A new processing approach for reducing computational complexity in cloud-RAN mobile networks

Cloud computing is considered as one of the key drivers for the next generation of mobile networks (e.g. 5G). This is combined with the dramatic expansion in mobile networks, involving millions (or even billions) of subscribers with a greater number of current and future mobile applications (e.g. IoT). Cloud Radio Access Network (C-RAN) architecture has been proposed as a novel concept to gain the benefits of cloud computing as an efficient computing resource, to meet the requirements of future cellular networks. However, the computational complexity of obtaining the channel state information in the full-centralized C-RAN increases as the size of the network is scaled up, as a result of enlargement in channel information matrices. To tackle this problem of complexity and latency, MapReduce framework and fast matrix algorithms are proposed. This paper presents two levels of complexity reduction in the process of estimating the channel information in cellular networks. The results illustrate that complexity can be minimized from O(N3) to O((N/k)3), where N is the total number of RRHs and k is the number of RRHs per group, by dividing the processing of RRHs into parallel groups and harnessing the MapReduce parallel algorithm in order to process them. The second approach reduces the computation complexity from O((N/k)3) to O((N/k)2:807) using the algorithms of fast matrix inversion. The reduction in complexity and latency leads to a significant improvement in both the estimation time and in the scalability of C-RAN networks

Serum SmD autoantibody proteomes are clonally restricted and share variable-region peptides

This article is under embargo for 12 months from the date of publication [Publication date: 7 Jan 2015] in accordance with publisher copyright policy.Recent advances in mass spectrometry-based proteomic methods have allowed variable (V)- region peptide signatures to be derived from human autoantibodies present in complex serum mixtures. Here, we analysed the clonality and V-region composition of immunoglobulin (Ig) proteomes specific for the immunodominant SmD protein subunit of the lupus-specific Sm autoantigen. Precipitating SmD-specific IgGs were eluted from native SmD-coated ELISA plates preincubated with sera from six patients with systemic lupus erythematosus (SLE) positive for anti-Sm/RNP. Heavy (H)- and light (L)-chain clonality and V-region sequences were analysed by 2-dimensional gel electrophoresis and combined de novo database mass spectrometric sequencing. SmD autoantibody proteomes from all six patients with SLE expressed IgG1 kappa restricted clonotypes specified by IGHV3-7 and IGHV1-69 H-chains and IGKV3-20 and IGKV2-28 L-chains, with shared and individual V-region amino acid replacement mutations. Clonotypic sharing and restricted V-region diversity of systemic autoimmunity can now be extended from the Ro/La cluster to Sm autoantigen and implies a common pathway of anti-Sm autoantibody production in unrelated patients with SLE

Latency reduction by dynamic channel estimator selection in C-RAN networks using fuzzy logic

Due to a dramatic increase in the number of mobile users, operators are forced to expand their networks accordingly. Cloud Radio Access Network (C-RAN) was introduced to tackle the problems of the current generation of mobile networks and to support future 5G networks. However, many challenges have arisen through the centralised structure of C-RAN. The accuracy of the channel state information acquisition in the C-RAN for large numbers of remote radio heads and user equipment is one of the main challenges in this architecture. In order to minimize the time required to acquire the channel information in C-RAN and to reduce the end-to-end latency, in this paper a dynamic channel estimator selection algorithm is proposed. The idea is to assign different channel estimation algorithms to the users of mobile networks based on their link status (particularly the SNR threshold). For the purpose of automatic and adaptive selection to channel estimators, a fuzzy logic algorithm is employed as a decision maker to select the best SNR threshold by utilising the bit error rate measurements. The results demonstrate a reduction in the estimation time with low loss in data throughput. It is also observed that the outcome of the proposed algorithm increases at high SNR values

Security Methods in Internet of vehicles

The emerging wireless communication technology known as vehicle ad hoc networks (VANETs) has the potential to both lower the risk of auto accidents caused by drivers and offer a wide range of entertainment amenities. The messages broadcast by a vehicle may be impacted by security threats due to the open-access nature of VANETs. Because of this, VANET is susceptible to security and privacy problems. In order to go beyond the obstacle, we investigate and review some existing researches to secure communication in VANET. Additionally, we provide overview, components in VANET in details

An Efficient Algorithm for Solving Multi- Objective Problem

In this paper, we propose an algorithm (CLLE) to find efficient" solutions  for  multi-objective sequencing problem on one machine .The Objectives are total completion time(∑Cj),total lateness(∑Lj), maximum Lateness(Lmax) and maximum earliness (Emax). A collection of dependent  jobs(tasks)  has to work out sequenced on one tool ,jobs j (j=1,2,3,….,n)required processing  time  pj and  due history  dj.  Conclusions   is formulated  in the  from  the (CLLE) algorithm based on results  of  computational experiments

Multi-Order Statistical Descriptors for Real-Time Face Recognition and Object Classification

We propose novel multi-order statistical descriptors which can be used for high speed object classification or face recognition from videos or image sets. We represent each gallery set with a global second-order statistic which captures correlated global variations in all feature directions as well as the common set structure. A lightweight descriptor is then constructed by efficiently compacting the second-order statistic using Cholesky decomposition. We then enrich the descriptor with the first-order statistic of the gallery set to further enhance the representation power. By projecting the descriptor into a low-dimensional discriminant subspace, we obtain further dimensionality reduction, while the discrimination power of the proposed representation is still preserved. Therefore, our method represents a complex image set by a single descriptor having significantly reduced dimensionality. We apply the proposed algorithm on image set and video-based face and periocular biometric identification, object category recognition, and hand gesture recognition. Experiments on six benchmark data sets validate that the proposed method achieves significantly better classification accuracy with lower computational complexity than the existing techniques. The proposed compact representations can be used for real-time object classification and face recognition in videos. 2013 IEEE.This work was supported by NPRP through the Qatar National Research Fund (a member of Qatar Foundation) under Grant 7-1711-1-312.Scopu

A study on the feeding of shrimp larvae of Macrobrachium nipponense on algae in vitro

Experiments were carried out for the propagation and rearing of Macrobrachium nipponense and its feeding on algae, with the aim of determining the density, survival, and growth of larvae in vitro. Hatched larvae of zoae were reared at a density of 50 zoea/L with algae mixture: Chlorella vulgaris, Scenedesmus sp., Pediastrium sp., Microctinium sp., Navicula sp., Ulothrix sp., Cyclotella sp., Daitoma sp. at three concentrations of 0.5=A, 1.0=B, 1.5=C x 10⁵ cell/ml, and the survival % rates of zoea larvae were 45.00±5.00, 53.33±7.64, 50.00±5.00 respectively. Then, three densities: 25=A, 50=B, 75=C zoea/L were tested by feeding them with the best concentration of 1.0×10⁵ cell/ml for 10 days, with the result being survival % rates were  50.00±5.00, 51.67±7.64, 31.67±7.64, respectively. After that, stage post-larvae were reared at a density of (50=A,100=B, 150=C) Pl/pond and fed with a concentration of 1.0 x 10⁵ cell/ml of the algae mixture for 28 days, which resulted in survival % rates of 48.33±7.64, 40.00±5.00, 33.33±7.64, and this stage, weight was 50.67±2.08, 50.00±2.00, 40.33±2.52mg respectively. The results of the analysis of survival rates for different densities of zoea larvae found significant differences (P < 0.05) between density C and density of both A and B, of which there were no significant differences (P > 0.05) between them. There were no significant differences (p > 0.05) in the survival rates of zoea in different concentrations of the selected algae. Also, there were no significant differences (p > 0.05) in the survival rates and weight rate of post-larvae when fed on algae (B)