Data structures for virtual-circuit implementation

Fingerprint authentication is widely used thanks to its simple process and low cost, but it is vulnerable to fake fingerprints. Many researchers have been working on presentation attack detection to ensure the security of fingerprint systems. However, the existing studies only focus on improving the detection accuracy, and let processing time and memory requirement be out of focus. Hence, it is difficult to integrate the existing algorithms to embedded and mobile systems. This paper proposes a method to detect presentation attacks using a small fully convolutional network. The proposed network is designed using the structure of the fire module of SqueezNet. The use of the fire module results in a network which has around 0.5 million parameters. The proposed network is trained using images of $32\times 32$ , $48\times 48$ , or $64\times 64$ pixels. Since the network has no fully connected layer, it can interfere with images of any size. This advantage helps to improve the detection rate and allows the proposed algorithm to be easily integrated into fingerprint systems. The experiments show an average detection error of 1.43%, which is comparable with the state-of-the-art accuracy, while the processing time and memory requirement are much reduced.

Optimal allocation and processing time decisions on non-identical parallel CNC machines: ϵ-constraint approach

This paper concentrates on the issue of detecting abnormal flows in distributed monitoring systems, which has many network management applications such as anomaly detection and traffic engineering. Collecting massive network traffic in real‐time remains a large challenge due to the limited system resource. Most existing approaches perform abnormal flow detection at one measurement point, while they cause large computation and memory overhead for recovering abnormal flows. In this paper, we propose a novel data streaming method that supports accurate abnormal flow detection with a low memory requirement. The key idea of our method is that each monitor compresses flow information to summary data structure, sends the generated data structure to the controller; then the controller aggregates the received data structures, recovers candidates of abnormal flows and estimates their size and change to find abnormal flows on the basis of the aggregated data structure. The experimental results based on real network traffic show that the proposed approach can detect up to 97% of abnormal flows with low memory and update requirements in comparison with related approaches.

A Solid Freeform Fabrication (SFF) system using Selective Laser Sintering (SLS) is currently recognized as a leading process and SLS extends the applications to machinery and automobiles due to the various materials employed. Especially, accuracy and processing time are very important factors when the desired shape is fabricated with Selective Laser Sintering (SLS), one of Solid Freeform Fabrication (SFF) system. In the convectional SLS process, laser spot size is fixed during laser exposing on the sliced figure. Therefore, it is difficult to accuracy and rapidly fabricates the desired shape. In this paper, to deal with those problems a SFF system having ability of changing spot size is developed. The system provides high accuracy and optimal processing time. Specifically, a variable beam expander is employed to adjust spot size for different figures on a sliced shape. Therefore, design and performance estimation of the SFF system employing a variable beam expander are achieved and the mechanism will be addressed to measure the real spot size generated from the variable beam expander. Also, the reduction of total processing time is an important issue in SFF system. A digital mirror system (DMS) is a system which scans the laser beam with different spot size. The spot size is selected based on the slicing section to decrease and accuracy of the process time and improve the processing efficiency. In this study, the optimal scan path generation for DMS will be addressed, and this development will improve the whole processing efficiency and accuracy through the scan efficiency by considering the existing scan path algorithm and heat energy distribution.

This paper considers scheduling large-scale divisible load on heterogeneous single-level tree networks to minimize the whole processing time. A more realistic and general model, i.e., both processors and links may have different speeds and arbitrary start-up costs, and with nonblocking mode of communication, is introduced. Under such environment, results are obtained including: (1) closed-form expressions both for the processing time and the fraction of load for each processor are derived; (2) the influence of start-up costs on the optimal processing time is analyzed; and (3) for bounded number of processors with large-scale workload, optimal sequence and algorithms for distributing divisible load are proposed. Numerical examples are presented to illustrate the analysis.

