In LoRa (Long Range), when a collision occurs in the network, each end-device has to retransmit its colliding frame, which reduces the throughput, and increases the energy consumption of the end-devices and the delay of the frames. In this paper, we propose an algorithm to decode colliding synchronized LoRa signals and thus improve the overall performance of the network. Indeed, we use successive transmissions of bitmaps by the end-devices to determine the correct symbols of each colliding frame, instead of retransmitting the whole frames. Simulation results show that our algorithm is able to significantly improve the overall throughput of LoRaWAN, and to decrease the energy consumption and the delay of the transmitters.
In @cite_4 , the authors made a study regarding the CSS modulation technique. They show that some CSS symbols are not orthogonal. Their simulations show that the achievable range of the CSS technique is lower than an ultra-narrowband solution, but the robustness against interference is higher.
@cite_4
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Long range low power is a family of technologies promising to connect thousands of sensors to the future internet of things. Within this family of possible technology choices, two different branches have emerged: the standards based on spreaded wideband communication and the standards based on narrowband communication, both promising to reach long range connectivity at very low power. More specifically, this paper focuses on chirp spread spectrum (CSS). This paper presents a detailed model of CSS, showing that the symbols are not perfectly orthogonal. The results show the potential communication ranges are close to narrowband networks and the robustness against interfering signals is 22 dB better for spreading factor 10 than BPSK. Although wideband communication is robust to interference, in long range communication, this robustness is insufficient due to the long range and the larger footprint. The large propagation losses due to the long range and the larger footprint make CSS prone to collisions with other noise sources, possibly larger than the coding gain. Motivated by the enormous amounts of data collected in a large IT service provider organization, this paper presents a method for quickly and automatically summarizing and extracting meaningful insights from the data. Termed Clustered Subset Selection (CSS), our method enables program-guided data explorations of high-dimensional data matrices. CSS combines clustering and subset selection into a coherent and intuitive method for data analysis. In addition to a general framework, we introduce a family of CSS algorithms with different clustering components such as k-means and Close-to-Rank-One (CRO) clustering, and Subset Selection components such as best rank-one approximation and Rank-Revealing QR (RRQR) decomposition. From an empirical perspective, we illustrate that CSS is achieving significant improvements over existing Subset Selection methods in terms of approximation errors. Compared to existing Subset Selection techniques, CSS is also able to provide additional insight about clusters and cluster representatives. Finally, we present a case-study of program-guided data explorations using CSS on a large amount of IT service delivery data collection. In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user, which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS, the strategy for combining the individual sensing results of the SUs is learned autonomously with a CNN using training sensing samples regardless of whether the individual sensing results are quantized or not. Moreover, both spectral and spatial correlation of individual sensing outcomes are taken into account such that an environment-specific CSS is enabled in DCS. Through simulations, we show that the performance of CSS can be greatly improved by the proposed DCS. In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user (PU) which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS which is hard to compute and also hard to use in practice, the strategy for combining the individual sensing results of the SUs is learned with a CNN using training sensing samples. Accordingly, an environment-specific CSS which considers both spectral and spatial correlation of individual sensing outcomes, is found in an adaptive manner regardless of whether the individual sensing results are quantized or not. Through simulation, we show that the performance of CSS can be improved by the proposed DCS with low complexity even when the number of training samples is moderate. Mining Software Repositories (MSR) is an applied and practise-oriented field aimed at solving real problems encountered by practitioners and bringing value to Industry. We believe that empirical studies on both Open Source Software (OSS) and Closed or Proprietary Source (CSS PSS) is required in MSR research to increase generalizability or transferability of findings and reduce external (or threats) validity concerns. Furthermore, we believe that a collaboration between University and Industry is must or important in achieving the stated goals and agenda of MSR research (such as deployment and technology transfer). We analyse past five years of research papers published in MSR series of conferences (2010-2014) and count the number of studies using solely OSS data or solely CSS data or both OSS and CSS data. We also count the number of papers published by authors solely from Universities, solely from Industry and from both University and Industry. We present our findings which indicate lack of University-Industry collaboration (measured using co-authorship in scientific publications) and paucity of empirical studies on CSS PSS data. Our analysis reveals that out of 187 studies over a period of 5 years, 90:9 studies are conducted solely on OSS dataset. We present our findings which indicate that only 14:43 of the studies involve a University-Industry collaboration. Wireless power transfer (WPT) provides a novel solution to the painstaking power-charging issue in wireless sensor networks. However, due to the propagation loss, the fast attenuation in energy transfer efficiency over the transmission distance is the main impediment to the WPT application. In this paper, we apply the simultaneous wireless information and power transfer (SWIPT) to a wirelessly powered sensor network, where each node has two circuits, which operate on energy harvesting mode and information decoding mode separately. We propose a novel cooperative SWIPT scheme (CSS) for this system. First, we present a conflict-free schedule initialization algorithm for CSS. For a given conflict-free schedule, we formulate a resource allocation problem to maximize the network energy efficiency, which is then transformed to an equivalent convex optimization problem and resolved via dual decomposition. Finally, a heuristic algorithm is presented to achieve the transmission schedule with the maximum energy efficiency and the corresponding resource assignment policy. Simulation results indicate that the CSS can significantly improve the energy efficiency of the wirelessly powered sensor network. We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques which are used for pattern classification. In this regard, unsupervised (e.g., K-means clustering and Gaussian mixture model (GMM)) and supervised (e.g., support vector machine (SVM) and weighted K-nearest-neighbor (KNN)) learning-based classification techniques are implemented for CSS. For a radio channel, the vector of the energy levels estimated at CR devices is treated as a feature vector and fed into a classifier to decide whether the channel is available or not. The classifier categorizes each feature vector into either of the two classes, namely, the "channel available class" and the "channel unavailable class". Prior to the online classification, the classifier needs to go through a training phase. For classification, the K-means clustering algorithm partitions the training feature vectors into K clusters, where each cluster corresponds to a combined state of primary users (PUs) and then the classifier determines the class the test energy vector belongs to. The GMM obtains a mixture of Gaussian density functions that well describes the training feature vectors. In the case of the SVM, the support vectors (i.e., a subset of training vectors which fully specify the decision function) are obtained by maximizing the margin between the separating hyperplane and the training feature vectors. Furthermore, the weighted KNN classification technique is proposed for CSS for which the weight of each feature vector is calculated by evaluating the area under the receiver operating characteristic (ROC) curve of that feature vector. The performance of each classification technique is quantified in terms of the average training time, the sample classification delay, and the ROC curve. Our comparative results clearly reveal that the proposed algorithms outperform the existing state-of-the-art CSS techniques. We explore the feasibility of linear interference alignment using finite signaling dimensions and symbol extensions. In the non-zero total intersection regime, we show that the number of sources is upperbounded by a function of channel diversity. The available diversity places a fundamental constraint on the number of signal spaces that can overlap at one destination (where they are undesired) while maintaining the resolvability of a subset of those signals (desired signals) at another destination. Specifically, the number of such signal spaces cannot be larger than the channel diversity. This is the diversity constraint that we identify in this paper. Not only is the proposed method applicable for X channels, interference channels, and their rank-deficient counterparts, it can also be used for both circular symmetric signaling (CSS) and asymmetric complex signaling (ACS) over a combination of frequency and MIMO channels with arbitrary symbol extensions. Programmers frequently turn to the web to solve problems and find example code. For the sake of brevity, the snippets in online instructions often gloss over the syntax of languages like CSS selectors and Unix commands. Programmers must compensate by consulting external documentation. In this paper, we propose language-specific routines called Tutorons that automatically generate context-relevant, on-demand micro-explanations of code. A Tutoron detects explainable code in a web page, parses it, and generates in-situ natural language explanations and demonstrations of code. We build Tutorons for CSS selectors, regular expressions, and the Unix command “wget”. We demonstrate techniques for generating natural language explanations through template instantiation, synthesizing code demonstrations by parse tree traversal, and building compound explanations of co-occurring options. Through a qualitative study, we show that Tutoron-generated explanations can reduce the need for reference documentation in code modification tasks. We present a method for fast, approximate registration of whole-slide images (WSIs) of histopathology serial sections. Popular histopathology slide registration methods in the existing literature tend towards intensity-based approaches. 1, 2 Further input, in the form of an approximate initial transformation to be applied to one of the two WSIs, is then usually required, and this transformation needs to be optimised. Such a transformation is not readily available in this context and thus there is a need for fast approximation of these parameters. Fast registration is achieved by comparison of the external boundaries of adjacent tissue sections, using local curvature on multiple scales to assess similarity. Our representation of curvature is a modified version of the Curvature Scale Space (CSS) 3 image. We substitute zero crossings with signed local absolute maxima of curvature to improve the registration's robustness to the subtle morphological differences of adjacent sections. A pairwise matching is made between curvature maxima at scales increasing exponentially, the matching minimizes the distance between maxima pairs at each scale. The boundary points corresponding to the matched maxima pairs are used to estimate the desired transformation. Our method is highly robust to translation, rotation, and linear scaling, and shows good performance in cases of moderate non-linear scaling. On our set of test images the algorithm shows improved reliability and processing speed in comparison to existing CSS based registration methods. Abstract Cooperative spectrum sensing (CSS) is an effective approach to improve the detection performance in cognitive radio (CR) networks. The CSS process imposes some additional security threats to the CR networks. One of the common threats is primary user emulation attack (PUEA). In this particular type of attack, some malicious users try to imitate primary signal characteristics and defraud CR users to prevent them from accessing the idle frequency bands. Here, we propose a novel cooperative sensing scheme with attack-aware capability in the presence of a malicious PUEA. More precisely, the probabilities of the presence of a PUEA fake signal in both presence and absence of the licensed PU signal are estimated. Then, the obtained parameters are used to determine the optimal threshold that minimizes the total error probability. Simulation results are presented to indicate the performance improvement of the proposed method against PUEA compared with the conventional method. JavaScript is a powerful and flexible prototype-based scripting language that is increasingly used by developers to create interactive web applications. The language is interpreted, dynamic, weakly-typed, and has first-class functions. In addition, it interacts with other web languages such as CSS and HTML at runtime. All these characteristics make JavaScript code particularly error-prone and challenging to write and maintain. Code smells are patterns in the source code that can adversely influence program comprehension and maintainability of the program in the long term. We propose a set of 13 JavaScript code smells, collected from various developer resources. We present a JavaScript code smell detection technique called JSNOSE. Our metric-based approach combines static and dynamic analysis to detect smells in client-side code. This automated technique can help developers to spot code that could benefit from refactoring. We evaluate the smell finding capabilities of our technique through an empirical study. By analyzing 11 web applications, we investigate which smells detected by JSNOSE are more prevalent. Modern web applications consist of a significant amount of client- side code, written in JavaScript, HTML, and CSS. In this paper, we present a study of common challenges and misconceptions among web developers, by mining related questions asked on Stack Over- flow. We use unsupervised learning to categorize the mined questions and define a ranking algorithm to rank all the Stack Overflow questions based on their importance. We analyze the top 50 questions qualitatively. The results indicate that (1) the overall share of web development related discussions is increasing among developers, (2) browser related discussions are prevalent; however, this share is decreasing with time, (3) form validation and other DOM related discussions have been discussed consistently over time, (4) web related discussions are becoming more prevalent in mobile development, and (5) developers face implementation issues with new HTML5 features such as Canvas. We examine the implications of the results on the development, research, and standardization communities. In this paper, we investigate a new waveform for UWB systems obtained by the combination of Orthogonal Frequency Division Multiplex (OFDM) and Code Division Multiple Access (CDMA). The proposed system, called Spread Spectrum - Multi-Carrier - Multiple Access (SS-MC-MA) turns out to be a judicious solution to combat frequency selectivity and narrowband interferers, and to manage the coexistence of several users and piconets. It is shown that the addition of a degree of freedom brought by the spreading component of SS-MC-MA allows to optimize jointly the assignment of the number of used codes and coding rates in order to make the system more robust. Through simulations, it is demonstrated that the new system can outperform Multi-Band OFDM Alliance (MBOA) for low data rates and is able to provide wider range of rates. We study indexing techniques for main memory, including hash indexes, binary search trees, T-trees, B+-trees, interpolation search, and binary search on arrays. In a decision-support context, our primary concerns are the lookup time, and the space occupied by the index structure. Our goal is to provide faster lookup times than binary search by paying attention to reference locality and cache behavior, without using substantial extra space. We propose a new indexing technique called -Sensitive Search Trees" (CSS-trees). Our technique stores a directory structure on top of a sorted array. Nodes in this directory have size matching the cache-line size of the machine. We store the directory in an array and do not store internal-node pointers; child nodes can be found by performing arithmetic on array osets. We