Rainfall and precipitation influenced by synoptic systems such as monsoons play a major role in the Philippines’ climate variability and contribute to natural disasters like landslides and floods. The monsoon season brings heavy rainfall to much of the country, but modeling this rainfall remains challenging due to the complexity of deep convection processes. This study uses an unsupervised machine learning method, the Self-Organizing Map (SOM), to analyze deep convective properties during the monsoon season in the Philippines while accounting for key meteorological factors. The SOM is trained on 20 years (2000–2019) of data from the European Centre for Medium-Range Weather Forecasts Reanalysis 5, centered over the Philippines. Input variables include daily 0400 UTC geopotential height anomalies at 850 hPa. Additional variables—such as cloud fraction, water vapor, and other convective properties—are taken from the Moderate Resolution Imaging Spectroradiometer Level 3 global gridded data and the Clouds and the Earth’s Radiant Energy System Synoptic fluxes and cloud data. The properties derived from the datasets are projected onto each SOM regime. The results reveal a clear seasonal separation where dry season regimes are associated with low cloud fraction and water vapor, while wet season regimes exhibit high cloud fraction and water vapor. The patterns also correspond to reduced shortwave and enhanced longwave fluxes. The findings demonstrate that SOMS can effectively link large-scale atmospheric dynamics with local cloud and radiation properties, providing insight into the drivers of monsoons in the region.

This paper introduces a novel reinforcement learning framework for portfolio optimization that leverages the complex statistical properties of financial markets through fractional Brownian motion (fBM). Unlike traditional methods that rely on memoryless or mean-reverting processes, our approach captures the long-range dependencies, persistence, and anti-persistence observed in empirical asset returns. Central to this framework is a meta-controller that dynamically calibrates the underlying Hurst parameter, enabling the trading agent to switch adaptively among specialized strategies trained for different market regimes. By integrating non-Markovian dynamics into the simulation environment and employing a hierarchical control structure, our method allows the agent to learn more robust and context-aware policies. Empirical evaluations demonstrate that agents operating under this adaptive, fBM driven paradigm achieve near-optimal performance in fluctuating market conditions, underscoring the model’s potential to better mirror real-world complexity and enhance decision making in financial applications.

This paper presents a method to automatically predict the survival rate of patients with a glioma brain tumor by classifying the patients MRI image using machine learning (ML) methods. The dataset used in this study is BraTS 2017, which provides 163 samples; each sample has four sequences of MRI brain images, the overall survival time in days, and the patients age. The dataset is labeled into three classes of survivors: short-term, mid-term, and long-term. To improve the prediction results, various types of features were extracted and trained by various ML methods. Features considered included volumetric, statistical and intensity texture, histograms and deep features; ML techniques employed included support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant, tree, ensemble and logistic regression. The best prediction accuracy based on classification is achieved by using deep learning features extracted by a pre-trained convolutional neural network (CNN) and was trained by a linear discriminant.

Many studies have been conducted on Handwritten Signature Verification. Researchers have taken many different approaches to accurately identify valid signatures from skilled forgeries, which closely resemble the real signature. The purpose of this paper is to suggest a method for validating written signatures on bank checks. This model uses a convolutional neural network (CNN) to analyze pixels from a signature image to recognize abnormalities. We believe the feature extraction capabilities of a CNN can optimize processing time and feature analysis of signature verification. Unique characteristics from signatures can be accurately and rapidly analyzed with multiple layers of receptive fields and hidden layers. Our method was able to correctly detect the validity of the inputted signature approximately 83 percent of the time. We tested our method using the SIGCOMP 2011 dataset. The main contribution of this method is to detect and decrease fraud committed, especially in the banking industry. Future uses of signature verification could include legal documents and the justice system.

One of the fastest growing industries in America is autonomous vehicle technology. The main motivation is to decrease the number of accidents each year. This leads to the challenge presented in this paper, preventing the spoofing of signals coming into autonomous vehicles. The increased complexity of these vehicles creates more vulnerabilities for attackers to take advantage of. Authentication of vehicular ad hoc networks is one method to help stop the potential hacking of autonomous vehicles. We propose a two-factor authentication for GPS signals by synchronizing with Stratum-1 clocks and digital signatures to prevent man-in-the-middle attacks. The experiment uses a computer to simulate a GPS signal that is sent to a Raspberry Pi 3 along with a timestamp and hashed key using RSA-1024. The Raspberry Pi 3 represents the vehicle. The method presented will prevent GPS spoofing attacks that are using modified or corrupted messages, an impersonation attack, or a roadside unit replication attack.

