Reviewerly/exHarmony huggingface dataset:
Practical DSP for Cortex-M Microcontrollers

In this chapter, we examine the architectural extensions included in the Cortex-M4/M7 to support Digital Signal Processing (DSP). These include the “Single Instruction Multiple Data” instructions, an enhanced MAC unit, and an optional Hardware Floating Point Unit. Arm also publishes a free DSP library and this chapter will look at using this library to implement an FFT as well as IIR and FIR filters. This chapter also provides a short overview of Machine learning support provided by CMSIS-DSP and CMSIS-NN.
Developing Software for the Cortex-M Family

This chapter guides you through installation of the Keil MDK tool chain for Cortex-M microcontrollers. A hands-on example also walks you through setting up the first project to build and debug an initial microcontroller program.
Armv8-M Architecture and Processors

Armv8-M and Armv8.1 are the latest architectural specifications for Cortex-M processors. Together, they define additional security and high-performance compute extensions. Armv8-M introduces Arm’s TrustZone technology to Cortex-M processors. TrustZone is a hardware security model that forms the security foundation for IoT and connected devices. The Armv8.1 defines the Helium Vector Extension that provides the processing power required for Machine Learning and Digital Signal Processing algorithms.
Exploring the Hidden Meaning of Care through the Life History of a 53-year-old Woman

This study is based on the story of a woman who has been caring for children and the elderly for a living. As a mother of three children she supported her mother-in-law with Alzheimer's for over 20 years. This paper tried to explore the meaning of care through the life history of a 53-year-old woman and discover its implications for pedagogy. As results, first, the meaning that 'life is a period of vulnerability(Donna J. Haraway)' came to light through the characters surrounding this woman's family history; Second, the fact that the relationship between the caregiver and the person being cared for is the core of a good care relationship became clear; Third, since care is already a relationship in itself, care work is a labor of love and relationships that cannot be standardized or objectified. Finally, the meaning of care work illuminated that self-care and care for others are constantly interacting. The family discord experienced by the participant due to solitary care work gave social, political and economic suggestions that care work was passed entirely on to women. This life history research with a participant K suggested also possibilities of inventing a new viewpoint of humanity and a new mode of human relationship through care work. So this study presented a new task for pedagogy in that care is the knowledge that anyone should and can learn.
Feasibility of material, language and media aspects in the development of environmental pollution learning tools

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation D. Fatmawati, Sajidan, Nurma Yunita Indriyanti; Feasibility of material, language and media aspects in the development of environmental pollution learning tools. AIP Conference Proceedings 27 January 2023; 2540 (1): 110012. https://doi.org/10.1063/5.0125115 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Development of website-based learning media using wordpress on virus material to empowered students’ learning motivation

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ihrom Rosyadi, Slamet Santosa, Umi Fatmawati; Development of website-based learning media using wordpress on virus material to empowered students’ learning motivation. AIP Conference Proceedings 27 January 2023; 2540 (1): 020023. https://doi.org/10.1063/5.0112435 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Distance estimation between moving objects using monocular camera

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation M. R. Habibi, Mohammad Isa Irawan, Budi Setiyono; Distance estimation between moving objects using monocular camera. AIP Conference Proceedings 27 January 2023; 2540 (1): 080019. https://doi.org/10.1063/5.0111106 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Inner implementation and application of hash map

Discuss about what is hash map how it internally work and there complexity for insert , deletion , updation ,get an element. Also understand the use case of hashing function in hash map and how to increasing the efficiency of Hash map through Rehashing technique .It use hashing technique and also see the real time application of hash map
Secured Communication Using Hash Function and Steganography

Two key fields that are extensively employed for data security are cryptography and steganography. With the help of these technologies, data security is offered in the banking system. The proposed method uses a hash function to virtualize bank cheque transactions. The cryptographic hash function algorithm is used to encrypt information. This paper uses steganography to develop an efficient algorithm for embedding data in images, which provides a better security pattern for transferring messages over a network. To achieve a secure transfer, the sender and receiver’s authentication details are masked. The projected method improves the integrity and confidentiality of the system.
Privacy Preserving Mechanism for Medical Data Stored in Cloud During Health Emergencies

