There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The distributed nature and dynamic topology of Wireless Sensor Networks (WSNs) introduces very special requirements in routing protocols that should be met. The most important feature of a routing protocol, in order to be efficient for WSNs, is the energy consumption and the extension of the network's lifetime. During the recent years, many energy efficient routing protocols have been proposed for WSNs. In this paper, energy efficient routing protocols are classified into four main schemes: Network Structure, Communication Model, Topology Based and Reliable Routing. The routing protocols belonging to the first category can be further classified as flat or hierarchical. The routing protocols belonging to the second category can be further classified as Query-based or Coherent and non-coherent-based or Negotiation-based. The routing protocols belonging to the third category can be further classified as Location-based or Mobile Agent-based. The routing protocols belonging to the fourth category can be further classified as QoS-based or Multipath-based. Then, an analytical survey on energy efficient routing protocols for WSNs is provided. In this paper, the classification initially proposed by Al-Karaki, is expanded, in order to enhance all the proposed papers since 2004 and to better describe which issues/operations in each protocol illustrate/enhance the energy-efficiency issues.

Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey

https://hal.archives-ouvertes.fr/hal-01009386/document

Energy efficiency in wireless sensor networks: A top-down survey

Gamma and neutron detection are typical methods used for detection of Weapons of Mass Destruction (WMD). However, radiation related to gamma-rays can be easily shielded. Moreover, environmental conditions affect the efficiency of current methods for detection of fissile materials, and also reduce the range of detection. Here, a wideband target recognition method is proposed with the ability to detect a target− in particular the WMD− from a distance and identify the type of the target. As wideband data includes a broad range of frequencies, it can reveal information about both the surface of the target and its content. At first, the presence of the target and its location are estimated. Then, the estimated target is recognized by evaluating collected wideband data with information from wideband signature library which has already been built. Based on the experimental results, it is concluded that the proposed technique can be a promising approach for standoff target detection. As an example, the approach could identify the stainless steel sheet as the target with the best accuracy of 74%. Although the proposed method is designed for WMD detection, it can be extended to any application of target detection. General Terms Remote sensing

/pdf/remote-detection-of-weapons-of-mass-destruction-using-7mg6iwe3yi.pdf

Remote Detection of Weapons of Mass Destruction using Wideband Radar

Distinctive from traditional wireless ad hoc networks, wireless sensor networks (WSN) comprise a large number of low-cost miniaturized nodes each acting autonomously and equipped with short-range wireless communication mechanism, limited memory, processing power, and a physical sensing capability. Since sensor networks are resource constrained in terms of power, bandwidth and computational capability, an optimal system design radically changes the performance of the sensor network. Here, a comprehensive information dissemination scheme for wireless sensor networks is performed. Two main research issues are considered: (1) a collaborative flow of information packet/s from the source to sink and (2) energy efficiency of the sensor nodes and the entire system. For the first issue, we designed and evaluated a reactive and on-demand routing paradigm for distributed sensing applications. We name this scheme as IDLF-Information Dissemination via Label Forwarding. IDLF incorporates point to point data transmission where the source initiates the routing scheme and disseminates the information toward the sink (destination) node. Prior to transmission of actual data packet/s, a data tunnel is formed followed by the source node issuing small label information to its neighbors locally. These labels are in turn disseminated in the network. By using small size labels, IDLF avoids generation of unnecessary network traffic and transmission of duplicate packets to nodes. To study the impact of node failures and to improve the reliability of the network, we developed another scheme which is an extension to IDLF. This new scheme, RM-IDLF - Reliable Multipath Information dissemination by Label Forwarding, employ an alternate disjoint path. This alternate path scheme (RM-IDLF) may have a higher path cost in terms of energy consumption, but is more reliable in terms of data packet delivery to sink than the single path scheme (IDLF). In the latter scheme, the protocol establishes multiple (alternate) disjoint path/s from source to destination with negligible control overhead to balance load due to heavy data traffic among intermediate nodes from source to the destination. Another point of interest in this framework is the study of trade-offs between the achieved routing reliability using multiple disjoint path routing and extra energy consumption due to the use of additional path/s. Also, the effect of the failed nodes on the network performance is evaluated within the sensor system. 
Performance of the label dissemination scheme is evaluated and compared with the classic flooding and SPIN. (Abstract shortened by UMI.)

/pdf/a-reliable-and-resource-aware-framework-for-data-razndiqgiv.pdf

A reliable and resource aware framework for data dissemination in wireless sensor networks

https://digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=1033&context=epscor

Survey of Environmental Data Portals: Features, Characteristics, and Reliability Issues

Three case studies in this report provide an analysis between two different machine learning algorithms in computer vision (CV) systems, with the difference in automatic feature learning as found in Deep Learning (DL), and manual hand-crafting feature extraction/selection, as found in traditional CV methods. Furthermore, this report focuses on state-of-the-art between Convolution Neural Networks (CNNs) and Scale-Invariant Feature Transform (SIFT) related-models.

Performance Comparison of Algorithms Involving Automatic Learned Features and Hand-Crafted Features in Computer Vision

The design of networks connecting a large number of processors that support efficient parallel and distributed computing has mreceivedextensive attention. Networks can be abstracted as undL rected graphs, in which vertices and edges correspond to processors and bidirectional communication links between processors, respectively. Desirable network properties include high connectivity, high fault-tolerance, small dhrneter and degree, simple data routing algorithms, high degree of symmetry, feasibility for hardware implementation, etc. These parameters are interrelated. The contradictory requirements for network design make the trade-offs of different propetiies necessary. The search for better interconnection networks is continuing.

Shahram Latifi

Papers

Remote Detection of Weapons of Mass Destruction using Wideband Radar

A reliable and resource aware framework for data dissemination in wireless sensor networks

Survey of Environmental Data Portals: Features, Characteristics, and Reliability Issues

Performance Comparison of Algorithms Involving Automatic Learned Features and Hand-Crafted Features in Computer Vision

Sparse hypercube-like interconnection networks