A fast improved fat tree encoder for wave union TDC in an FPGA

Up to now, the wave union method can achieve the best timing performance in FPGA-based TDC designs. However, it should be guaranteed in such a structure that the non-thermometer code to binary code （NTH2B） encoding process should be finished within just one system clock cycle. So the implementation of the NTH2B encoder is quite challenging considering the high speed requirement. Besides, the high resolution wave union TDC also demands that the encoder convert an ultra-wide input code to a binary code. We present a fast improved fat tree encoder （IFTE） to fulfill such requirements, in which bubble error suppression is also integrated. With this encoder scheme, a wave union TDC with 7.7 ps RMS and 3.8 ps effective bin size was implemented in an FPGA from Xilinx Virtex 5 family. An encoding time of 8.33 ns was achieved for a 276-bit non-thermometer code to a 9-bit binary code conversion. We conducted a series of tests on the oscillating period of the wave union launcher, as well as the overall performance of the TDC; test results indicate that the IFTE works well. In fact, in the implementation of this encoder, no manual routing or special constraints were required; therefore, this IFTE structure could also be further applied in other delay-chain-based FPGA TDCs.

Using Computational Intelligence Algorithms to Solve the Coalition Structure Generation Problem in Coalitional Skill Games

Coalitional skill games （CSGs） are a simple model of cooperation in an uncertain environment where each agent has a set of skills that are required to accomplish a variety of tasks and each task requires a set of skills to be completed, but each skill is very hard to be quantified and can only be qualitatively expressed. Thus far, many computational questions surrounding CSGs have been studied. However, to the best of our knowledge, the coalition structure generation problem （CSGP）, as a central issue of CSGs, is extremely challenging and has not been well solved. To this end, two different computational intelligence algorithms are herein evaluated： binary particle swarm optimization （BPSO） and binary differential evolution （BDE）. In particular, we develop the two stochastic search algorithms with two-dimensional binary encoding and corresponding heuristic for individual repairs. After that, we discuss some fundamental properties of the proposed heuristic. Finally, we compare the improved BPSO and BDE with the state-of-the-art algorithms for solving CSGP in CSGs. The experimental results show that our algorithms can find the same near optimal solutions with the existing approaches but take extremely short time, especially under the large problem size.

A quality status encoding scheme for PCB-based products in IoT-enabled remanufacturing

In this paper,a binary-extensible quality status encoding scheme,named IQSCT(IoT quality status code table),is proposed for the PCB-based product with available recovery options in remanufacturing.IQSCT is achieved by code evolution based on binary logic,in which the product flow and the quality information flow are integrated,and three key features of PCB-based product(PCB-module association,assembly-disassembly logic,and disassembly risk)are involved in production costing.With IQSCT,the manufacturer can have better decisions to reduce remanufacturing cost and improve resource utilization,which is verified by a case study based on the real data from BOM cost and corresponding estimation of Apple iPhone 11 series.