Design development of a static sunshade using small scale modeling technique

The rising global demand for energy has triggered emphasis on conservation of energy. Buildings are one of the important energy consuming sectors. Passive solar architecture encompasses a wide range of strategies and options resulting in energy efficient building design and increased occupant's comfort. Passive solar design, aiming at increasing direct solar gains during winter period and to avoid overheating during summer period should make use of specific shading devices over energy efficient window. The static sunshades are most effective for solar control inside the buildings.Countries like India have composite climate, which can be classified under summer, winter and rainy season. Depending on the seasonal requirements, this paper introduces a new geometry of a static sunshade, designed by calculating the sun angles for the two dates. The static sun shading design methodology is validated with the help of small scale modeling experimentation technique, carried out in Pilani, Rajasthan (India). Although insulating materials can be used as a part of a building structure, its feasibility should be checked before particular application. In the present paper, the two small-scale experimental models of actual construction material with varying static sunshades, i.e. horizontal and the proposed one have been constructed and analyzed with the models of insulating material (Polyurethane Foam [PUF]). Depending upon the solar intersection over south facade wall, sunlit area and shaded area have been correlated with temperature inside the models to decide the effectiveness of the proposed sunshade.     

A Novel Heterogeneous Clustering Protocol for Lifetime Maximization of Wireless Sensor Network

The network lifetime of Wireless Sensor Network (WSN) is one of the most challenging issues for any network protocol. The nodes in the network are densely deployed and are provided with limited power supply. The routing strategy is treated as an effective solution to improve the lifetime of the network. The cluster based routing techniques are used in the WSN to enhance the network lifespan and to minimize the energy consumption of the network. In this paper, an energy efficient heterogeneous clustering protocol for the enhancement of the network lifetime is proposed. The proposed protocol uses the sensor energy for the clustering process in a well-organized manner to maximize the lifetime of network. The MATLAB simulator is used for implementing the clustering model of proposed protocol and for measuring the effectiveness of the proposed technique the comparison is performed with the various existing approaches such as Stability Election Protocol, Distributed Energy Efficient Clustering and Adaptive Threshold Energy Efficient cross layer based Routing.

     

A Low-Power Wide-Dynamic-Range Analog VLSI Cochlea

Low-power wide-dynamic-range systems are extremely hard to build. The biological cochlea is one of the most awesome examples of such a system: It can sense sounds over 12 orders of magnitude in intensity, with an estimated power dissipation of only a few tens of microwatts. In this paper, we describe an analog electronic cochlea that processes sounds over 6 orders of magnitude in intensity, and that dissipates 0.5 mW. This 117-stage, 100 Hz to 10 KHz cochlea has the widest dynamic range of any artificial cochlea built to date. The wide dynamic range is attained through the use of a wide-linear-range transconductance amplifier, of a low-noise filter topology, of dynamic gain control (AGC) at each cochlear stage, and of an architecture that we refer to as overlapping cochlear cascades. The operation of the cochlea is made robust through the use of automatic offset-compensation circuitry. A BiCMOS circuit approach helps us to attain nearly scale-invariant behavior and good matching at all frequencies. The synthesis and analysis of our artificial cochlea yields insight into why the human cochlea uses an active traveling-wave mechanism to sense sounds, instead of using bandpass filters. The low power, wide dynamic range, and biological realism make our cochlea well suited as a front end for cochlear implants.     

Rate-distortion optimized tree-structured compression algorithms for piecewise polynomial images.

This paper presents novel coding algorithms based on tree-structured segmentation, which achieve the correct asymptotic rate-distortion (R-D) behavior for a simple class of signals, known as piecewise polynomials, by using an R-D based prune and join scheme. For the one-dimensional case, our scheme is based on binary-tree segmentation of the signal. This scheme approximates the signal segments using polynomial models and utilizes an R-D optimal bit allocation strategy among the different signal segments. The scheme further encodes similar neighbors jointly to achieve the correct exponentially decaying R-D behavior (D(R) - c(o)2(-c1R)), thus improving over classic wavelet schemes. We also prove that the computational complexity of the scheme is of O(N log N). We then show the extension of this scheme to the two-dimensional case using a quadtree. This quadtree-coding scheme also achieves an exponentially decaying R-D behavior, for the polygonal image model composed of a white polygon-shaped object against a uniform black background, with low computational cost of O(N log N). Again, the key is an R-D optimized prune and join strategy. Finally, we conclude with numerical results, which show that the proposed quadtree-coding scheme outperforms JPEG2000 by about 1 dB for real images, like cameraman, at low rates of around 0.15 bpp.     

