Systems and Communications Commons^™

Editorial Introduction To The Northeast Journal Of Complex Systems (Nejcs), Hiroki Sayama, Georgi Georgiev

Northeast Journal of Complex Systems (NEJCS)

Editorial Introduction to the Northeast Journal of Complex Systems (NEJCS)

Fractality And Power Law Distributions: Shifting Perspectives In Educational Research, Matthijs Koopmans

Northeast Journal of Complex Systems (NEJCS)

The dynamical character of education and the complexity of its constituent relationships have long been recognized, but the full appreciation of the implications of these insights for educational research is recent. Most educational research to this day tends to focus on outcomes rather than process, and rely on conventional cross-sectional designs and statistical inference methods that do not capture this complexity. This presentation focuses on two related aspects not well accommodated by conventional models, namely fractality (self-similarity, scale invariance) and power law distributions (an inverse relationship between frequency of occurrence and strength of response). Examples are presented for both phenomena …

A Harmless Wireless Quantum Alternative To Cell Phones Based On Quantum Noise, Florentin Smarandache, Robert Neil Boyd, Victor Christianto

Branch Mathematics and Statistics Faculty and Staff Publications

In the meantime we know that 4G and 5G technologies cause many harms to human health. Therefore, here we submit a harmless wireless quantum alternative to cell phones. It is our hope that this alternative

Subsurface Mimo: A Beamforming Design In Internet Of Underground Things For Digital Agriculture Applications, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required for the degree of …

Crude Palm Oil Prediction Based On Back Propagation Neutral Network Approach, Hijratul Aini, Haviluddin Haviluddin

Knowledge Engineering and Data Science

Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in …

The Diffusion Of Ict For Corruption Detectionin Open Government Data, Darusalam Darusalam, Jamaliah Said, Normah Omar, Marijn Janssen, Kazi Sohag

Knowledge Engineering and Data Science

Corruption occurs in many places within the government. To tackle the issue, open data can be used as one of the tools in creating more insight into the government. The premise of this paper is to support the notion that data opening can bring up new ways of fighting corruption. The current paper aimed at investigating how open data can be employed to detect corruption. This open data is trivial due to challenges like information asymmetry among stakeholders, data might only be opened partly, different sources of data need to be combined, and data might not be easy to use, …

Adam Optimization Algorithmfor Wide And Deep Neural Network, Imran Khan Mohd Jais, Amelia Ritahani Ismail

Knowledge Engineering and Data Science

The objective of this research is to evaluate the effects of Adam when used together with a wide and deep neural network. The dataset used was a diagnostic breast cancer dataset taken from UCI Machine Learning. Then, the dataset was fed into a conventional neural network for a benchmark test. Afterwards, the dataset was fed into the wide and deep neural network with and without Adam. It was found that there were improvements in the result of the wide and deep network with Adam. In conclusion, Adam is able to improve the performance of a wide and deep neural network.

Selection Of Marine Security Policyusing Fuzzy-Ahp Topsis Hybrid Approach, Hozairi Hozairi, Buhari Buhari, Heru Lumaksono, Marcus Tukan

Knowledge Engineering and Data Science

The research was focused on the integration of Fuzzy set theory with Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to choose the optimum maritime security policy to achieve Indonesia recognition as the world's maritime axis. The method used is AHP with fuzzy based enhancement. Here, the weight of each criterion is calculated to overcome the criticism of the scale of unbalanced rating, uncertainty, and inaccuracy in the pairwise of comparison process. The best recommendation for Indonesian maritime policies is multi task single agency which is greatly infuenced by several factors such as …

High Dimensional Data Clustering Using Self-Organized Map, Ruth Ema Febrita, Wayan Firdaus Mahmudy, Aji Prasetya Wibawa

Knowledge Engineering and Data Science

As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution.

Research Methodology Adopted In Developing Tansse-L System, Ellen Kalinga

Tanzania Journal of Engineering and Technology (TJET)

Research methodology is among the very important part in a research work. It is the heart which describe the activities necessary for the completion of the research work. Research methodology provides a plan of investigation considered to obtain answers to research problems and it depends on the context of application. This paper presents the research methodology adopted when developing Tanzania Secondary Schools e-Learning (TanSSe-L) system, a learning management system (LMS) which was successfully developed through customization of Moodle open source LMS. TanSSe-L system is a context centered platform for secondary schools in Tanzania. TanSSe-L system was developed using a number …

