Reverse Mapping Research Articles

Reversible data hiding in JPEG bitstream using optimal VLC mapping

The traditional RDH method for JPEG bitstream is conducted by building the mapping between the variable length codes (VLC). However, the capacity is limited, and the file size may not be well preserved as the capacity is increased. This is because that the trade-off between the capacity and the file size has not been deeply investigated, neither explicitly formulated nor appropriately optimized. In this paper, we propose to take the file size preservation into consideration and minimize the file size increase for a given capacity. We use the value transfer matrix to simulate a theoretical model and then design some optimization rules to reach the reversible solution. Consequently, a better reversible VLC mapping can be obtained in terms of both the capacity and the file size preservation. The experimental results show that the proposed method can increase the capacity with a relatively low cost of file size increase.

Quantitative Prediction of the Structure and Viscosity of Aqueous Micellar Solutions of Ionic Surfactants: A Combined Approach Based on Coarse-Grained MARTINI Simulations Followed by Reverse-Mapped All-Atom Molecular Dynamics Simulations.

We address the problem of the quantitative prediction of micelle formation in dilute aqueous solutions of ionic surfactants using sodium dodecyl sulfate (SDS) as a model system through a computational approach that involves three steps: (a) execution of coarse-grained simulations based on the MARTINI force field (with slightly modified parameters to afford the formation of large micelles); (b) reverse mapping of the final self-assembled coarse-grained configuration into an all-atom configuration; and (c) final relaxation of this all-atom configuration through short-time (on the order of a few tens of nanoseconds), detailed isothermal-isobaric molecular dynamics simulations using the CHARMM36 force field. For a given concentration of the solution in SDS molecules, the modified MARTINI-based coarse-grained simulations lead to the formation of large micelles characterized by mean aggregation numbers above the experimentally observed ones. However, by reintroducing the detailed chemical structure through a strategy that solves a well-defined geometric problem and re-equilibrating, these large micellar aggregates quickly dissolve to smaller ones and equilibrate to sizes that perfectly match the average micelle size measured experimentally at the given surfactant concentration. From the all-atom molecular dynamics simulations, we also deduce the surfactant diffusivity DSDS and the zero-shear rate viscosity, η0, of the solution, which are observed to compare very favorably with the few experimental values that we were able to find in the literature.

Abstract 65: Immediate Lymphatic Reconstruction After Axillary Lymphadenectomy Makes A Difference: A Two Year Comparative Analysis

Purpose: Axillary lymph node dissection (ALND) in the treatment of breast cancer increases the risk of iatrogenic lymphedema. Current rates after ALND range from 11-30%. We hypothesize that our lymphedema prevention surgical (LPS) paradigm, ALND with axillary reverse mapping (ARM) and lymphatico-venous bypass (LVB), lowers the risk of lymphedema. Here, we present findings from a case control study from patients undergoing this procedure. Methods: A review of our prospectively maintained lymphedema surgical registry was performed. Ninety-six consecutive patients with complete ALND underwent LPS at our institution from 9/2016-10/2019. A control group was selected consisting of 92 patients who underwent ALND without LPS in a concurrent time interval from 9/2016-11/2017 to prevent surgical technique or learning curve bias. Patients were followed for both signs and symptoms of lymphedema throughout the post treatment interval and underwent serial assessments for lymphedema via standardized arm circumference measurements by a certified lymphedema therapist. Lymphedema was defined as more than a 10% difference in volume between upper limbs in conjunction with characteristic symptoms and signs. Demographic, procedural and oncologic data was compared between groups. Results: Lymphedema occurrence rates were significantly different between control and treatment groups during the followup period (16.3% non-LPS vs. 5.3% LPS, p=0.006). Of the 15 non-LPS patients who developed lymphedema, 14 received PMRT and 5 had neoadjuvant chemotherapy. Five LPS patients acquired lymphedema, two had PMRT and all five underwent neoadjuvant chemotherapy. Clinical staging differed with a higher proportion of advanced nodal stage in LPS patient group. The mean lymph nodes removed per case was slightly higher in the LPS group (14.4 vs. 11.2). Rates of post mastectomy radiation therapy (PMRT) were similar (77% to 89%, respectively), but neoadjuvant chemotherapy rates were lower in non-LPS patients (43% vs 74%). The follow-up time was 15.3 months for the LPS group and 31.3 months for the non-LPS group. Following reverse mapping in LPS, an average of 2.1 blue transected lymphatics were identified per case. An average of 0.1 blue lymphatic vessels was left in continuity per case and 1.6 vessel anastomoses performed per case with intussusception being performed more frequently than end to end technique (55% vs 44%). Conclusion: In one of the largest controlled studies in this topic to date, our findings support that optimizing lymphatic preservation and restoring antegrade lymphatic flow with LPS significantly decreases short term lymphedema rates in patients undergoing axillary lymphadenectomy for breast cancer.

Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process

Intelligent manufacturing requires significant technological interventions to interface manufacturing processes with computational tools in order to dynamically mold the systems. In this era of the 4th industrial revolution, Artificial neural network (ANNs) is a modern tool equipped with a better learning capability (based on the past experience or history data) and assists in intelligent manufacturing. This research paper reports on ANNs based intelligent modelling of a turning process. The central composite design is used as a data-driven modelling tool and huge input–output is generated to train the neural networks. ANNs are trained with the data collected from the physics-based models by using back-propagation algorithm (BP), genetic algorithm (GA), artificial bee colony (ABC), and BP algorithm trained with self-feedback loop. The ANNs are trained and developed as both forward and reverse mapping models. Forward modelling aims at predicting a set of machining quality characteristics (i.e. surface roughness, cylindricity error, circularity error, and material removal rate) for the known combinations of cutting parameters (i.e. cutting speed, feed rate, depth of cut, and nose radius). Reverse modelling aims at predicting the cutting parameters for the desired machining quality characteristics. The parametric study has been conducted for all the developed neural networks (BPNN, GA-NN, RNN, ABC-NN) to optimize neural network parameters. The performance of neural network models has been tested with the help of ten test cases. The network predicted results are found in-line with the experimental values for both forward and reverse models. The neural network models namely, RNN and ABC-NN have shown better performance in forward and reverse modelling. The forward modelling results could help any novice user for off-line monitoring, that could predict the output without conducting the actual experiments. Reverse modelling prediction would help to dynamically adjust the cutting parameters in CNC machine to obtain the desired machining quality characteristics.

Efficient FPGA implementation of corrected reversible contrast mapping algorithm for video watermarking

This paper analyses and rectifies the shortcomings of reversible contrast mapping (RCM) algorithm for invisible watermarking. The proposed corrected RCM algorithm is tested by taking a gray scaled video input. The quality of services and structural similarity index matrix (SSIM) of each frame of the input video are tested in software environment. The video is obtained by OV7670 camera through Zed-board in fully field programmable gate array (FPGA) based hardware environment. FPGA devices based corrected high level synthesis of the proposed algorithm is presented. The suggested system engages pipeline structure and practices parallelism to achieve high performance. The quality of services and SSIM are also tested using FPGA devices and the comparative results with software implementations are explained. To process thirty (640 × 480) image blocks with 150 MHz clock we obtain a latency of 876.626 ns with throughput 62.328 Mbps. The critical path for single cycle is 5.992 ns. The number of resources essential is similar for watermark decoding with an improved schedule. The results acquired after implementation on Xilinx Virtex 7-XC7V2000T and programmable system-on-chip (Zynq - XC7Z030) FPGA devices confirm the practicability of real-time use with low cost and great speed.

Reverse Axillary Mapping in Breast Radiation Treatment Planning

Reverse Axillary Mapping in Breast Radiation Treatment Planning

Evaluation of axillary reverse mapping (ARM) in clinically axillary node negative breast cancer patients - Randomised controlled trial.

Axillary lymph node dissection (ALND) is an important procedure for control of axillary nodal metastasis in breast cancer patients. Lymphedema, restriction of shoulder movement and axillary nodal recurrence are the most disabling complications of the procedure. Axillary reverse mapping (ARM) procedure for arm lymph node identification emerged as a step for their preservation during ALND. Here we are testing the effect of ARM on lymphedema development and whether it compromises oncological safety in early breast cancer patients. 98 clinically node free breast cancer female patients undergoing completion ALND after positive sentinel lymph node biopsy were recruited in the study. They were put into group A (49 patients with ARM+ve preservation ALND) and group B (49 patients in the conventional ALND group). ARM procedure was performed in both groups, ARM positive nodes were preserved in group A, marked and taken out with other axillary LN in group B. The outcome was histopathology of ARM+ve LN, development of arm lymphedema, and restriction of shoulder movement on follow-up. ARM was positive in 46 patients (93.8%) in group A and 43 patients (87.8%) in group B, ARM+ve LN revealed positive metastasis only in 1 patient (2.3%) in group B. Lymphedema developed in 3 (6.5% patients in group A and 9 patients (20.9%) in group B. Restriction of shoulder movement showed a non-significant difference between the two groups. Axillary reverse mapping and preservation of arm lymphatics helped to decrease the lymphedema rate without compromising oncological safety in early breast cancer.

