Efficient Detection Algorithm Research Articles

COMSATS Face: A Dataset of Face Images with Pose Variations, Its Design, and Aspects

Due to the three-dimensional formation and flexibility, a human face may appear different in numerous events. Researchers are developing robust and efficient algorithms for face detection, face recognition, and face expression analysis, causing several difficulties due to face poses, illumination, face expression, head orientation, occlusion, hairstyle, etc. To determine the effectiveness of the algorithms, it needs to be tested using a specific benchmark of face images/databases. Face pose is an important factor that severely reduces the recognition ability. In this paper, two contributions are made: (i) a dataset of face images with multiple poses is introduced. The dataset includes 850 images of 50 individuals under 17 different poses (0°, 5°, 10°, 15°, 20°, 25°, 30°, 35°, 55°, -5°, -10°, -15°, -20°, -25°, -30°, -35°, -55°). These images were captured closed to real-world conditions in the time span of five months in COMSATS University, Abbottabad Campus. Face images included in this dataset can reveal the efficiency and robustness of future face detection and face recognition algorithms. (ii) A comparative analysis of three face recognition algorithms such as PAL, PCA, and LDA is presented based on the proposed face database.

Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey

Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field.

A novel blockchain-enabled heart disease prediction mechanism using machine learning

Heart disease is one of the leading causes for death in men and women across the globe. Several characteristics that can be monitored to predict the heart disease in the earlier stage are blood pressure, cholesterol level, blood sugar level and body weight. The technology is revolutionizing the existing healthcare infrastructure. With the inclusion of Internet of Things (IoT), now we can monitor patients remotely, store their data, and process it for further analysis. However, the need is to propose new and advanced secured algorithms for fast processing and efficient detection of events. In this article, a machine learning based Sine Cosine Weighted K-Nearest Neighbour (SCA_WKNN) algorithm is proposed for the heart disease prediction that learns from the data being stored in blockchain. Since the data stored in the blockchain are tamper resistant, it acts as an authentic source for learning data and also as a secure storage environment for patient information. The performance of proposed SCA_WKNN is assessed in comparison with other algorithms in terms of accuracy, precision, recall, F-score, and root mean square error. Our analysis indicates that SCA_WKNN achieves 4.59% and 15.61% maximum accuracy than W K-NN and K-NN, respectively. Also, blockchain-based storage is compared with peer-to-peer storage in terms of latency and throughput. The blockchain-based decentralized storage achieves 25.03% maximum throughput than peer-to-peer storage.

Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection

Accurate and efficient pedestrian detection is crucial for the intelligent transportation system regarding pedestrian safety and mobility, e.g., Advanced Driver Assistance Systems, and smart pedestrian crosswalk systems. Among all pedestrian detection methods, the vision-based detection method is demonstrated to be the most effective in previous studies. However, the existing vision-based pedestrian detection algorithms still have two limitations that restrict their implementations, those being real-time performance as well as the resistance to the impacts of environmental factors, e.g., low illumination conditions. To address these issues, this study proposes a lightweight Illumination and Temperature-aware Multispectral Network (IT-MN) for accurate and efficient pedestrian detection. The proposed IT-MN is an efficient one-stage detector. For accommodating the impacts of environmental factors and enhancing the sensing accuracy, thermal image data is fused by the proposed IT-MN with visual images to enrich useful information when visual image quality is limited. In addition, an innovative and effective late fusion strategy is also developed to optimize the image fusion performance. To make the proposed model implementable for edge computing, the model quantization is applied to reduce the model size by 75% while shortening the inference time significantly. The proposed algorithm is evaluated by comparing it with the selected state-of-the-art algorithms using a public dataset collected by in-vehicle cameras. The results show that the proposed algorithm achieves a low miss rate and inference time at 14.19% and 0.03 seconds per image pair on GPU. Besides, the quantized IT-MN achieves an inference time of 0.21 seconds per image pair on the edge device, demonstrating the potentiality of deploying the proposed model on edge devices as a highly efficient pedestrian detection algorithm.

