Functional testing of HDL spec$cations is one of the most promising approaches for the verijication of the f i n etionalities of a design before synthesis. The contribution of this work is the development of a test generation algorithm targeting a new coverage metric (called bit-coverage) that provides full statement coverage, branch coverage, condition coverage and partial path coverage for behaviorally sequential models. The behavioral test sequences can be also the only way to evaluate testability of VHDL model for which a gate-level representation is not available (e.g third-party cores), since the behavioral error model is characterized also by a high correlation with the RT and gate-level stuck-at fault model. Moreovec the preciseness of the proposed coverage metric makes the identijied test sequences more effective in identifiing design errors, than other test patterns developed by following standard coverage metrics.
The paper esamines the potentialities of genetic algorithms (GA ' a) with respect to the development of high-level TPG's. It summarizes atfirst the most relevant test pattern genemtion techniques based on genetic algorithms (GA ' s ) . This analysis distinguishes the considered techniques with respect to the abstraction level of the design under test. I n particular, the effectiveness of gate-level GA-based TPG's is compared with the effectiveness of high-level CA-based TPG's. Differences are deeply investigated. They mainly concern the way genetic operators ezploit specific simulation information to heuristically guide the genetic evolution. Moreover, a functional testing fmmework is described and used to actually measure on high-level descriptions the effectiveness of sophisticated CA-based TPC's in comparison to mndom approaches. Results are reported on a variety of benchmnrks. '1. INTRODUCTIONGA's [I] have been shown to he effective when solving search problems. In the area of test generation of VLSI designs, GA's have been adopted as TPG engines at gate level and high level (e.g., RTL and behavioral). Section 1.1 and 1.2 show their respective characteristics, which are summarized and compared in Section 1.3. Section 2 describes the proposed testing framework, which allows to compare the reviewed GA techniques.Experimental results, presented in Sect.ion 3, show evidences i n the use of GA's for high-level TPG. GA's for Gate-level TestingGA's have been used at first as a framework for simulationbased test generation in 121 and [3]. Both techniques target gatelevel descriptions. The former adopt a logic simulator to evaluate the generated test sequences, thus the generated test sets have often a lower fault coverage than that generated by a deterministic test generat.or. The later targets only combinational circuits. The adopted crossover operator exploits problem specific knowledge, thus making hard to identify the real contribution of GA's independently from the adopted heuristics. The performance of the CRlS test generator [Z] has been improved in [4] by using a fault simulation procedure to evaluate the generated sequences. The fault coverage increases as well as the execution time. The adopted fitness function is defined as: vi,j I (Np -Np) I< a (1) where t,he symbol NF and NF represent the number of changes of suh-circuits i and j due to the application of a sequence of 'Research activity partially supported by the IST-2001-34607 SYXIBAD European Project. length R and a is an appropriate small integer value. Other effective solutions targeting synchronous circuits have been proposed in [5] and [6]. The latter represents an extension of the former work, where up to 64 faults have been targeted simultaneously. GA's are used to propagate the induced faulty values to the design primary outputs. These techniques are suitable only for designs with a reset state. The adopted fitness function is: p= 1 ,"=I where ny.te and nF.r are respectively the number of gates and flip-flops, 4 is the k-th vector of sequen...
Urban air pollution causes deleterious effects on human health and the environment. To meet stringent\ud standards imposed by the European Commission, advanced measurement methods are required. Remote\ud sensing techniques, such as light detection and ranging (LiDAR), can be a valuable option for evaluating\ud particulate matter (PM), emitted by vehicles in urban traffic, with high sensitivity and in shorter time intervals.\ud Since air quality problems persist not only in large urban areas, a measuring campaign was specifically performed\ud in a suburban area of Crotone, Italy, using both a compact LiDAR system and conventional instruments\ud for real-time vehicle emissions monitoring along a congested road. First results reported in this paper show\ud a strong dependence between variations of LiDAR backscattering signals and traffic-related air pollution levels.\ud Moreover, time-resolved LiDAR data averaged in limited regions, directly above conventional monitoring stations\ud at the border of an intersection, were found to be linearly correlated to the PM concentration levels with a correlation\ud coefficient between 0.75 and 0.8
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