The software and hardware development fields evolved along separate paths through the end of the 20th century. We seem to have come full circle, however. The previously rigid hardware on which our programs run is softening in many ways. Embedded systems are largely responsible for this softening. These hidden computing systems drive the electronic products around us, including consumer products like digital cameras and personal digital assistants, office automation equipment like copy machines and printers, medical devices like heart monitors and ventilators, and automotive electronics like cruise controls and antilock brakes.
Embedded systems force designers to work under incredibly tight time-tomarket, power consumption, size, performance, flexibility, and cost constraints.
Many technologies introduced over the past two decades have sought to help satisfy these constraints. To understand these technologies, it is important to first distinguish the underlying embedded systems elements.
I implement Dijkstra s Single Source Shortest Path, say SSP, algorithm for directed graphs using a simple data structure, say simple scheme, Fibonacci heaps, say F-heap scheme, and Pairing heaps, say P-heap scheme, and measure the relative performance of the three implementations.
物流分析工具包。Facility location: Continuous minisum facility location, alternate location-allocation (ALA) procedure, discrete uncapacitated facility location
Vehicle routing: VRP, VRP with time windows, traveling salesman problem (TSP)
Networks: Shortest path, min cost network flow, minimum spanning tree problems
Geocoding: U.S. city or ZIP code to longitude and latitude, longitude and latitude to nearest city, Mercator projection plotting
Layout: Steepest descent pairwise interchange (SDPI) heuristic for QAP
Material handling: Equipment selection
General purpose: Linear programming using the revised simplex method, mixed-integer linear programming (MILP) branch and bound procedure
Data: U.S. cities with populations of at least 10,000, U.S. highway network (Oak Ridge National Highway Network), U.S. 3- and 5-digit ZIP codes
his folder contains the following files:
1. 02490rxP802-15_SG3a-Channel-Modeling-Subcommittee-Report-Final.doc: This is the final
report of the channel modeling sub-committee.
2. cmx_imr.csv (x=1, 2, 3, and 4) represent the files containing the actual 100 channel
realizations for CM1, CM2, CM3, and CM4. The columns are organized as (time, amp, time, amp,...)
3. cmx_imr_np.csv (x=1, 2, 3, and 4) represent the files containing the number of paths in
each of the 100 multipath realizations.
4. cmx_imr.mat (x=1, 2, 3, and 4) are the .mat files that can be loaded directly into
Matlab (TM).
5. *.m files are the Matlab (TM) files used to generate the various channel realizations.
this m file can Find a (near) optimal solution to the Traveling Salesman Problem (TSP) by setting up a Genetic Algorithm (GA) to search for the shortest path (least distance needed to travel to each city exactly once)
Notes:
1. Input error checking included
2. Inputs can be specified in any order, so long as the parameter pairs are specified as a parameter , value
