In this study, we present exact and heuristics algorithms for a traffic sensors location problem called the screen line problem. It is a problem of how to locate traffic sensors on a transportation network where all the origin/destination node pairs are fully separated. The problem experiences two main complexity dimensions that obstruct finding an efficient solution algorithm for large-scale networks: its mathematical formulation, which is proved in the literature to be NP-hard, and an inherent combinatorial complexity due to the need for a network complete path enumeration. In this study, the problem is reformulated as a set covering problem. Thereafter, the dual formulation is recalled showing that the shortest path-based column generation method would yield as many paths as necessary and hence circumvent the intractability of the full path enumeration task. This path generation technique enables applying both the proposed heuristics and exact methods to the problem. In addition, the gap value between the heuristics and the exact algorithms is set to be examined statistically. For evaluation, three networks of different sizes were used to track the scalability of proposed algorithms. The methodology showed high efficiency to deal with up to 10,000 demand node pairs in addition to the capability of producing practical solutions with respect to normal traffic flow conditions. The proposed heuristics algorithm stipulates a gap value of less than 25% with more than 99% confidence.

Recently, Wireless Body Area Network (WBAN) has revolutionized e-health-care. WBAN boosts monitoring vital signs utilizing tiny wireless sensors implanted in or around the human body. In February 2012, the IEEE 802.15.6 WBAN standard was released for low-power and short-range communication around the human body. The standard defines one medium access control layer and three different physical layers: narrow band , ultra-wideband, and Human Body Communication (HBC) layers. We are motivated by exploiting the human body as a communication medium. We propose a novel optimized architecture for the HBC baseband transceiver based on deep learning. The receiver utilizes two deep neural networks: one for frame synchronization to recover data and timing precisely and the other for the channel decoder to improve transceiver performance and reduce power consumption. In addition, low-complexity Preamble/SFD generator, Walsh modulation, and FSC spreader modules are proposed to reduce the power consumption while preserving the transceiver performance. Compared with the traditional hard-decision channel decoder, the proposed neural network decoder improves the block error rate by 2 dB. The proposed HBC transceiver supports 1.312 Mbps data rate at 42 MHz clock rate. The transceiver is implemented in RTL and synthesized on 90 nm CMOS technology. It consumes 493 pJ/bit on the receiver side and 105 pJ/bit on the transmitter side.