Multiplication-accumulation (MAC) operation plays a crucial role in digital signal processing (DSP) applications, such as image convolution and filters, especially when performed on floating-point numbers to achieve high-level of accuracy. The performance of MAC module highly relies upon the performance of the multiplier utilized. This work offers a distinctive and efficient floating-point Vedic multiplier (VM) called adjusted-VM (AVM) to be utilized in MAC module to meet modern DSP demands. The proposed AVM is based on Urdhva-Tiryakbhyam-Sutra (UT-Sutra) approach and utilizes an enhanced design for the Brent-Kung carry-select adder (EBK-CSLA) to generate the final product. A (6*6)-bit AVM is designed first, then, it is extended to design (12*12)-bit AVM which in turns, utilized to design (24*24)-bit AVM. Moreover, the pipelining concept is used to optimize the speed of the offered (24*24)-bit AVM design. The proposed (24*24)-bit AVM can be used to achieve efficient multiplication for mantissa part in binary single-precision (BSP) floating-point MAC module. The proposed AVM architectures are modeled in VHDL, simulated, and synthesized by Xilinx-ISE14.7 tool using several FPGA families. The implementation results demonstrated a noticeable reduction in delay and area occupation by 33.16% and 42.42%, respectively compared with the most recent existing unpipelined design, and a reduction in delay of 44.78% compared with the existing pipelined design.
In a counterfeit clever control procedure, another productive methodology for an indoor robot localization framework is arranged. In this paper, a new mathematic calculation for the robot confinement framework utilizing light sensors is proposed. This procedure takes care of the issue of localization (position recognizing) when utilizing a grid of LEDs distributed uniformly in the environment, and a multi- portable robot outfitted with a multi-LDRs sensor and just two of them activate the visibility robot. The proposed method is utilized to assess the robot's situation by drawing two virtual circles for each two LDR sensors; one of them is valid and the other is disregarded according to several suggested equations. The midpoint of this circle is assumed to be the robot focus. The new framework is simulated on a domain with (n*n) LEDs exhibit. The simulation impact of this framework shows great execution in the localization procedure.