Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Identification of Mixed Retinal Cells in Time-Lapse Fluorescent Microscopy Images using High-Dimensional DBSCAN

Despite providing high spatial resolution, functional imaging remains largely unsuitable for high-throughput experiments because current practices require cells to be manually identified in a time-consuming procedure. Against this backdrop, we seek to integrate such high-resolution technique in high-throughput workflow by automating the process of cell identification. As a step forward, we attempt to identify mixed retinal cells in time-lapse fluorescent microscopy images. Unfortunately, usual 2D image segmentation as well as other existing methods do not adequately distinguish between time courses of different spatial locations. Here, the task gets further complicated due to the inherent heterogeneity of cell morphology. To overcome such challenge, we propose to use a high-dimensional (HiD) version of DBSCAN (density based spatial clustering of applications with noise) algorithm, where difference in such time courses are appropriately accounted. Significantly, outcome of the proposed method matches manually identified cells with over 80% accuracy, marking more than 50% improvement compared to a reference 2D method.
Fast and Efficient Web Caching Methods Regarding the Size and Performance Measures per Data Object

Caching methods are developed since 50 years for paging in CPU and database systems, and since 25 years for web caching as main application areas among others. Pages of unique size are usual in CPU caches, whereas web caches are storing data chunks of different size in a widely varying range. We study the impact of different object sizes on the performance and the overhead of web caching. This entails different caching goals, starting from the byte and object hit ratio to a generalized value hit ratio for optimized costs and benefits of caching regarding traffic engineering (TE), reduced delays and other QoS measures. The selection of the cache contents turns out to be crucial for the web cache efficiency with awareness of the size and other properties in a score for each object. We introduce a new class of rank exchange caching methods and show how their performance compares to other strategies with extensions needed to include the size and scores for QoS and TE caching goals. Finally, we derive bounds on the object, byte and value hit ratio for the independent request model (IRM) based on optimum knapsack solutions of the cache content.
A morphological way to remove baseline and spike separation in EEG

In this work, we have considered the problem of inconsistent sequence order and varying quality of decompositions by independent component analysis (ICA) and presented an alternate way to separate source signals from the electroencephalogram (EEG) using morphological component analysis (MCA) method based on explicit dictionary of independent redundant bases. Using correlation, qualitatively we have compared ICA with MCA’s capability to segregate baseline and spike in different EEG data.
Design Considerations for Artefact-Free Optoelectronic Systems

This paper proposes design considerations that need to be followed in order to eliminate potential sources of artefact that could distort a recorded neural signal. The artefact that appears in a recorded signal has a combination of potential sources each of which contributes towards its formation. As such, these sources of artefact have been addressed in three main categories: a) electronics artefact, b) encapsulation artefact and c) interface artefact. Each source (component) is analyzed further and appropriate design techniques and considerations are suggested towards its mitigation.
A Two-stage Personnel Recommendation Method Directed Towards Mobile Crowdsourcing Testing

Nowadays, millions of mobile applications bring convenience to people’s lives, but at the same time, real scenes are difficult to simulate, recruitment of testers and other problems. At present, numerous mobile application crowdsourcing test platforms are emerging. They effectively solve the problem of mobile application testing by recruiting a large number of testers online and using real scene testing. However, with the registration of a large number of testers and the release of test tasks, the platform is faced with the problem that task publishers have difficulty in selecting high-quality personnel and personalized matching tasks, which severely restricts the development of mobile application crowdsourcing testing. Aiming at these problems, this paper firstly analyzes the characteristics of mobile application crowdsourcing test. Then the classifier indexer, personnel predictor and task predictor are generated. On this basis, two-stage staff recommendation algorithm tstage-pi and two-stage task recommendation algorithm tstage-ti are proposed to solve these problems. Through the two algorithms proposed in this paper, the matching degree between testers and crowdsourcing testing tasks can be greatly improved.
Effective Compression Modeling for Packaged Integrated Circuit with Compressive Sensing

In this paper, the compressive sensing theory is applied to the integrated circuit compression modeling. The microprocessor chip is divided into a number of dense micro-regions, and the temperature information of each region in a certain time domain is collected to form a temperature parameter matrix of the time domain and the region. The high-dimensional temperature parameter matrix is mapped to the low-dimensional space by principal component analysis to obtain the critical point temperature of the chip. By observing the critical point temperature, the chip can be used to recover the temperature parameter of the dense distribution. This is the compression modeling method of integrated circuit chips, which is of great significance for the early warning and protection of integrated circuit reliability. The experiment compares the effects of the compressed sensing modeling method and the traditional modeling method. The experimental results show that the recovery efficiency and accuracy of the model are improved by nearly 1.5 times.
Deep Learning for Automating the Organization of Institutional Dermatology Image Stores*

