Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.
- Keywords
- Epilepsy, deep brain stimulation, distributed computing, implantable devices, seizure detection, seizure prediction,
- Publication type
- Journal Article MeSH
This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras.
- Keywords
- collaboration, distributed processing, human–robot interaction, obstacles detection, sensors network, workspace monitoring,
- MeSH
- Computer Communication Networks * MeSH
- Publication type
- Journal Article MeSH
Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study's goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes.
- Keywords
- CLIP, JTOS, SDN, dynamic environment, framework, task scheduling,
- MeSH
- Cloud Computing * MeSH
- Internet of Things * MeSH
- Delivery of Health Care MeSH
- Publication type
- Journal Article MeSH
Many hospitals and medical clinics have been using a wearable sensor in its health care system because the wearable sensor, which is able to measure the patients' biometric information, has been developed to analyze their patients remotely. The measured information is saved to a server in a medical center, and the server keeps the medical information, which also involves personal information, on a cloud system. The server and network devices are used by connecting each other, and sensitive medical records are dealt with remotely. However, these days, the attackers, who try to attack the server or the network systems, are increasing. In addition, the server and the network system have a weak protection and security policy against the attackers. In this paper, it is suggested that security compliance of medical contents should be followed to improve the level of security. As a result, the medical contents are kept safely.
- MeSH
- Algorithms MeSH
- Monitoring, Ambulatory instrumentation MeSH
- Biometry MeSH
- Medical Records MeSH
- Cloud Computing * MeSH
- Confidentiality MeSH
- Electronic Health Records MeSH
- Internet MeSH
- Medical Informatics instrumentation MeSH
- Humans MeSH
- Delivery of Health Care MeSH
- Programming Languages MeSH
- Data Collection MeSH
- Information Storage and Retrieval methods MeSH
- Computer Security * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
In stochastic neuronal models, an interspike interval corresponds to the time interval during which the process imitating the membrane potential reaches a threshold from an initial depolarization. For neurons with an extensive dendritic structure, a stochastic process combining diffusion and discontinuous development of its trajectory is considered a good description of the membrane potential. Due to a lack of analytical solutions of the threshold passage distribution for such a process, a method for computer simulation is introduced here. For the diffusion Ornstein-Uhlenbeck process with exponentially distributed moments of constant jumps a program is given. The relation between the simulation step, accuracy of simulation and amount of computing time required is discussed.
The work proposes a synthesis method of capacitive fractional-order impedance element which is composed of homogenous distributed resistive-capacitive (RC) structures (lines). The method employs genetic algorithm and searches for optimal connection schemes and parameters of the partial RC structures. The synthesis algorithm is described in detail including the coding of the properties of the structures for the purpose of the genetic algorithm. The user interface of the design tool is introduced and the input and output parameters of the synthesis are explained. The algorithm was verified by computer simulations and particularly by measurements of element samples fabricated in thick-film technology. The results correspond to the required impedance characteristics, which confirm the validity of the synthesis method.
- Keywords
- Circuit synthesis, Distributed resistive-capacitive structure, Fractional-order element, Fractional-order impedance,
- Publication type
- Journal Article MeSH
- Review MeSH
The optimal siting and sizing of DGs are vital for the efficient operation of both radial and microgrid distribution systems. From an operational perspective, minimizing real power loss, reducing voltage deviation, and improving voltage stability index are the three primary objectives considered in this study. This manuscript addresses these issues by proposing a novel quasi-oppositional forensic-based investigation (QOFBI) algorithm, an evolutionary meta-optimization technique designed to optimize the location and sizing of DGs under various operating conditions, while adhering to system constraints. The approach introduces a weighting factor-based multiobjective formulation, where optimal weighting factors are computed dynamically. This ensures a balanced approach to minimizing power loss, voltage deviation, and enhancing voltage stability. Extensive simulations were conducted on the IEEE 33-bus and IEEE 69-bus standard distribution systems, evaluating the impact of DG placement with varying power factors under operational constraints. The results demonstrate the superiority of the proposed approach in terms of faster convergence, reduced complexity, and improved performance compared to existing optimization methods. The QOFBI algorithm achieves a 94.44% reduction in active power loss, highlighting its robust performance across different operational scenarios. These findings underscore the potential of QOFBI as a highly effective tool for DG optimization in modern distribution systems, offering both operational efficiency and system reliability.
