A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity recognition remains a challenging task. This is partly due to the broad range of human activities as well as the rich variation in how a given activity can be performed. Using features that clearly separate between activities is crucial. In this paper, we propose an approach to automatically extract discriminative features for activity recognition. Specifically, we develop a method based on Convolutional Neural Networks (CNN), which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains. In addition, a modified weight sharing technique, called partial weight sharing, is proposed and applied to accelerometer signals to get further improvements. The experimental results on three public datasets, Skoda (assembly line activities), Opportunity (activities in kitchen), Actitracker (jogging, walking, etc.), indicate that our novel CNN-based approach is practical and achieves higher accuracy than existing state-of-the-art methods.

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Spatial reuse in a mesh network can allow multiple communications to proceed simultaneously, hence proportionally improve the overall network throughput. To maximize spatial reuse, the MAC protocol must enable simultaneous transmitters to maintain the minimal separation distance that is sufficient to avoid interference. This paper demonstrates that physical carrier sensing enhanced with a tunable sensing threshold is effective at avoiding interference in 802.11 mesh networks without requiring the use of virtual carrier sensing. We present an analytical model for deriving the optimal sensing threshold given network topology, reception power, and data rate. A distributed adaptive scheme is also presented to dynamically adjust the physical carrier sensing threshold based on periodic estimation of channel conditions in the network. Simulation results are shown for large-scale 802.11b and 802.11a networks to validate both the analytical model and the adaptation scheme. It is demonstrated that the enhanced physical carrier sensing mechanism effectively improves network throughput by maximizing the potential of spatial reuse. With dynamically tuned physical carrier sensing, the end to end throughput approaches 90% of the predicted theoretical upper-bound assuming a perfect MAC protocol, for a regular chain topology of 90 nodes.

The vast available spectrum in the millimeter wave (mmWave) bands offers the possibility of multi-Gbps data rates for fifth generation (5G) cellular networks. However, mmWave capacity can be highly intermittent due to the vulnerability of mmWave signals to blockages and delays in directional searching. Such highly variable links present unique challenges for adaptive control mechanisms in transport layer protocols and end-to-end applications. This paper considers the fundamental question of whether TCP -the most widely used transport protocol -will work in mmWave cellular systems. The paper provides a comprehensive simulation study of TCP considering various factors such as the congestion control algorithm, including the recently proposed TCP BBR, edge vs. remote servers, handover and multiconnectivity, TCP packet size and 3GPP-stack parameters. We show that the performance of TCP on mmWave links is highly dependent on different combinations of these parameters, and identify the open challenges in this area.

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Abstract-Cognitive radio has become an effective theory to solve the inefficiency of the spectrum usage, and cooperative spectrum sensing among the secondary users to detect the primary user accurately is broadly studied before. In this paper, we employ a double threshold method in energy detector to perform spectrum sensing, while a fusion center in the cognitive radio network collects the local decisions and observational values of the secondary users, and then makes the final decision to determine whether the primary user is absence or not. Simulation results will show that the spectrum sensing performance in AWGN channels is improved significantly under the proposed scheme as opposed to the conventional method.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

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