Journal: BMC Biology

Article Title: LocalZProjector and DeProj: a toolbox for local 2D projection and accurate morphometrics of large 3D microscopy images

doi: 10.1186/s12915-021-01037-w

Figure Lengend Snippet: Presentation of LocalZProjector and DeProj . The toolbox is made of two tools, LocalZProjector a Fiji tool that generates 2D projections from 3D, multi-channel time-lapse images, and DeProj , a MATLAB function that uses the height-map output of LocalZProjector and the segmentation results on the projection output to measure accurately the morphology metrics of the cells in the projected tissue

Article Snippet: • Other requirements : at least MATLAB R2019b and the Image Processing Toolbox.

Techniques:

Journal: APL Bioengineering

Article Title: Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces

doi: 10.1063/5.0250296

Figure Lengend Snippet: Contribution ratio ( % Contribution ) of MUA and LFP features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the LFP frequency bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.

Article Snippet: Frequency-spectrum features were widely used for processing LFPs; therefore, LFP raw data were further down-sampled to a 1-kHz sampling rate and converted to power spectral density using a short-time Fourier transform with a Hanning window of 1 f m ms in length and time step of 33-ms, where f m is the minimum frequency of each LFP frequency bands (using the spectrogram function from the Signal Processing Toolbox, MATLAB R2019a, MathWorks).

Techniques: Labeling

Journal: Batteries

Article Title: SOC Estimation of a Rechargeable Li-Ion Battery Used in Fuel-Cell Hybrid Electric Vehicles—Comparative Study of Accuracy and Robustness Performance Based on Statistical Criteria. Part I: Equivalent Models

doi: 10.3390/batteries6030042

Figure Lengend Snippet: Figure 20. Simulink Simscape Diagram of Li-ion model. The values of the parameters from Simulink diagram are allocated in a MATLAB script that runs first for initialization, and then is running the Simulink model to extract these values from MATLAB workspace.

Article Snippet: Batteries 2020, 6, x FOR PEER REVIEW 13 of 38 An accurate simplified thermal model is provided in MATLAB R2019b library, at MATLAB/Simulink/Simscape/Battery, for a Li-ion gene ic battery model, implemented in Simulink Simscape as is shown in Figure 8, as is developed in [14].

Techniques:

Journal: Batteries

Article Title: SOC Estimation of a Rechargeable Li-Ion Battery Used in Fuel-Cell Hybrid Electric Vehicles—Comparative Study of Accuracy and Robustness Performance Based on Statistical Criteria. Part I: Equivalent Models

doi: 10.3390/batteries6030042

Figure Lengend Snippet: Figure 29. The MATLAB simulation result of unregulated RPM speed of induction motor.

Article Snippet: Batteries 2020, 6, x FOR PEER REVIEW 13 of 38 An accurate simplified thermal model is provided in MATLAB R2019b library, at MATLAB/Simulink/Simscape/Battery, for a Li-ion gene ic battery model, implemented in Simulink Simscape as is shown in Figure 8, as is developed in [14].

Techniques: