Miracle-dz/UniFAR-FTUs huggingface dataset:
DOC-SLAM: Robust Stereo SLAM with Dynamic Object Culling

To improve the accuracy of estimating camera trajectory in dynamic scenes, this paper proposes Dynamic Object Culling SLAM(DOC-SLAM), a stereo SLAM system that achieves good performance by culling actual moving objects in highly dynamic environments. DOC-SLAM combines the semantic information from panoptic segmentation with the point features from optical flow together to detect potential moving objects. And a moving consistency check module is designed to determine and remove the feature points in objects which are in motion so as to accomplish dynamic objects culling. Besides, for enhancing the robustness of our system, we devise a key point supplement strategy to provide sufficient and reliable key points for tracking. Meanwhile, the trajectory and landmarks are generated for localization and mapping of robots. The experimental evaluation on public datasets demonstrates that our DOC-SLAM can fit highly dynamic scenes.
Analysis of Financial Time Series Forecasting using Deep Learning Model

Time series data analysis and its forecasting is a foremost trend of stock market prediction. Accurate prediction of stocks brings more profit to market traders and helps in financial decision making. There are various machine learning and deep learning models assist to predict the stock market accuracy. Recent work concludes that various models like Support Auto Regressive Integrated Moving Average (ARIMA), Vector Machine (SVM), Artificial Neural Network (ANN), XGBoost, and Recurrent Neural Network (RNN) were preferred to obtain improved accuracy. In this study, a stacked Long Short-Term Memory (LSTM) model is proposed to predict the stock market accuracy and proposed model is compared with Moving Average (MA) and XGBoost models. The experiments are performed on the historical dataset of Infosys Limited of Bombay Stock Exchange, India (BSE30). The model is also evaluated through performance measures Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) and found that proposed stacked LSTM model outperformed the benchmark model.
Platinum Nanoparticle–Based Assay for Proteins by Resonance Light Scattering

ABSTRACT A simple, sensitive, platinum nanoparticle–based assay for trace amounts of protein by resonance light scattering (RLS) was studied. The RLS intensities for bovine serum albumin (BSA) and human serum albumin (HSA) were remarkably enhanced due to the interaction with Pt nanoparticles at pH 3.5 or 4.0, respectively, and the maximum RLS peak appeared at 390 nm. The RLS intensity increased proportionally with Pt concentration below 3.0 × 10−5 mol · L−1 but declined gradually above 4.0 × 10−5 mol · L−1. BSA within the range 0.1–3.0 µg · mL−1 and HSA of 0.1–2.7 µg · mL−1 can be detected with this method, and the detection limits were 4.2 and 10.7 ng · mL−1, respectively. The method was satisfactorily applied to the quantitative determination of total proteins in human serum samples with a maximum relative standard deviation (RSD) lower than 2.8% and recoveries in the range 100.4–102%.
A Review on Edge Intelligence based Collaborative Learning Approaches

Edge Intelligence (EI) based collaborative learning approaches have been researched in the recent years by several researchers. As majority of existing collaborative learning approaches are designed in the context of servers, design and development of novel systems are required in order to apply collaborative learning in resource constrained devices. Researchers possess different interpretations of the existing collaborative learning approaches. This paper presents an analysis of the existing work conducted on collaborative learning approaches on the domain of EI. The strongholds and drawbacks of the existing work will be analyzed to identify an ideal collaborative learning approach to be applied on the Internet of things (IoT) edge. The analysis will further include an investigation into the future enhancements and research gaps. The partitioned model training approach has been identified as the most ideal approach for the IoT edge based on the conducted critical analysis process. The reduction of communication overhead in the partitioned model training approach and the application of the partitioned model training approach in other unexplored deep learning model architectures have been identified as future research directions. This paper is a work in progress section of an ongoing research and in the future the findings will be used to design a system to apply the collaborative learning approach in IoT Edge and bridge the existing research gaps.
Effect of Absolute Cosine Value Regularization on VGG-19

The search for a more accurate Convolutional Neural Network (CNN) is a continuous process of unlimited possibilities. In order to simplify this process, the effects of algorithms on a network must be examined individually in order to determine their merits. The algorithm examined by this paper is a filter weight matrix regularizer designed to promote optimal information distillation. Absolute Cosine Value Regularization (ACVR) is a regularization technique hypothesized to increase the representational power of CNNs by using a Gradient Descent Orthogonalization algorithm to force the vectors that constitute their filters at any given convolutional layer to occupy unique positions in $\mathbb{R}^{n}$. This method has previously been given a mathematical definition, implementation description, and in addition, has been demonstrated to be effective at producing high diversity filter vectors in $\mathbb{R}^{3}$. However, the effect of this Regularizer on a full-scale CNN architecture has yet to be fully examined. This paper aims to determine the merits of this Regularizer by presenting experimental results generated by training the well-established CNN architecture VGG-19 with, and without its presence, using the CIFAR-10 image classification data set. This paper then goes on to propose the Dynamic-ACVR (D-ACVR) algorithm, demonstrating that at optimal configurations, this Regularization scheme can increase network accuracy by up to 3.12%.
A Robust Infrared Transducer Beyond 2K ×2K Pixels

A robust MEMS infrared thin film transducer beyond 2000 ×2000 pixels was proposed and fabricated for the first time. This novel transducer had excellent mechanical stability, time response and state-of-the-art pixel scale. The simulation and measured results of the transient temperature and radiation intensity were well consistent. High resolution infrared image was generated using the robust transducer. These results proved that the robust transducer is promising in infrared image generation of high frame rate, high resolution, and especially in large scale of pixels.
Improved Ring-Down Time and Axial Resolution of pMUTs via a Phase-Shift Excitation Scheme

We have proposed a phase-shift cancellation scheme to reduce the ring down period of ultrasonic transducers and increase axial resolutions. A piezoelectric micromachined ultrasonic transducer (pMUT) consisting of 80 elements with circular diaphragms of 415 μm in radius made of 2 μm-thick AlN layer has a measured resonant frequency of 160 kHz. The ring down period is minimized by emitting ten pulses followed by six additional pulses with a 180° phase difference. Results show the ring down period is shortened by 13% and 7.5% for a single element and the 80-element array, respectively. In addition, the axial distance sensing resolution is increased by 30% and 25% for the single element and the array structure, respectively. These results demonstrate the effectiveness of the proposed scheme for applications at a short distance and/or high axial resolution, including short-distance medical ultrasonic imaging, fingerprints, gesture identifications, and 3D imaging.
A novel translation estimation for essential matrix based stereo visual odometry

Visual Odometry (VO) plays an important role in autonomous navigation systems for vehicle localization. For traditional stereo visual odometry (SVO), we can estimate the rotation and translation of camera motion either simultaneously or separately where 3D information reconstructed from the stereo image is used as the input of the translation estimation. The accuracy of pose estimation is dependent on the uncertainty of 3D features as well as their portion used. This paper presents a novel translation estimation for essential matrix-based SVO to avoid the effectiveness of 3D feature uncertainty from stereo disparity. The rotation is extracted accurately from essential matrix of each pair of consecutive image frames on the left side; with a pre-estimated rotation matrix, the translation is rapidly and accurately estimated by solving a proposed linear closed-form only using 2D features as input with one-point RANSAC. The experimental results on the autonomous driving testing dataset (KITTI) indicate that the proposed approach enhances 20 % accuracy compared to traditional approaches in the same experimental scenario.
AdMat: A CNN-on-Matrix Approach to Android Malware Detection and Classification

The availability of big data and affordable hardware have enabled the applications of deep learning on different tasks. With respect to security, several attempts have been made to transfer deep learning’s application from the domain of image recognition or natural language processing into malware detection. In this study, we propose AdMat - a simple yet effective framework to characterize Android applications by treating them as images. The novelty of our study lies in the construction of an adjacency matrix for each application. These matrices act as “input images” to the Convolutional Neural Network model, allowing it to learn to differentiate benign and malicious apps, as well as malware families. During the experiment, we found that AdMat was able to adapt to a variety of training ratios and achieve the average detection rate of 98.26% in different malware datasets. In classification tasks, it also successfully recognized over 97.00% of different malware families with limited number of training data.
A Geographical Object Extraction Method Using User's Trajectories for Generating Artificial Maps

There have been many studies on automatic generation of artificial maps, but few have focused on the objects displayed on artificial maps. Therefore, in this research, we analyzed an object extraction method for automatically generating artificial map. In this research, objects are extracted using Biased Page Rank. Biased Page Rank is a method for improving the quality of search results by changing the rank source according to the interests and interests of individual users. By using this method for object extraction, it is possible to display the objects with a high evaluation value among the objects existing in a specific area on artificial map.
Design and Simulation Of BUCK-BOOST Type Dual Input DC-DC Converter for Battery Charging Application in Electric Vehicle

Electric Vehicles (EVs) are increasing in numbers at a rapid rate. In this phase change of the automobile sector for charging the electrical vehicles is one major barrier. Also, due to the limited range of single charge, range anxiety is an unresolved issue. This can be overcome by using on-road dynamic wireless charging. Also, to make the journey green, an on-board solar PV roof is considered as another charging source. To control the charging voltage for PV and dynamic charging, the buckboost converter is required as factors such as speed, ground clearance (different brands of EVs), misalignment, etc. can increase or decrease the charging voltage. Thus, this paper analysed a dual input buck-boost converter (DIBBC) and proposes charging of EV battery by solar PV and DynamicWPT. Investigation related to PV is not discussed in the paper. DIBBC small-signal model is derived and used to design a controller for three switches. Simulink model of the DIBBC is developed and the results of the simulation and performance of the DIBBC are discussed.
Deep Learning for Camera Autofocus

