Many digital signal processing algorithms are first developed in floating point and later converted into fixed point for digital hardware implementation. During this conversion, more than 50% of the design time may be spent for complex designs, and optimum wordlengths are searched by trading off hardware complexity for arithmetic precision at system outputs. We propose a fast algorithm for searching for an optimum wordlength. This algorithm uses sensitivity information of hardware complexity and system output error with respect to the signal wordlengths, while other approaches use only one of the two sensitivities. This paper presents various optimization methods, and compares sensitivity search methods. Wordlength design case studies for a wireless demodulator show that the proposed method can find an optimum solution in one fourth of the time that the local search method takes. In addition, the optimum wordlength searched by the proposed method yields 30% lower hardware implementation costs than the sequential search method in wireless demodulators. Case studies demonstrate the proposed method is robust for searching for the optimum wordlength in a nonconvex space. Table 1: Fixed-point conversion approaches for integer wordlength (IWL) and for fractional wordlength (FWL) determination. Analytical approach Statistical approach Range model for IWL Error model for FWL Range statistic for IWL Error statistic for FWL Wadekar [8] Constantinides [9] C m a r [ 10] C m a r [ 10] Stephenson [11] Shi [12] Kim [13] Kum [14] Nayak [15] --Shi [12]Table 2: Optimum wordlength search methods. Cost sensitivity Error sensitivity Nonsensitivity Local search [3] Sequential search [5] Exhaustive search [4] Evolutive search [6] Max-1 search [16] Branch and bound [3] -Preplanned search [5]Complexity-and-distortion measure search-proposed
Digital Twin, as an emerging technology related to Cyber-Physical Systems (CPS) and Internet of Things (IoT), has attracted increasing attentions during the past decade. Conceptually, a Digital Twin is a digital replica of a physical entity in the real world, and this technology is leveraged in this study to design a cooperative driving system at non-signalized intersections, allowing connected vehicles to cooperate with each other to cross intersections without any full stops. Within the proposed Digital Twin framework, we developed an enhanced first-in-firstout (FIFO) slot reservation algorithm to schedule the sequence of crossing vehicles, a consensus motion control algorithm to calculate vehicles' referenced longitudinal motion, and a modelbased motion estimation algorithm to tackle communication delay and packet loss. Additionally, an augmented reality (AR) humanmachine-interface (HMI) is designed to provide the guidance to drivers to cooperate with other connected vehicles. Agent-based modeling and simulation of the proposed system is conducted in Unity game engine based on a real-world map in San Francisco, and the human-in-the-loop (HITL) simulation results prove the benefits of the proposed algorithms with 20% reduction in travel time and 23.7% reduction in energy consumption, respectively, when compared with traditional signalized intersections.
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