Activation detection in fMRI using a maximum energy ratio statistic obtained by adaptive spatial filtering

An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data.     

Quality-aware images.

We propose the concept of quality-aware image, in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system, which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain.     

SCM: Secure and accountable TLS certificate management

In classical public‐key infrastructure (PKI), the certificate authorities (CAs) are fully trusted, and the security of the PKI relies on the trustworthiness of the CAs. However, recent failures and compromises of CAs showed that if a CA is corrupted, fake certificates may be issued, and the security of clients will be at risk. As emerging solutions, blockchain‐ and log‐based PKI proposals potentially solved the shortcomings of the PKI, in particular, eliminating the weakest link security and providing a rapid remedy to CAs' problems. Nevertheless, log‐based PKIs are still exposed to split‐world attacks if the attacker is capable of presenting two distinct signed versions of the log to the targeted victim(s), while the blockchain‐based PKIs have scaling and high‐cost issues to be overcome. To address these problems, this paper presents a secure and accountable transport layer security (TLS) certificate management (SCM), which is a next‐generation PKI framework. It combines the two emerging architectures, introducing novel mechanisms, and makes CAs and log servers accountable to domain owners. In SCM, CA‐signed domain certificates are stored in log servers, while the management of CAs and log servers is handed over to a group of domain owners, which is conducted on the blockchain platform. Different from existing blockchain‐based PKI proposals, SCM decreases the storage cost of blockchain from several hundreds of GB to only hundreds of megabytes. Finally, we analyze the security and performance of SCM and compare SCM with previous blockchain‐ and log‐based PKI schemes.     

EETP-MAC: energy efficient traffic prioritization for medium access control in wireless body area networks

Wireless body area network (WBAN) has witnessed significant attentions in the healthcare domain using biomedical sensor-based monitoring of heterogeneous nature of vital signs of a patient’s body. The design of frequency band, MAC superframe structure, and slots allocation to the heterogeneous nature of the patient’s packets have become the challenging problems in WBAN due to the diverse QoS requirements. In this context, this paper proposes an Energy Efficient Traffic Prioritization for Medium Access Control (EETP-MAC) protocol, which provides sufficient slots with higher bandwidth and guard bands to avoid channels interference causing longer delay. Specifically, the design of EETP-MAC is broadly divided in to four folds. Firstly, patient data traffic prioritization is presented with broad categorization including Non-Constrained Data (NCD), Delay-Constrained Data (DCD), Reliability-Constrained Data (RCD) and Critical Data (CD). Secondly, a modified superframe structure design is proposed for effectively handling the traffic prioritization. Thirdly, threshold based slot allocation technique is developed to reduce contention by effectively quantifying criticality on patient data. Forth, an energy efficient frame design is presented focusing on beacon interval, superframe duration, and packet size and inactive period. Simulations are performed to comparatively evaluate the performance of the proposed EETP-MAC with the state-of-the-art MAC protocols. The comparative evaluation attests the benefit of EETP-MAC in terms of efficient slot allocation resulting in lower delay and energy consumption.

     

Filter-Bank Modulation Based Signal Design and Transmission Techniques for Intensity Modulated MIMO Visible Light Communication Systems

Wireless Personal Communications - Current research in wireless communication undoubtedly points towards the tremendous advantages of using visible light as a spectrum for significantly boosting...     

A Full Connectable and High Scalable Key Pre-distribution Scheme Based on Combinatorial Designs for Resource-Constrained Devices in IoT Network

Considering the internet of things (IoT), end nodes such as wireless sensor network, RFID and embedded systems are used in many applications. These end nodes are known as resource-constrained devices in the IoT network. These devices have limitations such as computing and communication power, memory capacity and power. Key pre-distribution schemes (KPSs) have been introduced as a lightweight solution to key distribution in these devices. Key pre-distribution is a special type of key agreement that aims to select keys called session keys in order to establish secure communication between devices. One of these design types is the using of combinatorial designs in key pre-distribution, which is a deterministic scheme in key pre-distribution and has been considered in recent years. In this paper, by introducing a key pre-distribution scheme of this type, we stated that the model introduced in the two benchmarks of KPSs comparability had full connectivity and scalability among the designs introduced in recent years. Also, in recent years, among the combinatorial design-based key pre-distribution schemes, in order to increase resiliency as another criterion for comparing KPSs, attempts were made to include changes in combinatorial designs or they combine them with random key pre-distribution schemes and hybrid schemes were introduced that would significantly reduce the design connectivity. In this paper, using theoretical analysis and maintaining full connectivity, we showed that the strength of the proposed design was better than the similar designs while maintaining higher scalability.

     

BSLPSDN: Base station location privacy via software-defined networking (SDN) in wireless sensor networks

Base station's location privacy in a wireless sensor network (WSN) is critical for information security and operational availability of the network. A key part of securing the base station from potential compromise is to secure the information about its physical location. This paper proposes a technique called base station location privacy via software-defined networking (SDN) in wireless sensor networks (BSLPSDN). The inspiration comes from the architecture of SDN, where the control plane is separated from the data plane, and where control plane decides the policy for the data plane. BSLPSDN uses three categories of nodes, namely, a main controller to instruct the overall operations, a dedicated node to buffer and forward data, and lastly, a common node to sense and forward the packet. We employ three kinds of nodes to collaborate and achieve stealth for the base station and thus protecting it against the traffic-analysis attacks. Different traits of the WSN including energy status and traffic density can actively be monitored by BSLPSDN, which positively affects the energy goals, expected life of the network, load on common nodes, and the possibility of creating diversion in the wake of an attack on the base station. We incorporated multiple experiments to analyze and evaluate the performance of our proposed algorithm. We use single controller with multiple sensor nodes and multiple controllers with multiple sensor nodes to show the level of anonymity of BS. Experiments show that providing BS anonymity via multiple controllers is the best method both in terms of energy and privacy.     

A novel algorithm to study the impact of the mismatch on analog building blocks: a case study in basic 35 nm CMOS amplifiers

In this paper, the context of modeling of the impact of mismatch and statistical variations on analogue circuit building blocks is emphasized. The aim is to develop a new algorithm which predicts the statistical behavior of important parameters of an amplifier including output resistance, voltage gain and trans-conductance. The relative error of standard deviation of statistical parameters will remain less than 5% compared with the most accurate Monte-Carlo (MC) simulations using atomistic library model-cards. In comparison with other models which are based on the normal distribution of parameters, the proposed model does not need this limiting presumption. On the other hand, the proposed algorithm is more efficient compared with time consuming MC atomistic simulations.     

Wireless Brain Computer Interface for Smart Home and Medical System

Wireless Personal Communications - The number of aged and disabled people has been increasing worldwide. To look after these people is a big challenge in this era. However, scientists overcome the...