Influence Spread In Two-Layer Interdependent Networks: Designed Single-Layer Or Random Two-Layer Initial Spreaders?, Hana Khamfroush, Nathaniel Hudson, Samuel Iloo, Mahshid R. Naeini

Computer Science Faculty Publications

Influence spread in multi-layer interdependent networks (M-IDN) has been studied in the last few years; however, prior works mostly focused on the spread that is initiated in a single layer of an M-IDN. In real world scenarios, influence spread can happen concurrently among many or all components making up the topology of an M-IDN. This paper investigates the effectiveness of different influence spread strategies in M-IDNs by providing a comprehensive analysis of the time evolution of influence propagation given different initial spreader strategies. For this study we consider a two-layer interdependent network and a general probabilistic threshold influence spread model …

A Cooperative Overlay Approach At The Physical Layer Of Cognitive Radio For Digital Agriculture, Abdul Salam, Umit Karabiyik

Faculty Publications

In digital agriculture, the cognitive radio technology is being envisaged as solution to spectral shortage problems by allowing agricultural cognitive users to co-exist with noncognitive users in the same spectrum on the field. Cognitive radios increase system capacity and spectral efficiency by sensing the spectrum and adapting the transmission parameters. This design requires a robust, adaptable and flexible physical layer to support cognitive radio functionality. In this paper, a novel physical layer architecture for cognitive radio based on cognition, cooperation, and cognitive interference avoidance has been developed by using power control for digital agriculture applications. The design is based on …

Urban Underground Infrastructure Monitoring Iot: The Path Loss Analysis, Abdul Salam, Syed Shah

Faculty Publications

The extra quantities of wastewater entering the pipes can cause backups that result in sanitary sewer overflows. Urban underground infrastructure monitoring is important for controlling the flow of extraneous water into the pipelines. By combining the wireless underground communications and sensor solutions, the urban underground IoT applications such as real time wastewater and storm water overflow monitoring can be developed. In this paper, the path loss analysis of wireless underground communications in urban underground IoT for wastewater monitoring has been presented. It has been shown that the communication range of up to 4 kilometers can be achieved from an underground …

An Underground Radio Wave Propagation Prediction Model For Digital Agriculture, Abdul Salam

Faculty Publications

Underground sensing and propagation of Signals in the Soil (SitS) medium is an electromagnetic issue. The path loss prediction with higher accuracy is an open research subject in digital agriculture monitoring applications for sensing and communications. The statistical data are predominantly derived from site-specific empirical measurements, which is considered an impediment to universal application. Nevertheless, in the existing literature, statistical approaches have been applied to the SitS channel modeling, where impulse response analysis and the Friis open space transmission formula are employed as the channel modeling tool in different soil types under varying soil moisture conditions at diverse communication distances …

Feasibility And Security Analysis Of Wideband Ultrasonic Radio For Smart Home Applications, Qi Xia

School of Computing: Dissertations, Theses, and Student Research

Smart home Internet-of-Things (IoT) accompanied by smart home apps has witnessed tremendous growth in the past few years. Yet, the security and privacy of the smart home IoT devices and apps have raised serious concerns, as they are getting increasingly complicated each day, expected to store and exchange extremely sensitive personal data, always on and connected, and commonly exposed to any users in a sensitive environment. Nowadays wireless smart home IoT devices rely on electromagnetic wave-based radio-frequency (RF) technology to establish fast and reliable quality network connections. However, RF has its limitations that can negatively affect the smart home user …

Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required derived the degrees of …

Preserving Privacy In Automotive Tire Pressure Monitoring Systems, Kenneth L. Hacker

Theses and Dissertations

The automotive industry is moving towards a more connected ecosystem, with connectivity achieved through multiple wireless systems. However, in the pursuit of these technological advances and to quickly satisfy requirements imposed on manufacturers, the security of these systems is often an afterthought. It has been shown that systems in a standard new automobile that one would not expect to be vulnerable can be exploited for a variety of harmful effects. This thesis considers a seemingly benign, but government mandated, safety feature of modern vehicles; the Tire Pressure Monitoring System (TPMS). Typical implementations have no security-oriented features, leaking data that can …

A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language …

A Theoretical Model Of Underground Dipole Antennas For Communications In Internet Of Underground Things, Abdul Salam, Mehmet C. Vuran, Xin Dong, Christos Argyropoulos, Suat Irmak