A common challenge faced by researchers associated with healthcare institutions is that data of interest are often contained in electronic medical informatics systems that are centered on optimizing clinician/clinician and patient/clinician communication. While this focus naturally enhances the primary goal of care delivery, it is often suboptimal for secondary research purposes. For example at our institution while it is easy for a clinician to view images associated with a specific patient visit, it remains a challenge for an investigator to assemble a cohort of specific images in order to further research objectives. In order to address this important optimization gap we have developed a system for automated image categorization based on a deep neural network. This image classifier organizes the contents of an electronic health record system in a manner which is more amenable to further research by specifically dividing all available images into a lexicon of subclasses. While the current study is focused on dermatology-related images collected by a combined primary and tertiary care center, we expect similar approaches to aid a variety of institutions and clinical specialties.
Optimal Coil Design for Maximum Power Transfer Efficiency in Resonantly Coupled Systems

Maintaining maximum power transfer efficiency (PTE) is one of the main challenges in resonant inductive power transfer (IPT) systems. Maximum PTE can be achieved if the coupling between transmitter and receiver coils is strong. One way of achieving this is to geometrically optimise a coil by employing small ohmic resistance combined with high self-inductance. In this paper a design method for an optimum coil geometry which offers maximum PTE has been introduced. The proposed technique, in addition to minimising the system’s physical size, provides high level of PTE for both strongly- and loosely-coupled links. A design example for a typical IPT system is presented that shows, with a proper selection of strong coupling factor (e.g.: C = 220.), the designed coil geometry can provide maximum PTE of 95.4% for coupling coefficient K = 1. Also, for a loose inductive link with K = 0.215, maximum calculated and measured PTE values are 89% and 86%, respectively.
Design of a Ferrite-Less Power Pad for Wireless Charging Systems of Electric Vehicles

Inductive power transfer (IPT) systems unleashed a new era for wireless charging systems of electric vehicles (EVs). EVs can be charged stationary or in motion through IPT systems. Different types of power pads are introduced to increase the efficiency of IPT systems, among them, the most commonly used power pads is circular structure, another type with good potential is double D (DD) structure as recommended in Society of Automotive Engineers (SAE) recommended practice J2954. In this paper, a new power pad structure is proposed by combining circular and DD power pads to further improve the performance. Physical models of circular and DD power pads are first built by using specifications in SAE J2954, these models are then combined to form the proposed power pad. The design and simulation of the proposed power pad are carried out using the Finite Element Analysis (FEA) simulation software, ANSYS Maxwell 3D. Simulation results indicate that the proposed power pad shows improved performance compared to circular and DD power pads.
How Height and Weight of Patients with Spinal Cord Injury Affect the Spring Locations of Unpowered Energy-stored Exoskeleton

The paper focused on the effects of body dimensions of patients with spinal cord injury (SCI) on the spring locations of unpowered energy-stored exoskeleton (ES-EXO) based on energy-stored element optimization method. The optimal spring locations and muscle activations of patients with different combinations of height and weight were simulated in AnyBody Modeling System. The spring locations of ES-EXO changed slightly with height, and the weight affected the spring locations of S12 and S22 significantly. SCI patients with increasing height propelled themselves forwards by altering the muscle force distribution of latissimus dorsi, pectoralis major and deltoid, generating more obliquus internus abdominis (OI) and rectus abdominis force in support phase and less OI force in swing phase. SCI patients with increasing weight also adopted different distribution of thorax and shoulder muscle forces for ambulation. The patients with larger weight were likely to enhance OI force so as to provide sufficient hip flexion moment in swing phase.
Closed-loop modeling of the heart-rate reflex for improved diagnosis and monitoring of Mild Cognitive Impairment

Analysis of beat-to-beat spontaneous cerebral hemodynamic data has yielded predictive dynamic models of cerebral hemodynamics and has shown previously that patients with Mild Cognitive Impairment (MCI) exhibit significantly reduced cerebral vasomotor reactivity to CO2 relative to cognitively normal control subjects [1]. The present work examines the heart-rate reflex (HRR) dynamics of 46 MCI patients compared to 20 control subjects, using closed-loop modeling of HRR under resting conditions of spontaneous variations of arterial blood pressure (baroreflex) and end-tidal CO2 (chemoreflex). These subject-specific predictive dynamic models are obtained via the methodology of Principal Dynamic Modes [2] and allow the computation of model-based markers of baroreflex and chemoreflex function. We found that the chemoreflex gain is significantly weakened in MCI patients relative to controls (p=0.0086), while the baroreflex is not significantly affected. These findings offer another tool for diagnosis and monitoring of MCI (via model-based markers), when used in conjunction with current methods.
Text Document Clustering: Issues and Challenges

