RESUMEN
In the Internet of Things, sensor nodes collect environmental information and utilize lossy compression for saving storage space. To achieve this objective, high-efficiency compression of the continuous source should be studied. Different from existing schemes, lossy source coding is implemented based on the duality principle in this work. Referring to the duality principle between the lossy source coding and the channel decoding, the belief propagation (BP) algorithm is introduced to realize lossy compression based on a Gaussian source. In the BP algorithm, the log-likelihood ratios (LLRs) are iterated, and their iteration paths follow the connecting relation between the check nodes and the variable nodes in the protograph low-density parity-check (P-LDPC) code. During LLR iterations, the trapping set is the main factor that influences compression performance. We propose the optimized BP algorithms to weaken the impact of trapping sets. The simulation results indicate that the optimized BP algorithms obtain better distortion-rate performance.
RESUMEN
Passive radio-frequency identification (RFID) systems have been widely applied in different fields, including vehicle access control, industrial production, and logistics tracking, due to their ability to improve work quality and management efficiency at a low cost. However, in an intersection situation where tags are densely distributed with vehicle gathering, the wireless channel becomes extremely complex, and the readers on the roadside may only decode the information from the strongest tag due to the capture effect, resulting in tag misses and considerably reducing the performance of tag identification. Therefore, it is crucial to design an efficient and reliable tag-identification algorithm in order to obtain information from vehicle and cargo tags under adverse traffic conditions, ensuring the successful application of RFID technology. In this paper, we first establish a Nakagami-m distributed channel capture model for RFID systems and provide an expression for the capture probability, where each channel is modeled as any relevant Nakagami-m distribution. Secondly, an advanced capture-aware tag-estimation scheme is proposed. Finally, extensive Monte Carlo simulations show that the proposed algorithm has strong adaptability to circumstances for capturing under-fading channels and outperforms the existing algorithms in terms of complexity and reliability of tag identification.
RESUMEN
Ethylene diamine tetraacetic acid (EDTA) could form stable complexes with nickel due to its strong chelation. Ni-EDTA has significant impacts on human health because of its acute toxicity and low biodegradability, thus some appropriate approaches are required for its removal. In this research, a Ti3+ self-doped TiO2 nanotube arrays electrode (ECR-TiO2 NTA) was prepared and employed in electrochemical degradation of Ni-EDTA. The oxygen evolution potential of ECR-TiO2 NTA was 2.6 V vs. SCE. More than 96% Ni-EDTA and 88% TOC was removed after reaction for 120 min at current density 2 mA cm-2 at pH 4.34. The degradation of Ni-EDTA was mainly through the cleavage of amine group within Ni-EDTA and furthermore decomposed it into small molecular acids and inorganic ions including NH4+and NO3-. The electro-deposition of nickel ions at cathode was confirmed by XPS and was greatly affected by the pH of solution. The effects of current density, initial Ni-EDTA concentration, initial pH of solution and HCO3- concentration on Ni-EDTA degradation were investigated. The results exhibited that the ECR-TiO2 NTA had excellent efficiencies in electrochemical degradation of Ni-EDTA. The LSV analysis suggested that Ni-EDTA oxidation on ECR-TiO2 NTA anode and the production of hydroxyl radical (·OH) on the anode played an important role in the removal of Ni-EDTA.
