Computer on Modules
Multi tool use Computer on Modules
Computer on Modules (CoM) / System on Modules (SoM) offer reliable and cost-effective embedded platform for building end-products. Toradex offers extensive range of CoMs using leading Arm based System on Chips (SoC).
- Ready-to-use, Computing solution
Apalis and Colibri CoMs are pin-compatible within the family
- Proven Solution and access to latest technology
- Accelerate time-to market and reduce development time
- Reliable and long-term product availability
- It runs under extreme conditions, high vibration, high humidity
How CoMs can help you in product development?
Wondering why you should choose Toradex CoMs?
Apalis Arm® Family
Apalis Arm family Computer on Modules (CoMs) offers advanced computing with supreme graphics at optimal power consumption, along with support of high speed interfaces and extensive multimedia formats. Includes free BSPs & support for Windows Embedded Compact and Linux (Yocto project)
- Advanced computing
- Supreme graphics
- High speed connectivity and advanced multimedia
- Free BSPs & OS support (WEC, Embedded Linux)
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Colibri Arm® Family
Colibri Arm family offers an extensive portfolio of pin-compatible System on Modules(SoMs). These SoMs have small form factor, complemented with the presence of many industrial and connectivity interfaces. Includes free BSPs & support for Windows Embedded Compact and Linux (Yocto project)
- SODIMM sized Computer on Modules
- Cost-performance optimized designs
- Various industrial and connectivity interfaces
- Free BSPs & OS support (WEC, Embedded Linux)
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NVIDIA Tegra
CoMs powered by NVIDIA® Tegra™ TK1, T30 and T20 SoCs. Offers high performance with supreme graphics
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NXP i.MX
CoMs powered by NXP® (formerly Freescale) i.MX 8, i.MX 7 and i.MX 6 SoCs. Low power, scalable performance & industrial I/Os
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NXP Vybrid
CoMs powered by NXP® (formerly Freescale) Vybrid™ SoCs. Ideal for cost-sensitive industrial products
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up vote 2 down vote favorite There is a clear pattern that show for two separate subsets (set of columns); If one value is missing in a column, values of other columns in the same subset are missing for any row. Here is a visualization of missing data My tries up until now, I used ycimpute library to learn from other values, and applied Iterforest. I noted, score of Logistic regression is so weak (0.6) and thought Iterforest might not learn enough or anyway, except from outer subset which might not be enough? for example the subset with 11 columns might learn from the other columns but not from within it's members, and the same goes for the subset with four columns. This bar plot show better quantity of missings So of course, dealing with missings is better than dropping rows because It would affect my prediction which does contain the same missings quantity relatively. Any better way to deal with these ? [EDIT] The nullity pattern is confirmed: machine-learning cor...