The text document clustering has become one of the foremost research areas in the field of data mining. The exponential growth of the textual data in today’s digital world has bound the organizations to confront several challenges in organizing and browsing process. Although laborious work is done to make the process of clustering the text documents effortless, yet there are certain difficulties that emerge while dealing with a high volume of text documents. In this paper, the problems and challenges that come across while clustering a huge amount of text data are discussed and explained in detail.
Energy Consumption Analysis And Load Management For Smart Home

The goal of the proposed work is to minimize the energy consumption in a particular home or a region. The focus of energy consumption for smart home has been sensing depth on collecting as much as data as possible from each home or region. The paper presents have designed and deployed a smart system which leads each person to understand about the importance of intelligent buildings or smart home and their aim in reducing the energy consumption. The essence of this paper is all about collecting the different datasets for different home or different region as much as possible. It contains the information about different home appliances from a hair dryer to a refrigerator and their electricity usage for every second, temperature prediction, humidity and so on. The data that have collected has served as the foundation of this paper. And the datasets have described about those datasets and the tools that has used for implementing. The datasets and the tools are provided below so that it will be useful for further research in future on designing smart homes.
Real-time Resources Allocation Framework for Multi-Task Offloading in Mobile Cloud Computing

Cloudlet can effectively reduce the computing load and communication delay of the remote cloud. However, since the cloudlet does not have the powerful computing performance of the remote cloud, as the number of users changes, the resources that the cloudlet can provide for each user change. In addition, as the user has mobility and the service coverage of the cloudlet is limited, the user may get out of the service coverage of the cloudlet during the task execution. In this case, the user will not receive the calculation results, which will lead to the failure of cloud computing. In order to allocate the necessary and sufficient resources to the users, this paper proposes a real-time resource allocation framework. A task movement record-based particle swarm optimization (MRPSO) algorithm is introduced to solve the problem of real-time resource allocation and task failure. Experiments show that the proposed method can provide an effective solution which performs faster than the original PSO method.
Effective control of three power source dc micro grid using Smart meter

In proposed DC micro grid (MG) system, dc boost converter is implemented separately for different renewable energy sources like PV array, fuel cell and battery. These power sources are used, to deliver certain amount of power, but due to the occurrence of fault in the system, one of the power source will fail to operate and other two power sources should supply the power. Implementing Smart Meter (SM) is necessary as it indicates real power, reactive power, voltage, current and power factor simultaneously in lesser time period to utility grid during fault condition, for effectively operating the dc micro grid. MATLAB Simulink is used to model this dc micro grid topology.
Consumer Acceptance of Internet of Things (IoT): Smart Home Context

ABSTRACT The increasing number of devices connected to the internet paves the way for the new era of the internet of things (IoT) computing in which almost every object surrounding us can be transformed into smart entities by equipping objects with sensors, actors, and connectivity technologies. In this study, we seek to examine the consumer acceptance of IoT in smart home context. Drawing on the unified theory of acceptance and use of technology (UTAUT2), we propose an integrative theoretical model that extends UTAUT2 and offers a more comprehensive picture of the factors influencing IoT acceptance and use. The results show that performance expectancy, effort expectancy, social influence, hedonic motivation, and price value are significant predictors of smart home acceptance and use. The results also show that trust and security risk play a significant role in smart home acceptance. We conclude with a discussion of the theoretical and practical implications.
An MR Radiomics Framework for Predicting the Outcome of Stereotactic Radiation Therapy in Brain Metastasis*

Despite recent advances in cancer treatment, patients with brain metastasis still suffer from poor overall survival (OS) after standard treatment. Predicting the treatment outcome before or early after the treatment can potentially assist the physicians in improving the therapy outcome by adjusting a standard treatment on an individual patient basis. In this study, a data-driven computational framework was proposed and investigated to predict the local control/failure (LC/LF) outcome in patients with brain metastasis treated with hypo-fractionated stereotactic radiation therapy (SRT). The framework extracted several geometrical and textural features from the magnetic resonance (MR) images of the tumour and edema regions acquired for 38 patients. Subsequent to a multi-step feature reduction/selection, a quantitative MR biomarker consisting of two features was constructed. A support vector machine classifier was used for outcome prediction using the constructed MR biomarker. The bootstrap . 632+ and leave-one-patient-out cross-validation methods were used to assess the model’s performance. The results indicated that the outcome of LF after SRT could be predicted with an area under the curve of 0.80 and a cross-validated accuracy of 82%. The results obtained implied a good potential of the proposed framework for local outcome prediction in patients with brain metastasis treated with SRT and encourage further investigations on a larger cohort of patients.
A Network-Driven Prior Induced Bregman Model for Parallel MR Imaging*

