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arXiv 2021年

作者： Rowhanimanesh, Alireza Mohammad, R. Akbarzadeh, T. Department of Electrical Engineering University of Neyshabur Neyshabur Iran Department of Electrical Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran

This paper proposes a mathematical approach for robust control of a nanoscale drug delivery system in treatment of atherosclerosis. First, a new nonlinear lumped model is introduced for mass transport in the arterial wall, and its accuracy is evaluated in comparison with the original distributed-parameter model. Then, based on the notion of sliding-mode control, an abstract model is designed for a smart drug delivery nanoparticle. In contrast to the competing strategies on nanorobotics, the proposed nanoparticles carry simpler hardware to penetrate the interior arterial wall and become more technologically feasible. Finally, from this lumped model and the nonlinear control theory, the overall system's stability is mathematically proven in the presence of uncertainty. Simulation results on a well-known model, and comparisons with earlier benchmark approaches, reveals that even when the LDL concentration in the lumen is high, the proposed nanoscale drug delivery system successfully reduces the drug consumption levels by as much as 16% and the LDL level in the Endothelium, Intima, Internal Elastic Layer (IEL) and Media layers of an unhealthy arterial wall by as much as 14.6%, 50.5%, 51.8%, and 64.4%, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Diseases

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Universal swarm computing by nanorobots

arXiv

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arXiv 2021年

Realization of universal computing units for nanorobots is highly promising in creating new and wide arrays of applications, particularly in the realm of distributed computation. However, such realization is also a challenging problem due to the physical limitations of nanometer-sized designs such as in computation, sensory and perception as well as actuation. This paper proposes a theoretical foundation for solving this problem based on a novel notion of distributed swarm computing by basis agents (BAs). The proposed BA is an abstract model for nanorobots that can compute a very simple basis function called B-function. It is mathematically shown here that a swarm of BAs has the universal function approximation property and can accurately approximate functions. It is then analytically demonstrated that a swarm of BAs can be easily reprogrammed to compute desired functions simply by adjusting the concentrations of BAs in the environment. We further propose a specific structure for BAs which enable them to perform distributed computing such as in the aqueous environment of living tissues and nanomedicine. The hardware complexity of this structure aims to remain low to be more reasonably realizable by today’s technology. Finally, the performance of the proposed approach is illustrated by a simulation example. Copyright © 2021, The Authors. All rights reserved.

关键词： Nanorobotics

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Optimal Energy Scheduling Considering Consumers Preference in a Smart Home

Optimal Energy Scheduling Considering Consumers Preference i...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Mohsen Bihamta Toosi Habib Rajabi Mashhadi (Center of Excellence on Soft Computing and Intelligent Information Processing) Ferdowsi University of Mashhad Mashhad Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

Residential buildings are estimated to have a significant percentage of energy consumption in the world. Demand-side management (DSM) improves the grid stability by providing economic incentives with increasing flexibility in demand and reducing a peak to average ratio. The success of applications in DSM heavily depends on the welfare and satisfaction of consumers, or a significant reduction of the customers electricity bill. In this paper, the genetic algorithm is used to optimize the problem. Moreover, two scenarios are discussed. In the first one, occupants have announced their preferred time of household appliances usage. On this basis, the scheduled for the use of household appliances is optimally determined in order to minimize the cost of electricity usage in addition to meeting end-user preferences on when to use devices. In the second one, customers are offered a significant reduction in their electricity bill by changing the operation time of some of their household appliances. The proposed model is implemented for the hottest day of 2018 in New York City. The results demonstrate that the consumers preference is satisfied, and the peak load is declined down to the value, which is desirable for the utility. Besides, the model doesn't produce a new peak load when the power price is low. Hence the system stability is increased, which reflects the significant effectiveness of this model.

关键词： Water heating Home appliances HVAC State of charge Electrical engineering Batteries Resistance heating

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Learning efficient text-to-image synthesis via interstage cross-sample similarity distillation

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Science China(information Sciences) 2021年第2期64卷 19-30页

作者： Fengling MAO Bingpeng MA Hong CHANG Shiguang SHAN Xilin CHEN School of Information Science and Technology ShanghaiTech University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

For a given text, previous text-to-image synthesis methods commonly utilize a multistage generation model to produce images with high resolution in a coarse-to-fine manner. However, these methods ignore the interaction among stages, and they do not constrain the consistent cross-sample relations of images generated in different stages. These deficiencies result in inefficient generation and discrimination. In this study, we propose an interstage cross-sample similarity distillation model based on a generative adversarial network(GAN) for learning efficient text-to-image synthesis. To strengthen the interaction among stages,we achieve interstage knowledge distillation from the refined stage to the coarse stages with novel interstage cross-sample similarity distillation blocks. To enhance the constraint on the cross-sample relations of the images generated at different stages, we conduct cross-sample similarity distillation among the stages. Extensive experiments on the Oxford-102 and Caltech-UCSD Birds-200-2011(CUB) datasets show that our model generates visually pleasing images and achieves quantitatively comparable performance with state-of-the-art methods.

关键词： generative adversarial network (GAN) text-to-image synthesis knowledge distillation

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Taguchi-based Optimal Dry Electrode Design

Taguchi-based Optimal Dry Electrode Design

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Sorour Zakeri-K Mohsen Ghanaei Hediye Hakkak Mohammad- R. Akbarzadeh-T Alireza Akbarzadeh Dept. of Electrical Engineering Ferdowsi University of Mashhad(FUM) Mashhad Iran Dept. of Mechanical Engineering Ferdowsi University of Mashhad(FUM) Mashhad Iran Dept. of Electrical Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad(FUM) Mashhad Iran Dept. of Mechanical Engineering Center of Advanced Rehabilitation and Robotics Research(FUM CARE) Ferdowsi University of Mashhad(FUM) Mashhad Iran

Surface Electromyography (sEMG) electrodes, including both wet and dry classes, play a significant role in the EMG signals analysis and recording of muscle activation area. Users of prosthesis hands must occasionally manipulate and move their limbs for several hours during daily tasks. Hence, high-quality and durable muscle activation is crucial. Although the designs of electrodes with different probe shapes, sizes, metals, and roughness are presented in previous studies, finding the optimal electrode design is an open problem. Hence, this paper proposes a different application of the Taguchi method to specify a reduced set of examinations for determining the optimal electrode design. This study considers the length, width, and distance of probes as controllable parameters as well as subject dominant hand, skin humidity, and muscle fatigue as data recording noises. The design of the experiment process (DOE) selects reasonable orthogonal arrays for the inner and outer arrays of the DOE table. The probes of the electrodes are made of silver due to their high conductivity, less oxidation, and fewer skin allergies in the presented study. In this investigation, the primary focus lies in electrode optimization through the decrease in the error of grasp force prediction. The Support Vector Machine (SVM) method is used to evaluate the error of grasp force. For this purpose, the signal-to-noise ratio (SNR) assessment is performed, identifying each parameter’s optimum level and significance. Results indicate that the electrodes with probes of 1 cm in length, 1 cm in width, and 2 cm apart lead to better sEMG signals. These results offer a design for significantly improved muscle activation signal based on the FUMCARE sEMG setup to use in hand prosthesis applications.

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Real-time Slip Detection and Force Control for Prosthetic Hands

Real-time Slip Detection and Force Control for Prosthetic Ha...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Hediye Hakkak Mohsen Ghanaei Sorour Zakeri-K Mohammad- R. Akbarzadeh-T Alireza Akbarzadeh Dept. of Electrical Engineering Ferdowsi University of Mashhad(FUM) Mashhad Iran Dept. of Mechanical Engineering Ferdowsi University of Mashhad(FUM) Mashhad Iran Dept. of Electrical Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad(FUM) Mashhad Iran Dept. of Mechanical Engineering Center of Advanced Rehabilitation and Robotics Research(FUM CARE) Ferdowsi University of Mashhad(FUM) Mashhad Iran

Object slippage is a significant challenge toward stable grasp for prosthesis hands due to uncertainties, such as changes in object weight or fast maneuvers. Slippage can negatively affect a user’s inclination for long-term use of a prosthesis. The present study suggests a control algorithm for object slippage using surface electromyography (sEMG) and a force-sensitive resistor (FSR) mounted on the prosthesis fingertip. For this purpose, the grasp procedure is divided into four phases, labeled as open, stay open, close, and stay closed, based on the sEMG signals. In addition, a proportional-integral (PI) controller is used to adjust for the desired grasp force. The algorithm uses a decision tree (DT) classifier to determine either the occurrence of slippage or the presence of a stable grasp. A data set is experimentally collected using the FUM 3-Fingered Myo hand. Objects with increasing weight are used, and the corresponding FSR changes are recorded. Results show that the proposed DT method predicts slippage occurrence with 95.6% accuracy with an average detection time of 16.26 ms. Moreover, the proposed method can successfully anticipate slippage at least 80 ms before it actually occurs and, consequently, avoid object slippage by increasing the grasp force.

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Solving a Multi-Traveling Salesmen Problem using a Mamdani Fuzzy Inference Engine and Simulated Annealing Search Algorithm

Solving a Multi-Traveling Salesmen Problem using a Mamdani F...

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International eConference on Computer and Knowledge Engineering (ICCKE)

作者： Fatemeh Hassanpour Mohamad-R. Akbarzadeh-T Scientific Center of Soft Computing and Intelligent Information Processing Ferdowsi University Mashhad Scientific Center of Soft Computing and Intelligent Information Processing Professor Ferdowsi University Mashhad

ISBN: (数字)9781728185668

ISBN: (纸本)9781728185675

The multi-traveling salesmen problem (MTSP) is an extended situation of the standard traveling salesman problem (TSP), in which there is more than one salesman. In this matter, several salesmen are determined to visit N city with the goal of the shortest route to selling their goods, assuming they have crossed all of them, via just once each. The aim is to minimize the total cost of travel for salesman. Thus, it can be modeled as an optimization problem. Regarding the complexity degree, this problem is well known as a NP-Hard problem. Therefore, several meta-heuristic algorithms have been developed at the frontiers of knowledge to solve this problem. Nevertheless, the computational and time complexities are the most important challenges of such algorithms. In this paper, we first convert the MTSP using a fuzzy approach with a linear complexity level, to several TSPs. Then, we solve each problem using the simulated annealing (SA) optimization algorithm. In this way, the time complexity of the system is significantly reduced using the proposed method as well as the accuracy of the system is satisfactory. To assess the proposed algorithm, this method is implemented in the TSPLIB library dataset.

关键词： Urban areas Optimization Fuzzy logic Genetic algorithms Knowledge engineering Standards Simulated annealing

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Unsupervised Muscle Fatigue Clustering Based on sEMG Signals using DeepCNN Autoencoders

Unsupervised Muscle Fatigue Clustering Based on sEMG Signals...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Mohammad Javad Javanbakht Mohammad Reza Fouji Amin Akhavan Saffar Mohammad- R. Akbarzadeh-T Alireza Akbarzadeh Dept. of Electrical Engineering Center of Advanced Rehabilitation and Robotics Research (FUM CARE) Ferdowsi University of Mashhad Mashhad Iran Dept. of Computer Engineering Center of Advanced Rehabilitation and Robotics Research (FUM CARE) Ferdowsi University of Mashhad Mashhad Iran Dept. of Electrical Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran Dept. of Mechanical Engineering Center of Advanced Rehabilitation and Robotics Research (FUM CARE) Ferdowsi University of Mashhad Mashhad Iran

Muscle fatigue assessment is key for optimizing physiotherapy, rehabilitation, and exercise. Muscle fatigue detection not only helps in reducing rehabilitation discomfort but also assists athletes in working out efficiently. The main motivation behind this research is to address the pressing need for a precise and reliable tool to assess muscle fatigue during physical activities. In addition to enhancing therapeutic interventions, this approach improves microtears-induced muscle strengthening by advancing the understanding of muscle fatigue levels. Previous studies have mainly employed supervised approaches to detect fatigue by processing surface electromyography (sEMG) signals through traditional methods in the Time, Frequency, and Time-Frequency domains. These approaches are vulnerable to challenges, such as signal sensitivity, noise interference, and inter-individual variability. Thus, the proposed method uses the spring energy model of the sEMG signal because of its excellent approximation with muscle characteristics. In order to handle noisy and ambiguous data, this paper employs a self-supervised Deep Convolutional Neural Network (DeepCNN) Autoencoder for feature extraction and K-means clustering due to their strong performance. Five healthy participants carried out two experiments using a knee rehabilitation robot (FUM-Physio). The results showcase a well-ordered clustering that closely mirrors the progression of muscle fatigue during these experiments. This indicates the robustness and generality of the proposed method.

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Biosensors Localization for Swarm Medical Delivery

Biosensors Localization for Swarm Medical Delivery

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Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Nasibeh Rady Raz Mohammad-R. Akbarzadeh-T Saeed Setayeshi Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran Amirkabir University of Technology Tehran Iran

ISBN: (数字)9781728175539

ISBN: (纸本)9781728172217

Medical Delivery which is the propagation of indispensable nutrition i.e. oxygen in the body is used specifically in the field of Emergency Medicine (EM). In the EM, drug and nutrition delivery as early as possible to the target organ is the key factor. In this paper, the swarm of biosensors is injected into the body in the two groups of carriers and external trackable nodes. The carrier nodes deliver and spread the material to the target, while the trackable nodes are considered as the anchor points with the known coordination and bonding ability to the certain organs. Here, to localize the carrier node, we perform two estimation algorithms. As soon as the carriers are located in the area of anchor points, their local nutrition propagation is started to the organ in the emergency state. The PF approach usage in the proposed method leads to a better result in organ survival and precise node localization.

关键词： Biosensors Biological systems Machine learning algorithms Particle filters Estimation Approximation algorithms Wireless sensor networks

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FCSR-GAN: Joint Face Completion and Super-Resolution via Multi-Task Learning

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 109-121页

作者： Cai, Jiancheng Han, Hu Shan, Shiguang Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Peng Cheng Laboratory Shenzhen518055 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing100190 China

Combined variations containing low-resolution and occlusion often present in face images in the wild, e.g., under the scenario of video surveillance. While most of the existing face image recovery approaches can handle only one type of variation per model, in this work, we propose a deep generative adversarial network (FCSR-GAN) for performing joint face completion and face super-resolution via multi-task learning. The generator of FCSR-GAN aims to recover a high-resolution face image without occlusion given an input low-resolution face image with occlusion. The discriminator of FCSR-GAN uses a set of carefully designed losses (an adversarial loss, a perceptual loss, a pixel loss, a smooth loss, a style loss, and a face prior loss) to assure the high quality of the recovered high-resolution face images without occlusion. The whole network of FCSR-GAN can be trained end-to-end using our two-stage training strategy. Experimental results on the public-domain CelebA and Helen databases show that the proposed approach outperforms the state-of-the-art methods in jointly performing face super-resolution (up to 8×) and face completion, and shows good generalization ability in cross-database testing. Our FCSR-GAN is also useful for improving face identification performance when there are low-resolution and occlusion in face images. The code of FCSR-GAN is available at: https://***/swordcheng/FCSR-GAN. © 2019 IEEE.

关键词： Generative adversarial networks

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