Transmission Line Parameter Error Identification and Estimation in Three-Phase Networks

This paper concerns the detection and identification of errors in the parameters of three-phase transposed as well as untransposed transmission line (TL) models used by various network applications. Such detailed models are increasingly needed in particular by power system applications where loads may not be balanced and/or TLs may not be symmetrical, and a full detailed three-phase solution may have to be obtained. To address this problem, the paper proposes an efficient algorithm for detection, identification, and estimation of parameter errors using synchronized phasor measurements. The suspect TL is detected using the modal domain networks in the first stage. The developed algorithm extends the previously developed largest normalized Lagrange multiplier (NLM) test for positive sequence parameters to the full coupled three-phase lines. An estimation method is also proposed for estimating the erroneous parameters, which takes into account the correlation of the parameters. To illustrate the effectiveness of the method, several tests are performed on the IEEE 118-bus system and a large 3474-bus utility system.

Research on DoS Traffic Detection Model Based on Random Forest and Multilayer Perceptron

Denial of service (DoS) attack is a typical and extremely destructive attack, which poses a serious threat to the Internet security and is highly concealed, making it difficult to detect. In response to this problem, the paper proposes an efficient DoS attack traffic detection method, Random Forest and Multilayer Perceptron hybrid network attack detection algorithm (RF-MLP). At first, it is adopted that the random forest algorithm can be used for feature selection and the optimal threshold can be determined by drawing a learning curve; therefore the optimal feature subset is determined. Then the optimal feature subset is used as the input of the multilayer perceptron for training. We will analyze the experimental results obtained using different configurations by varying the number of training neurons and the number of hidden layers of the multilayer perceptron network in order to improve the accuracy and reduce the number of false results. Using the real network traffic CICIDS2017 dataset and UNSW-NB15 dataset to evaluate the method in this paper, the results show that the model can effectively detect and classify DoS attacks, the accuracy rate can reach 99.83% and 93.51%, and there is also a significant reduction in the false alarm rate, verifying the effectiveness of the method and its ease of use.

Banknote and obstacle detection system for visually impaired people

ABSTRACT This paper presents the development of two perception modules for visually impaired people. In the first module, a robust YOLO-based neural network model is proposed to recognise the denomination of American, European, Mexican, and Colombian banknotes, achieving a detection performance over 97 % over all denominations. In the second module, an efficient obstacle detection algorithm based on stereo-vision is developed to prevent collisions while walking. Several performance tests and simulations were carried out using each module, proving that each is accurate enough to help blind people to complete the proposed perception tasks.

Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control

In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments. In this article, we propose to incorporate multi-armed bandit approaches into sequential change-point detection to develop an efficient bandit change-point detection algorithm based on the limiting Bayesian approach to incorporate a prior knowledge of potential changes. Our proposed algorithm, termed Thompson-Sampling-Shiryaev-Roberts-Pollak (TSSRP), consists of two policies per time step: the adaptive sampling policy applies the Thompson Sampling algorithm to balance between exploration for acquiring long-term knowledge and exploitation for immediate reward gain, and the statistical decision policy fuses the local Shiryaev–Roberts–Pollak statistics to determine whether to raise a global alarm by sum shrinkage techniques. Extensive numerical simulations and case studies demonstrate the statistical and computational efficiency of our proposed TSSRP algorithm.

Real-Time Embedded Implementation of Improved Object Detector for Resource-Constrained Devices

Artificial intelligence (A.I.) has revolutionised a wide range of human activities, including the accelerated development of autonomous vehicles. Self-navigating delivery robots are recent trends in A.I. applications such as multitarget object detection, image classification, and segmentation to tackle sociotechnical challenges, including the development of autonomous driving vehicles, surveillance systems, intelligent transportation, and smart traffic monitoring systems. In recent years, object detection and its deployment on embedded edge devices have seen a rise in interest compared to other perception tasks. Embedded edge devices have limited computing power, which impedes the deployment of efficient detection algorithms in resource-constrained environments. To improve on-board computational latency, edge devices often sacrifice performance, creating the need for highly efficient A.I. models. This research examines existing loss metrics and their weaknesses, and proposes an improved loss metric that can address the bounding box regression problem. Enhanced metrics were implemented in an ultraefficient YOLOv5 network and tested on the targeted datasets. The latest version of the PyTorch framework was incorporated in model development. The model was further deployed using the ROS 2 framework running on NVIDIA Jetson Xavier NX, an embedded development platform, to conduct the experiment in real time.

