Search

Traditional model compression techniques are dependent on handcrafted features and require domain experts, with a tradeoff between model size, speed, and accuracy. This study proposes a new approach toward resolving model compression problems. Our approach combines reinforcement-learning-based automated pruning and knowledge distillation to improve the pruning of unimportant network layers and the efficiency of the compression process. We introduce a new state quantity that controls the size of the reward and an attention mechanism that reinforces useful features and attenuates useless features to enhance the effects of other features. The experimental results show that the proposed model is superior to other advanced pruning methods in terms of the computation time and accuracy on CIFAR-100 and ImageNet dataset, where the accuracy is approximately 3% higher than that of similar methods with shorter computation times. [ABSTRACT FROM AUTHOR]

Depth images obtained from lightweight, real-time depth estimation models and consumer-oriented sensors typically have low-resolution issues. Traditional interpolation methods for depth image up-sampling result in a significant information loss, especially in edges with discontinuous depth variations (depth discontinuities). To address this issue, this paper proposes a semi-coupled deformable convolution network (SCD-Net) based on the idea of guided depth map super-resolution (GDSR). The method employs a semi-coupled feature extraction scheme to learn unique and similar features between RGB images and depth images. We utilize a Coordinate Attention (CA) to suppress redundant information in RGB features. Finally, a deformable convolutional module is employed to restore the original resolution of the depth image. The model is tested on NYUv2, Middlebury, Lu, and a Real-Sense real-world dataset created using an Intel Real-sense D455 structured-light camera. The super-resolution accuracy of SCD-Net at multiple scales is much higher than that of traditional methods and superior to recent state-of-the-art (SOTA) models, which demonstrates the effectiveness and flexibility of our model on GDSR tasks. In particular, our method further solves the problem of an RGB texture being over-transferred in GDSR tasks. [ABSTRACT FROM AUTHOR]

To solve the problem of complex network models with a large number of redundant parameters, a pruning algorithm combined with an attention mechanism is proposed. Firstly, the basic training is performed once, and the network model is then re-trained with the attention mechanism for the baseline. The obtained model is pruned based on channel correlation, and finally a simplified model is obtained via continuous cyclic iteration while the accuracy rate is kept as close as possible to that of the baseline model. The algorithm was experimentally validated on ResNet based on different datasets, and the results showed that the algorithm provided strong adaptability to different datasets and different network structures. For the CIFAR-100 dataset, ResNet50 was pruned to reduce the amount of model parameters by 80.3% and the amount of computation by 69.4%, while maintaining accuracy. For the ImageNet dataset, the ResNet50 parameter volume was compressed by 2.49 times and the computational volume was compressed by 3.01 times. The ResNet101 parameter volume was reduced by 61.2%, and the computational volume was reduced by 68.5%. Compared with the traditional fixed threshold, the model achieves better results in terms of detection accuracy, compression effect, and inference speed. [ABSTRACT FROM AUTHOR]

In the single pruning algorithm, channel pruning or filter pruning is used to compress the deep convolution neural network, and there are still many redundant parameters in the compressed model. Directly pruning the filter will largely cause the loss of key information and affect the accuracy of model classification. To solve these problems, a parallel pruning algorithm combined with image enhancement is proposed. Firstly, in order to improve the generalization ability of the model, a data enhancement method of random erasure is introduced. Secondly, according to the trained batch normalization layer scaling factor, the channels with small contribution are cut off, the model is initially thinned, and then the filters are pruned. By calculating the geometric median of the filters, redundant filters similar to them are found and pruned, and their similarity is measured by calculating the distance between filters. Pruning was done using VGG19 and DenseNet40 on cifar10 and cifar100 data sets. The experimental results show that this algorithm can improve the accuracy of the model, and at the same time, it can compress the calculation and parameters of the model to a certain extent. Finally, this method is applied in practice, and combined with transfer learning, traffic objects are classified and detected on the mobile phone. [ABSTRACT FROM AUTHOR]