As the healthcare industry becomes more reliant upon electronic records, the amount of medical data available for analysis increases exponentially. While this information contains valuable statistics, the sheer volume makes it difficult to analyze without efficient algorithms. By using machine learning to classify medical data, diagnoses can become more efficient, accurate, and accessible for the public. After choosing k-Nearest Neighbors for its simplicity, we applied it to datasets compiled by the University of California, Irvine Machine Learning Repository to diagnose two conditions - chronic kidney failure and heart disease - with an accuracy of approximately 90%. In the future, similar methods can be used on a larger scale to bring ease of use to the field of medical diagnostics.

This paper discusses a meta-heuristic echolocation mathematical model, as a possible method to discover adjacent vehicles and road-side units in a smart transportation setting with levels 3, 4, and 5 autonomous vehicles. The operation of IoAV based on monitoring several parameters as well as major obstacles for the proliferation of level 4 and 5 autonomous vehicles are also analyzed. In this paper, we make the first attempt to analyze autonomous vehicles from a microscopic level, focusing on each vehicle and their communications. Simulation results demonstrated that the proposed model incurs minimal computation and communication overheads.

This paper proposes a prototype of a level 3 autonomous vehicle using Raspberry Pi, capable of detecting the nearby vehicles using an IR sensor. We make the first attempt to analyze autonomous vehicles from a microscopic level, focusing on each vehicle and their communications with the nearby vehicles and road-side units. Two sets of passive and active experiments on a pair of prototypes were run, demonstrating the interconnectivity of the developed prototype. Several sensors were incorporated into an emulation based on System-on-Chip to further demonstrate the feasibility of the proposed model.

Hyperspectral sensors provide 3-D images with high spatial and spectral resolution. Acquired data can be utilized in diverse applications such as detection and control of hazardous agents in atmosphere and water, military targets, and so on. Over the last decade, hyperspectral remote sensing algorithms for target detection have evolved from the spectral-based methods, which only use spectral information, to more recent methods based on spatial-spectral information. Spatial information plays a crucial role to improve the efficiency of the algorithms. Furthermore, the parallelization of the algorithms reduces the computation time. Developments in the area of commodity computing provide affordable approach for target detection applications with real-time constraint. We will give a scientific overview of recent target detection algorithms which try to overcome existing limitations (e.g. spectral variability or background interference) in hyperspectral remote sensing. Unlike current target detection methods in literature, this study explains and assesses different aspects of developments in target detection algorithms comprehensively. In particular, this study focuses on development in atmospheric correction methods which especially deal with background interference, development in methods based on spectral information and spectral-spatial information (both methods especially deal with spectral variability), and parallelization of the algorithms. With consideration of hyperspectral data challenges in real-world, an optimum approach is the adaptive algorithm based on spatial-spectral information in which their computation is performed in parallel.

Network congestion is one of challenging tasks in communication networks and leads to queuing delay, packet loss or the blocking of new connections. Here, a cognitive method is proposed to deal with network congestion. Unlike previous methods for congestion control, the proposed method is an intelligent approach for congestion control when the link capacity and information inquiries are unknown or variable. Based on simulation results, the cognitive method is capable of optimally adjusting the available bandwidth, to provide optimal communication services in network.

The star graph is a hierarchical graph, and rich in containing substars (or graphs with smaller size but with the same topological properties as the original). Given a set of faulty nodes, we determine the ability to recover substars of a given dimension from the original network. A study of the number of faulty nodes that are required to damage every subnetwork of a given size is also presented.

This paper addresses a constrained two-terminal reliability measure referred to as distance reliability (DR) between the source node u and the destination node I with the shortest distance, in an n-dimensional star network, Sn. The shortest distance restriction guarantees the optimal communication delay between processors and high link/node utilization across the network. This paper uses a combinatorial approach by limiting the number of node, link and node/link failures. For each failure model, two different cases depending on the relative positions of u and I, are analyzed to compute DR. Furthermore, DR for the antipodal communication, where every node must communicate with its antipode, is investigated as a special case. For this case, lower bound on DR of those disjoint paths is also derived.

An algorithm, which performs connected components detection in the course of decoding ITU-T (former CCITT) facsimile Group 3/4, i.e., MH/MR/MMR compressed images is presented. New definitions of mode color and a new transition element are introduced that allow MR/MMR codes to analyze and derive information about connection of black runs in two adjacent scan lines in the course of decoding. The experiments on the standard set of eight CCITT documents have shown that, on the average, the complexity of direct processing of MR/MMR codes is lower by a factor of 20 and 2.5 than that for raster images and MH codes processing respectively. Data structures for image vector description are discussed.

