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A real-time digital time-stamp sorting algorithm used in the In-Beam positron emission tomography (In-Beam PET) is presented. The algorithm is operated in the field programmable gate array (FPGA) and a small amount of registers, MUX and memory cells are used. It is developed for sorting the data of annihilation event from front-end circuits, so as to identify the coincidence events efficiently in a large amount of data. In the In-Beam PET, each annihilation event is detected by the detector array and digitized by the analog to digital converter (ADC) in Data Acquisition Unit (DAQU), with a resolution of 14 bits and sampling rate of 50 MS/s. Test and preliminary operation have been implemented, it can perform a sorting operation under the event count rate up to 1 MHz per channel, and support four channels in total, count rate up to 4 MHz. The performance of this algorithm has been verified by pulse generator and 22Na radiation source, which can sort the events with chaotic order into chronological order completely. The application of this algorithm provides not only an efficient solution for selection of coincidence events, but also a design of electronic circuit with a small-scale structure.

Multiple-object tracking is a fundamental computer vision task which is gaining increasing attention due to its academic and commercial potential. Multiple-object detection, recognition and tracking are quite desired in many domains and applications. However, accurate object tracking is very challenging, and things are even more challenging when multiple objects are involved. The main challenges that multiple-object tracking is facing include the similarity and the high density of detected objects, while also occlusions and viewpoint changes can occur as the objects move. In this article, we introduce a real-time multiple-object tracking framework that is based on a modified version of the Deep SORT algorithm. The modification concerns the process of the initialization of the objects, and its rationale is to consider an object as tracked if it is detected in a set of previous frames. The modified Deep SORT is coupled with YOLO detection methods, and a concrete and multi-dimensional analysis of the performance of the framework is performed in the context of real-time multiple tracking of vehicles and pedestrians in various traffic videos from datasets and various real-world footage. The results are quite interesting and highlight that our framework has very good performance and that the improvements on Deep SORT algorithm are functional. Lastly, we show improved detection and execution performance by custom training YOLO on the UA-DETRAC dataset and provide a new vehicle dataset consisting of 7 scenes, 11.025 frames and 25.193 bounding boxes.

Homomorphic sorting is an operation that blindly sorts a given set of encrypted numbers without decrypting them (thus, there is no need for the secret key). In this article, we propose a new, efficient, and scalable method for homomorphic sorting of numbers: polynomial rank sort algorithm. To put the new algorithm in a comparative perspective, we provide an extensive survey of classical sorting algorithms and networks that are not directly suitable for homomorphic computation. We also include, in our discussions, two of our previous algorithms specifically designed for homomorphic sorting operation: direct and greedy sort , and explain how they evolve from classical sorting networks. We theoretically show that the new algorithm is superior in terms of multiplicative depth when compared with all other algorithms. When batched implementation is used, the number of comparisons is reduced from $\mathcal {O}(N^2)$ O ( N 2 ) to $\mathcal {O}(N)$ O ( N ) provided that the number of slots is larger than or equal to the number of elements in the set. Our software implementation results confirm that the new algorithm is several orders of magnitude faster than many methods in the literature. Also, the polynomial sort algorithm scales better than the fastest algorithm in the literature to the best our knowledge although for small sets the execution times are comparable. The proposed algorithm is amenable to parallel implementation as most time consuming operations in the algorithm can naturally be performed concurrently.

Computer and communication systems and networks deal with many cases that require rearrangement of data either in descending or ascending order. This operation is called sorting, and the purpose of an efficient sorting algorithm is to reduce the computational complexity and time taken to perform the comparison, swapping, and assignment operations. In this article, we propose an efficient mean-based sorting algorithm that sorts integer/non-integer data by making approximately the same length independent quasi-sorted subarrays. It gradually finds sorted data and checks if the elements are partially sorted or have similar values. The elapsed time, the number of divisions and swaps, and the difference between the locations of the sorted and unsorted data in different samples demonstrate the superiority of the proposed algorithm to the Merge, Quick, Heap, and conventional mean-based sorts for both integer and non-integer large data sets which are random or partially/entirely sorted. Numerical analyses indicate that the mean-based pivot is appropriate for making subarrays with approximately similar lengths. Also, the complexity study shows that the proposed mean-based sorting algorithm offers a memory complexity same as the Quick-sort and a time complexity better than the Merge, Heap, and Quick sorts in the best-case. It is similar to the Merge and Heap sorts in view of the time complexity of the worst-case much better than the Quick-sort while these algorithms experience identical complexity in the average-case. In addition to finding part by part incremental (or decremental) sorted data before reaching the end, it can be implemented by parallel processing the sections running at the same time faster than the other conventional algorithms due to having independent subarrays with similar lengths.

A common procedure performed throughout biomedical research is the selection and isolation of biological entities such as organelles, cells and organoids from a mixed population. In this review, we describe the development and application of microraft arrays, an analysis and isolation platform which enables a vast range of criteria and strategies to be used when separating biological entities.(1) The microraft arrays are comprised of elastomeric microwells with detachable polymer bases (microrafts) that act as capture and culture sites as well as supporting carriers during cell isolation. The technology is elegant in its simplicity and can be implemented for samples possessing tens to millions of objects yielding a flexible platform for applications such as single-cell RNA sequencing, subcellular organelle capture and assay, high-throughput screening and development of CRISPR gene-edited cell lines, and organoid manipulation and selection. The transparent arrays are compatible with a multitude of imaging modalities enabling selection based on 2D or 3D spatial phenotypes or temporal properties. Each microraft can be individually isolated on demand with retention of high viability due to the near zero hydrodynamic stress imposed upon the cells during microraft release, capture and deposition. The platform has been utilized as a simple manual add-on to a standard microscope or incorporated into fully automated instruments that implement state-of-the-art imaging algorithms and machine learning. The vast array of selection criteria enables separations not possible with conventional sorting methods, thus garnering widespread interest in the biological and pharmaceutical sciences.

