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An Application Specific Inflexible FPGA (ASIF) is a tailored design, for a given group of known circuits, which is generated by extensively reducing the routing resources of an FPGA. In an ASIF, different dynamically reconfigurable application circuits are initially mapped and tested on an FPGA fabric. Subsequently, the FPGA fabric is reduced to achieve an efficient architecture for known application circuits. However, a large portion of ASIF is still occupied by fully flexible logic blocks, containing the same amount of area and SRAM memory cells, as found in a traditional FPGA. Thus, here lies a potential to further optimize the logic blocks of an ASIF at the expense of removing or reducing their reconfigurability. This work optimizes the logic blocks of an ASIF through the SRAM-Table sharing technique, without compromising their reconfigurability. Moreover, the routing channels of ASIF are further optimized by applying the Boolean functions (Gates) insertion technique. The applied techniques (SRAM-Table sharing and Boolean functions insertion) not only reduce the area, delay and power, but also minimize the reconfiguration time, bitstream size and the size of external memory required to store the bitstream of circuits. This optimized version of ASIF is termed as ASIF[Formula: see text]. Furthermore, an embedded FPGA in a System-on-Chip that requires the partial dynamic reconfiguration for known circuits, can be automatically reduced to an ASIF[Formula: see text]. It is found through experimental results that an ASIF[Formula: see text] is 4–9[Formula: see text] area-efficient and requires [Formula: see text] lesser number of SRAM cells, as compared to the previously proposed ASIF for a group of 2–5 circuits. It also achieves 34–53[Formula: see text] area saving as compared to a traditional FPGA.

Elastography is an upcoming and promising imaging modality which is used to map as an image, termed elastogram, information related to tissue elastic properties. An elastogram reflects strain information resulted from an applied stress. This paper presents a study of various image texture features as applied to simulated elastograms for the purpose of obtaining effective texture features. These features are those that exhibit non-overlapping distributions for different modulus distributions. Among various texture features studied, five are found to be effective for simulated elastograms. These include fractal signature, wavelet energy, and entropy, contrast and energy extracted from the sum and difference histograms. Upon the availability of clinical data, the results of this study will be used to classify abnormalities into malignant or benign tissue classes.

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