Image Segmentation by Edge Partitioning over a Nonsubmodular Markov Random Field

Edge weight-based segmentation methods, such as normalized cut or minimum cut, require a partition number specification for their energy formulation. The number of partitions plays an important role in the segmentation overall quality. However, finding a suitable partition number is a nontrivial problem, and the numbers are ordinarily manually assigned. This is an aspect of the general partition problem, where finding the partition number is an important and difficult issue. In this paper, the edge weights instead of the pixels are partitioned to segment the images. By partitioning the edge weights into two disjoints sets, that is, cut and connect, an image can be partitioned into all possible disjointed segments. The proposed energy function is independent of the number of segments. The energy is minimized by iterating the QPBO-α-expansion algorithm over the pairwise Markov random field and the mean estimation of the cut and connected edges. Experiments using the Berkeley database show that the proposed segmentation method can obtain equivalently accurate segmentation results without designating the segmentation numbers

A Novel Chain Formation Scheme for Balanced Energy Consumption in WSN-based IoT Network

In the Internet of Things (IoT) technologies, wireless sensor networks (WSNs) are one essential part. The IoT network commonly consists of WSNs, where hundreds or even thousands of small sensors are capable of sensing, processing, and sending environmental phenomena in the targeted region. The energy consumption imbalance of sensors becomes the cause of the network performance decrement, as sensor nodes have limited energy available for operation after being randomly deployed. Therefore, more research is necessary for the design of energy-efficient routing algorithms in energy-constrained WSNs. This paper focuses on the chain-based routing algorithm, which is a popular algorithm for achieving energy efficiency in WSN-based IoT network. Chain-based routing algorithms offer numerous advantages for WSNs, such as energy conservation and extended lifetime of WSNs. However, they face challenges due to the issue of internal communication imbalance. The objective of our study is to design a novel chain formation scheme that improves the energy consumption imbalance caused by internal communication in WSN-based IoT network. The proposed scheme is categorized in three phases (initial communication phase, chain formation phase, and data collection phase). In the first phase, the sink acquires their location information from sensors deployed in the sensing region. Then the sensing region is separated into sub-regions and with the number of sensor nodes is balanced employing the concept of the k-dimensional binary tree (K-D-B-tree). The sub-regions are organized into a binary tree structure, which is then formed into a chain. Lastly, data is collected along the chain, and the selected representative sensor transmits the collected data to the sink. We utilized the OMNET++ simulator and demonstrated effective simulation results in terms of network lifetime and average residual energy. In the simulation results, a novel chain formation scheme outperforms the power-efficient gathering in sensor information systems (PEGASIS) and the concentric clustering scheme for efficient energy consumption in the PEGASIS (CCS)

Extract-and-Adaptation Network for 3D Interacting Hand Mesh Recovery

Understanding how two hands interact with each other is a key component of accurate 3D interacting hand mesh recovery. However, recent Transformer-based methods struggle to learn the interaction between two hands as they directly utilize two hand features as input tokens, which results in distant token problem. The distant token problem represents that input tokens are in heterogeneous spaces, leading Transformer to fail in capturing correlation between input tokens. Previous Transformer-based methods suffer from the problem especially when poses of two hands are very different as they project features from a backbone to separate left and right hand-dedicated features. We present EANet, extract-and-adaptation network, with EABlock, the main component of our network. Rather than directly utilizing two hand features as input tokens, our EABlock utilizes two complementary types of novel tokens, SimToken and JoinToken, as input tokens. Our two novel tokens are from a combination of separated two hand features; hence, it is much more robust to the distant token problem. Using the two type of tokens, our EABlock effectively extracts interaction feature and adapts it to each hand. The proposed EANet achieves the state-of-the-art performance on 3D interacting hands benchmarks. The codes are available at https://github.com/jkpark0825/EANet.Comment: Accepted at ICCVW 202

Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction

Despite recent advances in 3D human mesh reconstruction, domain gap between training and test data is still a major challenge. Several prior works tackle the domain gap problem via test-time adaptation that fine-tunes a network relying on 2D evidence (e.g., 2D human keypoints) from test images. However, the high reliance on 2D evidence during adaptation causes two major issues. First, 2D evidence induces depth ambiguity, preventing the learning of accurate 3D human geometry. Second, 2D evidence is noisy or partially non-existent during test time, and such imperfect 2D evidence leads to erroneous adaptation. To overcome the above issues, we introduce CycleAdapt, which cyclically adapts two networks: a human mesh reconstruction network (HMRNet) and a human motion denoising network (MDNet), given a test video. In our framework, to alleviate high reliance on 2D evidence, we fully supervise HMRNet with generated 3D supervision targets by MDNet. Our cyclic adaptation scheme progressively elaborates the 3D supervision targets, which compensate for imperfect 2D evidence. As a result, our CycleAdapt achieves state-of-the-art performance compared to previous test-time adaptation methods. The codes are available at https://github.com/hygenie1228/CycleAdapt_RELEASE.Comment: Published at ICCV 2023, 16 pages including the supplementary materia

A study on rheological properties of blood and improvements with high-voltage plasma discharge