The Shell sort algorithm is one of the most practically effective in-place sorting algorithms. However, it is difficult to execute this algorithm with its intended running time complexity on data encrypted using fully homomorphic encryption (FHE), because the insertion sort in Shell sort has to be performed by considering the worst-case input data. In this paper, in order for the sorting algorithm to be used on the FHE data, we modify the Shell sort with an additional parameter $\alpha $ , allowing exponentially small sorting failure probability. For a gap sequence of powers of two, the modified Shell sort with input array length $n$ is found to have the trade-off between the running time complexity of $O(n^{3/2}\sqrt {\alpha +\log \log n})$ and the sorting failure probability of $2^{-\alpha }$ . Its running time complexity is close to the intended running time complexity of $O(n^{3/2})$ and the sorting failure probability can be made very low with slightly increased running time. Further, the near-optimal window length of the modified Shell sort is also derived via convex optimization. The proposed analysis of the modified Shell sort is numerically confirmed by using randomly generated arrays. For the practical aspect, our modification can be applied to any gap sequence, and we show that Ciura’s gap sequence, which is known to have good practical performance, is also practically effective when our modified Shell sort is applied. We compare our modified Shell sort with other sorting algorithms with the FHE over the torus (TFHE) library, and it is shown that this modified Shell sort has the best performance in running time among in-place sorting algorithms on homomorphic encryption scheme.

In industrial production systems, manufacturers often face difficulties in sorting different types of objects. Color and height-based sorting which is done manually by human is quite a tedious task and its needs countless time as well. For manual sorting, many workers are required, which can be quite expensive. Moreover, robots that can sort only color or height can’t be effective when there is a need of products with same color with different heights and vice versa. In this paper, a computer vision based robotic sorter is proposed, which is capable of detecting and sorting objects by their colors and heights at the same time. This work isn’t done before as height sorting of same shapes is a new technique, which is done with color sorting techniques by computer vision. It is equipped with a robotic arm having 6 degree of freedom (DOF), by which it picks up and then place objects according to its color and height, to a predetermined place as per the production system requirement. A camera with the computer vision software detects various colors and heights. Haar Cascade algorithm has been used to sort the products. This multi-DOF robotic sorter can be a remarkably useful tool for automating the production process completely, where multiple conveyor belts are used, which can reduce time complexity as well. In the proposed system, the efficiency of color and height sorting is around 99%, which proves the efficiency of our system. The overall improvement in the efficiency of the production process can be significantly enhanced by using this system.

Modern nonvolatile memories (NVMs) are widely recognized as energy-efficient replacements of classical memory/storage media, such as SRAM, DRAM, and mechanical hard disk. Among the popular NVMs, the skyrmion racetrack memory (SK-RM) is well known for its high storage density and unique supports of insert/delete operations. However, the existing algorithms designed for classical media might experience serious performance degradation when working on the SK-RM, due to the distinct characteristics of SK-RM. Thus, the existing algorithms should be redesigned to adapt to the brand-new memory model based on the SK-RM, so as to fully reveal the potentials of SK-RM. In particular, many existing algorithms tend to access the in-memory data in a random-hopping fashion, which generates many time-consuming shift operations of SK-RM. It is therefore crucial for the existing algorithms to eliminate unnecessary shift operations of SK-RM to boost the performance of the algorithms. In many modern applications, such as multimedia and data analysis, it is a common operation to process two or more arrays/vectors of data to perform certain computation tasks. In the arrays/vectors, an appropriate data placement strategy is critical for avoiding unnecessary shift operations of SK-RM. The observation thus motivates this work in proposing a recursive back-to-back data placement manner to effectively reduces the shift operations of SK-RM. To demonstrate the back-to-back data placement, we take sorting algorithms as a case study, and propose a novel shift-limited sorting algorithm for SK-RM. Analytical studies show that the shift-limited sort effectively enhances the time complexity of classical merge sort from $\mathcal {O}(dn\lg n)$ to $\mathcal {O}(n\lg n)$ , where $d$ is the bit distance between adjacent access ports on the nanotracks of the SK-RM. After that, the efficacy of the proposed shift-limited sort is then verified by experimental studies, where the results are encouraging.

Nowadays, 5G profoundly impacts video surveillance and monitoring services by processing video streams at high-speed with high-reliability, high bandwidth, and secure network connectivity. It also enhances artificial intelligence, machine learning, and deep learning techniques, which require intense processing to deliver near-real-time solutions. In video surveillance, person tracking is a crucial task due to the deformable nature of the human body, various environmental components such as occlusion, illumination, and background conditions, specifically, from a top view perspective where the person’s visual appearance is significantly different from a frontal or side view. In this work, multiple people tracking framework is presented, which uses 5G infrastructure. A top view perspective is used, which offers broad coverage of the scene or field of view. To perform a person tracking deep learning-based tracking by detection framework is proposed, which includes detection by YOLOv3 and tracking by Deep SORT algorithm. Although the model is pre-trained using the frontal view images, even then, it gives good detection results. In order to further enhance the accuracy of the detection model, the transfer learning approach is adopted. In this way, a detection model takes advantage of a pre-trained model appended with an additional trained layer using top view data set. To evaluate the performance, experiments are carried out on different top view video sequences. Experimental results reveal that transfer learning improves the overall performance, detection accuracy, and reduces false positives. The deep learning detection model YOLOv3 achieves detection accuracy of 92% with a pre-trained model without transfer learning and 95% with transfer learning. The tracking algorithm Deep SORT also achieves excellent results with a tracking accuracy of 96%.