Most digital cameras use specialized autofocus sensors, such as phase detection, lidar or ultrasound, to directly measure focus state. However, such sensors increase cost and complexity without directly optimizing final image quality. This paper proposes a new pipeline for image-based autofocus and shows that neural image analysis finds focus 5-10x faster than traditional contrast enhancement. We achieve this by learning the direct mapping between an image and its focus position. In further contrast with conventional methods, AI methods can generate scene-based focus trajectories that optimize synthesized image quality for dynamic and three dimensional scenes. We propose a focus control strategy that varies focal position dynamically to maximize image quality as estimated from the focal stack. We propose a rule-based agent and a learned agent for different scenarios and show their advantages over other focus stacking methods.
ECG signal analysis using emerging tools in current scenario of health informatics

Health informatics is an important field which is responsible for overall growth of the country’s healthcare management. Today, cardiac related diseases are dominating around the world requires emerging tools in current healthcare scenario. Also, diagnosis of relevant pathologies should be completed before terminal stage. Traditional health care systems were required high computational cost. To analyze the cardiac status of the patient (subject), Electrocardiogram (ECG) is the main tool. ECG is accomplished in the form of signal consisting of three different waves known as P-wave, QRS complex- wave, and T-wave. Unfortunately, the screening of ECG may have minute variations in the obtained results which can lead to wrong heart treatment. This condition motivated to automate the medical informatics system based emerging tools such as 8th order IIR Butterworth filter for pre-processing, chaos analysis (or theory) for feature extraction, and random forest (RF) for classification purpose. The proposed technique is evaluated in terms of Recall (Re), Precision (Pr), and Accuracy (Acc) & obtained Re of 99.93%, Pr of 99.94%, and Acc of 99.86%. The proposed medical informatics system adds the merits of computer science, information science, & healthcare which prepares its major applications in intelligent systems, smart pacemaker, critical cardiac therapies, etc.
Mobility management in HetNets consider on QOS and improve Throughput

HetNet networks are one of the key features of LTE and the ones beyond. Despite the development created in HetNet, recent studies have shown that HOFs and PPs can cause serious problems given the small size of the cell. On these networks, given the difference in base station's coverage, the handover performance of the user equipment (UE) may significantly reduce; this is especially obvious in situations where UE passes through the small cells at a high-speed. In this paper, a context-aware mobility management (MM) approach is presented for small-cellular networks, where reinforcement learning. BSs collectively learn their long-term traffic loads and optimal extension of their cell range and schedule their UE according to the speed and rate of history data, along with the consideration of QOS, which is exchanged over rows. The proposed method not only provides better performance than MM in terms of power, but also provides better fairness conditions that reduce the likelihood of a Handover and Ping Pong failure. In the survey, macro and picocells learn how to optimize their longterm traffic, whereas in the short term, the UE communication process is based on history and timing based on speed and QOS. Thus, a long-term load balancing approach aims to improve overall system capacity while reducing the probability of Hand Over Failure (HoF) and Ping Pong (PP).
Comparative Analysis of Soil Properties to Predict Fertility and Crop Yield using Machine Learning Algorithms

Agriculture is an essential part of human lives. It is one of the major source of employment in India. More than half of the population depend upon agriculture. It is the backbone of our economy. Crop yield depends on many factors. One of the major factors which affect the yield of the crop is soil. Improvising the techniques to predict crop yield in different climatic conditions can help farmers and other stakeholders in better decision making in terms of agronomy and crop selection. Crop yield prediction includes forecasting the yield of the crop from previous historical data which consists of factors such as temperature, humidity, pH, rainfall and crop name. It gives us an idea for the finest predicted crop which will be cultivate in the field weather conditions. In the proposed work, a comparative analysis on soil properties to predict fertility and crop yield has been performed using machine learning algorithms. The analysis has been done on self –obtained dataset, for three crops - tomato, potato and chilli. The crop yield prediction has been done using K Nearest Neighbour algorithm, Naïve Bayes algorithm and Decision Trees classifier.
Antibacterial Activity of Silver Nanoparticles Synthesized by Bark Extract of Syzygium cumini