Sequential detection of a temporary change in multivariate time series

In this work, we aim to provide a new and efficient recursive detection method for temporarily monitored signals. Motivated by the case of the propagation of an event over a field of sensors, we assumed that the change in the statistical properties in the monitored signals can only be temporary. Unfortunately, to our best knowledge, existing recursive and simple detection techniques such as the ones based on the cumulative sum (CUSUM) do not consider the temporary aspect of the change in a multivariate time series. In this paper, we propose a novel simple and efficient sequential detection algorithm, named Temporary-Event-CUSUM (TE-CUSUM). By combining with a new adaptive way to aggregate local CUSUM variables from each data stream, we empirically show that the TE-CUSUM has a very good detection rate in the case of an event passing through a field of sensors in a very noisy environment.

Machine Learning Based Theft Detection Using Yolo Object Detection

Object detection and tracking could be an immense, vivacious however inconclusive and trending area of computer vision. Due to its immense use in official surveillances, tracking modules applied in security and lots of other’s applications have made researchers to devise a lot of optimized and specialized methods. For validation purpose live input video will be taken for the same where objects will be getting detected and it can be simulated same for real-time through external hardware added. In the end we see the proper optimized and efficient algorithm for object detection and alert for security. Object Detection is computer vision technique used to detect object and identify its localization. This technique is not only used to identify the location but also to identify which type of object it is. This CV technique is used to detect objects in real time while maintaining the level of accuracy. By bringing some advancement in it, this system can be very helpful for people to keep track of their precious things or devices which are very expensive and need to be protected. Open CV (Open-Source Computer Vision Library) is a library of programming functions mainly aimed at real- time computer vision. Open CV features GPU acceleration for real-time operations. This feature helps us to write computationally intensive codes in C/C++ and create a Python wrapper for it so that we can use these wrappers as Python modules.

Effective algorithms to detect stepping-stone intrusion by removing outliers of packet RTTs

An effective method to detect stepping-stone intrusion (SSI) is to estimate the length of a connection chain. This type of detection method is referred to as a network-based detection approach. Existing network-based SSI detection methods are either ineffective in the context of the Internet because of the presence of outliers in the packet round-trip times (RTTs) or inefficient, as many packets must be captured and processed. Because of the high fluctuation caused by the intermediate routers on the Internet, it is unavoidable that the RTTs of the captured packets contain outlier values. In this paper, we first propose an efficient algorithm to eliminate most of the possible RTT outliers of the packets captured in the Internet environment. We then develop an efficient SSI detection algorithm by mining network traffic using an improved version of k-Means clustering. Our proposed detection algorithm for SSI is accurate, effective, and efficient in the context of the Internet. Well-designed network experiments are conducted in the Internet environment to verify the effectiveness, correctness, and efficiency of our proposed algorithms. Our experiments show that the effective rate of our proposed SSI detection algorithm is higher than 85.7% in the context of the Internet.

Deconstructing gastrulation at single-cell resolution

Gastrulation movements in all animal embryos start with regulated deformations of patterned epithelial sheets, which are driven by cell divisions, cell shape changes, and cell intercalations. Each of these behaviors has been associated with distinct aspects of gastrulation1-4 and has been a subject of intense research using genetic, cell biological, and more recently, biophysical approaches.5-14 Most of these studies, however, focus either on cellular processes driving gastrulation or on large-scale tissue deformations.15-23 Recent advances in microscopy and image processing create a unique opportunity for integrating these complementary viewpoints.24-28 Here, we take a step toward bridging these complementary strategies and deconstruct the early stages of gastrulation in the entire Drosophila embryo. Our approach relies on an integrated computational framework for cell segmentation and tracking and on efficient algorithms for event detection. The detected events are then mapped back onto the blastoderm shell, providing an intuitive visual means to examine complex cellular activity patterns within the context of their initial anatomic domains. By analyzing these maps, we identified that the loss of nearly half of surface cells to invaginations is compensated primarily by transient mitotic rounding. In addition, by analyzing mapped cell intercalation events, we derived direct quantitative relations between intercalation frequency and the rate of axis elongation. This work is setting the stage for systems-level dissection of a pivotal step in animal development.