The most important process before packaging and preserving agricultural products is sorting operation. Sort of carrot by human labor is involved in many problems such as high cost and product waste. Image processing is a modern method, which has different applications in agriculture including classification and sorting. The aim of this study was to classify carrot based on shape using image processing technique. For this, 135 samples with different regular and irregular shapes were selected. After image acquisition and preprocessing, some features such as length, width, breadth, perimeter, elongation, compactness, roundness, area, eccentricity, centroid, centroid nonhomogeneity, and width nonhomogeneity were extracted. After feature selection, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were used to classify the features. The classification accuracies of the methods were 92.59 and 96.30, respectively. It can be stated that image processing is an effective way in improving the traditional carrot sorting techniques., Evaluation of image processing technique and discriminant analysis methods in waste control of carrot fruit in postharvest stage.

In a sensory or consumer setting, panelists are commonly asked to rank a set of stimuli, either by the panelist's liking of the samples, or by the samples' perceived intensity of a particular sensory note. Ranking is seen as a “simple” task for panelists, and thus is usually performed with minimal (or no) specific instructions given to panelists. Despite its common usage, seemingly little is known about the specific cognitive task that panelists are performing when ranking samples. It becomes quickly unruly to suggest a series of paired comparisons between samples, with 45 individual paired comparisons needed to rank 10 samples. Comparing a number of elements with regards to a scaled value is common in computer science, with a number of differing sorting algorithms used to sort arrays of numerical elements. We compared the efficacy of the most basic sorting algorithm, Bubble Sort (based on comparing each element to its neighbor, moving the higher to the right, and repeating), vs a more advanced algorithm, Merge Sort (based on dividing the array into sub arrays, sorting these sub arrays, and then combining), in a sensory ranking task of 6 ascending concentrations of sucrose (n = 73 panelists). Results confirm that as seen in computer science, a Merge Sort procedure performs better than Bubble Sort in sensory ranking tasks, although the perceived difficulty of the approach suggests panelists would benefit from a longer period of training. Lastly, through a series of video recorded one-on-one interviews, and an additional sensory ranking test (n = 78), it seems that most panelists natively follow a similar procedure to Bubble Sorting when asked to rank without instructions, with correspondingly inferior results to those that may be obtained if a Merge Sorting procedure was applied. Results suggests that ranking may be improved if panelists were given a simple set of instructions on the Merge Sorting procedure., Graphical abstract Image 1, Highlights • Ranking is common in both sensory and consumer testing. • Despite its wide adoption, no standard set of procedural instructions exists on how to perform a ranking exercise. • This report details 2 approaches, borrowed from computer science, to ensure that all panelists are ranking using the same strategy. • In addition, a separate panel were asked to rank the same samples without instruction, with in-depth follow up interviews on a smaller cohort to delineate the natural approach. • The strategies are contrasted in terms of ease, speed and accuracy.

Sorting is studied in this paper as an archetypal example to explore the optimizing power of consensus. In conceptualizing the consensus sort, the classical hill-climbing method of optimization is paired with the modern notion that value and fitness can be judged by data mining. Consensus sorting is a randomized sorting algorithm which is based on randomly selecting pairs of elements within an unsorted list (expressed in this paper as a permutation), and deciding whether to swap them based on appeals to a database of other permutations. The permutations in the database are all scored via some adaptive sorting metric, and the decision to swap depends on whether the database consensus suggests a better score as a result of swapping. This uninformed search process does not require the definition of the concept of sorting, but rather depends on selecting a metric which does a good job of distinguishing a good path to the goal, a sorted list. A previous paper has shown that the ability of the algorithm to converge on the goal depends strongly on the metric which is used, and analyzed the performance of the algorithm when number of inversions was used as a metric. This paper continues by analyzing the performance of a much more efficient metric, the number of cycles in the permutation.

The article describes a special application of own design that allows students studying algorithms for sorting and searching data to observe the process and analyze the advantages and disadvantages of several methods to better understand the principles of their work. Some algorithms for sorting and searching data are considered, existing software systems (Internet sites) for solving the problem, their features, advantages, and disadvantages are analyzed. The development of an object-oriented model of the software system by means of visual modeling UML (diagrams of use cases and a class diagram are presented) and a functional model in BPWin notation (first and second levels are given). The available algorithms are listed: Bubble Sort, Insert Sort, Selection Sort, Merge Sort, Quick Sort, Shaker Sort, Gnome Sort, Shell Sort, Binary Sort, Sequential Search, Binary Search. Since the real operating time of the algorithm on a modern computer is too short, and the user will not have time to understand the principles of its operation, it was decided to add a delay after each step, which significantly increased the time when demonstrating the operation of one algorithm. Examples of using the developed application are given data entry and demonstration of the sorting algorithm by exchanges, comparison of sorting algorithms for randomly filling an array of 25000 elements according to the criteria "Running time" and "Number of iterations". The possibility of changing the interface language is noted. The use of the help system is described. A typical sequence of work with the created application is considered. It is concluded that the developed application can become an additional element of information and communication teaching aids in the presentation of relevant disciplines – for example, "Algorithms and data structures" for specialties of the industry 12 "Information technology".

Microfluidic droplet sorting enables the high-throughput screening and selection of water-in-oil microreactors at speeds and volumes unparalleled by traditional well-plate approaches. Most such systems sort using fluorescent reporters on modified substrates or reactions that are rarely industrially relevant. We describe a microfluidic system for high-throughput sorting of nanoliter droplets based on direct detection using electrospray ionization mass spectrometry (ESI-MS). Droplets are split, one portion is analyzed by ESI-MS, and the second portion is sorted based on the MS result. Throughput of 0.7 samples s-1 is achieved with 98 % accuracy using a self-correcting and adaptive sorting algorithm. We use the system to screen ≈15 000 samples in 6 h and demonstrate its utility by sorting 25 nL droplets containing transaminase expressed in vitro. Label-free ESI-MS droplet screening expands the toolbox for droplet detection and recovery, improving the applicability of droplet sorting to protein engineering, drug discovery, and diagnostic workflows.

