Capture One Pro 7 Mac Torrent MAXSPEED 1
Lora Ceasor
When you are using a wifi adapter in monitor mode, you are not associated to an access point. It will simply listen on the configured channel(s) and capture any traffic it understands whether that is 802.11b, 802.11g, or 802.11n.
If you are associated to an access point, then you are running in promiscuous mode, and then you are generally only going to capture traffic to/from your station plus any broadcast/multicast/management frames.
Now the flip side is that you need to be fairly close to the source of any traffic running at 300Mbps to be able to capture it. The further you get away from the source, the slower the data rate of any usable data you will capture.
Is it possible to reduce this time? I tried set different parameters for capture request, such as TEMPLATE_ZERO_SHUTTER_LAG, NOISE_REDUCTION_MODE_OFF, EDGE_MODE_OFF, etc. But it has no any effect.If I try to capture burst, then all images, except first are comes very fast, no more then in 30-40ms. How can I reduce capturing time for first image?
that explains why the 1st capture is slow, but in burst mode the next images comes very fast. The requests and processing are queued and the 1st takes 300ms to arrive all the way back on the callback but the next one is already "right behind it".
I am trying to use tshark for reassembling and extracting NFS payloads. Because of the large amount of data I am processing (and some security concerns) I can not do this processing offline so I am trying to get tshark to run at or as close to wire speed as possible. I initially tried using tshark to do the packet capture but it was dropping too many packets so I am using another pcap based tool to do the packet capture (and successfully capturing and writing packets at wire speed) and then I am piping the output of tshark to another process that does processing on the payloads. So the whole setup looks something like this:
In my experiments, I have seen that tshark significantly lags wire speed (1 Gb/s). It's actual rate is roughly 25 MB/s and this lag manifests itself by tshark taking extra time after the packet capture is complete to finish at roughly the rate of 1 to 1 ie. if I do a packet capture for 30 minutes, it will take tshark a total of an hour to finish.
I should add, this is running in a virtualized linux environment with a relatively modern/ powerful server and I have already disabled host look up (as I know that can significantly slow down packet captures).
There is a pretty good chance, that the NFS dissector consumes much more resources, and thus it is so much slower. As I don't have a large NFS capture file, I cannot test it. However, you can test your environment with a large http capture file (easy to create). Then compare your results with mine. If tshark is still much slower, then it's related to your system (CPU, I/O, etc.) or to the tshark version (mine: 1.8.3 on Ubuntu 12.04). If your system is much faster with http, then it's the NFS dissector and there is probably nothing you can do, except speeding up the dissector by improving the code or by using an even faster system (CPU) ;-)
In this white-paper, we explore the effects of scene- and camera-motion on the performance of Intel RealSense depth cameras D400 series, and we specifically focus on introducing a new 300 fps high-speed capture mode for the D435 model. We show how this mode can be used to capture fast motion, but also how it can be used to enhance depth performance. To fully appreciate the need for high-speed capture, we also take the time in this paper to explore various motion artifacts and performance limitations of the current modes of operation of both the D415 and D435 models, and the considerations that need to be taken into account when capturing very high-speed motion. The D415 and D435 cameras are shown to behave differently, and artefacts are seen to depend on both distance to objects, lighting conditions, projector illumination, and whether the fast motion comes from the camera moving or objects moving in the scene.
The Intel RealSense Depth camera D435 is capable of capturing depth with a wide Field-of-View (FOV) of 90x58 degrees at 90fps with a resolution of 848 x 480, producing up to 36.6 Million depth points per second. In this paper we explore the benefits of introducing a new mode in which we configure the global shutters in the D435 stereo depth imager to capture at 300fps, albeit with a narrower vertical FOV of 840x100. This means we are performing about the same number of depth calculations per second and therefore not increasing the bandwidth of data being transmitted. This new mode is illustrated in Figure 1.
Fig. 1: Depth map from an Intel RealSense D435 depth camera operating at 848x480 (left image) versus the new high-speed capture mode (right image) with a resolution of 848x100, cropped to the vertical center.
This trade-off with vertical FOV allows us to explore three primary benefits of high-frame-rate capture. First, a higher frame rate obviously allows us to capture faster motion. This will enable usages such as the ability to capture optically both the speed and complete 3D trajectory of a ball flying at high speeds, like a baseball fast pitch. Second, we will show that under bright lighting conditions or high projector power, we can use this high frame-rate mode to reduce the depth measurement noise by capturing at high frame rates and applying multi-frame averaging to enhance the depth measurement while still achieving normal effective frame rates of, say, 30fps. The basic reasoning is that if the depth noise is stochastic from frame-to-frame, then we should in principle see a noise reduction of sqrt (300/30), or 3.16x. Finally, since the Intel on-chip Self-Calibration feature (covered in another white paper) scales in speed directly with the frame rate, introducing a 256x144 300fps mode, means that it can be sped up 3.3x compared to 90fps mode.
