Computing is not neutral. The way that we collect and manipulate data, the decisions that are programmed into autonomous devices, even the ways that we use each other's code reflects someone's values. Because technology has more and more influence in society, consideration of these values should be a part of a Computer Science student's education. The ACM/IEEE 2013 Curricula Guidelines for Computer Science programs reflects this imperative. The writers of the document suggest that between 11 and 16 of the core hours be devoted to Social Issues and Professional Practice. Where and how should we include the essential discussions of values, ethical considerations, social issues, and professional practice in the curricula? Should a whole course be devoted to the topic? If so, when in the program should it appear? Should we instead incorporate values topics related to the subject matter in each computing course? If so how? Perhaps we should leave the discussion to the philosophers or ethicists on campus. Will our students then be able to make the connection between computing and general ethics/values frameworks? Which aspects of values judgments should be addressed? Can we generalize in a way that will still apply to future advances in our fields? We'd like to hear from you.

This paper describes a sequence of assignments, each building upon the next, leading students to a working simulation of a simple 8-bit CPU (Central Processing Unit). The design features a classic Von Neumann architecture comprising a simple data path with a few registers, a simple ALU (Arithmetic Logic Unit), and a microprogram to direct all the control signals. The first step involves the design of the ALU which is capable of eight basic operations. The second step guides students to construct a datapath complete with several 8-bit registers. The third step involves the design and implementation of a control unit which uses a microprogram to implement machine code instructions. The microprogram implements nine basic machine language instructions which are sufficient for writing many simple programs. The final step involves adding program memory and an input and output device to form a simple working simulation of a computer. At this point, students may hand-assemble code for their CPU and simulate its execution. All simulations are performed using a free and open source simulator called Logisim which performs digital logic simulations with the ability to build larger circuits from smaller subcircuits. Students can set an adjustable clock rate and observe the internal CPU state and registers as it retrieves instructions and steps through the microcode. The basic CPU architecture provides many opportunities for more advanced exercises, such as adding an instruction fetch unit, adding pipelining, or adding more machine language instructions. The assignments were introduced in a second year course on computer organization, providing an effective hands-on approach to understanding how a CPU actually operates.

A network of real-time vision sensors is described using 100baseT Ethernet. Each vision sensor comprises a camera connected to a dedicated computer to form a "vision node". Each vision node is capable of capturing and processing images and communicating the results over the network to a master computer in real-time. Deterministic and timely operation using standard Ethernet is accomplished using three basic techniques: suppressing superfluous packets; using the UDP transport protocol; and using an application layer protocol based on master/slave round-robin polling. Deterministic communications also enable the acquisition times of the cameras to be synchronized over the Ethernet network. By synchronizing the vision nodes to interleave their capture times, network contention is avoided and the effective video sample rate is increased. An analysis is also performed to determine the maximum number of vision nodes that can be supported using this technique over 100baseT Ethernet.

A direct visual servoing system is described which employs a network of cameras providing high-speed vision feedback that is robust to occlusions. This system does not rely on any external position or velocity sensors, but directly sets motor current using visual feedback alone. The limitation of 60 Hz video is overcome with multiple RS-170 cameras, synchronized over a network in round-robin fashion, to capture video fields at different instants in time. Each camera has its own computer that processes video at field rates to determine the position of a planar robot joint using eigenspace methods. The eigenspace computations produce position and Euclidean distance measurements sent from each camera node over a network to a master servo computer. It is shown that the Euclidean distance from the manifold in eigenspace in the presence of random occlusions is statistically related to the position measurement error. Occlusions are thus considered as "noise," and the measurement error variance is estimated directly from Euclidean distance. The measurement error variance is applied directly to a Kalman filter, which weights feedback from each camera to provide improved position estimates. The Kalman filter also models the vision transport delays to provide timely position estimates to ensure the stable direct visual servoing of a planar robot. Simulation results illustrate improvement in dynamic performance as the number of cameras are increased. Experimental measurements were obtained for a network of four cameras performing direct visual servoing of a simple planar robot. The results demonstrate the step response, as well as stable servo-hold operation in the presence of full occlusions in a subset of cameras or with partial occlusions in all cameras.

A distributed camera system using off-the-shelf components is presented that demonstrates the capability to perform high-speed vision feedback suitable for applications such as direct visual servoing. The limitation of 60 Hz video sample rates is overcome by using multiple RS-170 cameras synchronized over a network to capture at different instants in time. Each camera node has its own computer that processes video at field rates to determine the pose of a planar robot joint using eigenspace methods. Position information is fed back over a network to a master computer to perform direct visual servoing. The resulting vision feedback from the multiple cameras uses a Kalman filter to estimate position and to model the vision computation and transport delays. Computer simulation results are provided as the number of cameras are varied. Finally, real-time experimental results are presented that verify the approach using a network of four cameras performing direct visual servoing of a simple planar robot.