In this paper, for web-based cloud computing services, we provide a novel fine-grained two-factor authentication access control mechanism. Our suggested 2FA access control system, which requires both a user secret key and a lightweight security device, employs an attribute-based access control mechanism in particular.
Quantum image K-nearest neighbor mean filtering

Quantum image filtering is an extension of classical image filtering algorithms, which mainly studies image filtering models based on quantum characteristics. The existing quantum image filtering focuses on noise detection and noise suppression, ignoring the effect of filtering on image boundaries. In this paper, a new quantum image filtering algorithm is proposed to realize the K-nearest neighbor mean filtering task, which can achieve the purpose of boundary preservation while suppressing noise. The main work includes: a new quantum compute module for calculating the absolute value of the difference between two non-negative integers is proposed, thus constructing the quantum circuit of the distance calculation module for calculating the grayscale distance between the neighborhood pixels and the center pixel; the existing quantum sorting module is improved to sort the neighborhood pixels with the distance as the sorting condition, and thus the quantum circuit of the K-nearest neighbor extraction module is constructed; the quantum circuit of the K-nearest neighbor mean calculation module is designed to calculate the gray mean of the selected neighbor pixels; finally, a complete quantum circuit of the proposed quantum image filtering algorithm is constructed, and carried out the image de-noising simulation experiment. The relevant experimental indicators show that the quantum image K-nearest neighbor mean filtering algorithm has the same effect on image noise suppression as the classical K-nearest neighbor mean filtering algorithm, but the time complexity of this method is reduced from $O\left(2^{2 n}\right)$ of the classical algorithm to $O\left(n^{2}+q^{2}\right)$.
Quantum delay metrology with complete frequency-bin Bell basis synthesizer

We generate all four frequency-bin Bell states via single and dual spectral-line pumping of spontaneous parametric down-conversion and demonstrate their sensitivity to the sum and difference of entangled-photon delays using spectral interferometry.
Unbiased quantum phase estimation

Quantum phase estimation algorithm (PEA) is one of the most important algorithms in early studies of quantum computation. However, we find that the PEA is not an unbiased estimation, which prevents the estimation error from achieving an arbitrarily small level. In this paper, we propose an unbiased phase estimation algorithm (UPEA) based on the original PEA. We also show that a maximum likelihood estimation (MLE) post-processing step applied on UPEA has a smaller mean absolute error than MLE applied on PEA. In the end, we apply UPEA to quantum counting, and use an additional correction step to make the quantum counting algorithm unbiased.
New quantum codes derived from the images of constacyclic codes

Assume that $q$ is a prime power and $m\geq 2$ is a positive integer. Cyclic codes over $\mathbb{F}_{q^{2m}}$ of length $n=\frac{q^{2m}-1}{\rho }$ with $\rho\mid (q-1)$, and constacyclic codes over $\mathbb{F}_{q^{2m}}$ of length $n=\frac{q^{2m}-1}{\rho }$ with $\rho\mid (q+1)$ are considered in this paper, respectively. Two classes of quantum codes are derived from the images of these codes by the Hermitian construction. Compared with the previously known quantum codes, the quantum codes in our scheme have better parameters.
Interactive Strategies of Chinese Cloud Classroom Based on the Perspective of Meta Universe

With the American social software Facebook officially announced its name change to “Meta”, the concept of “Meta Universe” has really entered the public field. The meta universe is an Internet social form based on the integrated application of various emerging information technologies. It has transformative value for the future development of all walks of life, including the education industry. Since the outbreak of covid-19, online Chinese learning of most foreign students has changed from a short-term accidental state to a normal state. In this way, we must pay attention to how to seize the opportunity of the development of meta universe technology, combine the meta universe cloud classroom with AI digital people, and create a new scene of educational application integrating multiple technologies under the new AI ecology. Only in this way can we build a new educational paradigm change, and solve some bottlenecks and difficulties in the current development of Chinese cloud classroom interaction for foreign students, so that classroom teaching can truly promote the development of students’ knowledge, ability and emotion, and finally cultivate students’ innovative spirit and practical ability.
A Python Framework for High-level Applications in Accelerator Operations

