Sortify was developed during the summer term 2014 in “Programming Intelligent Applications”. The goal is to rearrange your playlist and make it more harmonic.
The progress is described in the following gallery:
We used plot.ly to visualize the result and the guys wrote a blog post about out “German Robo-DJ”: http://blog.plot.ly/post/92062112527/the-data-of-music-the-anatomy-of-pain
Abstract Im nachfolgenden Paper soll es um die Performance Aussichten der neuen Dateiformate WebP und JPEG XR gehen. Die Geschwindigkeitseigenschaften werden durch einen Vergleich der Ladezeit und der Dateigröße mit JPEG betrachtet. Auf die technische Funktionsweise der beiden Dateiformate wird dabei nicht weiter eingegangen.
Abstract Queuing Theory deals with the mathematical consideration of waiting queues. This paper simulates a M/M/1 queue, which is the simplest model in Queuing Theory, with the help of the server software Rabbit MQ. Thereby the average number of users in the queuing system and in the queue is measured. Furthermore the average time in the queuing system per user and the waiting time in the queue per user is calculated. The resulting values of 25 runs are compared to the expected values of the equations by the Queuing Theory. The results show that the expected value is nearby the mean of the simulation and most likely in the 95% confidence interval around the mean value. Moreover the FIFO property of M/M/1 queues is confirmed.
My second Raspberry Pi is connected to my TV running Raspbmc to play video files. I liked the idea of a custom ambilight installation described by _nadnerb so I bought some WS2801 LEDs and installed Boblight. The LEDs are connected by SPI to the raspberry. Boblight is then able to control the LEDs based on the output.
You can see the mounted LEDs and the Boblight config in the following images:
Abstract This bachelor-thesis analyses the motion sequence of industrial robots for recurring cycles, which are generated by tasks. The goal of this bachelor-thesis is to indentify recurring cycles und find groups of cycles, which contain a high waiting time deviation. For this purpose the motion sequence of the robot is filtered, to ignore long downtimes for the analysis. Subsequent the recurring cycles are extracted from the movement data. Resulting cycles, that are very similar, are grouped at a similarity of 85%. The standard deviation of the standard deviations of every waiting time sequence is used to analyse the waiting times in clusters. Frequent occurring clusters, which have a high standard deviation of the standard deviations, are critical for the waiting time deviation. Clusters, that occure more than two times and have a standard deviation greater than 3.5 are marked as critical. Critical segments in critical clusters are identified by the standard deviation of the waiting times in each segment in critical clusters. Critical segments are segments, which standard deviation of the waiting times is greater than the mean value of the standard deviations of all segments in a cluster. With discovery of critical segments, the line and module of the robotprogramm on the specific segment are allocatable. Furthermore the average length of a task can be calculated. Afterwards the correlation of the clusters is reviewed. Here an inspection of the preceding cluster of critical clusters is made. At the end, the introduced method is verified by example data. In the absence of tagged measurement data, the waiting time deviation gets simulated. The simulation shows, that critical segments are identified by the introduced method.
