An Accelerated Hierarchical Approach for Object Shape Extraction and Recognition

We present a novel automatic supervised object recognition algorithm based on a scale and rotation invariant Fourier descriptors algorithm. The algorithm is hierarchical in nature to capture the inherent intra-contour spatial relationships between the parent and child contours of an object. A set of distance metrics are introduced to go along with the hierarchical model. To test the algorithm, a diverse database of shapes is created and used to train standard classification algorithms, for shape-labeling. The implemented algorithm takes advantage of the multi-threaded architecture and GPU efficient image-processing functions present in OpenCV wherever possible, speeding up the running time and making it efficient for use in real-time applications. The technique is successfully tested on common traffic and road signs of real-world images, with excellent overall performance that is robust to moderate noise levels

Infusing Raspberry Pi in the Computer Science Curriculum for Enhanced Learning

With the advent of cloud computing, the Internet of Things (IoT), and mobile computing, CS faculty are continuously revamping the curriculum material to address such burgeoning set of technologies in practical and relatable ways. Raspberry Pi (RPi) devices represent an ideal hardware/software framework that embodies all these technologies through its simple architecture, small form factor (that minimizes the volume and footprint of a desktop computer), and ability to integrate various sensors that network together and connect to the Cloud. Therefore, one of the strategies of Computer Science Department, to enhance depth of learning concepts, has been to infuse Raspberry Pi (RPi) in computer science courses. RPi has been incorporated since 2016 in targeted courses, notably, Computer Organization & Assembly Language, Computer Architecture, Database Management Design & Implementation, Unix/Linux Programming, Internet Programming, and Senior Project. An inexpensive credit card sized computer, an RPi lends itself to allow depth of learning of concepts. From implementing firewalls, intrusion detection systems, scripting, client-server based computing, distributed computing, to interfacing with sensors and actuators, a student is guided to polish concepts taught in a class through RPi Project Based Learning (RPBL). Computer science curriculum already provides breadth of learning. The infusion of RPi in key courses provides depth in targeted concepts. There are peripheral desirable consequences as well, including a student learning prevalently used Linux environment even though a targeted course may have nothing directly to do with Linux. Furthermore, RPi provides an opportunity for students to realize that software programs can be interfaced with sensors and actuators to provide immersed experience in programming. From simply interfacing a switch and a Light Emitting Diode (LED) to getting data from sensors, buffering, and uploading to the cloud, a student already would have touched upon multiple disciplines in computer science. This paper provides a blueprint to infusing RPi in the targeted courses, and how each RPi based project provides depth to a targeted concept

Weak Mitoticity of Bounded Disjunctive and Conjunctive Truth-Table Autoreducible Sets

Glaßer et al. (SIAMJCOMP 2008 and TCS 2009 (The two papers have slightly different sets of authors)) proved existence of two sparse sets A and B in EXP, where A is 3-tt (truth-table) polynomial-time autoreducible but not weakly polynomial-time Turing mitotic and B is polynomial-time 2-tt autoreducible but not weakly polynomial-time 2-tt mitotic. We unify and strengthen both of those results by showing that there is a sparse set in EXP that is polynomial-time 2-tt autoreducible but not even weakly polynomial-time Turing mitotic. All these results indicate that polynomial-time autoreducibilities in general do not imply polynomial-time mitoticity at all with the only exceptions of the many-one and 1-tt reductions. On the other hand, however, we proved that every autoreducible set for the polynomial-time bounded disjunctive or conjunctive tt reductions is weakly mitotic for the polynomial-time tt reduction that makes logarithmically many queries only. This shows that autoreducible sets for reductions making more than one query could still be mitotic in some way if they possess certain special properties