An efficient target detection algorithm via Karhunen‐Loève transform for frequency modulated continuous wave (FMCW) radar applications

This paper investigates an advanced effective signal processing technique to suppress noise, addressing a modern high-performance detection in the field of radar sensing. To achieve a higher accuracy, the frequency modulated continuous wave radar is taken as a case study to derive the algorithm based on Karhunen - Loève transform (KLT) before detection. KLT defines a linear projection of the signal statistics on the eigenfunctions domain, which makes the input-dependent signals orthogonal to each other under new eigen-basis and eigenvalues. The highest energy along slow time dimension of each range bin is concentrated in the transformed domain corresponding to the largest N eigenvalues. The performance of the algorithm is evaluated by different eigenvalue selection strategies. Numerical experiments are employed to obtain the relationship between signal-to-noise ratio and different eigenvalue selection strategies. Pertaining to the detection performance, constant false alarm ratio detector is applied to demonstrate the detection ability as a result of the processor by use of probability of detection (Pd).

An efficient ROI detection algorithm for Bangla text extraction and recognition from natural scene images

This research work plays a significant role in finding information from the scene images to fulfill the demand of real life applications like detection of license plate, navigation of robot and helping the visually impaired persons. Here, a new algorithm has been proposed and applied on the scene images to extract Region of Interest (ROI). All the Bangla words are then separated from a sentence by analyzing and applying the Connected Component (CC) method along with bounding box technology. Another new algorithm has been proposed and applied to apart and bring-out Bangla characters from the Bangla words. This algorithm works by the method of vertical scanning of the images of Bangla words. Finally, the extracted characters are recognized by using the Support Vector Machine (SVM) as a classifier which works with Histogram of Oriented Gradient (HOG) features. There are 500 scene images with variations in colors, writing styles and orientations in our designed database. The proposed algorithm yields the accuracy 92.70% and 93.23% in extraction of ROI and character respectively. In the recognition of Bangla characters (digits, Basic characters, and joined characters), the average accuracy is 99.16%. The recognition accuracy of Bangla characters using Convolutional Neural Network (CNN) is also calculated and the obtained result is 83.52%.

Compressive Sensing-Based Joint Activity and Data Detection for Grant-Free Massive IoT Access

Massive machine-type communications (mMTC) are poised to provide ubiquitous connectivity for billions of Internet-of-Things (IoT) devices. However, the required low-latency massive access necessitates a paradigm shift in the design of random access schemes, which invokes a need of efficient joint activity and data detection (JADD) algorithms. By exploiting the feature of sporadic traffic in massive access, a beacon-aided slotted grant-free massive access solution is proposed. Specifically, we spread the uplink access signals in multiple subcarriers with pre-equalization processing and formulate the JADD as a multiple measurement vectors (MMV) compressive sensing problem. Moreover, to leverage the structured sparsity of uplink massive access signals among multiple time slots, we develop two computationally efficient detection algorithms, which are termed as orthogonal approximate message passing (OAMP)-MMV algorithm with simplified structure learning (SSL) and accurate structure learning (ASL). To achieve accurate detection, the expectation maximization algorithm is exploited for learning the sparsity ratio and the noise variance. To further improve the detection performance, channel coding is applied and successive interference cancellation (SIC)-based OAMP-MMV-SSL and OAMP-MMV-ASL algorithms are developed, where the likelihood ratio obtained in the soft-decision can be exploited for refining the activity identification. Finally, the state evolution of the proposed OAMP-MMV-SSL and OAMP-MMV-ASL algorithms is derived to predict the performance theoretically. Simulation results verify that the proposed solutions outperform various state-of-the-art baseline schemes, enabling low-latency random access and high-reliable massive IoT connectivity with overloading.