Divide and Conquer is a well-known technique for designing algorithms. Many of the existing algorithms are a product of this popular algorithm design technique. Such include Quick sort and Merge sort sorting algorithms. These two algorithms have been widely employed for sorting, however, determining the most efficient among the two has always been a contentious issue. Most of the existing literature have compared these algorithms using machine dependent factors such as computational complexity but few have employed machine independent factors such as internal/external sorting, algorithm complexity: best, average, and worst cases, memory usage, stability etc. This study intends to contribute to this discuss using both machine dependent and independent factors. The implementation was carried out in MATLAB programming environment and the internal system clock was set to keep track of the time required for sorting. Results obtained revealed that in terms of computational speed using array of small sizes, Quick sort algorithm is faster, though Merge sort algorithm is faster with array of large sizes. Also, both algorithms are of O(nlogn) best case and average case complexity while the worst case for quicksort is O(n2) and that of merge sort remains unchanged. In terms of stability, Quick sort is stable while Merge sort is not. Despite the excellent performance of Merge sort algorithm, the need for an auxiliary memory for sorting makes it less preferable than Quick sort algorithm for applications where a good cache locality is of paramount importance.

Many sorting algorithms have been developed to enhance the performance of important applications from different domains such as commercial commuting, operational research and scientific simulation. In this paper, we propose a new in-place sorting algorithm called OneByOne (OBO) sort. When compared to selection sort and bubble sort algorithms, OBO demonstrated faster run time at different array sizes. OBO also showed nearly equal performance when compared to the insertion sort algorithm.

none 4 si Mobile devices performance and uptime heavily depend on energy consumed at the hardware and software level. Hence implementation of efficient algorithms has become a crucial aspect for increasing the performance of such systems and battery life for mobile devices. Sorting algorithms are implicitly the building block of many program implementation. Over the past years, researchers have spent more time optimizing hardware components to reduce their energy consumption. However, it has not been so clear which sorting algorithm is more energy efficient. In this study, we conduct a meta-analytical comparison of the energy consumed by the two most common sorting algorithms namely quick sort and merge sort. Our study mainly focused on energy consumption for mobile devices and embedded systems. For our meta-analysis and literature review, we took into consideration studies published not more than 20 years ago. The meta-analytical results show that there is no significant difference between both algorithms in terms of energy efficiency. none Dlamini, Gcinizwe; Jolha, Firas; Kholmatova, Zamira; Succi, Giancarlo Dlamini, Gcinizwe; Jolha, Firas; Kholmatova, Zamira; Succi, Giancarlo

Aiming to address the problem of counting multi-target moving vehicles in the various complex traffic environments, this paper proposes a detecting and tracking method based on YOLO (You Only Look Once) and Deep Sort, and evaluates its performance with public dataset (TUA-DETRAC) and two self-collection datasets. The YOLOv4 algorithm is firstly used to detect each moving vehicles, and then Deep Sort algorithm is adopted to track multi-target vehicles. The experimental results show that moving vehicles can be effectively detected and tracked in real time under different traffic environments including daytime, nighttime, rainy and crowded scenes. The experimental results show that the proposed method can reach 93% average detection accuracy with 20fps of tracking speed, and is capable of dealing with different traffic and climate conditions.

Sorting provides a method of rearrangement of elements in ascending or descending order. In this paper, we are introducing a new sorting algorithm called RevWay sort in which the two consecutive numbers are compared from left and then from right. This process is repeated \(((n/2)+1)\) times, where n is the total number of elements. We have compared running time of the proposed algorithm with other sorting algorithms. We run the algorithm starting from 10,000 to 50,000 elements. We found that the newly proposed RevWay sort yields lesser running time compared to bubble and selection sort. For 10,000 elements, RevWay sort takes 203.636 ms, whereas bubble sort takes 364.8243ms and selection sort consumes 337.5543 ms.

Sorting is an essential operation which is widely used and is fundamental to some very basic day to day utilities like searches, databases, social networks and much more. Optimizing this basic operation in terms of complexity as well as efficiency is cardinal. Optimization is achieved with respect to space and time complexities of the algorithm. In this paper, a novel left-field N-dimensional cartesian spaced sorting method is proposed by combining the best characteristics of bucket sort, counting sort and radix sort, in addition to employing hashing and dynamic programming for making the method more efficient. Comparison between the proposed sorting method and various existing sorting methods like bubble sort, insertion sort, selection sort, merge sort, heap sort, counting sort, bucket sort, etc., has also been performed. The time complexity of the proposed model is estimated to be linear i.e. O(n) for the best, average and worst cases, which is better than every sorting algorithm introduced till date.

In many industrial applications, the key job that needs to be accomplished in the final packaging segment is to sort the large amount of goods in the industry in the required manner. Material sorting in an industry is a repetitive manufacturing process, which would normally be carried out manually. With this continuous manual sorting procedure comes along the problems with accuracy of the output such as, wrong item in a wrong section or container/package. In-order to have an accurate product supplied to the market for the consumers our automatic sorting machine may instead be used to eliminate these disadvantages of conventional packaging by using the TCS3200 colour sensor that can distinguish the distinct coloured objects and categorize it effectively and quickly. With the help of the Arduino, the system can be programmed according to the desired work that needs to be done in industries, using the conveyor belt these objects can be transported easily and effectively without requiring the labour work and shifted to the particular direction to separate the coloured objects using servo motor. The servo can push the object to a different conveyor that can move the object to a different direction making it all more reliable and accurate. Objects can be sorted using a variety of techniques, including sorting by scale (height, length, etc. ), color, weight, computer vision (image processing), content of an object, and so on. Electromagnetic sorting methods, for example, are used to separate ferromagnetic materials from gas in thermal power plants. In this paper, we demonstrate how the sorting object functions using colour sensor. The framework accomplishes this task with the help of connected microcontroller ATmega 328p in the Arduino UNO, a color detector TCS3200 that helps to colour sensor which helps in sensing the colour of the object that is placed in front of it.