Distances between identical symbols in information strings (biological, language, computer programs (*.exe files) are described with a different precision with four distributions: exponential, Weibull, log-normal and negative binomial. The correlations are sometimes highly significant.
- MeSH
- DNA genetics MeSH
- Codon genetics MeSH
- Humans MeSH
- Molecular Sequence Data MeSH
- Computing Methodologies * MeSH
- Amino Acid Sequence MeSH
- Base Sequence MeSH
- Sequence Analysis, DNA statistics & numerical data MeSH
- Software MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- DNA MeSH
- Codon MeSH
Several constitutive models have been proposed for the description of mechanical behaviour of soft tissues containing collagen fibres. Some of the commonly used approaches accounting for the dispersion of fibre orientations are based on the summation of (mechanical) contributions of differently oriented fibre families. This leads to the need of numerical integration on the sphere surface, and the related numerical consumption is the main disadvantage of this category of constitutive models. The paper is focused on the comparison of various numerical integration methods applied to a specific constitutive model applicable for arterial walls. Robustness and efficiency of several integration rules were tested with respect to application in finite element (FE) codes. Among all the analysed numerical integration rules, the best results were reached by Lebedev quadrature; the related parameters for the specific constitutive model are presented in the paper. The results were implemented into the commercial FE code ANSYS via user subroutines, and their applicability was demonstrated by an example of FE simulation with non-homogenous stress field.
- Keywords
- Hyperfit software, arterial wall mechanics, constitutive modelling, distribution of collagen fibre orientation, numerical integration on the sphere,
- MeSH
- Arteries anatomy & histology physiology MeSH
- Models, Biological * MeSH
- Biomechanical Phenomena MeSH
- Collagen physiology MeSH
- Computer Simulation MeSH
- Computational Biology methods MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Collagen MeSH
In this work, a novel force equilibrium method called distributed dielectrophoretic cytometry (2DEP cytometry) was developed. It uses a dielectrophoresis (DEP)-induced vertical translation of live cells in conjunction with particle image velocimetry (PIV) in order to measure probabilistic distribution of DEP forces acting on an entire cell population. The method is integrated in a microfluidic device. The bottom of the microfluidic channel is lined with an interdigitated electrode array. Cells passing through the micro-channel are acted on by sedimentation forces, while DEP forces either oppose sedimentation, support sedimentation, or neither, depending on the dielectric (DE) signatures of the cells. The heights at which cells stabilize correspond to their DE signature and are measured indirectly using PIV, which enables simultaneous and high-throughput collection of hundreds of single-cell responses in a single PIV frame. The system was validated using polystyrene micro-particles. Preliminary experimental data quantify the DE signatures of immortalized myelogenous leukemia cell lines K562 and KG1. We show DEP-induced cell translation along the parabolic velocity profile can be measured by PIV with sub-micron precision, enabling identification of individual cell DE signatures. DE signatures of the selected cell lines are distinguishable. Throughput of the method enables measurement of DE signatures at 10 different frequencies in almost real time.
- Keywords
- Cytometry, Dielectrophoresis, Lab-on-chip, Microfluidics,
- MeSH
- K562 Cells MeSH
- Equipment Design MeSH
- Electric Stimulation MeSH
- Electrophoresis instrumentation MeSH
- Lab-On-A-Chip Devices MeSH
- Humans MeSH
- Computer Simulation MeSH
- Flow Cytometry instrumentation methods MeSH
- Stochastic Processes MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
