ROBUST ITERATIVEPRUNED-TREE DETECTION ANDLDPCC DECODING

A novel sub-optimal low-complexity equalization and turbo-iterative decoding scheme based on running the sum-product algorithm on an aggressively pruned tree is proposed in this paper for use in a multiple transmit and receive antenna (MIMO) system operating over severe frequency-selective fading inter-symbol interference (ISI) channels. The receiver deals with the issue of signal processing complexity which with a full-search equalization grows with power-law. The sum-product algorithm is applied to the pruned tree which is constructed by two main operations, a sphere list detection and a threshold-based tree search algorithms. At a particular node of the tree, only a number of most probable branches in the tree of hypothetical symbols are expanded and included in the list of candidates; at a particular tree-section, all but some of most probable candidatesare pruned. This pruned tree takes the soft input and generates the soft output, and is utilized in the turbo-iterative manner with the decoder of the low-density parity check code. We oobtained the approximated error probability using the pair-wise error calculation averaged over the fading ensemble, and use it to boundour simulation results. Our current simulation results are obtained for MIMO systems up to four transmit and four receive antennas, using 4-QAM symbols. They indicate the proposed receiverperforms extremely well. The proposed transceiver system is ideal for a system of higher spectral efficiency with even larger signal constellations. Adopting Hassbi-Vikalo's framework, we provide a method which enables a quick evaluation of the signal processing complexity required in the proposed algorithm at a given set of system parameters

Infrared Small Targets Detection based on MRF Model

AbstractAiming at the difficulty in detecting infrared dim small target under strong background clutter, we propose a novel algorithm of infrared dim small target detection based on Markov random fields(MRF) model. We at first use adaptive morphological filter to suppress the background clutter, then introduce new potential function and energy function according to MRF theory and infrared small target image features, and build a target detection model to confirm automatically the location and size of the target. Simulation results show that the algorithm is effective

When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Learning to segmentation without large-scale samples is an inherent capability of human. Recently, Segment Anything Model (SAM) performs the significant zero-shot image segmentation, attracting considerable attention from the computer vision community. Here, we investigate the capability of SAM for medical image analysis, especially for multi-phase liver tumor segmentation (MPLiTS), in terms of prompts, data resolution, phases. Experimental results demonstrate that there might be a large gap between SAM and expected performance. Fortunately, the qualitative results show that SAM is a powerful annotation tool for the community of interactive medical image segmentation.Comment: Preliminary investigatio

Approximation d'une assignation basique de croyance par compatibilité des éléments focaux

International audienceThe theory of belief functions is an important tool in the field of information fusion. However, the fusion of Basic Belief Assignments (BBAs) requires high computational cost and long computing time when a large number of focal elements are involved in the fusion rules. This problem becomes a bottleneck of application of Belief Functions (BF) in high-dimensional real problems. To overcome this drawback, many approaches were proposed to approximate BBAs to reduce the computational complexity in the fusion process. In this paper, we present a novel method based on the compatibility of focal elements to approximate a BBA by removing some focal elements of the original BBA. Besides, a new mass assignment strategy based on the distance of focal elements is proposed. Several examples, simulations and related analyses are provided to illustrate the interest and efficiency of the proposed method

Combination of Sources of Evidence with Distinct Frames of Discernment

International audienceMulti-source information fusion strategies in target recognition have been widely applied. Generally, each source is defined and modelled over a common frame composed of the hypotheses to discern. However, in practice, the independent sources of evidence can refer to distinct frames of discernment in terms of the hypotheses they consider. Under this condition, the classical combination process cannot be applied directly. Working with distinct frames of discernment for information fusion is a problem often encountered in the development of recognition systems which requires a particular attention. In order to combine such sources, this paper presents a new combination method which splits the process of fusion into two steps: construction of granular structure, calculation of belief mass, followed by the fusion process. Our simulations results show that the proposed method can effectively solve the problem of fusion of sources defined on distinct frames

