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Summary Main aim of this paper is to compare the classification accuracies of glacier change detection by following classifiers: sub-pixel classification algorithm, indices based supervised classification and object based algorithm using Landsat imageries. It was observed that shadow effect was not removed in sub-pixel based classification which was removed by the indices method. Further the accuracy was improved by object based classification. Objective of the paper is to analyse different classification algorithms and interpret which one gives the best results in mountainous regions. The study showed that object based method was best in mountainous regions as optimum results were obtained in the shadowed covered regions.

Modern and energy-optimized buildings often lack an intelligent and advanced control strategy. Instead, conventional rule-based control (RBC) strategies are still mainly used today, which do not exploit the full performance potential of these buildings. Model predictive control (MPC) has proven in simulation studies and pilot cases to be a promising approach to reduce the energy consumption of buildings, while improving occupants’ comfort. However, there is still a lack of implementing MPC in real, large-scale and fully occupied buildings, to further prove this potential in real building operations. This paper describes the implementation and operation of MPC in a large-sized, low-energy office building. The MPC controller was implemented in a section of the building during a three-month test period from February to April 2020, controlling the supply temperature of heating circuits for thermally activated building systems (TABS). Its performance was compared to the default rule-based control which is active in the other building sections. This allows for a detailed evaluation of MPC versus RBC under identical environmental and operational conditions. The MPC controlled building section used 30 % less heating energy than RBC controlled building sections, while the existing high level of thermal comfort could be maintained. Especially in transition periods (i. e. interseasonal periods like late winter/early spring) , the MPC is superior to the conventional heating-curve based control strategy, with heating energy savings of 75 %.

Summary Breast cancer is significant health problem diagnosed mostly in women worldwide. Therefore, early detection of breast cancer is performed with the help of digital mammography, which can reduce mortality rate. This paper presents wrapper based feature selection approach for wavelet co-occurrence feature (WCF) using Genetic Fuzzy System (GFS) in mammogram classification problem. The performance of GFS algorithm is explained using mini-MIAS database. WCF features are obtained from detail wavelet coefficients at each level of decomposition of mammogram image. At first level of decomposition, 18 features are applied to GFS algorithm, which selects 5 features with an average classification success rate of 39.64%. Subsequently, at second level it selects 9 features from 36 features and the classification success rate is improved to 56.75%. For third level, 16 features are selected from 54 features and average success rate is improved to 64.98%. Lastly, at fourth level 72 features are applied to GFS, which selects 16 features and thereby increasing average success rate to 89.47%. Hence, GFS algorithm is the effective way of obtaining optimal set of feature in breast cancer diagnosis.

Summary In this paper, we proposed superscalar pipelined inner product computation unit for signed-unsigned number operating at 16 GHz. This is designed using five stage pipelined operation with four 8 × 8 multipliers operating in parallel. Superscalar pipelined is designed to compute four 8 × 8 products in parallel in three clock cycles. In the fourth clock cycle of the pipeline operation, two inner products are computed using two adders in parallel. Fifth stage of the pipeline is designed to compute the final product by adding two inner partial products. Upon the pipeline is filled up, every clock cycle the new product of 16 × 16-bit signed unsigned number is obtained. The worst delay measured among the pipeline stage is 0.062 ns, and this delay is considered as the clock cycle period. With the delay of 0.062 ns clock cycle period, the pipeline stage can be operated with 16 GHz synchronous clock signal. Each superscalar pipeline stage is implemented using 45 nm CMOS process technology, and the comparison of results shows that the delay is decreased by 38%, area is reduced by 45% and power dissipation is saved by 32%.

Summary This paper presents a technique that aimed to accomplish an efficient balance between video compression using H.265 protocol and retention of 8K resolution. The study implements multi-level of optimization in the encoding process using H.265 where JPEG2000 standards play a crucial role. The study also applies a novel concept of orthogonal projection that manages pixels metadata required in every frame transition followed by motion compensation. By using multiple file formats of 30 video datasets, the outcome of the study is found to be accomplishing approximately 49% of enhancement in data quality and around 59% of improvement in video compression in comparison to the existing techniques of HEVC-based video compression.