Blood behaves as a shear-thinning non-Newtonian fluid where its viscosity varies due to both the deformability and aggregation of RBCs with the interaction with macro-molecules in blood plasma. The elevated whole blood viscosity (WBV), which indicates the increased frictional resistance between a moving blood and stationary vessel walls, has been suggested as one of the major determinants or risk factors of atherosclerosis diseases (i.e., cardiovascular diseases, stroke, and peripheral arterial diseases etc.) and microvascular disorders (i.e., diabetic retinopathy, nephrophathy, and neuropathy etc.) by causing both the endothelial injury of vessel walls and poor perfusion at capillaries.In order to investigate the shear-thinning non-Newtonian behavior of blood in regards to the effects of increased wall shear stress and impaired oxygen delivery on various diseases that might be caused by hyperviscosity, the present study was focused on the studies of rheological properties of blood by examining the WBV profiles over a pathologically wide range of shear rates using a scanning capillary tube viscometer (SCTV) and their improvements using high-voltage plasma discharge.Firstly, a new hematocrit-correction model using the Casson model was proposed to correct the measured WBVs of different blood samples with different hematocrits to a standard hematocrit of 45 %, a process which is needed to compare the effect of intrinsic rheological properties or other determinants on blood viscosity for different blood samples. Without the measurement of plasma viscosity, the new model showed about 4 to 6 times more accurate and less deviations than the conventional Matrai's model.Secondly, a new method of measuring the electric conductivity of whole blood was introduced for the purpose of hematocrit determination, demonstrating a simple but accurate hematocrit measurement by employing a low-frequency squarewave voltage signal in a conductance cell, without the usual error associated with the sedimentation of erythrocytes.Thirdly, a new physical treatment method with the application of highvoltage plasma discharges (i.e. DBD and corona discharge) followed by filtration of the coagulated particles was proposed. The results indicated that WBV could be reduced by 9.1 % and 17.7 % for systolic blood viscosity (SBV) and diastolic blood viscosity (DBV), respectively, from the baseline values when DBD-treated blood plasma was filtered prior to mixing with red blood cells. When treated with the corona discharge for 60 pulses, DBV and LDL concentration dropped by 30.1 % and 31.5 %, respectively, from the baseline values.Lastly, a new opaque standard viscosity fluid (SVF) was proposed using maltose with 55 % of concentration to replicate a shear-thinning non-Newtonian behavior of blood for different shear rates. The produced viscosity profiles from three different levels of SVFs provided low-, medium-, and high-standard viscosity fluids that can be used for the performance test of any blood viscometers over a wide range of shear rates. The applicability of new opaque SVFs was demonstrated by dye concentration test, repeatability test, and degradation test.Ph.D., Mechanical Engineering -- Drexel University, 201

Microfabricated Otto chip device for surface plasmon resonance based optical sensing

Surface plasmon resonance (SPR) based sensors are usually designed using the Kretschmann prism coupling configuration in which an input beam couples with a surface plasmon through a thin metal film. This is generally preferred by sensor developers for building planar devices instead of the Otto prism coupling configuration, which, for efficient coupling, requires the metal surface to be maintained at a distance on the order of the wavelength from the input prism surface. In this paper, we report on the microfabrication and characterization of an Otto chip device, which is suitable for applications of the SPR effect in gas sensing and biosensing

Acquired, Bilateral Nevus of Ota-like Macules (ABNOM) Associated with Ota's Nevus: Case Report

Ota's nevus is mongolian spot-like macular blue-black or gray-brown patchy pigmentation that most commonly ocurrs in areas innervated by the first and second division of the trigeminal nerve. Acquired, bilateral nevus of Ota-like macules (ABNOM) is located bilaterally on the face, appears later in life, is blue-brown or slate-gray in color. It is not accompanied by macules on the ocular and mucosal membranes. There is also debate as to whether ABNOM is part of the Ota's nevus spectrum. We report an interesting case of ABNOM associated with Ota's nevus. A 36-yr-old Korean women visited our clinic with dark bluish patch on the right cheek and right conjunctiva since birth. She also had mottled brownish macules on both forehead and both lower eyelids that have developed 3 yr ago. Skin biopsy specimens taken from the right cheek and left forehead all showed scattered, bipolar or irregular melanocytes in the dermis. We diagnosed lesion on the right cheek area as Ota's nevus and those on both forehead and both lower eyelids as ABNOM by clinical and histologic findings. This case may support the view that ABNOM is a separate entity from bilateral Ota's nevus

Screenomics : a new approach for observing and studying individuals' digital lives

This study describes when and how adolescents engage with their fast-moving and dynamic digital environment as they go about their daily lives. We illustrate a new approach—screenomics—for capturing, visualizing, and analyzing screenomes, the record of individuals’ day-to-day digital experiences. Sample includes over 500,000 smartphone screenshots provided by four Latino/Hispanic youth, age 14 to 15 years, from low-income, racial/ethnic minority neighborhoods. Screenomes collected from smartphones for 1 to 3 months, as sequences of smartphone screenshots obtained every 5 seconds that the device is activated, are analyzed using computational machinery for processing images and text, machine learning algorithms, human labeling, and qualitative inquiry. Adolescents’ digital lives differ substantially across persons, days, hours, and minutes. Screenomes highlight the extent of switching among multiple applications, and how each adolescent is exposed to different content at different times for different durations—with apps, food-related content, and sentiment as illustrative examples. We propose that the screenome provides the fine granularity of data needed to study individuals’ digital lives, for testing existing theories about media use, and for generation of new theory about the interplay between digital media and development