The problem of optimal divisible load distribution in distributed bus networks employing a heterogeneous cluster of processors is addressed. The objective is to minimize the total processing time of the entire load subject to the communication and computation delays. In the mathematical model we adopt, both the granularity of the load fractions and all the associated overheads (also referred to as start-up costs) in the process of communication and computation, are considered explicitly in the problem formulation. We introduce a directed flow graph model for representing the load distribution process. This representation is novel to this literature. With this model, we first derive a closed-form solution for an optimal processing time. We propose an integer approximation algorithm and derive ultimate performance bounds for the class of homogeneous networks. We then extend the problem to a special class of application problems in which the data partitioning is restricted to a finite number of partitions. For this case, we present a recursive procedure to obtain optimal processing time. We then present two different integer approximation algorithms-PIA and IIA that could generate integer load fractions and yield suboptimal solutions. The choice of these algorithms are also analyzed. All the results are extended to a class of homogeneous networks to obtain ultimate performance bounds. Several illustrative examples are provided for ease of explanation.

Automatic Optical Inspection (AOI) is any method of detecting defects during a Printed Circuit Board (PCB) manufacturing process. Early AOI methods were based on classic image processing algorithms using a reference PCB. The traditional methods require very complex and inflexible preprocessing stages. With recent advances in the field of deep learning, especially Convolutional Neural Networks (CNN), automating various computer vision tasks has been established. Limited research has been carried out in the past on using CNN for AOI. The present systems are inflexible and require a lot of preprocessing steps or a complex illumination system to improve the accuracy. This paper studies the effectiveness of using CNN to detect soldering bridge faults in a PCB assembly. The paper presents a method for designing an optimized CNN architecture to detect soldering faults in a PCBA. The proposed CNN architecture is compared with the state-of-the-art object detection architecture, namely YOLO, with respect to detection accuracy, processing time, and memory requirement. The results of our experiments show that the proposed CNN architecture has a 3.0% better average precision, has 50% less number of parameters and infers in half the time as YOLO. The experimental results prove the effectiveness of using CNN in AOI by using images of a PCB assembly without any reference image, any complex preprocessing stage, or a complex illumination system. Doi: 10.28991/HIJ-2022-03-01-01 Full Text: PDF

IoT devices rely on authentication mechanisms to render secure message exchange. During data transmission, scalability, data integrity, and processing time have been considered challenging aspects for a system constituted by IoT devices. The application of physical unclonable functions (PUFs) ensures secure data transmission among the internet of things (IoT) devices in a simplified network with an efficient time-stamped agreement. This paper proposes a secure, lightweight, cost-efficient reinforcement machine learning framework (SLCR-MLF) to achieve decentralization and security, thus enabling scalability, data integrity, and optimized processing time in IoT devices. PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication. An IoT network gathers information of interest from multiple cluster members selected by the proposed framework. In addition, the software-defined secured (SDS) technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform. Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime, energy, secured data retrieval rate, and performance ratio. By enabling the proposed framework, number of residual nodes is reduced to 16%, energy consumption is reduced by up to 50%, almost 30% improvement in data retrieval rate, and network lifetime is improved by up to 1000 msec.

The S-transform provides both the true frequency and globally referenced phase measurements characteristic of the Fourier transform and also generates local spectra, as does the wavelet transform. But the computational requirements of the 2D S-transform have limited its application in digital images processing in time. In order to reduce the processing time and memory requirement, we present a fast oversampling orthogonal method to compute the discrete 2D S-transform. Our method combines Brown's oversampling method with Drabycz's orthogonal method and reduces the time and memory consuming dramatically. We calculated the 2D discrete S-transform of the typical 24 bits bmp image in 0.05 seconds and the memory requirement is 8 times of the original image size. 256×256 24 bits bmp image in 0.05 seconds and the memory requirement is 8 times of the original image size.

Impostors are image-based primitives commonly used to replace complex geometry in order to reduce the rendering time needed for displaying complex scenes. However, a big problem is the huge amount of memory required for impostors. This paper presents an algorithm that automatically places impostors into a scene so that a desired frame rate and image quality is always met, while at the same time not requiring enormous amounts of impostor memory. The low memory requirements are provided by a new placement method and through the simultaneous use of other acceleration techniques like visibility culling and geometric levels of detail.

Carpooling is an effective solution to traffic congestion. It increases the usage rate of vehicles by employing empty seats as a transportation resource. In order to provide carpooling services to users, we developed an intelligent carpool system called BlueNet-Ride . After prospective carpoolers submit their requests through their smart handheld devices, this system provides appropriate matches by using the proposed Low-Complexity and Low-Memory Carpool Matching method. The compact genetic algorithm is applied to our Low-Complexity and Low-Memory Carpool Matching method, which involves three proposed modules: an Evolutionary Model Initialization module, an Evolutionary Process Operation module, and an Evolutionary Model Modification module. The Evolutionary Model Initialization module takes advantage of the manipulation of the evolving population on a probability distribution to achieve low-memory requirements during the evolution process of the carpool match solution. The Evolutionary Process Operation and Evolutionary Model Modification modules simulate genetic operations to accomplish superior matching within a short amount of time. The experimental results demonstrate that our Low-Complexity and Low-Memory Carpool Matching method achieves the highest degree of performance with regard to solution quality, processing time, and memory requirements of all evaluated methods.

The pyrolysis process has many models with different characteristics and specifications. Each model provides a different conversion value depending on the temperature value, length of time, and the number of raw materials used. Jengkok Tobacco waste was dangerous because it contains Arsenic (As), and was used as a biochar product with economic and strategic value through the extrusion model pyrolysis process. The purpose of this study was to determine the thermal conversion value (yield percentage) of the pyrolysis process of tobacco waste material into biochar at the optimal temperature and processing time. The specified variables consist of process temperatures (400, 450, 500, 550, and 600°C) and processing times (30, 35, and 40 minutes). The product of the process will be analyzed statistically using the Spearman rank correlation test and followed by Minitab to produce the optimal value. The results showed that the thermal conversion value in making biochar was 29.476% (»30%) at a process temperature of 500°C and a processing time of 30 minutes.

Mobility data of a moving object, called trajectory data, are continuously generated by vessel navigation systems, wearable devices, and drones, to name a few. Trajectory data consist of samples that include temporal, spatial, and other descriptive features of object movements. One of the main challenges in trajectory data analysis is to divide trajectory data into meaningful segments based on certain criteria. Most of the available segmentation algorithms are limited to processing data offline, i.e., they cannot segment a stream of trajectory samples. In this work, we propose an approach called Reactive Buffering Window - Trajectory Segmentation (RBW-TS), which partitions trajectory data into segments while receiving a stream of trajectory samples. Another novelty compared to existing work is that the proposed algorithm is based on multidimensional features of trajectories, and it can incorporate as many relevant features of the underlying trajectory as needed. This makes RBW-TS general and applicable to numerous domains by simply selecting trajectory features relevant for segmentation purposes. The proposed online algorithm incurs lower computational and memory requirements. Furthermore, it is robust to noisy samples and outliers. We validate RBW-TS on three use cases: (a) segmenting human-movement trajectories in different modes of transportation, (b) segmenting trajectories generated by vessels in the maritime domain, and (c) segmenting human-movement trajectories in a commercial shopping center. The numerical results detailed in the paper demonstrate that (i) RBW-TS is capable of detecting the true breakpoints of segments in all three usecases while processing a stream of trajectory points; (ii) despite low memory and computational requirements, the performance in terms of the harmonic mean of purity and coverage is comparable to that of state-of-the-art batch and online algorithms; (iii) RBW-TS achieves different levels of accuracy depending on the various internal parameter estimation methods used; and (iv) RBW-TS can tackle real-world trajectory data for segmentation purposes.