Abstract A Python graphical framework providing reusable components to facilitate the development of accelerator applications, that meet the basic requirements of experts and operators alike, is presented. Such a collective approach serves to bridge the gap between the expert developer and the operational team, resulting in applications that are inherently cohesive, durable and easily navigable. The operational advantages and underlying principles are exemplified in a reference application that provides executable examples of customary practices, and further highlights a number of composite and control system-enabled widgets.
Algorithm Application Based on YOLOX Model in Health Monitoring of Elderly Living Alone

Due to the outbreak of the COVID-19 novel coronavirus, the restrictions on population entry and exit have resulted in most elderly people staying at home alone, causing them a lot of inconvenience. Aiming at the problem that the elderly living alone at home may cause various diseases due to negative emotions but cannot be detected and solved in time, a method of detecting the facial expressions of the elderly is proposed to determine whether the elderly need timely care. YOLOX is the latest generation of YOLO series target detectors released by Megvii Technology in July 2021. It adopts the latest technology in the industry in recent years and surpasses existing similar products in performance and accuracy. If the YOLOX detector can be applied to the health monitoring of the elderly living alone under the current epidemic situation, it will be of great significance to improve detection rate and accuracy of detection and reduce labor costs.
YOLOx-M: Road Small Object Detection Algorithm Based on Improved YOLOx

In order to improve the detection accuracy of small objects in road scenarios, a detection algorithm, YOLOx-M is proposed. First, the last layer of the backbone uses the MobileVit Block network, which learns a stronger global representation. Moreover, we use the CIOU loss function because it is more sensitive to location information. Finally, a moderate increase for the input scale to improve road small object detection accuracy. Experiments conducted on BDD100k and self-built datasets show that YOLOx-M has a mAP@0.5 of 0.5860 for six categories in the dataset improves 0.0481 over YOLOx, while the detection speed is 31.69 fps, which satisfies the real-time requirements. YOLOx-M has significantly improved the detection accuracy for road small objects, providing a better algorithm for detecting road small objects in autonomous driving environments.
Video Stream Forwarding Algorithm Based on Multi-channel Ring Buffer Technology

With the development of the country and the progress of the society, people's daily life is more and more inseparable from electricity. With the rapid development of the speed and scale of my country's power grid construction, the scale of the power grid has jumped to the first place in the world. Guaranteeing the stability, continuity and quality of power supply has become an important task of the State Grid Corporation, which requires daily power inspections. However, at present, most of my country's electric power inspections rely on manual labor. On the one hand, due to the influence of geographical space, complex terrain, and changing weather, manual inspections have many limitations and dangers. On the other hand, due to the rising labor cost, the cost of power inspection is becoming more and more heavy. If power companies want to improve the efficiency and accuracy of inspections, they must use emerging technology push streaming live platforms to help inspectors in their daily inspections. In order to improve the inspection stability and inspection efficiency, this paper proposes and implements a multi-channel ring buffer video stream forwarding algorithm. This algorithm solves the problem of long delay in video stream forwarding on traditional inspection live broadcast platforms, so that inspectors can receive video streams in a timely and stable manner and play them smoothly. Based on the video fluency experiment in the production environment, the effectiveness of this algorithm is verified.
RetinaHand: Towards Accurate Single-Stage Hand Pose Estimation

Due to the high joint flexibility and deformation degree of hands, hand pose estimation is more challenging in the detection task. In order to ensure the accuracy of prediction, two-stage algorithms are proposed recently, which requires a huge and redundant model structure and is difficult to implement end-to-end deployment. In this paper, we propose a novel dynamic single-stage CNN (RetinaHand) for end-to-end 2D handpose estimation of RGB images based on RetinaNet. RetinaHand firstly extracts image features through the backbone with dynamic convolutional layers. In the neck module, we propose Context Path Aggregation Network (CPANet) that fuse different scale features and expands context information to improve performance. In addition, we use the idea of multi-task learning to add a keypoints heatmap regression branch on the basis of the existing classification and bounding box regression branch, and use multi-task loss training model. Experimental results on the Eric.Lee and Panoptic datasets consistently show that our proposed RetinaHand has comparable performance to existing hand pose estimation methods at more efficient inference rates.
Face Detection and Tracking Algorithm Based on Fatigue Driving

Aiming at the problem that drivers may have traffic accidents due to fatigue driving for a long time, a method of detecting and analyzing the facial features of drivers is proposed to study fatigue driving. The preprocessing operation of the video image is completed before detection, and the driver's face in the video image is detected by RestNet10-SSD target detection algorithm. The experimental results show that this method can better detect the face when the front, side and part of the driver's face are blocked. However, the detection rate of the algorithm is relatively slow and can not meet the real-time requirements, On this basis, Dlib tracking algorithm is used to optimize face detection. It turned out that the driver's face detection and tracking through the fusion algorithm not only has a high detection as well as recognition rate, but also meets the real-time requirements, and plays a key role in the subsequent fatigue driving detection.
Research on Image Description Generation Method Based on Visual Sentinel

Attention-based encoder-decoder framework has been currently utilize for image description. Many methods are to keep visual attention dynamic for each produced word. But the decoder does not need to obtain any visual information from the picture to forecast non-visual words at every moment. In addition, the development of non visual vocabulary may mislead and reduce the overall effectiveness of visual signals in generating guidelines.So we provides a new model AoANet++ to solve the above problems. At each time step, the model decides when to display the picture and when the language model generates the next word to produce a better sentence.The experimental results on MSCOCO dataset prove the effectiveness of this method, which improves the best CIDEr-D and SPICE scores of the initial report by 8.6% and 4.7% respectively.
Abdominal Multi-organ Localization with Adaptive Random Forest in CT Images

Medical image localization plays an important role in digital medical research, therapy planning, and delivery. However, the presence of noise and low contrast renders automatic abdominal multi-organ localization an extremely challenging task. In this study, we focus on an adaptive weighted random forest method for abdominal organ localization. Different from the traditional random forest, the proposed method first trains the weighted random regression forest, then iterates multiple weighted random forests to form a stronger one with the Adaptive Boosting technique, and finally performs the final regression through the random forest regressor to get the final bounding box. Our adaptive random forest algorithm can efficiently realize the direct mapping from voxel to organ position and size. Through the quantitative verification of CT scanning data, our method has higher accuracy than the original random forest method and AdaBoost method.
An Integrated Secure Scalable Blockchain Framework for IoT Communications

The Internet of Things (IoT) has shown great promise in the years since its invention and widespread acceptance by demonstrating its ability to adapt and improve manual processes while bringing them into the digital age. IoT's capacity to do so has elevated it to the ranks of the most promising technologies of our time. Despite the fact that IPv4 and IPv6 are being utilized to serve a growing number of devices in IoT connectivity, there are still issues with address space allocation and other security concerns, including scalability and poor access control methods. It is necessary to go through these difficulties and worries. Both of these organizations have spent a considerable amount of time in the vanguard of advancement in the study of IoT and Blockchain technology. Since IoT devices are capable of efficient two-way communication, integrating Blockchain technology is challenging. However, scalability is the biggest obstacle. The IoT Blockchain Framework discussed in the research article has the potential to be a game-changing solution to the issues that IoTs currently face, provided that it is used properly. Data access control and data interchange, transparency, and scalability without compromising privacy or dependability are all issues with the IoT paradigm that Blockchain technology may be able to efficiently address. Creating a local index that is scalable and does not interfere with either the local or global peer validation procedures is one way to limit the number of transactions that contact the global Blockchain. According to the findings, the blocks are significantly lighter and smaller than those seen in other parts of the world.
Using a Novel Hybrid Krill Herd and Bat based Recurrent Replica to Estimate &lt;br /&gt; the Sentiment Values of Twitter based Political Data

Big data is an essential part of the world since it is directly applicable to many functions. Twitter is an essential social network or big data replicating political information. However, big data sentiment analysis in opinion mining is challenging for complex information. In this approach, the Twitter-based political datasets are taken as input. Furthermore, the sentiment analysis of twitter-based political multilingual datasets like Hindi and English is not easy because of the complicated data. Therefore, this paper introduces a novel Hybrid Krill Herd and Bat-based Recurrent Replica (HKHBRR) to evaluate the sentiment values of twitter-based political data. Here, the fitness functions of the krill herd and bat optimization model are initialized in the dense layer to enhance the accuracy, precision, etc., and also reduce the error rate. Initially, Twitter-based political datasets are taken as input, and these collected datasets are also trained to this proposed approach. Moreover, the proposed deep learning technique is implemented in the Python framework. Thus, the outcomes of the developed model are compared with existing techniques and have attained the finest results of 98.68% accuracy and 0.5% error.