Faculty Publications

The realization of Internet of Underground Things (IOUT) relies on the establishment of reliable communication links, where the antenna becomes a major design component due to the significant impacts of soil. In this paper, a theoretical model is developed to capture the impacts of change of soil moisture on the return loss, resonant frequency, and bandwidth of a buried dipole antenna. Experiments are conducted in silty clay loam, sandy, and silt loam soil, to characterize the effects of soil, in an indoor testbed and field testbeds. It is shown that at subsurface burial depths (0.1-0.4m), change in soil moisture impacts …

Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This …

Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai

Electrical and Computer Engineering Faculty Publications

To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …

Artificial Intelligence In The Aviation Manufacturing Process For Complex Assemblies And Components, Elena Vishnevskaya, Ian Mcandrew, Michael Johnson

Publications

Aviation manufacturing is at the leading edge of technology with materials, designs and processes where automation is not only integral; but complex systems require more advanced systems to produce and verify processes. Critical Infrastructure theory is now used to protect systems and equipment from external software infections and cybersecurity techniques add an extra layer of protection. In this research, it is argued that Artificial Intelligence can reduce these risks and allow complex processes to be less exposed to the threat of external problems, internal errors or mistakes in operation.

Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep …

A Survey Of Techniques For Mobile Service Encrypted Traffic Classification Using Deep Learning, Pan Wang, Xuejiao Chen, Feng Ye, Zhixin Sun

Electrical and Computer Engineering Faculty Publications

The rapid adoption of mobile devices has dramatically changed the access to various net- working services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades. However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification. Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted …

Internet Of Things In Smart Agriculture: Enabling Technologies, Abdul Salam, Syed Shah

Faculty Publications

In this paper, an IoT technology research and innovation roadmap for the field of precision agriculture (PA) is presented. Many recent practical trends and the challenges have been highlighted. Some important objectives for integrated technology research and education in precision agriculture are described. Effective IoT based communications and sensing approaches to mitigate challenges in the area of precision agriculture are presented.

Storage Systems For Mobile-Cloud Applications, Nafize R. Paiker

Dissertations

Mobile devices have become the major computing platform in todays world. However, some apps on mobile devices still suffer from insufficient computing and energy resources. A key solution is to offload resource-demanding computing tasks from mobile devices to the cloud. This leads to a scenario where computing tasks in the same application run concurrently on both the mobile device and the cloud.

This dissertation aims to ensure that the tasks in a mobile app that employs offloading can access and share files concurrently on the mobile and the cloud in a manner that is efficient, consistent, and transparent to locations. …

Profiling And Identifying Individual Usersby Their Command Line Usage And Writing Style, Darusalam Darusalam, Helen Ashman

Knowledge Engineering and Data Science

Profiling and identifying individual users is an approach for intrusion detection in a computer system. User profiles are important in many applications since they record highly user-specific information - profiles are basically built to record information about users or for users to share experiences with each other. This research extends previous research on re-authenticating users with their user profiles. This research focuses on the potential to add psychometric user characteristics into the user model so as to be able to detect unauthorized users who may be masquerading as a genuine user. There are five participants involved in the investigation for …

Energy Efficiency Metrics Of University Data Centers, Leonel Hernandez, Genett Jimenez, Piedad Marchena

Knowledge Engineering and Data Science

The data centers are fundamental pieces in the network and computing infrastructure,and evidently today more than ever they are relevant. Since they support the processing, analysis, assurance of the data generated in the network and by the applications in the cloud, which every day increases its volume thanks to technologies such as Internet of Things, Virtualization, and cloud computing, among others. Precisely the management of this large volume of information makes the data centers consume a lot of energy, generating great concern to owners and administrators. Green Data Centers offer a solution to this problem, reducing the impact produced by …

Signature Pattern Recognition Using Kohonen Network, Nadia Roosmalita Sari, Mohammad Zoqi Sarwani, Yudha Alif Aulia, Wayan Firdaus Mahmudy

Knowledge Engineering and Data Science

A signature is a special form of handwriting that used for human identification process. The current identification process is extremely ineffective. People have to manually compare signatures with the previously stored data. This study proposed SOM Kohonen algorithm as the method of signature pattern recognition. This method has able to visualize high-dimensional data. The image processing method is used in this study in pre-processing data phase. The accuracy of SOM Kohonen was 70 %, indicated the method used was good enough for pattern recognition.

Digit Classification Of Majapahit Relic Inscriptionusing Glcm-Svm, Tri Septianto, Endang Setyati, Joan Santoso

Knowledge Engineering and Data Science

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition.Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture …