u/jfc123_boy

So the KOSPI index crashed around 9% today. Are there any cheap, non-hyped stocks with strong fundamentals and the potential for consistent growth that are worth a look? Hyundai, KT, and POSCO come to mind.

Boas, estou de olho num carro usado económico e fiável. Encontrei um Toyota Yaris 1.5 Hybrid de 2014, com mais ou menos 115000 km. O carro tem caixa e-ctv que ouvi falar bastante bem a nível de fiabilidade e faz consumos bastante bons a gasolina. Contudo, estou um pouco preocupado com a bateria.

Provavelmente a bateria tem de ser eventualmente substituída mesmo que a saúde dela esteja boa, estou a pensar em verificar pela porta OBD e app Dr. Prius.

A minha questão é quanto custa substituir uma bateria destas? Online vejo valores muito divergentes e acho que tenho de ter isso em consideração se prosseguir na compra deste carro.

Para além disso, se souberem de outros problemas que deva ter atenção, estejam à vontade.

Obrigado!

So I have an ST CSI camera connected to my jetson, which outputs RAW10 monochrome video output trough v4l2 on the jetson. The driver also allows some controls such as gain, and exposure.

The firmware of the camera has its own ISP, with features such as dark calibration, noise reduction, etc, but no features such as an Auto Exposure (AE) or lens shading correction.

I am new to this, but from what I understand the argus library from nvidia is very limited in what it can support freely.

Since these features are important for the project, what would be the most efficient path with the lowest latency possible?

I tought of using v4l2 DMA feature, read from memory and apply some efficient CUDA kernels, adjust gain and exposure trought the driver and write the final output also to memory.

But I also dont know if this is a "naive" implementation and if there is a more efficient solution for this case that is usually used. Any tips would be great!

Thanks!

So I have an ST CSI camera connected to my jetson, which outputs RAW10 monochrome video output trough v4l2 on the jetson. The driver also allows some controls such as gain, and exposure.

The firmware of the camera has its own ISP, with features such as dark calibration, noise reduction, etc, but no features such as an Auto Exposure (AE) or lens shading correction.

I am new to this, but from what I understand the argus library from nvidia is very limited in what it can support freely.

Since these features are important for the project, what would be the most efficient path with the lowest latency possible?

I tought of using v4l2 DMA feature, read from memory and apply some efficient CUDA kernels, adjust gain and exposure trought the driver and write the final output also to memory.

But I also dont know if this is a "naive" implementation and if there is a more efficient solution for this case that is usually used. Any tips would be great!

Thanks!

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