A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems

In smart systems, attackers can use botnets to launch different cyber attack activities against the Internet of Things. The traditional methods of detecting botnets commonly used machine learning algorithms, and it is difficult to detect and control botnets in a network because of unbalanced traffic data. In this article, we present a novel and highly efficient botnet detection method based on an autoencoder neural network in cooperation with decision trees on a given network. The deep flow inspection method and statistical analysis are first applied as a feature selection technique to select relevant features, which are used to characterize the communication-related behavior between network nodes. Then, the autoencoder neural network for feature selection is used to improve the efficiency of model construction. Finally, Tomek-Recursion Borderline Synthetic Minority Oversampling Technique generates additional minority samples to achieve class balance, and an improved gradient boosting decision tree algorithm is used to train and establish an abnormal traffic detection model to improve the detection of unbalanced botnet data. The results of experiments on the ISCX-botnet traffic dataset show that the proposed method achieved better botnet detection performance with 99.10% recall, 99.20% accuracy, 99.1% F1 score, and 99.0% area under the curve.

Application of LDA and word2vec to detect English off-topic composition.

This paper presents an off-topic detection algorithm combining LDA and word2vec aiming at the problem of the lack of accurate and efficient off-topic detection algorithms in the English composition-assisted review system. The algorithm uses the LDA model to model the document and train the document through the word2vec, and uses the semantic relationship between the document's topics and words to calculate the probability weighted sum for each topic and its feature words in the document, and finally selects the off-topic composition by setting a reasonable threshold. Different F values are obtained by changing the number of topics in the document, and the best number of topics is determined. Experimental results show that the proposed method is more effective than vector space model, can detect more off-topic compositions, and the accuracy is higher, the F value is more than 88%, which realizes the intelligent processing of off-topic detection of composition, and can be effectively applied in English composition teaching.

Improved Gauss–Seidel detector for large‐scale MIMO systems

Large-scale multiple-input multiple-output (LS-MIMO) is one of the promising technologies beyond the 5G cellular system in which large antenna arrays at the base station (BS) improve the system capacity and energy-efficiency. However, the large number of antennas at the BS makes it challenging to design low-complexity high-performance data detectors. Thus, a number of iterative detection methods, such as Gauss–Seidel and conjugate gradient, are introduced to achieve complexity-performance tradeoff. However, their performance deteriorates for the systems with small BS-to-user antenna ratio or for the channels that exhibit correlation. This paper proposes a new efficient iterative detection algorithm based on the improved Gauss–Seidel iteration to address this problem. The proposed method performs one conjugate gradient iteration that enables better performance with less number of iterations. A new hybrid iteration is introduced and a low-complexity initial estimation is utilised to enhance detection accuracy while reducing the complexity further. In addition, a novel preconditioning technique is proposed to maintain the benefits of the proposed detector in correlated MIMO channels. It is mathematically demonstrate that the proposed detector achieves low approximated error. Theoretical analysis and numerical results show that the proposed algorithm provides a faster convergence rate compared to conventional methods.

Real-time factory smoke detection based on two-stage relation-guided algorithm

Recently, air quality analysis based on image sensing devices has attracted much attention. Since most smoke images in real scenes have challenging variances, which is difficult for existing object detection methods. To keep real-time factory smoke under efficient and universal social supervision, this paper proposes a mobile-platform-running efficient smoke detection algorithm based on image analysis techniques. We introduce the two-stage smoke detection (TSSD) algorithm based on the lightweight detection framework, in which the prior knowledge and contextual information are modeled into the relation-guided module to reduce the smoke search space, which can therefore significantly improve the performance of the single-stage method. Experimental results show that the proposed TSSD algorithm can robustly improve the detection accuracy of the single-stage method and the model has good compatibility for image resolution inputs. Compared with various state-of-the-art detection methods, the accuracy AP_{mean} of our proposed TSSD model reaches 59.24%, even surpassing the current detection model Faster RCNN. In addition, the detection speed of our proposed model can reach 50 ms (20 FPS), meeting the real-time requirements. This knowledge-based system has the advantages of high stability, high accuracy, fast detection speed. It can be widely used in some scenes with smoke detection requirements, such as on the mobile terminal carrier, providing great potential for practical environmental applications.