Multi-threshold window discriminator based on SAR logic

The new X-ray imaging detectors allow capturing an X-ray image in various photon energy ranges in one shot. This technique is called X-ray color imaging, and it is becoming a promising method in fields such as medical imaging, computed tomography, and non-destructive material testing. To measure the energy spectrum in one shot, discriminant circuits need to be integrated into the pixel front-end electronics. Several solutions of in-pixel discriminators exist. However, current designs suffer from a low number of discrimination bins and need to adjust each threshold separately, leading to relatively complicated calibration procedures. This work introduces a novel design of a multi-threshold window discriminator based on successive approximation register logic. This circuit realizes in-pixel binning to ten equidistant windows. Two variables are used for tuning the multi-threshold window discriminator: offset of the first window and width of the windows. Setting these parameters allows the user to fulfill the need of the target application.

Systematization and Comparison of the Binary Successive Approximation Variants

This paper presents a systematization and a comparison of the binary successive approximation (SA) variants. Three different variants are distinguished and all of them are applied in the analog-to-digital conversion. Regardless of an analog-to-digital converter circuit solution, the adoption of the specific SA variant imposes a particular character of the conversion process and related parameters. One of them is the ability to direct conversion of non-removeable physical quantities such as time intervals. Referencing to this aspect a general systematization of the variants and a name for each of them is proposed. In addition, the article raises the issues related to the complexity of implementation and energy consumption for each of the discussed binary SA variants.

A 9-Bit 1-GS/s Hybrid-Domain Pseudo-Pipelined SAR ADC Based on Variable Gain VTC and Segmented TDC

This paper presents a 9-bit 1 GS/s successive approximation register (SAR) analog-to-digital converter (ADC). In this hybrid architecture, the pseudo-pipeline operation is realized, which increases the sampling rate effectively. The ADC adopts two key technologies: the variable gain voltage-to-time converter (VTC), which ensures the linearity is not sacrificed; the segmented time-to-digital converter (STDC), which further improves the linearity of time domain quantization. The prototype ADC is simulated in a standard 65-nm CMOS process with an active area of 0.038 mm2. The simulated SNDR and SFDR are 44.3 and 58 dB with a sampling rate of 1 GS/s. The FoMW and FoMS are 24.7 fJ/conv-step and 150.7 dB, respectively.

A comparative study of SAM and ADDIE models in simulating STEM instruction

The study compared exhaustively the Successive Approximation Model (SAM) and Analyze, Design, Develop, Implement and Evaluate (ADDIE) model on the teaching and learning of Science, Technology, Engineering and Mathematics subjects in Ghana. We selected a sample of 30 student-teachers who offered Mathematics and Science in the distance mode of the University of Education, Winneba, Ghana in the 2018/2019 academic year. The first stage of the analysis compared the models separately within the Vygotskian framework using pre-post experiemtal design. The second stage made comparisons between and within the two models. The results of both stages showed that student-teachers preferred mostly SAM to ADDIE instructional models. There were not only consistently higher mean gains in the latter model, but the group averages of student-teachers in the post-treatment results also demonstrated clear improvements. Again, student-teachers showed tremendous improvements in the conceptual understanding of both models. However, the Successive Approximation Model recorded much more improvements in both pre-treatment and post-treatment results. It was therefore imperative to conclude that the Successive Approximation Model was more properly situated in the context of teaching and learning Mathematics and Science. We, therefore, recommended experimental explorations of SAM for STEM.

A comparative study of SAM and ADDIE models in simulating STEM instruction

The study compared exhaustively the Successive Approximation Model (SAM) and Analyze, Design, Develop, Implement and Evaluate (ADDIE) model on the teaching and learning of Science, Technology, Engineering and Mathematics subjects in Ghana. We selected a sample of 30 student-teachers who offered Mathematics and Science in the distance mode of the University of Education, Winneba, Ghana in the 2018/2019 academic year. The first stage of the analysis compared the models separately within the Vygotskian framework using pre-post experiemtal design. The second stage made comparisons between and within the two models. The results of both stages showed that student-teachers preferred mostly SAM to ADDIE instructional models. There were not only consistently higher mean gains in the latter model, but the group averages of student-teachers in the post-treatment results also demonstrated clear improvements. Again, student-teachers showed tremendous improvements in the conceptual understanding of both models. However, the Successive Approximation Model recorded much more improvements in both pre-treatment and post-treatment results. It was therefore imperative to conclude that the Successive Approximation Model was more properly situated in the context of teaching and learning Mathematics and Science. We, therefore, recommended experimental explorations of SAM for STEM.