Multiscale Aggregate Networks with Dense Connections for Crowd Counting

The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo’10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.

Network-on-Chip for Low Power MAP Decoder Using Folded Technique and CORDIC Algorithm for 5G Network

With different constraint length (K), time scale, and code rate, modified MAP (maximum a posteriori) decoder architecture using folding technique, which has a linear life time chart, is developed, and dedicated turbo codes will be placed in a network-on-chip for various wireless applications. Folded techniques mitigated the number of latches used in interleaving and deinterleaving unit by adopting forward and backward resource utilizing method to M-2, where M is the number of rows and end-to-end delay get reduced to 2M. By replacing conventional full adder by high speed adder using 2 x 1 multiplexer to calculate the forward state metrics and reverse state metrics will minimize the power consumption utilization in an effective manner. In s similar way, CORDIC (Coordinated ROtation DIgital Computer) algorithm is used to calculate the LLR value and confer a highly precise value with less computational complexity by means of only shifting and adding methods.

Bit error rate performance analysis of AC-MAP in multiple input single output wireless relay network

In this paper, we propose a joint decoding scheme called AC-MAP decoder for multiple input single output (MISO) wireless cooperative communication network that consists of single source, single relay, and single destination. The proposed scheme is based on both Alamouti combining (AC) scheme and maximum a posteriori (MAP) decoder and is used to estimate the data at the destination. The AC-MAP decoder is optimal in the sense that it minimizes the end-to-end bit error rate (BER). In order to analyze performance of the proposed decoder, we derive a closed form expression for the upper bound (UB) on the end-to-end error probability. Distances between system nodes, transmit energy, and channel noise and fading effects are considered in the derivation of the UB. Numerical results show that the closed form UB is very tight and it almost coincides with the exact BER results obtained from simulations. Therefore, we use the derived UB expression to study the effects of the relay position on the BER performance and to find the optimal location of the relay node.