Definitive goal of various modern sorting algorithm is to re-arrange sequence of numbers or words in ascending or descending order. Compared to other exisiting sorting algorithms, the modern sorting algorithms used to provide execution time, running time and complexity based on the given number of inputs. Thus, the modern algoirithms helps us to know more about re-arranging or re-ordering numbers or words including how they fastly operate inputs in a specific manner of time. This paper focused on asymptotic analysis pertaining to the running time consumed by most recent sorting algorithms based on the number of elements in an array. Through computation of the running time, it is really possible to know how sorting algorithms tends to become not only efficient but also a manner its usefulness. Various contemporary sorting algorithms like gnome sort, shaker sort, radix sort, shell sort and comb sort have considered and being compared in this study.

Mobile smartphones/laptops are becoming much more powerful in terms of core count and memory capacity. Demanding games and parallel applications/algorithms can hopefully take advantages of the hardware. Our parallel MSPSort algorithm is one of those examples. However, MSPSort can be optimized and fine tuned even further to achieve its highest capabilities. To evaluate the effectiveness of MSPSort, two Linux systems are quad core ARM Cortex-A72 and 24-core AMD ThreadRipper R9-2920. It has been demonstrated that MSPSort is comparable to the well-known parallel standard template library sorting functions, i.e. Balanced QuickSort and Multiway MergeSort in various aspects such as run time and memory requirements.

Sorting plays a fundamental role in computer science. It has far reaching applications in database operations and data science tasks. An important class of sorting keys are strings and among all string sorting methods, radix sort is a simple but effective algorithm. Many works have been studied to accelerate radix string sort. One typical approach is to process multiple characters in each sorting pass. However, this approach incurs the crucial issue of the radix being too large. To address the problem, we introduce a novel multi-character encoding based method that can significantly reduce the radix. This new encoding scheme takes advantage of the sparse alphabet space usage as well as the sparsity of distinguishing prefixes of the inputs which are commonly seen in real-world datasets. Combining the effective encoding scheme with an adaptive sampling process to generate the encoding efficiently, our proposed sorting algorithm essentially blends radix sort with sample sort and achieves substantial improvement over other sorting approaches. The results on both real datasets and synthetic datasets show that our method yields an average 4.85× performance improvement over C++ STL sort[21], 1.47× improvement over the state-of-the-art Radix Sort on strings implementation[19] and 2.55× over the multikey quicksort[6]. Preliminary tests in a multi-core environment also show it is competitive or better than the most recent parallel string sorting algorithm pS5[8] which demonstrates the scalability of our method.

In this paper, we are watching out for the issue of different article tracking in a single packaging. To oblige the course of action, we at first recognize all of the articles in the packaging and subsequently designate wonderful ID’s to all of them until they move out of the edge of reference. We proposed to deal with this issue in three stages, which are according to the accompanying: perceiving all of the articles in the current edge followed by identifying them, and thereafter finally, we intend to follow them. We took this issue announcement considering the way that in spite of the way that we have various estimations identifying with this situation, the MOT-A Figure is not adequate. So to manage on something practically the same, we hope to use Kalman channels for redesigned results and differentiated our MOT-An estimation and right now open works in this space and the results were adequate. The key computation used by us was YOLO after which the articles are recognized and organized under various classes. After this, for settled motion Figure and feature extraction, Kalman channels were used to show these states and as of late perceived articles are settled of a current edge, behind the linking which is created for new acknowledgment. It is done using the Deep SORT algorithm, which is mainly a Deep alliance metric using a SORT computation. We used kalman channels because they improve the precision of our model and besides redesigned the result for a most part. On practically identical lines, we have used YOLO to do entity disclosure as well as ID, both at the similar timing. It can in like manner be treated as an identifier, which when is applied a lone neural association can expect the hopping box and do multiple class gathering. Our proposed plan can have distinctive multi-reason uses in our consistently life like it can hold people back from getting kept at a singular spot, especially during COVID times and raising the alert to caution people. We can similarly use this approach to manage address problems concerning manage the chiefs and perception zoo/biodiversity stops if there ought to emerge an event of contentions between different animals where checking each and every transgression gle area is no basic task.

A parallel even-odd sort algorithm has been identified and developed as a basis. The switching circuit consists of two alternating options for switching elements in pairs. These switchings are based on the local on-device assembling in pairs of array elements having adjacent indices. Moreover, such local on-device assembling of elements in pairs leads to “small” movements of elements along the length of array and the regular character of pairs generation. In each pair, we performed an operand comparison-exchange operation. Reducing the time of sorting is based on designing the switching options with a ring structure of elements. A comparison of classical and modified algorithms has shown the advantage of array ring structure, in which pairs of boundary elements are created at each sorting step. The given paper shows the advantage of the modified sorting over the standard even-odd sort by 15% −18% for the operation of industrial intelligent systems.

The issue of productivity and energy is an important objective of the optimization of parallel applications. Thesize of the problem for a large number of data on multiprocessor platforms forces the use of parallel algorithms.Efficient management of large memories using modern processors in Big data processing requires innovativetechniques and efficient algorithms. For years have found the results of tests conducted on methods for usein various computing environments and improvements. This article shows the energy consumption analysisby parallel sorting algorithms. Sort algorithms are used in information systems and databases, to select andorganize the information. The subject of this article is research into energy consumption and computationalcomplexity for parallel sorting methods by merging compared to classic methods. The tests carried out confirmthe reduction of energy consumption by using parallel sorting algorithms. The presented parallel fast sort andparallel modified merge sort for large task dimensions have less power consumption than classic methods andcan be used successfully in NoSQL databases.

