = 8.429, P <0.05 for model 1 and F (2, 38) = 8.648, P<0.05 for model 2. Moreover, Scatter plot shows (Figure1) that there is no funnel shaped or crescent shaped cloud. This indicates that two assumptions of linearity and homogeneity of variance have been met.

Figure 8. Speech signal after frequency wrapping-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Use of speech spectrum for changing the work domain on the signal from time to frequency is used via Fourier conventions. The rapid and practical way of estimating Spectrum in such applications is employment of rapid Fourier transform. The final stage is extracting particularities is use of discrete cosine to return particularities to the time domain and converse FFT approximation. Major advantage of this method is decrease of number of particularities of number of filter from f N to c N in which c f N ≤ N . In addition, doing so, includes making independent of the obtained particularity and rendering them non dependant which leads matrix covariance features to become axial. The following equation shows this point.

Figure 7. Speech signal after FFT-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Figure 2. MFCC Block Diagram Step 1-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Mel Frequency Cepstral Coefficients (MFCC) are coefficients that represent audio, based on perception. It is derived from the Fourier Transform (FFT) or the Discrete Cosine Transform (DCT) of the audio clip. The basic difference between the FFT/DCT and the MFCC is that in the MFCC, the frequency bands are positioned logarithmically (on the Mel scale) which approximates the human auditory system's response more closely than the linearly spaced frequency bands of FFT or DCT. This allows for better processing of data. The main purpose of the MFCC processor is to mimic the behavior of the human ears. Overall the MFCC process has 5 steps that show in figure 2.

Figure 11. A sample of speech to facial animation system-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

To increase the autonomy of deaf and hard of hearing people in their day-to-day professional and social lives, in this paper design and initial implementation of a new approach based on MFCC and Vector Quantization Method is described. This approach includes analyses of speech to animate the talking head. Our future work will include the conception of new test types and performance patterns. We are particularly interested in extending this approach to testing to include implementation of applications under real-time constraints

Figure 5. Mel-spaced filter bank-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Physiologic changes show that human comprehension of frequency content of sound does not obey a linear space. Therefore for each individual it is computed and measure with a real frequency of sound peak at Mel seal. Using the below equation one can change the frequency at Hertz scale to Mel Scale.

opencc-by-4.0Nov 2011View details →

Figure 7. The outcomes of the Monitoring and Control of the Greenhouse soil and climate Conditions for tomato crops-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

In the past generation greenhouses it was enough to have one cabled measurement point in the middle to provide the information to the greenhouse automation system. The system itself was usually simple without opportunities to control locally heating, lights, ventilation or some other activity, which was affecting the greenhouse interior climate. The optimal greenhouse climate and soil adjustment can enable us to improve productivity and to achieve remarkable energy savings. In this paper we proposed a multi-agent methodology for integrated management systems in greenhouses. In this regards wireless sensor networks play a vital role to monitor greenhouse and environment parameters. Each controlled process of the greenhouse environment is modeled as an autonomous agent with its own inputs, its own outputs and its own interactions with the other agents. Each agent acts autonomously, as it knows a priori the desired environmental set-points. In this way, any possible conflicting decisions of conventional environmental control methodologies are resolved through negotiations between the agents so that the possible optimal integrated solution is achieved. The developed system is simple, cost effective, and easily installable.

Figure 6. Structure of the MAS for integrated greenhouse management system.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The complete agents “environment” is shown in figure 6. Each agent receives the necessary environmental or plant condition measurements. While each agent keeps its autonomy and has its own “personal” goals, it interacts with the other agents through some specific communication language, in the “Agent discussion area”, where the overall goal is taken into account. This interaction takes place under the presence of information from the existing models and the target goals. At that point, each agent has decided on its best strategy.

Figure 2. Experimental wireless sensors setup in greenhouse-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

Figure 2 illustrates how the sensor nodes were deployed to the greenhouse block. The idea of the vertical deployment was to get a better understanding of the microclimate layers which typically exist in the greenhouse, and to figure out what kind of differences occur in the climate between lower and upper flora.

Figure 5. Multi-agent system information and knowledge scheme.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The negotiations between agents are subject to optimization based on “knowledge” that is derived from complete production models, yield models or even sparse models as expressed in fuzzy expert rules or practical rules of thumb. In addition, pest control and plant disease models provide additional information useful to the design of a successful strategy for optimal management [15] (illustrated in Figure 5).