Parametric Optimization of Complementary Split-Ring Resonator Dimensions for Planar Antenna Size Miniaturization

Wireless Personal Communications -     

ORuML: Optimized Routing in wireless networks using Machine Learning

Routing is a process of selecting a path in a network for delivering a packet from source node to destination node. Successful delivery of a message is a challenge, and therefore, this paper proposes an algorithm for a wireless network called Optimized Routing in wireless networks using Machine Learning (ORuML), which uses machine learning algorithm namely, K‐nearest neighbor (KNN), Support Vector Machine (SVM), and Multinomial Logistic Regression (MLR), to predict the network type of the source and destination nodes. The ML model is trained by using characteristic features of a node collected in real time such as battery power utilization, available internal storage, IP address, and range of a node. Intuitively, the MLR should outperform KNN and SVM in terms of accuracy and Area under ROC Curve (AUC). The proposed algorithm determines whether the source and destination nodes are co‐located and also, determines the best neighboring hop for efficient routing.     

Secure and efficient anonymous authentication scheme for three-tier mobile healthcare systems with wearable sensors

The mobility and openness of wireless communication technologies make Mobile Healthcare Systems (mHealth) potentially exposed to a number of potential attacks, which significantly undermines their utility and impedes their widespread deployment. Attackers and criminals, even without knowing the context of the transmitted data, with simple eavesdropping on the wireless links, may benefit a lot from linking activities to the identities of patient’s sensors and medical staff members. These vulnerabilities apply to all tiers of the mHealth system. A new anonymous mutual authentication scheme for three-tier mobile healthcare systems with wearable sensors is proposed in this paper. Our scheme consists of three protocols: Protocol-1 allows the anonymous authentication nodes (mobile users and controller nodes) and the HSP medical server in the third tier, while Protocol-2 realizes the anonymous authentication between mobile users and controller nodes in the second tier, and Protocol-3 achieves the anonymous authentication between controller nodes and the wearable body sensors in the first tier. In the design of our protocols, the variation in the resource constraints of the different nodes in the mHealth system are taken into consideration so that our protocols make a better trade-off among security, efficiency and practicality. The security of our protocols are analyzed through rigorous formal proofs using BAN logic tool and informal discussions of security features, possible attacks and countermeasures. Besides, the efficiency of our protocols are concretely evaluated and compared with related schemes. The comparisons show that our scheme outperforms the previous schemes and provides more complete and integrated anonymous authentication services. Finally, the security of our protocols are evaluated by using the Automated Validation of Internet Security Protocols and Applications and the SPAN animator software. The simulation results show that our scheme is secure and satisfy all the specified privacy and authentication goals.     

Full Eulerian deformable solid-fluid interaction scheme based on building-cube method for large-scale parallel computing

We propose a full Eulerian incompressible solid-fluid interaction scheme capable of achieving high parallel efficiency and easily generating meshes for complex solid geometries. While good scalability of a full Eulerian solid-fluid interaction formulation has been reported by Sugiyama et al, their analysis was carried out using uniform Cartesian mesh and an artificial compressibility method. Typically, it is more challenging to achieve good scalability for hierarchical Cartesian meshes and a fully incompressible formulation. In addition, the conventional full Eulerian methods require a large computational cost to resolve complex solid geometries due to the usage of uniform Cartesian meshes. In an attempt to overcome the aforementioned issues, we employ the building-cube method, where the computational domain is divided into cubic regions called cubes. Each cube is divided at equal intervals, the same number of cubes is assigned to each core, and the spatial loop processing is executed for each cube. The numerical method is verified by computing five numerical examples. In the weak scaling test, the parallel efficiency at 32768 cores with 32 cores as a reference is 93.6%. In the strong scaling test, the parallel efficiency at 32768 cores with 128 cores as a reference is 70.2%.     

A Semiempirical Model for Post-Yield Stress Instability in the Stress–Strain Response of 3D Lattice Structures under Compressive Loads

Mechanical behavior of lattice structures is important for a range of engineering applications. Herein, a new semiempirical model is proposed that describes the entire range of stress–strain response of lattice structures, including the stress-instability region which is modeled as an oscillator. The model can be fit to individual stress–strain curves to extract elastic modulus, yield stress, collapse stress, post-yield collapse ratio, densification strain, and the energy absorbed per unit volume. The model is fit to 119 unique experimental stress–strain curves from 13 research papers in literature covering four different lattice designs, namely, octet truss, body-centered cubic with vertical members, body-centered cubic, and hexagonal. Manufacturing methods (additive and conventional) and materials (metals and polymers) were also included in the analysis. The fitted model yields several new insights into the compression behavior of previously tested lattice structures and can be applied to additional lattice designs. Among other results, analysis of variance (ANOVA) reveals that the octet truss lattice demonstrates the highest post-yield collapse ratio and the smallest normalized energy absorption per unit volume amongst the lattice structures investigated. The proposed model is a powerful tool for designers to quantitatively compare and select 3D lattice structures with the desired mechanical characteristics.