摘要: Since its introduction more than six decades ago by Claude E. Shannon information theory has guided with two performance bounds, namely source-entropy II and channel capacity C, the design of sourced intelligence-space compressors... 展开 Since its introduction more than six decades ago by Claude E. Shannon information theory has guided with two performance bounds, namely source-entropy II and channel capacity C, the design of sourced intelligence-space compressors for communication systems, where the units of intelligence-space are `mathematical' binary digit (bit) units of a passing of time uncertainty nature. Recently, motivated by both a real-world radar problem treated in the first part of the present paper series, and previous uncertainty/certainty duality studies of digital-communication and quantized-control problems by the author, information theory was discovered to have a `certainty' time-dual that was named latency theory. Latency theory guides with two performance bounds, i.e. processor-ectropy K and sensor consciousness F the design of processing intelligence-time compressors for recognition systems, where the units of intelligence-time are `mathematical' binary operator (bor) units of a configuration of space certainty nature. Furthermore, these two theories have been unified to form a mathematical latency-information theory (M-LIT) for the guidance of intelligence system designs, which has been successfully applied to real-world radar. Also recently, M-LIT has been found to have a physical LIT (P-LIT) dual that guides life system designs. This novel physical theory addresses the design of motion life-time and retention life-space compressors for physical signals and also has four performance bounds. Two of these bounds are mover-ectropy A and channel-stay T for the design of motion life-time compressors for communication systems. An example of a motion life-time compressor is a laser system, inclusive of a network router for a certainty, or multi-path life-time channel. The other two bounds are retainer-entropy Nand sensor scope I for the design of retention life-space compressors for recognition systems. An example of a retention life-space compressor is a silicon semiconductor crystal, inclusive of a leadless chip carrier for an uncertainty, or noisy life-space sensor. The eight performance bounds of our guidance theory for intelligence and life system designs will be illustrated with practical examples. Moreover, a four quadrants (quadrants I and III for the two physical theories and quadrants II and IV for the two mathematical ones) LIT revolution is advanced that highlights both the discovered dualities and the fundamental properties of signal compressors leading to a unifying communication embedded recognition (CER) system architecture. 收起