Figure 1. Agent Architecture-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The multi agent method creates a non-structured environment using agents, these agents, in order to reach the global optimum, must be capable of performing transactions between each other, achieving several “deals”, which is performed though a common vocabulary, a finite set of information exchange, a finite set of possible actions, penalties, etc. The power of each agent depends on the degree of contribution of its represented process to the final output of the entire system. Each agent communicates with the environment and adapts to its own internal state as well as to the state of the entire system. In this systems, for the realization of the multi agent architecture the JADE 4.1.1 (Java Agent DEvelopment Framework) have been used [7].

Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The value of the final product incorporates not only quantity issues but also quality issues, which are difficult to be measured or even estimated. The environment of each agent is defined by the same parameters that define the physical environment in addition to internal states reported by each agent.

Figure 3. Photosynthetic activity in different wavelengths of light radiation. 5.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The greenhouse protects the plants from the extreme weather conditions. However, if the period of daylight prevents the photosynthetic activity, the plants do not grow. Horticultural lighting allows the grower to extend the growing season. It enables a year-round producing of plants or makes it possible for the grower to start sowing in early spring and continue season till the first frost. Plants need about 10-12 hours light to improve growth. When the plants are producing flowers or fruits the supplemental need of light per day increases up to 16 hours. Figure 3 shows the photosynthetic activity in different wavelengths of light radiation [16].

opencc-zeroApr 2024View details →

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Phenotypic data from: from buds to shoots: insights into grapevine development from the Witch's Broom bud sport

Background Bud sports occur spontaneously in plants when new growth exhibits a distinct phenotype from the rest of the parent plant. The Witch's Broom bud sport occurs occasionally in various grapevine (Vitis vinifera) varieties and displays a suite of developmental defects, including dwarf features and reduced fertility. While it is highly detrimental for grapevine growers, it also serves as a useful tool for studying grapevine development. We used the Witch's Broom bud sport in grapevine to understand the developmental trajectories of the bud sports, as well as the potential genetic basis. We analyzed the phenotypes of two independent cases of the Witch's Broom bud sport, in the Dakapo and Merlot varieties of grapevine, alongside wild type counterparts. To do so, we quantified various shoot traits, performed 3D X-ray Computed Tomography on dormant buds, and landmarked leaves from the samples. We also performed Illumina and Oxford Nanopore sequencing on the samples and called genetic variants using these sequencing datasets. Results The Dakapo and Merlot cases of Witch's Broom displayed severe developmental defects, with no fruit/clusters formed and dwarf vegetative features. However, the Dakapo and Merlot cases of Witch's Broom studied were also phenotypically different from one another, with distinct differences in bud and leaf development. We identified 968–974 unique genetic mutations in our two Witch's Broom cases that are potential causal variants of the bud sports. Examining gene function and validating these genetic candidates through PCR and Sanger-sequencing revealed one strong candidate mutation in Merlot Witch's Broom impacting the gene GSVIVG01008260001. Conclusions The Witch's Broom bud sports in both varieties studied had dwarf phenotypes, but the two instances studied were also vastly different from one another and likely have distinct genetic bases. Future work on Witch's Broom bud sports in grapevine could provide more insight into development and the genetic pathways involved in grapevine.

opencc-by-4.0Dec 2019View details →

Fig. 6 in Salivary gland proteome analysis of developing adult female HaemaphYSaliS longiCorniS ticks: molecular motor and TCA cycle-related proteins play an important role throughout development

Fig. 6 Phenotype associated with dynein, kinesin, isocitrate dehydrogenase and citrate synthase mRNA subjected to RNAi in female ticks via injection with the corresponding dsRNA. a Dynein dsRNA injection. b Kinesin dsRNA injection. c Isocitrate dehydrogenase dsRNA injection. d Citrate synthase dsRNA injection. e GFP dsRNA injection, control. f No injection, control. Scale-bars: 5 mm

opencc-by-4.0Dec 2019View details →

Fig. 7 in Salivary gland proteome analysis of developing adult female HaemaphYSaliS longiCorniS ticks: molecular motor and TCA cycle-related proteins play an important role throughout development

Fig. 7 Digital micrographs of salivary gland acinar morphological changes in unfed female H. longicornis after RNAi.The time at which the tick bit the host and began sucking blood was recorded as day 0. a–e Dynein dsRNA injection. f–j Kinesin dsRNA injection. k–o Isocitrate dehydrogenase dsRNA injection. p–t Citrate synthase dsRNA injection. u–y GFP dsRNA injection, control. Scale-bars: 25 µm