Download The Spike Pc For Android New Version

This is odd to hear of, @user633! Thank you for sharing your experience with us. Does this only happen on the android device you are using? If possible, see if the experience of data usage is the same on another mobile device. Some important factors to consider is data coverage/ provider, continuous lengthy streaming (more use generally), and please ensure there is not a vpn enabled on the mobile device.

As this is an android device, please check for any android apps that conflict with the Ring app . While you check these variables, I will be reaching out to you in a private message in the Community shortly, so please ensure to respond to me there.

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A spike is a product development method originating from extreme programming that uses the simplest possible program to explore potential solutions.[1] It is used to determine how much work will be required to solve or work around a software issue. Typically, a "spike test" involves gathering additional information or testing for easily reproduced edge cases. The term is used in agile software development approaches like Scrum or Extreme Programming.

A distinction can be made between technical spikes and functional spikes. The technical spike is used more often for evaluating the impact new technology has on the current implementation. A functional spike is used to determine the interaction with a new feature or implementation.

I am mentoring an FLL Team, as well as running robotics YouTube Channel, Tellurium Robotics. We just started using Spike this year, so we had to use Spike 3. I do have experience with spike 2, and am wondering if there is a way to get access to those blocks in Spike 3. Mainly, I just need to know where the motor degrees and motor rotation output blocks are, or if they even exist anymore.

Every time there is an interval change the Trainerroad app on my Android tablet shows either a spike or a drop in the power number. I use a dumb trainer and a 4iiii Precision power meter and pedaling at a steady cadence without changing gear.

You may be on to something there. I guess this is exactly what you would see if the smoothing starts over on zero at the start if each interval. But then the fix should also be easy for TrainerRoad. Why restart the smoothing? As a matter of fact I cannot see these spikes and drops in the data afterwards. They are only visible in the power numbers when riding.

I started using a powertap G3 and dumb trainer. I also noticed that when an interval starts/changes there is a split second spike in power. Around 7-10 watts. I did notice it significantly during the ramp test. Using macos. Ant only

More Info:

The app/game consists of a joystick, a player sprite, and multiple rock projectile sprites. The player is a surfer and uses the joystick to dodge oncoming rocks. The code works as intended, but after playing for about 30 to 40 seconds a noticeable lag spike occurs. It reoccurs about every 5 seconds after that. I understand that I am doing a lot of rendering and computing a lot of information within a short period of time(45ms) for App Inventor, but I am interested in why such a noticeable lag spike happens so late within the app and why it is not a constant drop in framerate as shown in other games or apps.

Thanks for the quick response . I took your word and changed the rendering time from 45ms to 200ms. Unfortunately, The same spike still happens around the same time. While less noticeable as the spikes blend more to the lower framerate, It is still noticeable and I also attempted to move the time from 200ms to 300ms and it is still relatively visible. Anything over 300ms per frame is something close to unusable.

The second major thing that game engines try to avoid is creating garbage that has to be collected. The spikes that you're observing are due to the virtual machine running the code needing to pause to clean up the results of intermediate computations. My guess is that on your device it takes about 30-40 seconds for the main memory available for the app to be exhausted and then the garbage collector (GC) runs. This frees up some memory enough until a few seconds later at which point the freed memory has been exhausted again and the GC runs. Rinse and repeat. The likely culprit for this is in the ChangeFrame function where you are toggling between two different images every other frame. App Inventor does not cache images, so the causes the image to be read from the assets every time, scaled, etc. and then drawn. The previous image will hang around in memory until the GC runs to clean it up, resulting in the spikes while the world is paused for the GC operation. Some devices use parallel GC so that this is not needed but the actual functionality will depend on the Android version, etc.

Because the use of animated frames was not extremely essential to my app I decided to just remove it altogether. This did bring the amount of freeze time for the spike down, but there is still a noticeable spike that lasts about 1 second. Perhaps, are there any other variables or objects that are considered "garbage" that would need to be thrown away?

If you use Google Analytics as a tool to understand how your audience engages with your digital collection, you may come across a spike of hits on a particular day or over a particular month, with no immediate reason why. Therefore, it's logical that you may want to investigate, in order to understand more about why it has occurred.

Step 1: Visit the Audience -> Overview report, and set the time period to encompass the hit spike. If the spike happens over less than a week, we can use a week time period, or if the spike happens over a month, we can choose a month. Be sure to also enable "Compare to: previous period" to help make differences in traffic stand out in the reports.

Because we enabled "Compare to: previous period" in Step 1, under each channel the % change is listed from the previous time period, and if this change is relatively high, that could indicate a source or sources of the hits for a hit spike.

Let's explore two hit spikes using the above suggested method. We will use hit spikes we noticed on elephind.com as examples here (Elephind is a historical newspaper search engine we created to make it possible to search a variety of online historic newspapers from one place). Although Elephind is a search engine rather than a digital collection, the same principles apply when investigating hit spikes.

Most of this spike happens within a week, so we can set the date range to 7 days beginning September 20th, and enable "Compare to: previous period" to compare with the previous week through the reports.

The larger circles indicating user numbers for the week of the hit spike are very clear compared with the previous week. Note that the finest granularity available in these location reports is to the level of city.

We can fairly safely conclude from the investigation of this hit spike that it was caused by a post on Eastman's Online Genealogy Newsletter, and as a result of that post a significant number of users arrived on elephind.com via referral, directly, via organic search, and via various social networks. The users that arrived during this event were situated in many countries across the world and seemingly from every single state across the United States and DC.

The second hit spike we'll look at took place earlier this year. Looking at the Audience -> Overview report for this year, we can see a more complex situation than with the first example, as this influx of traffic has multiple peaks and a long tail. The highest peak is April 18, 2021.

Referral is only a small amount of the total traffic of the hit spike, but aside from l.messenger.com, the major increases in traffic are actually from search engines. The first source, com.google.android.googlequicksearchbox, is actually part of one version of the Google Search app for Android, and duckduckgo.com and ecosia.org are also both search engines.

We did notice an article: makeuseof.com/tag/10-search-engines-explore-deep-invisible-web/ that since June 2017 has been causing intermittent small spikes of traffic to Elephind, but the latest spike occurred with only 89 users on 13 March 2021, which was a month before this large spike in traffic occurred.

We also noted that the link to Elephind from this site also includes the html attribute rel="noreferrer" which causes referral traffic to occur as direct traffic, so only some versions of this page actually show up as referral traffic. rel="noreferrer" is used in some specific cases as a privacy or security enhancement. These kinds of now common measures may have contributed to the outcome of this hit spike investigation, which is that we don't currently know what caused it. For now, that remains a mystery.

A spike story in Agile is a user story that needs more information so the team can estimate how long the story will take to complete. Agile teams typically have a set amount of time outlined for spikes, which is why spike stories are often referred to as timeboxed investigations.

Sometimes spike stories in Agile can be investigated in less time than the timebox, and sometimes spikes need more time than has been allotted. In this case, team members need to report the outcome of their investigation to the rest of the team, even if they will need more time to finish the investigation.

The goal of a spike story in Agile is not to determine the solution to a story, but rather to determine an estimate for how long the original story will take to complete. Spike stories might require team members to spend time splitting a story into smaller stories if the original user story is too large or complex, or it might require a team member to build an experiment to gather more information for the estimate. 006ab0faaa