The GPU is an effective architecture for sorting due to its massive parallelism and high memory bandwidth. However, for input datasets that exceed global memory capacity, the communication overhead between host (CPU) and GPU may degrade the overall performance of heterogeneous approaches. Thus, to achieve performance gains over multi-core parallel CPU algorithms, heterogeneous sorting using the GPU needs to obviate communication overheads. We provide a detailed overview of current host-GPU data transfer mechanisms and advance several methods of mitigating the associated performance bottlenecks. Using these methods, we develop a heterogeneous CPU/GPU sorting algorithm that effectively exploits the architecture. Furthermore, we demonstrate that, while out-of-place GPU sorting achieves the best performance, an in-place sort has the potential to further reduce some host-side bottlenecks, which encourages several future research priorities. Our approaches mitigate several bottlenecks, as demonstrated on single- and dual-GPU platforms, achieving speedups up to 3.47× over the parallel reference implementation on the CPU. We discuss future research for heterogeneous sorting in the multi-GPU NVLink era.

The present work is dedicated to the methods of sorting data and analysis of their complexity. Thereare several reasons for analysis of algorithms. One of them is necessity to evaluate the boundary values for theamount of memory or time required by some algorithm for successful data processing. The sorting process canimplemented by various algorithms. The choice of algorithm depends on the structure of the data being processed. Inpractice two classes of sorting are used: external and internal. If the amount of input data fits within the range ofavailable internal RAM they say about the algorithms for internal sorting. But if the input data are stored in files, i.e.external memory, they say about external sorting.This work demonstrates the fundamental algorithms of internal soritng with quadratic time and quickalgorithms with О(n*logn) complexity. Quick sorting algorithms such as merge sorting and Hoare“s quicksortalgorithms are given. Also simpler methods of internal sorting such as exchange sort, Shell“s method, insertion andselection algorithms are discussed as well. The article describes the idea behind these methods, agorithms on whichthey are based, complexity of these algorithms and provides concrete examples of programs.

Sorting is the most fundamental operation in database system. There are many classical sorting algorithms and among them the most commonly-used sorting algorithm in modern database system is merge sort. Merge sort is an efficient, general-purpose, comparison-based sorting algorithm. As merge sort is based on a divide and conquer model, it has found wide use in parallel computing algorithms. In this study, the authors present an adaptive sorting algorithm based on merge sort which is called array sort. Array sort not only takes advantages of merge sort, but also simplify the merge step by using a tag array. The proposed implementation consists of three phases: tag array creation phase, tag array split phase and list merge phase. Instead of just evaluating array sort in a single thread environment, the authors also run their experiments on multi-thread to test how array sort performs.

In the development of advanced software and hardware for modern computing, the interest is the improvement of methods of associative processing of information such as procedures of sorting and selection. That ensures the realization of effective search for the required information in the data arrays. The need for parallel non-processing of large amounts of information entails the appropriate organization of associative memory, as well as the development and using of perspective technical devices. The sorting is important procedure in such application areas as solving economic problems, managing databases, sorting of IP addresses in computer networks, processing signals and images (for example, in nonlinear median image filtering). The analysis of known sorting methods have shown that the most effective method of parallel sorting, taking into account its hardware implementation by the sorting network, is the pairwise exchange. At the same time, the degree of parallelism of any sorting method for its hardware implementation directly depends on the number of comparison schemes that work in parallel in each view. For a pairwise exchange method, the degree of parallelism is determined by the value ]n/2[, where n is the number of input numerical values ​​or the dimension of the input linear number array. In this article methods of implementing of the sorting algorithm by the method of pairwise exchange with the link topology between elements of the number array in the form of "tape" and "ring" are analyzed. For example, the parallel sorting algorithm using the pairwise exchange method is described. The simulation at a high-level C ++ language is done. The obtained statistical and graphic results of modeling are analyzed. The analysis of graphical modeling results shows the dependence of the form O(n) between the number of sort cycles and the dimensionality n of the input array. That confirms the effectiveness of the hardware implementation of sorting by pairwise exchange on the sorting network due to the regularity of the structure and connections in the sorting process. The ability to statistically determine not only the number of sorting cycles with a given dimension of the number array, but also the corresponding number of comparisons and transposition greatly extends the possibilities of improving the known and creating new ways of synchronous sorting of elements of a linear array by hardware in the form of a sorting network.

Although mutual information is a general method usually being used to measure the similarity of two images, the robustness is questionable due to the absence of spatial information. The purpose of this study is to develop a feasible sorting technique for 4D-CT. A novel sorting algorithm named mutual information and edge gradient (MIEG), which includes spatial information by combining mutual information with a term based on the edge gradient of the image, was proposed to sort sequential CT images. The edge of image was extracted by calculating the wavelet transform modulus maxima, and the gradient similarity coefficient of the edge image was calculated and used to multiply by mutual information to form the final similarity metric. This sorting technique was validated by comparing the 4D-CTs reconstructed using MIEG and Real-time Position Management system (Varian Medical Systems, Inc., Palo Alto, CA). Tumor motion trajectories derived from 4D-CTs were analyzed in three orthogonal directions. Their correlation coefficients (CC) and differences in tumor motion magnitude (Ds) were determined. In addition, Dice similarity coefficient (DSC) was used to measure how well the tumor volumes segmented from the two 4D datasets overlapped with each other. For all patients, the mean CC values were >0.95 in all directions. The mean Ds were

Non-dominated sorting is one of the prominent steps in developing any Pareto-dominance based multi-objective evolutionary algorithm. The computational complexity of any Pareto-dominance based multi-objective evolutionary algorithm primarily depends on this step. Thus, researchers are working on reducing the complexity of this step. Recently, an efficient approach for non-dominated sorting known as Best Order Sort (BOS) has been proposed. This approach is very efficient in terms of the number of comparisons between the solutions. Another advantage of this approach is that while comparing two solutions, the number of objectives which are compared is reduced from the actual number of objectives associated with each solution. However, in spite of these two advantages, this approach is not suitable in its current form for cases in which we have duplicate solutions. This paper generalizes BOS to handle duplicate solutions while retaining both of its advantages. We call this generalized version Generalized Best Order Sort (GBOS). The present work shows that BOS can be generalized to handle its limitation without compromising its time and space complexity.

In vivo microscopy is an important tool to study developing organs such as the heart of the zebrafish embryo but is often limited by slow image frame acquisition speed. While collections of still images of the beating heart at arbitrary phases can be sorted to obtain a virtual heartbeat, the presence of identical heart configurations at two or more heartbeat phases can derail this approach. Here, we propose a dual illumination method to encode movement in alternate frames to disambiguate heartbeat phases in the still frames. We propose to alternately acquire images with a ramp and pulse illumination then sort all successive image pairs based on the ramp-illuminated data but use the pulse-illwninated images for display and analysis. We characterized our method on synthetic data, and show its applicability on experimental data and found that an exposure time of about 7% of the heartbeat or more is necessary to encode the movement reliably in a single heartbeat with a single redundant node. Our method opens the possibility to use sorting algorithms without prior information on the phase, even when the movement presents redundant frames.

At present, the deep neural network model is commonly used to detect the vehicle in the video, and the detection accuracy is relatively high. However, the neural network model requires high computing performance and high demand for network transmission bandwidth. In many cases, the edge computing device used is of small computing power, so the neural network model is not applicable. However, background subtraction algorithm is easy to realize because of its low requirement on hardware calculation force and fast and accurate detection speed. Using SORT algorithm to track with accurate detection results can improve the speed again and reduce the consumption of computing resources. Therefore, this paper proposes an algorithm that uses the background subtraction algorithm to detect the vehicles in the video, and then uses the SORT algorithm to track the detected vehicles. The vehicle counter will automatically count when the vehicle in the video passes the traffic flow counting line. The accuracy of traffic flow counting results in this paper is 88%, which proves the feasibility and effectiveness of vehicle detection counting method based on background subtraction algorithm and SORT.

Surgical risk increases significantly when patients present with comorbid conditions. This has resulted in the creation of numerous risk stratification tools with the objective of formulating associated surgical risk to assist both surgeons and patients in decision-making. The Surgical Outcome Risk Tool (SORT) is one of the tools developed to predict mortality risk throughout the entire perioperative period for major elective in-patient surgeries in the UK. In this study, we enhance the original SORT prediction model (UK SORT) by addressing the class imbalance within the dataset. Our proposed method investigates the application of diversity-based selection on top of common re-sampling techniques to enhance the classifier's capability in detecting minority (‘mortality’) events. Diversity amongst training datasets is an essential factor in ensuring re-sampled data keeps an accurate depiction of the minority/majority class region, thereby solving the generalization problem of mainstream sampling approaches. We incorporate the use of the Solow-Polasky measure as a drop-in functionality to evaluate diversity, with the addition of greedy algorithms to identify and discard subsets that share the most similarity. Additionally, through empirical experiments, we prove that the performance of the classifier trained over diversity-based dataset outperforms the original classifier over ten external datasets. Our diversity-based re-sampling method elevates the performance of the UK SORT algorithm by 1.4%.

Quick Sort is a famous algorithm. It was the fastest algorithm at one point in time. However, sometimes it can give polynomial time complexity. The only thing that is important in this algorithm is the selection of Pivot Element. In this paper, we proposed a new algorithm, which is based on Quick Sort, and through our testing, we conclude that this algorithm is giving good results for small as well as large data sets.

Image processing methods can be broadly classified into hardware and software processing. Hardware is suitable for embedded systems because of its high performance and low power consumption. In hardware development, high-level synthesis is often used because of its ease of development. However, in order to generate high-performance hardware, it is necessary to write at the software level, considering the configuration of the hardware. Since sorting algorithms are often used inside image processing, it is necessary to generate high-performance sorting algorithm hardware. In previous research, methods for generating high-performance sorting hardware using high-level synthesis and performance comparisons have been conducted, but no comparison has been made for image processing as a whole. In this study, we will examine the dynamic background subtraction method, which is an image processing method that uses sorting algorithms. As a result, it was found that simple algorithms such as bubble sort and odd-even sort can realize pipeline processing, which is a feature of hardware, and produce high-performance image processing hardware.

Sorted data is essential. Apart from information presentation or manual retrieval of information, sorted data is beneficial even when using machines’ computational power. In many science and engineering fields, the sorting of extensive dataset is essential in matrix form. Matrix sort is an algorithm, which can sort a large amount of data in matrix form efficiently. In this paper, parallel algorithms are developed for the Matrix Sort algorithm (designed by S. Kavitha et al. Int J Comput Appl 143(9):1–6, 2016) [1]. This algorithm sorts the matrix rows and columns in parallel, subsequently applying the further procedure on resultant data. The implementations of parallel algorithms have been discussed by comparing the execution time results obtained in sequential and parallel form.

The method of confluence sort is improved by the spatial parallel separation process of sorting that is driven to the simultaneous receipt of elements of growing and handing down arrays. The basic development stages of flowchart graph of parallel sorting of data arrays with using of improved method of confluence are defined and considered. The concrete flowchart graph of the parallel data arrays sorting with the use of the improved method of confluence sort and referenced to architecture of graphic processor unit is developed. Development of software means for the parallel sorting of data arrays with the use of the improved method of confluence on the basis of complex approach that embraces: research, improvement and development of methods and algorithms of the parallel data arrays sorting; flowchart graphs of algorithms of the parallel sorting; architecture of graphic processor unit GPU and program model CUDA is proposed. It is shown that realization of the parallel data arrays sorting with the use of the improved method of confluence provides considerable reduction of sorting time in comparison with the use of only central processing unit.

