University of Bridgeport

About: University of Bridgeport is a education organization based out in Bridgeport, Connecticut, United States. It is known for research contribution in the topics: Wireless sensor network & Key distribution in wireless sensor networks. The organization has 1008 authors who have published 1639 publications receiving 22740 citations.

Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks

Abstract: Wireless Sensor Networks (WSNs) have become essential components for a variety of environmental, surveillance, military, traffic control, and healthcare applications. These applications face critical challenges such as communication, security, power consumption, data aggregation, heterogeneities of sensor hardware, and Quality of Service (QoS) issues. Service-Oriented Architecture (SOA) is a software architecture that can be integrated with WSN applications to address those challenges. The SOA middleware bridges the gap between the high-level requirements of different applications and the hardware constraints of WSNs. This survey explores state-of-the-art approaches based on SOA and Service-Oriented Middleware (SOM) architecture that provide solutions for WSN challenges. The categories of this paper are based on approaches of SOA with and without middleware for WSNs. Additionally, features of SOA and middleware architectures for WSNs are compared to achieve more robust and efficient network performance. Design issues of SOA middleware for WSNs and its characteristics are also highlighted. The paper concludes with future research directions in SOM architecture to meet all requirements of emerging application of WSNs.

Robust desynchronization of Parkinson’s disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation

Abstract: The hyperkinetic symptoms of Parkinson’s Disease (PD) are associated with the ensembles of interacting oscillators that cause excess or abnormal synchronous behavior within the Basal Ganglia (BG) circuitry. Delayed feedback stimulation is a closed loop technique shown to suppress this synchronous oscillatory activity. Deep Brain Stimulation (DBS) via delayed feedback is known to destabilize the complex intermittent synchronous states. Computational models of the BG network are often introduced to investigate the effect of delayed feedback high frequency stimulation on partially synchronized dynamics. In this study, we develop a reduced order model of four interacting nuclei of the BG as well as considering the Thalamo-Cortical local effects on the oscillatory dynamics. This model is able to capture the emergence of 34 Hz beta band oscillations seen in the Local Field Potential (LFP) recordings of the PD state. Train of high frequency pulses in a delayed feedback stimulation has shown deficiencies such as strengthening the synchronization in case of highly fluctuating neuronal activities, increasing the energy consumed as well as the incapability of activating all neurons in a large-scale network. To overcome these drawbacks, we propose a new feedback control variable based on the filtered and linearly delayed LFP recordings. The proposed control variable is then used to modulate the frequency of the stimulation signal rather than its amplitude. In strongly coupled networks, oscillations reoccur as soon as the amplitude of the stimulus signal declines. Therefore, we show that maintaining a fixed amplitude and modulating the frequency might ameliorate the desynchronization process, increase the battery lifespan and activate substantial regions of the administered DBS electrode. The charge balanced stimulus pulse itself is embedded with a delay period between its charges to grant robust desynchronization with lower amplitudes needed. The efficiency of the proposed Frequency Adjustment Stimulation (FAS) protocol in a delayed feedback method might contribute to further investigation of DBS modulations aspired to address a wide range of abnormal oscillatory behavior observed in neurological disorders.

Variations in Variational Autoencoders - A Comparative Evaluation

Abstract: Variational Auto-Encoders (VAEs) are deep latent space generative models which have been immensely successful in many applications such as image generation, image captioning, protein design, mutation prediction, and language models among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data can be generated from the encoded distribution. This concept has led to tremendous research and variations in the design of VAEs in the last few years creating a field of its own, referred to as unsupervised representation learning. This paper provides a much-needed comprehensive evaluation of the variations of the VAEs based on their end goals and resulting architectures. It further provides intuition as well as mathematical formulation and quantitative results of each popular variation, presents a concise comparison of these variations, and concludes with challenges and future opportunities for research in VAEs.

Bones of large dinosaurs (Prosauropoda and Stegosauria) from the Rhaetic Bone Bed (Upper Triassic) of Aust Cliff, southwest England

Abstract: Five shafts of large long bones of dinosaurs have been found since 1846 in the Rhaetic Bone bed of the Westbury Formation (Upper Triassic) at Aust Cliff near Bristol, Avon, southwestern England. Two bones (1 lost) are Dinosauria incertae sedis and a third (also lost), the longest and best preserved, was probably part of a femur of the melanorosaurid prosauropod Camelotia (Upper Triassic, England). The width of the other two femoral shafts is greater transversely than anteroposteriorly, as in Camelotia. However, these shafts are also straight in lateral view, a derived character that occurs only in sauropods and stegosaurs and some Cretaceous ankylosaurs and ornithopods. However, the shafts are curved in lateral view in basal ankylosaurs and Jurassic ornithopods. Unlike the femora of Upper Jurassic sauropods and stegosaurs, in which the bone is almost solid, the Aust shafts consist of a thin layer of compact cortical bone surrounding a large area, most of which is filled with very lightly constructed cancellous bone. However, cross-sections of the humerus just below the deltopectoral crest of the sauropod Isanosaurus (Upper Triassic, Thailand) and the stegosaur Dacentrurus (Upper Jurassic, England) are intermediate in their histology. The femoral shaft is hollow in some Jurassic stegosaurs, though not to the degree shown by those from Aust. The Aust shafts are truly columnar, agreeing with those of stegosaurs in the apparent lack of a prominent fourth trochanter and of the associated posteromedial depression, structures which are prominent in sauropods. These two shafts, with estimated femoral lengths of about 1000-1100 mm, are tentatively referred to the Stegosauria so stegosaurs probably reached a large size in the Upper Triassic ; the earliest definitive record of the group is Middle Jurassic (Bathonian) of England.