Sludge transforms into biochar: Doping calcium induces phosphorus transforming into a plant-available speciation

The mass-produced sewage sludge (SS) worldwide is regarded as an important phosphorus (P) pool with a P-content of 2-3% (dry basis). Pyrolytic conversion of SS into P-rich biochar has multiple environmental benefits: toxicity elimination, carbon sequestration and soil fertilization. It has been proved that P transforms into insoluble speciation such as Ca2P2O7 during pyrolysis, and this would be influenced significantly by inherent minerals such as Ca, Mg, Fe, Al, etc [1, 2]. With a purpose of enhancing biochar’s fertilizer efficiency to plant, we selected calcium (Ca) as an additive to SS and expected their thermal-chemical interaction would induce P transforming into a plant-available speciation. The sequential extraction experiments showed that after pyrolysis (biochar: SS500) the percent of the insoluble phosphates (HCl-extracted P) increased significantly from 8.28% to 76.6%, while the readily soluble P species being extracted by water, NaHCO3 and NaOH decreased sharply. Doping CaCl2 strengthened this transformation and the produced biochars at pyrolysis temperature of 500oC with 20% (w/w) Ca-doping (biochar: SS-Ca500) contained 84.1% insoluble phosphates and 5.28% Fe/Al mineral adsorbed P (NaOH-extracted P). It indicated that Ca could compete for more P than Fe/Al during pyrolysis. Instrumental analysis (XRD, NMR) showed that Ca promoted more formation of pyrophosphate and short-chain polyphosphates such as Ca5(PO4)3(OH), Ca5(PO4)3Cl, which are species facilitating plant-uptake while avoiding dissolution loss. This study gave an insight into P speciation transformation during biochar formation and suggested that P availability in biochars are controllable by doping minerals to structure a safe slow-release P fertilizer benefiting plant growth. Please click Additional Files below to see the full abstract

Rough Set Classifier Based on DSmT

International audienceThe classifier based on rough sets is widely used in pattern recognition. However, in the implementation of rough set-based classifiers, there always exist the problems of uncertainty. Generally, information decision table in Rough Set Theory (RST) always contains many attributes, and the classification performance of each attribute is different. It is necessary to determine which attribute needs to be used according to the specific problem. In RST, such problem is regarded as attribute reduction problems which aims to select proper candidates. Therefore, the uncertainty problem occurs for the classification caused by the choice of attributes. In addition, the voting strategy is usually adopted to determine the category of target concept in the final decision making. However, some classes of targets cannot be determined when multiple categories cannot be easily distinguished (for example, the number of votes of different classes is the same). Thus, the uncertainty occurs for the classification caused by the choice of classes. In this paper, we use the theory of belief functions to solve two above mentioned uncertainties in rough set classification and rough set classifier based on Dezert-Smarandache Theory (DSmT) is proposed. It can be experimentally verified that our proposed approach can deal efficiently with the uncertainty in rough set classifiers

Robot Map Building from Sonar and Laser Information using DSmT with Discounting Theory

In this paper, a new method of information fusion – DSmT (Dezert and Smarandache Theory) is introduced to apply to managing and dealing with the uncertain information from robot map building. Here we build grid map form sonar sensors and laser range finder (LRF). The uncertainty mainly comes from sonar sensors and LRF. Aiming to the uncertainty in static environment, we propose Classic DSm (DSmC) model for sonar sensors and laser range finder, and construct the general basic belief assignment function (gbbaf) respectively. Generally speaking, the evidence sources are unreliable in physical system, so we must consider the discounting theory before we apply DSmT. At last, Pioneer II mobile robot serves as a simulation experimental platform. We build 3D grid map of belief layout, then mainly compare the effect of building map using DSmT and DST. Through this simulation experiment, it proves that DSmT is very successful and valid, especially in dealing with highly conflicting information. In short, this study not only finds a new method for building map under static environment, but also supplies with a theory foundation for us to further apply Hybrid DSmT (DSmH) to dynamic unknown environment and multi-robots- building map together