In order to avoid the direct depth reconstruction of the original image pair and improve the accuracy of the results, we proposed a coarse-to-fine stereo matching network combining multi-level residual optimization and depth map super-resolution (ASR-Net). First, we used the u-net feature extractor to obtain the multi-scale feature pair. Second, we reconstructed global disparity in the lowest resolution. Then, we regressed the residual disparity using the higher-resolution feature pair. Finally, the lowest-resolution depth map was refined by using the disparity residual. In addition, we introduced deformable convolution and group-wise cost volume into the network to achieve adaptive cost aggregation. Further, the network uses ABPN instead of the traditional interpolation method. The network was evaluated on three datasets: scene flow, kitti2015, and kitti2012 and the experimental results showed that the speed and accuracy of our method were excellent. On the kitti2015 dataset, the three-pixel error converged to 2.86%, and the speed was about six times and two times that of GC-net and GWC-net. [ABSTRACT FROM AUTHOR]

To further reduce the size of the neural network model and enable the network to be deployed on mobile devices, a novel fusion pruning algorithm based on information entropy stratification is proposed in this paper. Firstly, the method finds similar filters and removes redundant parts by Affinity Propagation Clustering, then secondly further prunes the channels by using information entropy stratification and batch normalization (BN) layer scaling factor, and finally restores the accuracy training by fine-tuning to achieve a reduced network model size without losing network accuracy. Experiments are conducted on the vgg16 and Resnet56 network using the cifar10 dataset. On vgg16, the results show that, compared with the original model, the parametric amount of the algorithm proposed in this paper is reduced by 90.69% and the computation is reduced to 24.46% of the original one. In ResNet56, we achieve a 63.82%-FLOPs reduction by removing 63.53% parameters. The memory occupation and computation speed of the new model are better than the baseline model while maintaining a high network accuracy. Compared with similar algorithms, the algorithm has obvious advantages in the dimensions of computational speed and model size. The pruned model is also deployed to the Internet of Things (IoT) as a target detection system. In addition, experiments show that the proposed model is able to detect targets accurately with low reasoning time and memory. It takes only 252.84 ms on embedded devices, thus matching the limited resources of IoT. [ABSTRACT FROM AUTHOR]

In order to solve the problem of large model computing power consumption, this paper proposes a novel model compression algorithm. Firstly, this paper proposes an interpretable weight allocation method for the loss between a student network (a network model with poor performance), a teacher network (a network model with better performance) and real label. Then, different from the previous simple pruning and fine-tuning, this paper performs knowledge distillation on the pruned model, and quantifies the residual weights of the distilled model. The above operations can further reduce the model size and calculation cost while maintaining the model accuracy. The experimental results show that the weight allocation method proposed in this paper can allocate a relatively appropriate weight to the teacher network and real tags. On the cifar-10 dataset, the pruning method combining knowledge distillation and quantization can reduce the memory size of resnet32 network model from 3726 KB to 1842 KB, and the accuracy can be kept at 93.28%, higher than the original model. Compared with similar pruning algorithms, the model accuracy and operation speed are greatly improved. [ABSTRACT FROM AUTHOR]

Background: Recently, wearable technologies have gained attention in diverse applications of the medical platform to guarantee the health and safety of the sportsperson with the assistance of the Internet of things (IoT) device. The IoT device's topology varies due to the shift in users' orientation and accessibility, making it impossible to assign resources, and routing strategies have been considered the prominent factor in the current medical research. Further, for sportspersons with sudden cardiac arrests, hospital survival rates are low in which wearable IoT devices play a significant role.Objective: In this paper, the energy efficient optimized heuristic framework (EEOHF) has been proposed and implemented on a wearable device of the sportsperson's health monitoring system.Method: The monitoring system has been designed with cloud assistance to locate the nearest health centers during an emergency. The wearable sensor technologies have been used with an optimized energy-efficient algorithm that helps athletes monitor their health during physical workouts. The monitoring system has fitness tracking devices, in which health information is gathered, and workout logs are tracked using EEOHF. The proposed method is applied to evaluate and track the sportsperson's fitness based on case study analysis.Results: The simulation results have been analyzed, and the proposed EEOHF achieves a high accuracy ratio of 97.8%, a performance ratio of 95.3%, and less energy consumption of 9.4%, delay of 13.1%, and an average runtime of 98.2% when compared to other existing methods. [ABSTRACT FROM AUTHOR]

