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The capability of multiple valued logic (MVL) circuits to achieve higher storage density when compared to that of existing binary circuits is highly impressive. Recently, MVL circuits have attracted significant attention for the design of digital systems. Carbon nanotube field effect transistors (CNTFETs) have shown great promise for design of MVL based circuits, due to the fact that the scalable threshold voltage of CNTFETs can be utilized easily for the multiple voltage designs. In addition, resistive random access memory (RRAM) is also a feasible option for the design of MVL circuits, owing to its multilevel cell capability that enables the storage of multiple resistance states within a single cell. In this manuscript, a design approach for ternary combinational logic circuits while using CNTFETs and RRAM is presented. The designs of ternary half adder, ternary half subtractor, ternary full adder, and ternary full subtractor are evaluated while using Synopsis HSPICE simulation software with standard 32 nm CNTFET technology under different operating conditions, including different supply voltages, output load variation, and different operating temperatures. Finally, the proposed designs are compared with the state-of-the-art ternary designs. Based on the obtained simulation results, the proposed designs show a significant reduction in the transistor count, decreased cell area, and lower power consumption. In addition, due to the participation of RRAM, the proposed designs have advantages in terms of non-volatility.

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In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

Network-on-Chip (NoC) is fast emerging as an on-chip communication alternative for many-core System-on-Chips (SoCs). However, designing a high performance low latency NoC with low area overhead has remained a challenge. In this paper, we present a two-clock-cycle latency NoC microarchitecture. An efficient request masking technique is proposed to combine virtual channel (VC) allocation with switch allocation nonspeculatively. Our proposed NoC architecture is optimized in terms of area overhead, operating frequency, and quality-of-service (QoS). We evaluate our NoC against CONNECT, an open source low latency NoC design targeted for field-programmable gate array (FPGA). The experimental results on several FPGA devices show that our NoC router outperforms CONNECT with 50% reduction of logic cells (LCs) utilization, while it works with 100% and 35%~20% higher operating frequency compared to the one- and two-clock-cycle latency CONNECT NoC routers, respectively. Moreover, the proposed NoC router achieves 2.3 times better performance compared to CONNECT.

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