Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Cognitive radio & Wireless network. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Modulation Classification Based on Signal Constellation Diagrams and Deep Learning

Abstract: Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification.

How Much Language Is Enough? Theoretical and Practical Use of the Business Process Modeling Notation

Abstract: The Business Process Modeling Notation (BPMN) is an increasingly important industry standard for the graphical representation of business processes. BPMN offers a wide range of modeling constructs, significantly more than other popular languages. However, not all of these constructs are equally important in practice as business analysts frequently use arbitrary subsets of BPMN. In this paper we investigate what these subsets are, and how they differ between academic, consulting, and general use of the language. We analyzed 120 BPMN diagrams using mathematical and statistical techniques. Our findings indicate that BPMN is used in groups of several, well-defined construct clusters, but less than 20% of its vocabulary is regularly used and some constructs did not occur in any of the models we analyzed. While the average model contains just 9 different BPMN constructs, models of this complexity have typically just 4-5 constructs in common, which means that only a small agreed subset of BPMN has emerged. Our findings have implications for the entire ecosystems of analysts and modelers in that they provide guidance on how to reduce language complexity, which should increase the ease and speed of process modeling.

Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving for Cloud Storage

Abstract: Remote data integrity checking (RDIC) enables a data storage server, say a cloud server, to prove to a verifier that it is actually storing a data owner’s data honestly. To date, a number of RDIC protocols have been proposed in the literature, but most of the constructions suffer from the issue of a complex key management, that is, they rely on the expensive public key infrastructure (PKI), which might hinder the deployment of RDIC in practice. In this paper, we propose a new construction of identity-based (ID-based) RDIC protocol by making use of key-homomorphic cryptographic primitive to reduce the system complexity and the cost for establishing and managing the public key authentication framework in PKI-based RDIC schemes. We formalize ID-based RDIC and its security model, including security against a malicious cloud server and zero knowledge privacy against a third party verifier. The proposed ID-based RDIC protocol leaks no information of the stored data to the verifier during the RDIC process. The new construction is proven secure against the malicious server in the generic group model and achieves zero knowledge privacy against a verifier. Extensive security analysis and implementation results demonstrate that the proposed protocol is provably secure and practical in the real-world applications.

Effects of leader contingent and noncontingent reward and punishment behaviors on subordinate performance and satisfaction.

Abstract: This study investigated the nature of the relationships between leader reward and punishment behaviors and subordinate performance and satisfaction. Only performance-contingent reward behavior was found to affect subordinate performance significantly. Positive relationships were found between leader contingent reward behavior and employee satisfaction. Contingent punishment had no effects on subordinate performance or satisfaction.

Responsive polymer films and capsules via layer-by-layer assembly

Abstract: In recent years, the layer-by-layer (LbL) self-assembly technique has grown strong in its influence on chemistry, physics, materials science, and biology. One of the emerging trends in the area is the growing interest in design of polyelectrolyte multilayer films (PEMs) with specific response characteristics. This approach is significantly facilitated by the versatility and flexibility of the LbL technique. In this review, fundamental aspects of construction of PEMs responsive to a variety of external stimuli such as pH, ionic strength, temperature, light, magnetic field or specific biological moieties, and their possible applications are discussed.