The reliability of the Star Graph architecture is discussed. The robustness of the Star Graph network under node failures, link failures, and combined node and link failures is shown. The degradation of the Star Graph into Substar Graphs is used as the measure of system effectiveness in the face of failures. Models are provided for each of the failure and re-mapping modes evaluated herein, and the resilience of the Star Graph to failures is emphasized. This paper defines failure of a Star Graph as being when no fault-free (n - 1)-substars remain operational and the intermediate states are defined by the number of (n - 1)-substars that remain operational. A powerful tool (re-mapping) is introduced in which the number of operational (n 1)-substars can be maintained for longer periods, thus improving the overall MTTF (mean time to failure). For comparison the results of a similar reliability analysis of the hypercube is shown. The comparisons are considered conservative due to the failure model used herein for the star graph. One might apply re-mapping to hypercubes; while it would improve the overall MTTF of hypercubes, the hypercubes would still have an appreciably poorer performance than star graphs.

A new broadcasting method is presented for hypercubes with wormhole routing mechanism. The communication model assumed allows an n-dimensional hypercube to have at most n concurrent 110 communications along its ports. It further assumes a distance insensitivity of (n+1) with no intermediate reception capability for the nodes along the communication path. The approach is based on determination of the set of nodes (called stations) in the hypercube such that for any node in the network there is a station at distance of at most 1. Once stations are identified, parallel disjoint paths are formed from the source to all stations. The broadcasting is accomplished first by sending the message to all stations which will in turn inform the rest of the nodes of the message. To establish node-disjoint paths between the source node and all stations, we introduce a new routing strategy. We prove that multicasting can be done in one routing step as long as the number of destination nodes are at most n in an n-dimensional hypercube. The number of broadcasting steps using our routing is equal to or smaller than that obtained in an earlier work; this number is optimal for all hypercube dimensions n/spl les/12, except for n=10.

This paper presents a novel approach to document image compression that is efficient in both compression and processing flexibility. By proper exploitation of the structural characteristics of compressed data, one may obtain high performance for image operations with low complexity. Based on CCITT Group 4, an improved coding scheme (MG4), which exploits the 2-dimensional correlation between scan lines, is developed. Then such operations as skew detection, skew correction and connected component extraction are investigated and implemented. These operations are shown to run faster in the compressed domain than traditional methods.

We discuss the problem of packing hypercubes into an n-dimensional star graph S(n), which consists of embedding a disjoint union of hypercubes U into S(n) with load one. Hypercubes in U have from [n/2] to (n+1)/spl middot/[log/sub 2/ n]-2([lod/sub 2/n]+1)+2 dimensions, i.e., they can be as large as any hypercube which can be embedded with dilation at most four into S(n). We show that U can be embedded into S(n) with optimal expansion, which contrasts with the growing expansion ratios of previously known techniques. We employ several performance metrics to show that, with our techniques, a star graph can efficiently execute heterogeneous workloads containing hypercube, mesh, and star graph algorithms. The characterization of our packings includes some important metrics which have not been addressed by previous research (namely, average dilation, average congestion, and congestion). Our packings consistently produce small average congestion and average dilation, which indicates that the induced communication slowdown is also small. We consider several combinations of node mapping functions and routing algorithms in S(n), and obtain their corresponding performance metrics using either mathematical analysis or computer simulation.

In processing a page image by a given zoning algorithm (automatic or manual), a certain text string is generated which may not be the same as the correct string. The difference may be due to the incorrect reading order selected by the employed zoning algorithm or poor recognition of characters. A difference algorithm is commonly used to find the best match between the generated string and the correct string. The output of such an algorithm will then be a sequence of matched substrings which are not in the correct order. To determine the performance of a given zoning algorithm, it is of interest to find the minimum number of moves needed to obtain the correct string from the string generated by that algorithm. The problem can be modeled as a sorting problem where a string of n integers ordered in a random manner, must be sorted in ascending (or descending) order. In this paper, we derive bounds on the time complexity of sorting a given string and present a near-optimal algorithm for that.

The paper proposes designs of interconnection networks (graphs) which can tolerate link failures. The networks under study belong to a subclass of Cayley graphs whose generators are subsets of all possible transpositions. We specifically focus on star and bubble sort networks. Our approach is to augment existing dimensions (or generators) with one or more dimensions. If the added dimension is capable of replacing any arbitrary failed dimension, it is called a wildcard dimension. It is shown that, up to isomorphism among digits used in labeling the vertices, the generators of the star graph are unique. The minimum number of extra dimensions needed to acquire i wildcard dimensions is derived for the star and bubble sort networks. Interestingly, the optimally augmented star network coincides with the Transposition network, T/sub n/. Transposition networks are studied rigorously. These networks are shown to be optimally fault tolerant. T/sub n/ is also shown to possess wide containers with short length. Fault diameter of T/sub n/ is shown to be n. While the T can efficiently embed star and bubble sort graphs, it can also lend itself to an efficient embedding of meshes and hypercubes.

We consider the problem of simulating linear arrays and rings on the multiply twisted cube. We introduce a new concept, the reflected link label sequence, and use it to define a generalized Gray Code (GGC). We show that GGCs can be easily used to identify Hamiltonian paths and cycles in the multiply twisted cube. We also give a method for embedding a ring of arbitrary number of nodes into the multiply twisted cube.

