Results 1 - 10 of 75
Results 1 - 10 of 75. Search took: 0.018 seconds
|Sort by: date | relevance|
[en] Despite the scientific and engineering challenges facing the development of quantum computers, considerable progress is being made toward applying the technology to commercial applications. In this article, we discuss the solutions that some companies are already building using quantum hardware. Framing these as examples of combinatorics problems, we illustrate their application in four industry verticals: cybersecurity, materials and pharmaceuticals, banking and finance, and advanced manufacturing. While quantum computers are not yet available at the scale needed to solve all of these combinatorics problems, we identify three types of near-term opportunities resulting from advances in quantum computing: quantum-safe encryption, material and drug discovery, and quantum-inspired algorithms.
[en] One EMRAS II WG9 participant used the information from the modelling exercise (given in Appendix I) to estimate the source term from surface activity measurements. This was done through application of inverse fitting of a Gaussian dispersion model; using measurements for surface activity (Bq/m2), in particular, data from Test 2, for which all measurements were available at the time of this work. An optimization routine was written based on MINPACK to inversely determine the source term by means of the Levenberg-Marquardt algorithm and the Gaussian dispersion model, as implemented in HotSpot 2.07.1. The same empirical formulas used in HotSpot 2.07.01 were applied to estimate dispersion coefficients, wind velocity at a reference height, and other parameters; however, unlike HotSpot 2.07.01, integration of the depletion factor was carried out using a Gaussian integration, instead of the less precise trapezoidal rule.
[en] Energy is a critical issue in vehicle routing. UAV's or Drones have been the fundamental way in activities where a human has their limited skills to do. Drones save too much time in accomplishing such tasks. The growing usage of drones by commercial firms has contributed to developing a new Vehicle Routing Problem with Drones (VRPD). Self-driving cars and aircraft can be used to transfer packages from one place to another to customers. Vehicles and drones could have dependent or independent deliveries. A drone takes off from a vehicle for package delivery and then returns to the same vehicle after the delivery as long as the drone's energy restrictions are met. This paper tries to model the UAV's routing process to understand the problem clearly. Besides, the paper proposes a modified version of the Brainstorming algorithm to find the best routing for drones. Experiments are carried out in numerous environments and with various scenarios. The findings indicate the suggested algorithm to solve the drone routing problem is much effective with 70% enhancement than the random routing. (author)
[en] We propose a couple of oracle construction methods for quantum pattern matching. One of the constructs is based upon the conventional string comparison method. This, along with a unique input state preparation, when combined with the Grover’s search algorithm, results in a deterministic exact and partial pattern matching logic. The other method generates a superposition of Hamming distances between the searched pattern and all the substrings formed from the input string. The measurement statistics from a large ensemble would provide data on the closest match. We show that this method can leverage parallel computing for enhanced performance. Alternatively, it can also be combined with the minimum finding algorithm for a deterministic outcome. (author)
[en] To assess the interobserver reliability (IOR) of the Tile classification system, and its potential influence on outcomes, for the interpretation of CT images of pelvic fractures by radiologists and surgeons. Retrospective data (1/2008–12/2016) from 238 patients with pelvic fractures were analyzed. Mean patient age was 44 years (SD 20); 66% were male. There were 54 Tile A, 82 Tile B, and 102 Tile C type injuries. The 30-day mortality rate was 15% (36/238). Six observers, three radiologists, and three surgeons with different levels of experience (attending/resident/intern) classified each fracture into one of the 26 second-order subcategories of the Tile classification. Weighted kappa coefficients were used to assess the IORs for the three main categories and nine first-order subcategories. The overall IORs of the Tile system for the main categories and first-order subcategories were moderate (kappa = 0.44) and fair (kappa = 0.31), respectively. IOR was fair to moderate among radiologists, but only fair among surgeons. By level of training, IOR was moderate between attendings and between residents, whereas it was only fair between interns. IOR was moderate to substantial (kappa = 0.56–0.70) between the radiology attending and resident. Association of the Tile fracture type with 30-day mortality was present based on two out of six observer ratings. The overall IOR of the Tile classification system is only fair to moderate, increases with the level of rater experience and is better among radiologists than surgeons. In the light of these findings, results from studies using this classification system must be interpreted cautiously.
[en] The advancement in the field of precision agriculture has opened doors for site-specific weed management. There is a growing need to control the amount of herbicide sprayed on weeds to reduce economic and environmental losses. In the field of precision agriculture, incorporation of machine learning techniques has enabled the farmers to automate the process of controlling weed using an adequate number of herbicides for different species in-situ. This study aims to explore various parameters of Computer Vision and Machine Learning algorithms and methods used by researchers to develop Artificial Intelligence models to remove weeds from agricultural fields. More than twenty state-of-the-art algorithms have been studied in this paper. We categorized these algorithms into five categories based on different features i.e. visual, shape, spatial, and spectral. At the end of this study, a comprehensive table is presented containing details of algorithms in terms of limitations and accuracy. (author)
[en] Cyber security is one of the major concerns of today’s connected world. For all the platforms of today’s communication technology such as wired, wireless, local and remote access, the hackers are present to corrupt the system functionalities, circumvent the security measures and steal sensitive information. Amongst many techniques of hackers, port scanning and Distributed Denial of Service (DDoS) attacks are very common. In this paper, the benefits of machine learning are taken into consideration for classification of port scanning and DDoS attacks in a mix of normal and attack traffic. Different machine learning algorithms are trained and tested on a recently published benchmark dataset (CICIDS2017) to identify the best performing algorithms on the data which contains more recent vectors of port scanning and DDoS attacks. The classification results show that all the variants of discriminant analysis and Support Vector Machine (SVM) provide good testing accuracy i.e. more than 90%. According to a subjective rating criterion mentioned in this paper, 9 algorithms from a set of machine learning experiments receive the highest rating (good) as they provide more than 85% classification (testing) accuracy out of 22 total algorithms. This comparative analysis is further extended to observe training performance of machine learning models through k-fold cross validation, Area Under Curve (AUC) analysis of the Receiver Operating Characteristic (ROC) curves, and dimensionality reduction using the Principal Component Analysis (PCA). To the best of our knowledge, a comprehensive comparison of various machine learning algorithms on CICIDS2017 dataset is found to be deficient for port scanning and DDoS attacks while considering such recent features of attack. (author)
[en] To have the effective speech communication, the information should be clearly passed in a noise-free environment. However, in real-world environment, the existence of background noise degrades the performance of the system. Based on the blind source separation strategy, various adaptive algorithms are designed and implemented using both dispersive impulse response and sparse impulse response. Even though the existing dual fast normalized least mean square algorithm works well under different noisy situations and gives a good performance, the problem is that it involves large number of processing steps. To overcome the complexity in finding the signal prediction parameter and to improve the performance of speech enhancement, we propose three adaptive filtering algorithms namely revised twofold rapid normalized least mean square algorithm, diminished twofold normalized least mean square algorithm and upgraded balanced two fold normalized least mean square algorithm (UBTNLMS). Taking the performance objective criteria into account, these algorithms have been tested for segmental signal-to-noise ratio, segmental mean square error, signal-to-noise ratio, mean square error and cepstral distance. On comparing the performance of the existing and proposed algorithms, UBTNLMS performs better than the other algorithms. (author)