Atomic-level reconstruction of biomolecules by a rigid-fragment- and local-frame-based (RF-LF) strategy.

Coarse-grained (CG) model has been a powerful tool in bridging the gap between theoretical studies and experimental phenomena in biological computing field. The reconstruction from a CG model to an atomic-detail structure is especially important in CG studies of biological systems. In this work, a rigid-fragment- and local-frame-based (RF-LF) backmapping method was proposed to achieve reverse mapping from CG models to atomic-level structures. The initial atomic-level structures were further refined to yield the final backmapping ones. With the popular Martini force field, the performance of the RF-LF method was extensively examined in the CG → AA (CG to AA) backmapping of protein/DNA/RNA systems. Besides, the RF-LF method was also extended to the backmapping of the TMFF model. Numerical results illustrate that the RF-LF backmapping method is generic and parameter-free and can provide a promising way to tackle atomic-level studies in CG models.

Reversible data hiding for JPEG images based on pairwise nonzero AC coefficient expansion

JPEG (Joint Photographic Experts Group) is the most common format in our daily life for storing and transmitting photographic images. Reversible data hiding (RDH) for JPEG images currently is one of the hotspots in the field of data hiding. However, different from RDH in uncompressed images, any modification in the compressed domain may produce distinct visual distortion and significantly increase the file size of the marked image. In this paper, a new JPEG RDH scheme based on pairwise nonzero AC coefficient expansion (pairwise NACE) is proposed. Firstly, by taking every two adjacent nonzero alternating current (AC) coefficients together to generate the nonzero-AC-coefficient pairs (NACPs), a sharp two-dimensional (2D) histogram can be constructed. Then a new reversible 2D mapping is carefully designed, and data are embedded based on the modification of these NACPs according to the mapping. Furthermore, an adaptive embedding strategy based on block and DCT frequency selection is proposed in order to preserve the visual quality and reduce the file size increase of the marked JPEG image. Experimental results demonstrate that our proposed method can achieve better performance both in visual quality and file size preservation compared to the previous works.

Cycle-IR: Deep Cyclic Image Retargeting

Supervised deep learning techniques have achieved great success in various fields due to getting rid of the limitation of handcrafted representations. However, most previous image retargeting algorithms still employ fixed design principles such as using gradient map or handcrafted features to compute saliency map, which inevitably restricts its generality. Deep learning techniques may help to address this issue, but the challenging problem is that we need to build a large-scale image retargeting dataset for the training of deep retargeting models. However, building such a dataset requires huge human efforts. In this paper, we propose a novel deep cyclic image retargeting approach, called Cycle-IR, to firstly implement image retargeting with a single deep model, without relying on any explicit user annotations. Our idea is built on the reverse mapping from the retargeted images to the given images. If the retargeted image has serious distortion or excessive loss of important visual information, the reverse mapping is unlikely to restore the input image well. We constrain this forward-reverse consistency by introducing a cyclic perception coherence loss. In addition, we propose a simple yet effective image retargeting network (IRNet) to implement the image retargeting process. Our IRNet contains a spatial and channel attention layer, which is able to discriminate visually important regions of input images effectively, especially in cluttered images. Given arbitrary sizes of input images and desired aspect ratios, our Cycle-IR can produce visually pleasing target images directly. Extensive experiments on the standard RetargetMe dataset show the superiority of our Cycle-IR.

基于反向映射的元素图像生成算法

基于反向映射的元素图像生成算法

Reversible Logic Synthesis Using Binary Decision Diagrams With Exploiting Efficient Reordering Operators