An enumeration of star graphs is given which has many useful properties. For example an arbitrary prefix or suffix is connected; indeed the diameter is O(n). As a consequence, there is an O(n) interval broadcast algorithm. Prefixes which have t(n-1)! vertices for some t are especially well-behaved. The topology of, embeddings in, and algorithms for these graphs are considered, making use of the enumeration.>

Introduces a new measure of conditional connectivity for large regular graphs by requiring each vertex to have at least g good neighbors in the graph. Based on this requirement, the vertex connectivity for the n-dimensional cube is obtained, and the minimal sets of faulty nodes that disconnect the cube are characterized.>

The star graph has been known as an attractive candidate for interconnecting a large number of processors. The hierarchy of the star graph allows the assignment of its special subgraphs (substars), which have the same topological features as the original graph, to a sequence of incoming tasks. The paper proposes a new code, called star code (SC), to recognize available substars of the required size in the star graph. It is shown that task allocation based on the SC is statically optimal. The recognition ability of a given SC or a class of SC's is derived. The optimal number of SC's required for the complete substar recognition in an n-dimensional star is shown to be 2/sup n-2/.>

Introduces a new interconnection network for massively parallel systems called the incomplete star graph. The authors describe unique ways of interconnecting and labeling the nodes and routing point-to-point communications within this network. In addition, they provide an analysis of a special class of incomplete star graph called C/sup n/spl minus/1/ graph and obtain the diameter and average distance for this network. For the C/sup n/spl minus/1/ graph, an efficient broadcasting scheme is presented. Furthermore, it is proven that a C/sup n/spl minus/1/ with N nodes (i.e. N=m(n/spl minus/1)!) is Hamiltonian if m=4 or m=3k, and k/spl ne/2.>

This paper introduces a new interconnec tion network for massively parallel systems referred to as star connected cycles (SCC) graph. The SCC presents a fixed degree structure that results in several advantages over variable degree graphs like the star graph and the n-cube. The description of the SCC graph given in this paper in cludes issues such as labeling of nodes, degree, diameter, symmetry, fault tolerance and Cayley graph representation. The paper also presents an optimal routing algorithm for the SCC and a comparison with other interconnection net works. Our results indicate that for even n, an n-SCC and a CCC of similar sizes have about the same diameter.

The authors present a routing algorithm that uses the depth first search approach combined with a backtracking technique to route messages on the star graph in the presence of faulty links. The algorithm is distributed and requires no global knowledge of faults. The only knowledge required at a node is the state of its incident links. The routed message carries information about the followed path and the visited nodes. The algorithm routes messages along the optimal, i.e., the shortest path if no faults are encountered or if the faults are such that an optimal path still exists. In the absence of an optimal path, the algorithm always finds a path between two nodes within a bounded number of hops if the two nodes are connected. Otherwise, it returns the message to the originating node. The authors provide a performance analysis for the case where an optimal path does not exist. They prove that for a maximum of n-2 faults on a graph with N=n! nodes, at most 2i+2 steps are added to the path, where i is O( square root n). Finally, they use the routing algorithm to present an efficient broadcast algorithm on the star graph in the presence of faults.>

It is shown that the diameter of an n-dimensional hypercube can only increase by an additive constant of 1 when (n-1) faulty processors are present. Based on the concept of forbidden faulty sets, which guarantees the connectivity of the cube in the presence of up to (2n-3) faulty processors. It is shown that the diameter of the n-cube increases to (n-2) as a result of (2n-3) processor failures. It is also shown that only those nodes whose Hamming distance is (n-2) have the potential to be located at two ends of the diameter of the damaged cube. It is proven that all the n-cubes with (2n-3) faulty processors and a fault-diameter of (n+2) are isomorphic. A generalization to the subject study is presented.>

A new hypercube-type structure, the folded hypercube (FHC), which is basically a standard hypercube with some extra links established between its nodes, is proposed and analyzed. The hardware overhead is almost 1/n, n being the dimensionality of the hypercube, which is negligible for large n. For this new design, optimal routing algorithms are developed and proven to be remarkably more efficient than those of the conventional n-cube. For one-to-one communication, each node can reach any other node in the network in at most (n/2) hops (each hop corresponds to the traversal of a single link), as opposed to n hops in the standard hypercube. One-to-all communication (broadcasting) can also be performed in only (n/2) steps, yielding a 50% improvement in broadcasting time over that of the standard hypercube. All routing algorithms are simple and easy to implement. Correctness proofs for the algorithms are given. For the proposed architecture, communication parameters such as average distance, message traffic density, and communication time delay are derived. In addition, some fault tolerance capabilities of this architecture are quantified and compared to those of the standard cube. It is shown that this structure offers substantial improvement over existing hypercube-type networks in terms of the above-mentioned network parameters.>