Background: Nowadays, smart healthcare minimizes medical facilities costs, ease staff burden, achieve unified control of materials and records, and enhance patients' medical experience. Smart healthcare treatments have critical barriers to improving patient outcomes, reducing the regulatory burden, and promoting the transition from volume to benefit.Objective: In this paper, the Internet of Things-assisted Intelligent Monitoring Model (IoT-IMM) has been proposed to improve patient health and maintain health records.Method: The advanced IoT sensors can monitor patient health and insert into the patients' bodies. Information collected can be analyzed, aggregated, and mined to predict diseases at an early stage. For that, an enhanced deep learning network using Bayes theorem (EDLN-BT) benefits to obtain and verify various patient health data in a specific aspect, making it easy to supervise the patient's activities.Results: The IoT-IMM-based EDLN-BT results show the smart health care monitoring has undergone substantial growth, improving patient satisfaction for the quality of the healthcare services offered in hospitals and many other healthcare facilities. It helps predict health diseases with increased accuracy, prediction rate with minimal residual error delay, and energy consumption. [ABSTRACT FROM AUTHOR]

Background: Soccer is one of the world's most successful sports with several players. Quality player's activity management is a tough job for administrators to consider in the Internet of Things (IoT) platform. Candidates need to predict the position, intensity, and path of the shot to look back on their results and determine the stronger against low shot and blocker capacities.Objective: In this paper, the IoT-assisted wearable device for activity prediction (IoT-WAP) model has been proposed for predicting the activity of soccer players.Method: The accelerometer built wearable devices formulates the impacts of multiple target attempts from the prevailing foot activity model that reflect a soccer player's characteristics. The deep learning technique is developed to predict players' various actions for identifying multiple targets from the differentiated input data compared to conventional strategies. The Artificial Neural Network determines a football athlete's total abilities based on football activities like transfer, kick, run, sprint, and dribbling.Results: The experimental results show that the suggested system has been validated from football datasets and enhances the accuracy ratio of 97.63%, a sensitivity ratio of 96.32%, and a specificity ratio of 93.33% to predict soccer players' various activities. [ABSTRACT FROM AUTHOR]

Background: Physical health monitoring may take several forms, from individual quality changes to complex health checks carried out by health staff. Present health issues are detected with monitoring, and potential health problems are expected. Wearable sensors provide users with ease in everyday tracking, although many issues must be addressed in such sensor systems. The devices take a long time to obtain the requisite detection and diagnostic expertise and produce false alarms.Objective: In this paper, the Internet of Things-assisted Health Condition Monitoring system (IoT-HCMS) has been proposed to track and analyze the patient physical health condition.Method: The proposed IoT-HCMS utilizes the intelligent monitoring model to follow the patient physical health day by day activities and instantaneously generate the health records. The system will indeed support patients in tracking psychological signs to minimize risks to their well-being.Results: The experimental results show that the IoT-HCMS improves accuracy in patient health monitoring and has less response time. [ABSTRACT FROM AUTHOR]

Background: Internet of Things (IoT) technology provides a tremendous and structured solution to tackle service deliverance aspects of healthcare in terms of mobile health and remote patient tracking. In medicine observation applications, IoT and cloud computing serves as an assistant in the health sector and plays an incredibly significant role. Health professionals and technicians have built an excellent platform for people with various illnesses, leveraging principles of wearable technology, wireless channels, and other remote devices for low-cost healthcare monitoring.Objective: This paper proposed the Fog-IoT-assisted multisensor intelligent monitoring model (FIoT-MIMM) for analyzing the patient's physical health condition.Method: The proposed system uses a multisensor device for collecting biometric and medical observing data. The main point is to continually generate emergency alerts on mobile phones from the fog system to users. For the precautionary steps and suggestions for patients' health, a fog layer's temporal information is used.Results: Experimental findings show that the proposed FIoT-MIMM model has less response time and high accuracy in determining a patient's condition than other existing methods. Furthermore, decision making based on real-time healthcare information further improves the utility of the suggested model. [ABSTRACT FROM AUTHOR]

Background: Health monitoring is important for early disease diagnosis and will reduce the discomfort and treatment expenses, which is very relevant in terms of prevention. The early diagnosis and treatment of multiple conditions will improve solutions to the patient's healthcare radically. A concept model for the real-time patient tracking system is the primary goal of the method. The Internet of things (IoT) has made health systems accessible for programs based on the value of patient health.Objective: In this paper, the IoT-based cloud computing for patient health monitoring framework (IoT-CCPHM), has been proposed for effective monitoring of the patients.Method: The emerging connected sensors and IoT devices monitor and test the cardiac speed, oxygen saturation percentage, body temperature, and patient's eye movement. The collected data are used in the cloud database to evaluate the patient's health, and the effects of all measures are stored. The IoT-CCPHM maintains that the medical record is processed in the cloud servers.Results: The experimental results show that patient health monitoring is a reliable way to improve health effectively. [ABSTRACT FROM AUTHOR]

Background: The modern Internet of Things (IoT) makes small devices that can sense, process, interact, connect devices, and other sensors ready to understand the environment. IoT technologies and intelligent health apps have multiplied. The main challenges in the sports environment are playing without injuries and healthily.Objective: In this paper the Internet of Things-based Smart Wearable System (IoT-SWS) is introduced for monitoring sports person activity to improve sports person health and performance in a healthy way.Method: Wearable systems are commonly used to capture individual sports details on a real-time basis. Collecting data from wearable devices and IoT technologies can help organizations learn how to optimize in-game strategies, identify opponents' vulnerabilities, and make smarter draft choices and trading decisions for a sportsperson.Results: The experimental result shows that IoT-SWS achieve the highest accuracy of 98.22% and efficient in predicting the sports person's health to improve sports person performance reliably. [ABSTRACT FROM AUTHOR]

Background: In recent years the Internet of Things (IoT) has become a popular technological culture in the physical education system. Though several technologies have grown in the physical education system domain, IoT plays a significant role due to its optimized health information processing framework for students during workouts.Objective: In this paper, an advanced dynamic information processing system (ADIPS) has been proposed with IoT assistance to explore the traditional design architecture for physical activity tracking.Method: To track and evaluate human physical activity in day-to-day living, a new paradigm has been integrated with wearable IoT devices for effective information processing during physical workouts. Continuous observation and review of the condition and operations of various students by ADIPS helps to evaluate the sensed information to analyze the health condition of the students.Results: The result of ADIPS has been implemented based on the performance factor correlation with the traditional system. [ABSTRACT FROM AUTHOR]

Background: Physical health is vital to the improvement of our skills and the enhancement of eye movements. The coordination of good body movement helps to establish a safe position of the body. The challenging characteristics of physical education include insufficient time allocation, inadequately trained teachers, and inadequate provision of the equipment is considered as an important factor.Objective: In this paper, IoT-based Computational Narrowband Physical Health Framework (IoT-CNPHF) has been proposed to strengthen adequate time allocation, appropriately qualified teachers, and sustainable provision in the physical education system.Method: Massive extended range analysis is introduced to enhance the duration and time allotted for physical activity that helps in creating awareness about the importance of physical activities and sports in our daily life. The multimodal supervised technique is incorporated with IoT-CNPHF to improve the knowledge of physical education for the teachers and to provide suitable provision for students in the physical education system.Results: The simulation analysis is performed based on accuracy, performance, and its efficiency proves the reliability of the proposed framework. [ABSTRACT FROM AUTHOR]

Background: Physical exercise programs are required to improve students' physical ability, physical fitness, self-responsibility, and satisfaction to remain physically active for a lifetime. The supporting system's demanding characteristics include lack of school leadership support, and lack of communication skills among students is considered an essential factor in the physical education system.Objective: In this paper, an Internet of Things (IoT)-based intelligent physical support framework (IoT-IPSF) has been proposed to encourage education leadership and student social interaction in the physical education system.Method: Training service analysis is introduced to improve adequate leadership support, helping in the physical education system's growth. Self-determination analysis is integrated with IoT-IPSF to enhance effective communication among school teachers, educational experts, and curriculum officers in the physical education system.Results: The simulation results show that the proposed method achieves a high accuracy ratio of 98.7%, an efficiency ratio of 95.6, student performance 97.8%, fitness level 82.3%, activity involvement 94.5% compared to other existing models. [ABSTRACT FROM AUTHOR]

Background: Internet of Things (IoT) is a hopeful advancement that is an accurate international link for smart devices for total initiatives. Physical Education (PE) builds students' abilities and trust to engage in various physical activities, both within and outside their classrooms. The challenging characteristics in the learning management system include lack of setting a clear goal, lack of system integration, and failure to find an implementation team is considered as an essential factor.Objective: In this paper, an IoT-based technological acceptance learning management framework (IoT-TALMF) has been proposed to identify the objectives, resource allocation, and effective team for group work in the physical education system.Method: Physical Educators primarily use the learning management framework as databases of increased management components, choosing to interact with students, teammates, organizations. Statistical course content analysis is introduced to identify and set clear goals that motivate students for the physical education system. The course instructor learning technique is incorporated with IoT-TALMF to improve system integration based on accuracy and implement an effective team to handle unexpected cost delays in the physical education system.Results: The numerical results show that the IoT-TALMF framework enhances the identity accuracy ratio of 97.33%, the performance ratio of students 96.2%, and the reliability ratio of 97.12%, proving the proposed framework's reliability. [ABSTRACT FROM AUTHOR]