compare the algorithms based on their time and space requirements. We have implemented all of the techniques, and present a performance study on two popular modern machines. We demonstrate that with This research was supported by a David and Lucile Packard Foundation Fellowship in Science and Engineering, by an NSF Young Investigator Award, by NSF grant number IIS-98-12014, and by NSF CISE award CDA-9625374. Orthogonal frequency-division multiplexing (OFDM) is a modulation technology that has been widely adopted in many new and emerging broadband wireless and wireline communication systems. Due to its capability to transmit a high-speed data stream using multiple spectral-overlapped lower-speed subcarriers, OFDM technology offers superior advantages of high spectrum efficiency, robustness against inter-carrier and inter-symbol interference, adaptability to server channel conditions, etc. In recent years, there have been intensive studies on optical OFDM (O-OFDM) transmission technologies, and it is considered a promising technology for future ultra-high-speed optical transmission. Based on O-OFDM technology, a novel elastic optical network architecture with immense flexibility and scalability in spectrum allocation and data rate accommodation could be built to support diverse services and the rapid growth of Internet traffic in the future. In this paper, we present a comprehensive survey on OFDM-based elastic optical network technologies, including basic principles of OFDM, O-OFDM technologies, the architectures of OFDM-based elastic core optical networks, and related key enabling technologies. The main advantages and issues of OFDM-based elastic core optical networks that are under research are also discussed. Ultra-reliable communications over wireless will open the possibility for a wide range of novel use cases and applications. In cellular networks, achieving reliable communication is challenging due to many factors, particularly the fading of the desired signal and the interference. In this regard, we investigate the potential of several techniques to combat these main threats. The analysis shows that traditional microscopic multiple-input multiple-output schemes with 2x2 or 4x4 antenna configurations are not enough to fulfil stringent reliability requirements. It is revealed how such antenna schemes must be complemented with macroscopic diversity as well as interference management techniques in order to ensure the necessary SINR outage performance. Based on the obtained performance results, it is discussed which of the feasible options fulfilling the ultra-reliable criteria are most promising in a practical setting, as well as pointers to supplementary techniques that should be included in future studies. In this letter, we focus on the evaluation of link-level performance of LoRa technology, in the usual network scenario with a central gateway and high-density deployment of end-devices. LoRa technology achieves wide coverage areas, low power consumption and robustness to interference thanks to a chirp spread-spectrum modulation, in which chirps modulated with different spreading factors (SFs) are quasi-orthogonal. We focus on the performance analysis of a single receiver in presence of collisions. First, we analyze LoRa modulation numerically and show that collisions between packets modulated with different SFs can indeed cause packet loss if the interference power received is strong enough. Second, we validate such findings in experiments based on commercial devices and software-defined radios. Contradicting the common belief that SFs can be considered orthogonal, our results demonstrate that inter-SF collisions are indeed an issue in LoRa networks and, thus, allocating higher SFs to users far from the gateway might not necessarily improve their link capacity, in case of congested networks. In this paper we study the impact of imperfect-orthogonality in LoRa spreading factors (SFs) in simulation and real-world experiments. First, we analyze LoRa modulation numerically and show that collisions between packets of different SFs can indeed cause packet loss if the interference power received is strong enough. Second, we validate such findings using commercial devices, confirming our numerical results. Third, we modified and extended LoRaSim, an open-source LoRa simulator, to measure the impact of inter-SF collisions and fading (which was not taken into account previously in the simulator). Our results show that non-orthogonality of the SFs can deteriorate significantly the performance especially of higher SFs (10 to 12) and that fading has virtually no impact when multiple gateways are available in space diversity. It is well known that when a connectionist network is trained on one set of patterns and then attempts to add new patterns to its repertoire, catastrophic interference may result. The use of sparse, orthogonal hidden-layer representations has been shown to reduce catastrophic interference. The author demonstrates that the use of sparse representations may, in certain cases, actually result in worse performance on catastrophic interference. This paper argues for the necessity of maintaining hidden-layer representations that are both as highly distributed and as highly orthogonal as possible. The author presents a learning algorithm, called context-biasing, that dynamically solves the problem of constraining hiddenlayer representations to simultaneously produce good orthogonality and distributedness. On the data tested for this study, context-biasing is shown to reduce catastrophic interference by more than 50 compared to standard backpropagation. In particular, this technique succeeds in reducing catastrophic interference on data where sparse, orthogonal distributions failed to produce any improvement.