With the continuous shrinkage of transistor sizes in very large scale integrated circuits, power consumption forms a serious concern to be tackled. With their ability to allow for zero energy dissipation, reversible circuits have been considered as promising solution to meetup with low power design requirements. Moreover, advances in their synthesis methodologies can be easily applied to quantum circuits due to the inherited reversibility of the latter. Although numerous algorithms have been proposed in the literature to synthesize reversible circuits and map them into their corresponding quantum circuits, the scalability and computational effort of such algorithms form a serious concern when synthesizing large size input functions. Binary Decision Diagram-based synthesis for reversible circuits has shown great evidence in realizing reversible circuits with low quantum cost through exploiting proper reduction rules for smaller graph size. However, the order of the variables in the decision diagram impacts its overall size, and thus, the cost of its corresponding reversible circuit. While several reordering algorithms have been proposed in this manner, their direct impact on the quantum cost has not been considered. In this article, a Binary Decision Diagram-based algorithm for reversible circuit synthesis is proposed to synthesize reversible circuits for a given Boolean function with low quantum cost through exploiting a linearized relationship between the decision diagram size and the corresponding quantum cost. Thereafter, different decision diagram reordering algorithms have been integrated with the proposed algorithm and compared in terms of their impact on the quantum cost. Experimental results show that Genetic Algorithm-based reordering for decision diagram, supported with, cycle crossover, inverse mutation, and tournament selection, results in the least quantum cost of the output circuit if compared with other algorithms due to its property in preserving the nodes of the decision diagram in their near-optimal locations during the optimiation recipe.

Parallel Hardware Implementation of Efficient Embedding Bit Rate Control Based Contrast Mapping Algorithm for Reversible Invisible Watermarking

This paper presents an improved reversible contrast mapping (RCM) algorithm for reversible invisible watermarking (RIW) in both software and hardware platforms. Based on well-known parameters for RIW like distortion, embedding bit rate, payload size and data hiding capacity, an efficient embedding bit rate control based contrast mapping (EBCRCM) algorithm is proposed. An adaptive linear contrast mapping on pixel intensity value is asserted with RCM that controls the embedding bit rate without changing the embedding capacity to maintain distortion. Xilinx system generator (XSG) and VIVADO tool construct the novel VLSI architecture that needs 173.362 ns latency for 100 MHz clock with throughput 46.146 Mbps and 5.8 ns critical path for single cycle of embedding process. The proposed algorithm is verified in MATLAB tool based software platform by taking different types of multimedia data like gray-scale images, color images and video signals. Implementation of low hardware resources based VLSI architecture through zed-board in real time field programmable gate array (FPGA) platform confirms the capability of high speed, low cost and real-time use. 100% agreement is observed from software simulations and hardware platforms.

An End-to-End Deep Learning Network for 3D Object Detection From RGB-D Data Based on Hough Voting

Existing outdoor three-dimensional (3D) object detection algorithms mainly use a single type of sensor, for example, only using a monocular camera or radar point cloud. However, camera sensors are affected by light and lose depth information. When scanning a distant object or an occluded object, the data collected by the short-range radar point cloud sensor are very sparse, which affects the detection algorithm. To address the above challenges, we design a deep learning network that can combine the texture information of two-dimensional (2D) data and the geometric information of 3D data for object detection. To solve the problem of a single sensor, we use a reverse mapping layer and an aggregation layer to combine the texture information of RGB data with the geometric information of point cloud data and design a maximum pooling layer to deal with the input of multi-view cameras. In addition, to solve the defects of the 3D object detection algorithm based on the region proposal network (RPN) method, we use the Hough voting algorithm implemented by a deep neural network to suggest objects. Experimental results show that our algorithm has a 1.06% decrease in average precision (AP) compared to PointRCNN in easy car object detection, but our algorithm requires 37.7% less time to calculate than PointRCNN under the same hardware environment. Moreover, our algorithm improves the AP by 1.14% compared to PointRCNN in hard car object detection.

Segment-Based Multiple-Base Compressed Addressing for Flexible JavaScript Heap Allocation

Many Internet-of-Things (IoT) systems use lightweight JavaScript engines to support easy programming in microcontrollers. Lightweight JavaScript engines use several techniques for memory optimization, such as static heap reservation and compressed addressing. Recent IoT systems also use several external libraries and a larger on-chip memory to support abundant functionalities, such as machine learning and connectivity. However, as the JavaScript heap space is not resizable owing to the memory optimizations, the JavaScript engine or an external library is prone to fail the memory allocation in these devices. To address this problem, we propose a flexible memory optimization technique, segment-based multiple-base compressed addressing (SMBCA) , which compresses a pointer indicating a JavaScript object allocated to a resizable heap based on multiple base addresses. SMBCA comprises two components: a dynamic segment allocator (DSA) and a multiple-base compressed address translator (MBCAT). DSA dynamically allocates the JavaScript heap in segment units. Meanwhile, MBCAT converts a low-bitwidth address into a full-bitwidth address and vice versa, based on the multiple base addresses. To reduce the address compression overhead of MBCAT, we propose a software cache technique, reverse map cache (RMC) . We found that the SMBCA reduces average memory usage by 43.9% compared to the existing lightweight JavaScript engines when running SunSpider benchmarks, V8 benchmarks, and real-world applications. We also showed that the RMC reduces the average address compression latency of MBCAT by 34.9% when running the SunSpider benchmarks.