The generalized |$k$| -connectivity of a graph |$G$| is a parameter that can measure the reliability of a network |$G$| to connect any |$k$| vertices in |$G$|⁠ , which is a generalization of traditional connectivity. Let |$S\subseteq V(G)$| and |$\kappa _{G}(S)$| denote the maximum number |$r$| of edge-disjoint trees |$T_{1}, T_{2}, \cdots , T_{r}$| in |$G$| such that |$V(T_{i})\bigcap V(T_{j})=S$| for any |$i, j \in \{1, 2, \cdots , r\}$| and |$i\neq j$|⁠. For an integer |$k$| with |$2\leq k\leq n$|⁠ , the generalized |$k$| -connectivity of a graph |$G$| is defined as |$\kappa _{k}(G)= min\{\kappa _{G}(S)|S\subseteq V(G)$| and |$|S|=k\}$|⁠. In this paper, we introduce a family of regular graph |$G_{n}$| that can be constructed recursively and each vertex with exactly one outside neighbor. The generalized |$3$| -connectivity of the regular graph |$G_{n}$| is studied, which attains a previously proven upper bound on |$\kappa _{3}(G)$|⁠. As applications of the main result, the generalized |$3$| -connectivity of some important networks including some known results such as the alternating group network |$AN_{n}$|⁠ , the star graph |$S_{n}$| and the pancake graphs |$P_{n}$| can be obtained directly. [ABSTRACT FROM AUTHOR]

A smart city refers to a city equipped with the basic infrastructure to provide a good quality of life and a clean and sustainable environment to its citizens using smart technology-based solutions. This article (Zhang & Liu, 2022) conducts APP page management through technical analysis of mobile devices and user experience-oriented design. The selected papers present in-depth studies of practical issues and challenging problems in Big Data Analytics with IoT-oriented Infrastructures for Future Smart Cities. [Extracted from the article]

Given a source node and a node set, the node-to-set disjoint paths problem is to find disjoint paths from the source node to all nodes from the node set. In this paper, we propose an improved algorithm for the node-to-set disjoint paths problem on bi-rotator graphs. Our result improves the complexity of previous result from to , where n is the dimension of the input graph. [ABSTRACT FROM AUTHOR]

Given a complete graph G = ( V , E ) with a metric cost function c : E → R + and two vertex subsets R ⊂ V and R ′ ⊆ R , a partial-terminal Steiner tree is a Steiner tree which contains all the vertices in R such that all the vertices in R ′ are leaves. The partial-terminal Steiner tree problem (PTSTP) is to find a partial-terminal Steiner tree with the minimum cost. The problem has been shown to be NP-hard and MAX SNP-hard, even when the edge costs are restricted in { 1 , 2 } , namely, the ( 1 , 2 ) -partial-terminal Steiner tree problem ( PTSTP ( 1 , 2 ) ) . In this paper, we consider PTSTP ( 1 , 2 ) . The previous best-known approximation ratio of PTSTP ( 1 , 2 ) was at most 25 14 . In this paper, we propose a polynomial-time approximation algorithm that improves the approximation ratio from 25 14 to 5 3 . [ABSTRACT FROM AUTHOR]

Tallini and Bose defined a random walk function on which an efficient encoding/decoding algorithm for second-order spectral-null (2-OSN) codes was based. Later, Yang enhanced Tallini-Bose code by using quasi 2-OSN codes. Both 2-OSN codes encode the information word into a balanced word and then append extra bits to make the length of this balanced word equal to 0 (or 2) modulo 4. The main innovation is to eliminate the need for appended bits, thereby increasing the code rate. [ABSTRACT FROM AUTHOR]

Abstract: For a given graph , the interval completion problem of G is to find an edge set F such that the supergraph of G is an interval graph and is minimum. It has been shown that it is equivalent to the minimum sum cut problem, the profile minimization problem and a kind of graph searching problems. Furthermore, it has applications in computational biology, archaeology, and clone fingerprinting. In this paper, we show that it is NP-complete on split graphs and propose an efficient algorithm on primitive starlike graphs. [Copyright &y& Elsevier]