A novel robust package-height estimation and parcel-size classification scheme is proposed in this work. A pertinent prototype system consisting of a millimeter-wave radar, a shipment track, and a stepping motor has been built for proof of concept. Three new schemes using the range-profile data produced by the mmWave radar, namely, the detection based-on cumulative distribution (DBCD) scheme, the detection based-on peak-value clustering (DBPC) scheme, and the DBCD–DBPC hybrid scheme are introduced here. For benchmark evaluation of the proposed new system, two practical scenarios, namely, the undisturbed scenario (no people is close to the system) and the disturbed scenario (some people is close to the system), are tested. This new system can overcome the serious drawback of the existing camera-assisted systems, which have to rely on sufficient lighting conditions, and operate normally even in complete darkness. Meanwhile, the proposed new system can accommodate parcels/packages in arbitrary orientations, which cannot be allowed by the current camera-assisted systems, during the parcel-size measurements. According to the test experiments, the proposed DBCD–DBPC hybrid scheme can reach up to a very high average classification accuracy of 96.4% for both undisturbed and disturbed scenarios. The proposed novel package-height estimation and parcel-size classification technique in this work can be very useful for intelligent inventory management and smart logistics in the future.

Constrained random simulation is becoming the mainstream methodology to verify system-wide properties in functional verification. It is a must to develop a high-throughput constrained random pattern generator, which is able to support arbitrary distribution. In this paper, we propose a novel range-splitting heuristic and a solution-density estimation technique to conquer the challenges of random pattern generators proposed in the recent literature. The solution densities can significantly increase by pruning infeasible subspaces. On the other hand, the estimated solution densities stored on a range-splitting tree statistically predict the distribution of solutions. Therefore, the generated patterns are ensured to meet the desired distribution with high throughput. Experimental results show that our framework achieves more than 10X speedup on average when compared to a commercial generator.

To verify system-wide properties on SoC designs in Constrained Random Verification (CRV), the default set of constraints to generate patterns could be overridden frequently through the complex testbench. It usually results in the degradation of pattern generation speed because of low hit-rate problems. In this paper, we propose a technique to preprocess the solution space under each constraint set. Regarding the similarity between constraint sets, the infeasible subspaces under a constraint set help identify the infeasible subspaces under another constraint set. The profiled results under each constraint set are then stored in a distinct range-splitting tree (RS-Tree). These trees accelerate pattern generation under multiple constraint sets and, simultaneously, ensure the produced patterns are evenly-distributed. In our experiments, our framework achieved 10X faster pattern generation speed than a state-of-art tool in average.

Constrained random verification (CRV) methodology has been identified as an efficient solution to functional verification challenges. In practical cases, it is required to implement constraints with variable ordering to put essential efforts on cared patterns. However, handling constraints with variable ordering may encounter performance degradation of pattern generation speed and distribution. To resolve these challenges, we provide a preprocessing technique to analyze solution space by adaptively splitting ranges of variables and prove the feasibility of each subspace. This analysis allows us to perform effective range-reduction to enhance pattern generation speed and ensure the desired distribution. From the experimental results, our framework outperforms a state-of-art tool with 10X speedup in average and retains better stability of performance with the increase of number of variable orders.

Constrained random simulation is becoming the mainstream methodology in functional verification. In order to achieve the verification closure, a high-throughput and evenly-distributed constrained random pattern generator has become a must. In this paper, we propose a novel Range-Splitting heuristic and a Solution-Density Estimation technique (RSSDE) to partition sample space. The chosen cutting planes target to prune more infeasible subspaces so that the solution densities in other subspaces increase correspondingly. In addition, with statistics-based analyses, the estimated solution densities precisely predict the distribution of solutions. The intermediate statistical information is recorded in a range-splitting tree (RS-tree). By top-down random walking on the RS-tree, random pattern generation produces evenly-distributed patterns with high throughput. Experimental results show that our framework guarantees evenly-distributed stimuli and achieves more than 10× speedup in average when compared to a state-of-the-art commercial generator.

Functional rectification in late design stages has been a crucial process in modern complex system design. This paper proposes a robust functional ECO engine, which applies SAT proof minimization and interpolation techniques to automate patch construction to make old implementation and golden specification functionally equivalent. The SAT proof minimization technique provides a sound and efficient way of fixing easy errors, and the interpolation technique provides a complete and robust way of fixing remaining errors. Experimental results show that our engine performs robustly to generate small patches in fixing various design rectification instances.