Axillary reverse mapping with gamma probe: A concept to prevent lymphedema in early breast cancer patients.

Axillary reverse mapping with gamma probe: A concept to prevent lymphedema in early breast cancer patients.

Video Imprint.

A new unified video analytics framework (ER3) is proposed for complex event retrieval, recognition and recounting, based on the proposed video imprint representation, which exploits temporal correlations among image features across video frames. With the video imprint representation, it is convenient to reverse map back to both temporal and spatial locations in video frames, allowing for both key frame identification and key areas localization within each frame. In the proposed framework, a dedicated feature alignment module is incorporated for redundancy removal across frames to produce the tensor representation, i.e., the video imprint. Subsequently, the video imprint is individually fed into both a reasoning network and a feature aggregation module, for event recognition/recounting and event retrieval tasks, respectively. Thanks to its attention mechanism inspired by the memory networks used in language modeling, the proposed reasoning network is capable of simultaneous event category recognition and localization of the key pieces of evidence for event recounting. In addition, the latent structure in our reasoning network highlights the areas of the video imprint, which can be directly used for event recounting. With the event retrieval task, the compact video representation aggregated from the video imprint contributes to better retrieval results than existing state-of-the-art methods.

T-Entropy formulae for concrete classes of transfer operators

t-Entropy is a principal object of the spectral theory of operators, generated by dynamical systems, namely, weighted shift operators and transfer operators. In essence t-entropy is the Fenchel – Legendre transform of the spectral potential of an operator in question and derivation of explicit formulae for its calculation is a rather nontrivial problem. In the article explicit formulae for t-entropy for two the most exploited in applications classes of transfer operators are obtained. Namely, we consider transfer operators generated by reversible mappings (i. e. weighted shift operators) and transfer operators generated by local homeomorphisms (i. e. Perron – Frobenius operators). In the first case t-entropy is computed by means of integrals with respect to invariant measures, while in the second case it is computed in terms of integrals with respect to invariant measures and Kolmogorov – Sinai entropy.

Prevention of lymphedema via axillary reverse mapping for arm lymph-node preservation following breast cancer surgery: a randomized controlled trial

BackgroundBreast cancer, with an incidence of 32%, is the most frequent cancer among Egyptian women. The frequency of arm lymphedema after axillary surgery for breast cancer ranges from 7 to 77%. Axillary reverse mapping is a technique aimed to distinguish and conserve upper-limb lymphatics and lymph nodes during the course of axillary surgery and could help to prevent arm lymphedema.MethodsPatients (n = 48) were prepared for axillary lymph-node dissection. The study group and the control group each contained 24 individuals. In the study group, following dye injection, stained arm lymph nodes and lymphatics were conserved during axillary dissection, whereas control-group participants underwent the conventional procedure. All participants were re-evaluated after 6 months, and the incidence of lymphedema was recorded by measuring arm circumference at a level 10 cm proximal to the medial epicondyle. Arm lymphedema was defined as a change in the circumference of the ipsilateral upper extremity > 2 cm during the follow-up period.ResultsAge, tumor size and N stage were not significantly different between the study and control groups. Lymph-node visualization was achieved in 20 participants (83.3%) in the study group. Suspicious stained lymph nodes were surgically removed from four individuals but showed no metastatic involvement. In 20 individuals in the study group, no stained lymph nodes were removed. The incidence of lymphedema in the control group was 16.7%, and the incidence in the study group was 4.2%.ConclusionsAxillary reverse mapping is a minimally invasive technique that can be performed during axillary lymph-node dissection, helping to prevent the subsequent development of arm lymphedema.Trial registration#SCURCTN3276, retrospectively registered on 11 April 2017 at Research Ethics Committee at the Faculty of medicine-Suez Canal University.

Axillary Reverse Mapping Using Methylene Blue Subcutaneous Injection Can Identify Arm Lymph Nodes and Vessels, Measuring Arm Size for Lymphedema, Histopathological Examination of Arm Lymph Nodes Included With Axillary Lymph Node Dissection

Axillary Reverse Mapping Using Methylene Blue Subcutaneous Injection Can Identify Arm Lymph Nodes and Vessels, Measuring Arm Size for Lymphedema, Histopathological Examination of Arm Lymph Nodes Included With Axillary Lymph Node Dissection