To generalize the Fisher Discriminant Analysis (FDA) algorithm to the case of discriminant functions belonging to a nonlinear, finite dimensional function space F (Nonlinear FDA or NFDA), it is sufficient to expand the input data by computing the output of a basis of F when applied to it [1,2,3,4]. The solution to NFDA can then be found like in the linear case by solving a generalized eigenvalue problem on the between- and within-classes covariance matrices (see e.g. [5]). The goal of NFDA is to find linear projections of the expanded data (i.e., nonlinear transformations of the original data) that minimize the variance within a class and maximize the variance between different classes. Such a representation is of course ideal to perform classification. The application of NFDA to pattern recognition is particularly appealing, because for a given input signal and a fixed function space it has no parameters and it is easy to implement and apply. Moreover, given C classes only C - 1 projections are relevant [5]. As a consequence, the feature space is very small and the algorithm has low memory requirements and high speed during recognition. Here we apply NFDA to a handwritten digit recognition problem using the MNIST database, a standard and freely available set of 70,000 handwritten digits (28 × 28 pixels large), divided into a training set (60,000 digits) and a test set (10,000 digits). Several established pattern recognition methods have been applied to this database by Le Cun et al. [6]. Their paper provides a standard reference work to benchmark new algorithms. We perform NFDA on spaces of polynomials of a given degree d, whose corresponding basis functions include all monomials up to order d in all input variables. It is clear that the problem quickly becomes intractable because of the high memory requirements. For this reason, the input dimensionality is first reduced by principal component analysis. On the preprocessed data we then apply NFDA by expanding the training patterns in the polynomial space and solving the linear FDA eigenvalue problem. As mentioned above, since we have 10 classes we only need to compute the first 9 eigenvectors. Since the within-class variance is minimized, the patterns belonging to different classes tend to cluster in the feature space when projected on the eigenvectors. For this reason we classify the digits with a simple method such as Gaussian classifiers. We perform simulations with polynomials of degree 2 to 5. With polynomials of degree 2 the explosion in the dimensionality of the expanded space with increasing number of input dimensions is relatively restricted, so that it is possible to use up to 140 dimensions. With higher order polynomials one has to rely on a smaller number of input dimensions, but since the function space gets larger and includes new nonlinearities, one obtains a remarkable improvement in performance. The best performance is achieved with polynomials of degree 3 and 35 input dimensions, with an error rate of 1.5% on test data. This error rate is comparable to but does not outperform that of the most elaborate algorithms (Table 1). The performance of NFDA is however remarkable considering the simplicity of the method and the fact that it has no a priori knowledge on the problem, in contrast for example to the LeNet-5 algorithm [6] which has been designed specifically for handwritten character recognition. In addition, for recognition, NFDA has to store and compute only 9 functions and has thus small memory requirements and a high recognition speed. It is also possible to formulate NFDA using the kernel trick, in which case one can in principle use function spaces of infinite dimensionality [1,2,3,4]. However, the limiting factor in that formulation is the number of training patterns, which makes it not realistic for this application. The performance of NFDA could be further improved using for example a more problem-specific preprocessing of the patterns (e.g., by increasing the size of the training set with new patterns generated by artificial distortion of the original one), boosting techniques, or mixture of experts with other algorithms [5,6].

This paper presents a new FSVQ scheme called Finite-State Residual Vector Quantization (FSRVQ) in which each state uses a Residual Vector Quantizer (RVQ) to encode the input vector. Furthermore, a novel tree- structured competitive neural network is proposed to jointly design the next-state and the state-RVQ codebooks for the proposed FSRVQ. Joint optimization of the next-state function and the state-RVQ codebooks eliminates a large number of redundant states in the conventional FSVQ design; consequently, the memory requirements are substantially reduced in the proposed FSRVQ scheme. The proposed FSRVQ can be designed for high bit rates due to its very low memory requirements and low search complexity of the state-RVQs. Simulation results show that the proposed FSRVQ scheme outperforms the conventional FSVQ schemes both in terms of memory requirements and perceptual quality of the reconstructed image. The proposed FSRVQ scheme also outperforms JPEG (current standard for still image compression) at low bit rates.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.