One of the main topics in computer science is how to perform data classification without requiring plenty of resources and time. The sorting algorithms Quicksort, Mergesort, Timsort, Heapsort, Bubblesort, Insertion Sort, Selection Sort, Tree Sort, Shell Sort, Radix Sort, Counting Sort, are the most recognized and used. The existence of different sorting algorithm options led us to ask: What is the algorithm that us better execution times? Under this context, it was necessary to understand the various sorting algorithms in C and Python programming language to evaluate them and determine which one has the shortest execution time. We implement algorithms that help create four types of integer arrays (random, almost ordered, inverted, and few unique). We implement eleven classification algorithms to record each execution time, using different elements and iterations to verify the accuracy. We carry out the research using the integrated development environments Dev-C++ 5.11 and Sublime Text 3. The products allow us to identify different situations in which each algorithm shows better execution times.

In the present computer system, the data processing aspect, occupies the enormous processing frequency, approximately has the nearly 50% above CPU processing time is USES in the sort data. It can be seen that the data sorting algorithm has a high requirement on its own execution speed, so it is particularly important to implement a fast and good sorting algorithm. In this paper. The traditional merge sort algorithm uses two-way merge sort, which needs the same size of auxiliary space and data to be sorted, so it is necessary to improve it. The traditional merge sort method and an improved in-situ merge algorithm based on hand method are introduced, which is devoted to providing theoretical basis for improving the traditional data sort method.

Aiming at the main sorting stage of known radar signals in the radar signal sorting system, using supervised learning algorithms in machine learning to replace the traditional sorting algorithms which based on pulse repetition interval. The experimental results show that the supervised learning algorithms can successfully sort overlapping multi-type radar pulse signals, and the sorting accuracy of some algorithms exceeded 95%. It is feasible to apply the supervised learning algorithm to the main sorting stage of known radar signals.

Sorting is a central problem in computer science and one of the key components of many applications. To the best of our knowledge, no reactive programming implementation of sorting algorithms has ever been presented. In this paper we present reactive implementations of so-called sorting networks. Sorting networks are networks of comparators that are wired up in a particular order. Data enters a sorting network along various input wires and leaves the sorting network on the same number of output wires that carry the data in sorted order. This paper shows how sorting networks can be expressed elegantly in a reactive programming language by aligning the visual representation of a sorting network with the canonical DAG representation of reactive programs. We use our own experimental language called Haai to do so. With a limited number of built-in higher-order reactive programs, we are able to express sorting networks for bubble sort, insertion sort, bitonic sort, pairwise sort and odd-even merge sort.

Sorting does not only occupy a central place in computer science; it also constitutes a building block in machine learning algorithms. This paper draws a useful connection between sorting and clustering, leading to a general approach for accelerating sorting algorithms. Basically, clustering is presented as an effective way to implement the well-known divide-and-conquer strategy. The key idea is that unless an algorithm takes (sub-)linear time, which is an impossible average complexity for sorting, if a k-means-type clustering is applied to the input data, the sum of the individual sorting times will be less than the time for the original input, and sorted versions of the partitions only need be concatenated. Quadratic time, which is common among well-known algorithms, can be accelerated up to $k$ times. Although any clustering algorithm can be used, to minimize overhead, we propose a simplified one-pass k-means-type algorithm comprising a single iteration of Lloyds standard k-means algorithm. We sample k items as partition prototypes; then each item in the data is grouped with its nearest prototype. As proof of concept, we present experiments involving 3 well-known quadratic-time algorithms, namely Insertion Sort, Bubble Sort and Selection Sort.

Model engineering is conducive to decision makers by describing and solving problems in a reasonable way. Various sorting methods have been widely used in the field of model engineering, however, they are rarely visualized and clearly presented to decision makers. In this paper, in order to sort cities in China according to their sustainable development risks and visualize the result by representing it on a two dimensional plane, a novel method combined with AHPSortII and GAIA(graphical analysis for interactive aid) has been proposed, which can visualize and solve the multi-criteria sorting problems with less pairwise comparisons. The proposed method can help decision makers have a clearer understanding of the result obtained by the sorting method and make better decisions. Finally, a case study is provided to illustrate validity of the proposed method.

Based on the quick sorting algorithm, this paper uses verilog language to implement the RTL design of the ID sorting module applied to the CAN bus controller, and performs functional simulation on the module. Simulation results show that the ID sorting module designed in this paper can quickly sort up to 64 different ID numbers and output correct sequence to meet the design requirements. Using a parametric design strategy, the ID sorting module designed in this paper can configure parameters to achieve the function of sorting a larger number of random sequences to meet different needs, and has high scalability and practicality.

Sorting is a classic problem in computer science. Different kinds of sorting algorithms are required in different application scenarios. With regard to the real-time data processing applications implemented on FPGA, a faster throughput and more resource efficient sorting algorithm is needed to complete the data sorting. And the pipelined implementation of sorting algorithm is essential for improving the overall throughput. In this paper, a serial comparison based iterative sort algorithm is proposed and its implementation on FPGA is elaborated. To take advantages of the parallel characteristics of FPGA, the pipelined sorting module is realized by concatenating multiple serial comparison sorting submodules. Compared to other sorting algorithms implemented on FPGA, the serial comparison based iterative sort algorithm has the merit of requiring fewer resource consumptions, consuming less executing time and generating faster overall data throughput. The algorithm and its pipelined implementation have been successfully applied to the median filter of OS-CFAR processing in millimetre-wave MIMO radar, and their performance have been validated.