The unique property of the silver nanoparticles having the antimicrobial activity drags the major attention towards the present nanotechnology. The environmentally nontoxic, ecofriendly, and cost-effective method that has been developed for the synthesis of silver nanoparticles using plant extracts creates the major research interest in the field of nanobiotechnology. The synthesized silver nanoparticles have been characterized by the UV-visible spectroscopy, atomic force microscopy (AFM), and scanning electron microscopy (SEM). Further, the antibacterial activity of silver nanoparticles was evaluated by well diffusion method, and it was found that the biogenic silver nanoparticles have antibacterial activity against Escherichia coli (ATCC 25922), Staphylococcus aureus (ATCC 29213), Pseudomonas aeruginosa (ATCC 27853), Azotobacter chroococcum WR 9, and Bacillus licheniformis (MTCC 9555).
Body Shape-Guided Motion Retargeting to Reduce Effort on Human to Humanoid Landmark Placements

Motion retargeting reduces the animator’s efforts to create robot motion by adapting human motion. However, it still requires a number of manual landmark placements to achieve satisfactory whole-body retargeted motion. Therefore, to reduce efforts on placing landmarks for corresponding minor body parts, this paper proposes a whole-body motion retargeting method for a general humanoid robot that considers the body shape similarity in addition to traditional landmark-based similarity. An additional strategy that matches the volumetric distribution of the body shape between a human and a robot is presented as guidance to handle redundancy from fewer landmarks and to force consistent outcomes from ambiguous landmark placements. The kinematically constrained Gaussian mixture model, originally used as a volumetric model-based human tracking method, is adapted and modified to manage both the shape and the landmarks in the proposed method. The shape and landmark similarity metrics are respectively introduced, and the overall similarity metric is defined by composing the sum of both metrics with weighting coefficients to control the balance between two policies by animators. Then the expectation-maximization based optimization is utilized to calculate target robot angles with human demonstration frame by frame. Experimental results validate the effectiveness of body shape matching, controllability through weighting coefficients, noise robustness on self-retargeting, and generality on applying different humanoid robots.
Optimization of PBKDF2 Using HMAC-SHA2 and HMAC-LSH Families in CPU Environment

Password-Based Key Derivation Function 2 (PBKDF2) is widely used cryptographic algorithm in order to generate secure keys to a password in various occasions. For example, it is used for file encryption and implementation of authentication systems, and so on. However, the generated derived key has a lower entropy than a general cryptography key, so its use is limited. To compensate for this the number of iteration counts of PBKDF2 should be increased. As the number of repetitive tasks increases, the entropy of the derived key increases, but it takes more time to generate the derived key. We present various optimization methods of PBKDF2. The main idea of our proposed method is reducing redundant block operations and optimizing the internal process of underlying Pseudo Random Function (PRF). In other words, we integrate several redundant operations and make full use of constant values used in PBKDF2. We use two HMAC algorithms: one using SHA-2 family and one using LSH family as the PRF of PBKDF2 (SHA-2 family is the most widely used hash functions, and LSH family is the latest hash function recently developed in South Korea). With our techniques, our implementations outperform Korea Internet & Security Agency (KISA) implementation by 121.26%, 325.91%, and 231.89% for using SHA256, LSH256, and LSH512 respectively; and also outperform OpenSSL implementation by 39.59% using SHA512. In addition, we show that the internal process of PBKDF2 can be computed independently. With our multi thread technique, our PBKDF2 implementations outperform KISA implementation by 2,152.66%, 1,986.85%, and 1,591.36% for using SHA256, LSH256, and LSH512 respectively; and our PBKDF2-HMAC-SHA512 implementation outperforms OpenSSL implementation by 523.57%. With our proposed implementation techniques, higher security can be achieved with more iteration operations. Furthermore, our optimization techniques can be easily expanded to optimize the performance of PBKDF2 on GPGPU and embedded devices.
Active Distance Learning of Embedded Systems

The move from face-to-face to distance learning poses a challenge for courses that rely on hands-on experience such as embedded systems. In this course, students need to work with hardware and software to achieve various learning objectives. For full advantage, the hands-on experience should be aligned with the acquisition of related concepts and procedural knowledge. The alignment of conceptual learning with hands-on experience is a big challenge, in general, and for distance learning, in particular. This article describes how different learning technologies can be integrated to achieve such alignment for embedded systems in a distance learning mode. A framework for active, lecture-free learning was established using a learning management system, YouTube, various web resources, a hardware kit, and a software development environment. The learning activities were implemented as ungraded quizzes on Moodle with different types of questions. These include review questions, conceptual questions, procedural questions, brainstorming questions, code analysis questions, and code creation questions. Our students used the provided hardware kit and the software development environment to complete the learning activities throughout the semester without listening to any live or recorded lecture from our end. This instructional design was evaluated by analyzing learning data generated by Moodle as well as self-report data. The results show high student engagement and positive perceptions of the course content and the learning method. We believe that the proposed pedagogical framework of this design is of general value and can be adopted in other engineering courses with similar requirements of hands-on experience in distance learning.