Compressed sensing based parallel imaging (CS-PI) has attracted great attention in fast magnetic resonance imaging (MRI) community. In particular, Bregman iterative model has shown encouraging performance in solving this problem. However, its regularization term still has large room for improvement. In this work, we propose a network-driven prior induced Bregman model, dubbed as Breg-EDAEP, for CS-PI task. In the present model, the implicit property among different channel MR images is preliminarily explored by the network to obtain more structure details in iterative reconstruction procedure. Experiments on various acceleration factors and sampling patterns have shown that the proposed method outperforms the state-of-the-art algorithms. Breg-EDAEP possesses strong capability to restore image details and preserves well structure information.
Image-Based Humanoid Robot Pose Recognition System

In this paper, an image-based humanoid robot pose recognition system is designed and implemented to recognize humanoid robot pose and some objects position in the field. Robot Operating System (ROS) is used to establish the software development framework for the image recognition system. The image recognition system includes feature matching, region of interest (ROI) capture, background subtraction, edge detection, and object segmentation. Then the Inverse Perspective Mapping (IPM) is used to determine the location of humanoid robot and some objects. Experimental results show that the proposed robot pose recognition system can simultaneously recognize and track both of obstacle, humanoid robot and its pose.
Design and Implementation of DUFAB Hand, A Low-Cost Myoelectric Prosthetic Hand

In this paper, the design and implementation of a low-cost myoelectric prosthetic hand prototype are presented. Our main contributions are - (i) proposing a new Computer Aided Design (CAD) suitable for laser cutter for prosthetic hand and (ii) a new feature, XCORR that calculates cross-correlation between two channels of EMG signals for same gesture, is introduced to classify Electromyography (EMG) signals. Individual parts are designed in a 2D plane and assembled to make a 3D structure in CAD software. Acrylic is used as the main material. The prosthetic hand can exhibit 6-degree of freedom (DOF), i.e., six gestures which are required to perform essential daily tasks and grabbing objects. Commonly used features, such as SMAT, HJORTH parameters are extracted from raw EMG signals and combined with newly introduced feature XCORR to classify 6 types of individual and combined finger movements. Using the classified EMG signal as input we have controlled the designed prosthetic hand in both simulation and hardware level. The controller is designed in such a way that the angular displacement of each finger meets a predefined angle value for each classified gesture. For classification of EMG signals, the highest accuracy is observed as 93.9% with a low computational load. The proposed hand will be affordable and suitable for amputees and help them regain their ability to work.
Implementation and Characterization of a Memristive Memory System

Memristors are one of the promising emerging technologies to address several challenges faced by the computing system of the day. However, a sizeable portion of the works in the literature are not supported by practical implementations or their details are kept as trade secrets. In this work, we propose and implement a writing and reading circuit for a memristive memory system and present our measurement results. A key feature of the proposed system is that it does not need any read-out compensation and virtually no refreshing (due to readout). However, we observed that by the passage of the time (and irrespective of not applying any inputs) some information loss happens, which necessitates refreshing and dictates its frequency. We associate this phenomenon, which has not been reported in the literature before, to what we call “leakage current”. We anticipate this paper to be a starting point for seeing more implementation-based works in the literature, modeling the leakage current phenomenon, and incorporating such design and consideration into the design process of memristive systems.
Is It Enough to just Rely on Near-End, Middle, and Far-End Points to get Feasible Relay Coordination?

Power system protection is a very crucial branch of electric power engineering. This branch is divided into many sub-branches, such as: protection design, relaying and algorithms, fault location, and recently optimal relay coordination (ORC). Since the end of the eighties of the last century, ORC becomes one of the hot topics covered in the literature. Many analytical and numerical techniques have been presented as effective tools to solve this highly constrained, nonlinear, non-convex mixed-integer optimization problem. However, these optimizers are built based on a hypothesis that feasible optimal solutions can be guaranteed if the discrimination margin between the operating times of each primary and backup (P/B) relay pair is satisfied at some three-phase fault points specified on each line. This paper tries to study different design criteria, used during solving ORC problems, to answer the main question raised in the title.
An intelligent and private method to profile social network users

In this paper, we propose an intelligent and private method to profile social network users. The scenario is constituted by the social network platform and an advertiser willing to expose its products to potentially interested users. The intelligence is in the use of text analysis services (like Wikify! ) to extract knowledge from the social networks posts and get measure criteria to compare them, by using Rough Set Theory. The private layer guarantees that neither of the parties in the scenario can gain any advantage by knowing information (tastes, users topic adherence, etc.) of the other party.