In the following we start by explaining how to enable and use high-speed capture mode.
The high-speed depth capture will be released in firmware version 5.12.4.0. It will be available for the download from the Firmware Releases section [1]. To install firmware, Device Firmware Update (DFU) tool is available, or it can be installed directly with the Intel RealSense Viewer. After the firmware installation, launch the Intel RealSense Viewer application bundled in Intel RealSense SDK to check if it can be set into high-speed depth capture mode 848x100 at 300fps (for D435 cameras). Also note that for best high-speed operation, it is recommended to not have any other devices connected to the USB port, and to limit the use of simultaneous applications that place a high load on the host CPU.
We recommended to NOT enable the RGB camera stream simultaneously, as it will only operate at 30fps, capture will not be synchronized, and depth capture can become unstable on some PC platforms. Instead, 300fps monochrome image streams can be enabled (RS2_STREAM_INFRARED) as shown below. In these 2 streams, index 1 is the left infrared stream which is aligned perfectly to the depth stream as shown in Figure 2.
Having described the mechanisms of capture, we now turn to specific examples and considerations for capturing high speed motion. We start by noting that the definition of "fast" is actually not an absolute number, because the motion observed by the camera changes with the distance to the moving object. Clearly, the closer the moving object is to the camera, the more transverse movement there will be between captured frames.
The higher the frame rate, the smaller the change in position or size will become between consecutive frames for moving objects. As the distance between the moving object and the depth camera increases, the movement of objects in consecutive image decreases. Therefore, if high speed capture is desired, one could argue that the distance between the camera and the object should simply be increased, if physically permitted. Whilst this is true, there are two limitations: 1. The accuracy of range of D400 Series depth cameras scales quadratically with the inverse of the distance to the object, and 2. The object image size shrinks and the transverse resolution degrades linearly with range to object. The relationship between depth camera and a moving object is shown in the Figure 3 below.
Typically, f is about 645 pixels for the D435 1280x720 resolution mode, and about 940 pixels for the D415 1280x720 resolution mode. Target object size in captured image ri [pixel] is expressed by the following equation.
Fig. 5: Speed measurement of moving cylinders from Figure 4. LEFT: 400fps capture, and RIGHT: 30fps capture.
Each image shows original depth (upper), moving object depth and motion vector (middle), and measured speed (bottom). At 400fps, the object speed of 15 m/sec is measured correctly, whereas for the 30fps mode it fails.
In this experiment, we measured up to about 15 m/sec object speeds, limited by the fan rotation speed, and compared 400fps vs 30 fps captures. This example shows how the 30fps mode is not able to sample enough images in time to properly capture the velocity. As seen in Table 1 (last column), the interframe motion is only 32 pixels when operating at 400 fps, but would be 426 pixels at 30fps, which is too large for accurate depth vector estimation.
When discussing motion capture by imaging systems, a complete analysis should clearly also address all aspects of capture, including image blur and system shutter artefacts. It is well known that Global Shutter imagers are generally preferred for high speed motion capture because they do not suffer from rolling shutter artefacts [3]. This is also one clear difference between the Intel RealSense Depth Camera D415 which uses rolling shutters, and the D435 model which uses global shutters. Another important difference is that the D435 model has about 5x the light sensitivity. This is extremely important for fast image capture. Concretely this means that while the D415 in a low-light office environment might need 33ms to capture a good quality image, the D435 would only need less than 6ms.
In this section we will concentrate on quantifying the motion artefact differences between the D435 and D415 models, so we can better illustrate the conditions under which a D415 model can sufficiently capture motion, and the conditions under which it would become necessary to switch to a D435. It turns out that the motion sensitivity can be very different depending on many factors, such as whether the object or camera is moving, or whether the pattern projector is on or off.
In the first experiment, we use a rotating blade with fixed position cameras, and adjust the blade rotation rate from maximum speed down to rest. The blade is placed in front of a surface (wall or curtain). The Intel RealSense depth camera is mounted on a tripod and pointed at the blade. The camera distance is set such that the blade fills most of the FOV (1m for D415, 0.68m for D435). The blade is spun and a sequence of frames (depth, RGB, or point cloud) is captured. Tests are run with both D415 and D435 cameras running at 30 fps and 1280x720 resolution. A variety of conditions are captured with different configurations of blade and background texture, with and without emitter.