In this work, we propose a novel approach to prioritize the depth map computation of multi-view stereo (MVS) to obtain compact 3D point clouds of high quality and completeness at low computational cost. Our prioritization approach operates before the MVS algorithm is executed and consists of two steps. In the first step, we aim to find a good set of matching partners for each view. In the second step, we rank the resulting view clusters (i.e. key views with matching partners) according to their impact on the fulfillment of desired quality parameters such as completeness, ground resolution and accuracy. Additional to geometric analysis, we use a novel machine learning technique for training a confidence predictor. The purpose of this confidence predictor is to estimate the chances of a successful depth reconstruction for each pixel in each image for one specific MVS algorithm based on the RGB images and the image constellation. The underlying machine learning technique does not require any ground truth or manually labeled data for training, but instead adapts ideas from depth map fusion for providing a supervision signal. The trained confidence predictor allows us to evaluate the quality of image constellations and their potential impact to the resulting 3D reconstruction and thus builds a solid foundation for our prioritization approach. In our experiments, we are thus able to reach more than 70% of the maximal reachable quality fulfillment using only 5% of the available images as key views. For evaluating our approach within and across different domains, we use two completely different scenarios, i.e. cultural heritage preservation and reconstruction of single family houses., Comment: This paper was accepted to ISPRS Journal of Photogrammetry and Remote Sensing (https://www.journals.elsevier.com/isprs-journal-of-photogrammetry-and-remote-sensing) on March 21, 2018. The official version will be made available on ScienceDirect (https://www.sciencedirect.com)

Summary In this paper, we present a software tool capable of predicting the final quality of triple play services by using the most common assessment metrics. The quality of speech and video in network environment is a growing concern of all the internet service providers to carry the multimedia traffic without the excessive delays and losses, which degrade the quality of multimedia as it is perceived by the end users. Prediction mathematical model is based on results obtained from many performed testing scenarios simulating real behavior in the network. Based on the proposed model, speech or video quality is calculated with regard to policies applied for packet processing by routers and to the level of total network utilization. The application cannot only predict QoS parameters but also generate the source code of particular QoS policy setting according to the user interaction and apply the policy to the routers in the network. Contribution of the work consists of a new software tool enables network administrators and designers to improve and optimize network traffic efficiently.

Summary A cellular neural network (CNN) is a massively parallel analog array processor capable of solving various complex processing problems by using specific templates that characterize the synaptic connections. The hardware implementation and applications of CNN have attracted a great deal of attention. Recently, memristors with nanometer-scale and variable gradual conductance have been exploited to make compact and programmable electric synapses. This paper proposes and studies a novel memristive CNN (Mt-CNN) with time-variant templates realized by memristor crossbar synaptic circuits. The template parameters are estimated analytically. The Mt-CNN provides a promising solution to hardware realization of real-time template updating processes, which can be used to effectively deal with various complicated problems of cascaded processing. Its effectiveness and advantages are demonstrated by practical examples of edge detection on noisy images.

Summary Mobile is becoming a part of everyone's life and as their power of computation and storage is rising and cost is coming down. Most of mobile phone users have a lot of private data which they want to protect from others ( La Polla et al., 2013 ). It means user must be authenticated properly for accessing the mobile resources. Normally user is authenticated using text passwords, PIN, face recognition or patterns etc. All these methods are used but they have some shortcomings. In this paper we have seen various existing methods of mobile authentications and proposed our improved mobile authentication IPCT scheme. We have compared our Image Pass Code with tapping scheme with existing techniques and shown that our scheme is better than existing techniques.

This paper analyses the fuel consumption of interferometric radar missions employing small satellite formations like, e.g., Cross-track Pendulum, Cartwheel, CarPe, or Trinodal Pendulum. Individual analytic expressions are provided for each of the following contributions: separation from a simultaneously injected master satellite, formation set-up, orbit maintenance, formation maintenance, and distance maintenance. For this, a general system of equations is derived describing the relative motion of the small satellites in a co-rotating reference frame. The transformation into Keplerian elements is carried out. To evaluate fuel consumption, three master satellites are assumed in different orbital heights, which are typical for Earth observation missions. The size of the exemplarily analysed formations is defined by remote sensing aspects and their respective fuel requirements are estimated. Furthermore, a collision avoidance concept is introduced, which includes a formation separation and formation set-up after a desired time period.

The software system ABACUS is an object-oriented framework for the implementation of branch-and-cut and branch-and-price algorithms. This paper shows the basics of its application to combinatorial and mixed integer optimization problems.