Every day we share our personal information through digital systems which are constantly exposed to threats. For this reason, security-oriented disciplines of signal processing have received increasing attention in the last decades: multimedia forensics, digital watermarking, biometrics, network monitoring, steganography and steganalysis are just a few examples. Even though each of these fields has its own peculiarities, they all have to deal with a common problem: the presence of one or more adversaries aiming at making the system fail. Adversarial Signal Processing lays the basis of a general theory that takes into account the impact that the presence of an adversary has on the design of effective signal processing tools. By focusing on the application side of Adversarial Signal Processing, namely adversarial information fusion in distributed sensor networks, and adopting a game-theoretic approach, this thesis contributes to the above mission by addressing four issues. First, we address decision fusion in distributed sensor networks by developing a novel soft isolation defense scheme that protect the network from adversaries, specifically, Byzantines. Second, we develop an optimum decision fusion strategy in the presence of Byzantines. In the next step, we propose a technique to reduce the complexity of the optimum fusion by relying on a novel near-optimum message passing algorithm based on factor graphs. Finally, we introduce a defense mechanism to protect decentralized networks running consensus algorithm against data falsification attacks.

Spectrum sensing allows cognitive radio systems to detect relevant signals in despite the presence of severe interference. Most of the existing spectrum sensing techniques use a particular signal-noise model with certain assumptions and derive certain detection performance. To deal with this uncertainty, learning based approaches are being adopted and more recently deep learning based tools have become popular. Here, we propose an approach of spectrum sensing which is based on long short term memory (LSTM) which is a critical element of deep learning networks (DLN). Use of LSTM facilitates implicit feature learning from spectrum data. The DLN is trained using several features and the performance of the proposed sensing technique is validated with the help of an empirical testbed setup using Adalm Pluto. The testbed is trained to acquire the primary signal of a real world radio broadcast taking place using FM. Experimental data show that even at low signal to noise ratio, our approach performs well in terms of detection and classification accuracies, as compared to current spectrum sensing methods.

One of the most satisfying aspects of working at Cisco is tackling a thorny problem of engineering or computational science, and seeing your work reflected in a global standard or a shipping product within a few years. In wireless networking in particular, we have teams on the leading edge of research, software and hardware engineering, and on applying AI to wireless systems. There are many interesting problems our engineers have solved. But there are many we still haven't. I was recently asked to present to a National Science Foundation workshop on "Future Wireless Research Challenges."

Spectrum sensing is of critical importance in any cognitive radio system. When the primary user's signal has uncertain parameters, the likelihood ratio test (LRT), which is the theoretically optimal detector, generally has no closed-form expression. As a result, spectrum sensing under parameter uncertainty remains an open question, though many detectors exploiting specific features of a primary signal have been proposed and have achieved reasonably good performance. In this paper, a neural network is trained as a detector for modulated signals. The result shows by training on an appropriate dataset, the neural network gains robustness under uncertainties in system parameters including the carrier frequency offset, carrier phase offset, and symbol time offset. The result displays the neural network's potential in exploiting implicit and incomplete knowledge about the signal's structure.

NASA spacecraft typically rely on human-controlled radio systems to communicate with Earth. As collection of space data increases, NASA looks to cognitive radio, the infusion of artificial intelligence into space communications networks, to meet demand and increase efficiency. "Modern space communications systems use complex software to support science and exploration missions," said Janette C. Briones, principal investigator in the cognitive communication project at NASA's Glenn Research Center in Cleveland, Ohio. "By applying artificial intelligence and machine learning, satellites control these systems seamlessly, making real-time decisions without awaiting instruction." To understand cognitive radio, it's easiest to start with ground-based applications.

NASA scientists are planning to use artificial intelligence to better manage the increasing communications between its spacecraft and the Earth. NASA spacecraft typically rely on human-controlled radio systems to communicate with Earth. Cognitive radio, the infusion of artificial intelligence into space communications networks, could meet demand and increase efficiency, researchers said. "Modern space communications systems use complex software to support science and exploration missions," said Janette C Briones, from the NASA's Glenn Research Center in the US. "By applying artificial intelligence and machine learning, satellites control these systems seamlessly, making real-time decisions without awaiting instruction," said Briones.

As collection of space data increases, NASA is exploring the infusion of Artificial Intelligence (AI) into space communications networks to meet demand and increase efficiency. Software-defined radios like cognitive radio use AI to employ underutilised portions of the electromagnetic spectrum without human intervention. The Federal Communications Commission (FCC) permits a cognitive radio to use the frequency while unused by its primary user until the user becomes active again. "Modern space communications systems use complex software to support science and exploration missions. By applying AI and machine learning, satellites control these systems seamlessly, making real-time decisions without awaiting instruction," Janette C. Briones, Principal Investigator at NASA's Glenn Research Centre in Cleveland, Ohio, said in a statement on Saturday.

Jan Krikke After 50 years of research and tinkering, machine translation might be ready to compete with human translators. Several companies have announced breakthroughs or substantial progress in MT research in recent months. In January, for example, Steven Klein, CEO of New York-based Meaningful Machines, announced that his company successfully tested new translation algorithms that he says could lead to translation engines replacing human translators. "While our current prototype is already outperforming other systems on limited resources," says Klein, "we expect to see significant improvement to our quality as both the target language corpus and the dictionary continue to increase in size, with a realistic goal of reaching human quality." "Although the prototype is only partially complete," says Klein, "we recently began blind testing from Spanish to English, and our system is already performing at higher quality levels on the BLEU (Bilingual Evaluation Understudy) scale than any system we are aware of--0.6092. Systran, whose Spanish-to-English system is one of the best, scored a 0.5494 when we ran it through the same test, and the Systran system has been through many decades of development and incremental improvements." Meaningful Machines' test has not been independently verified, and the goal of reaching near-human quality translation will probably depend on some degree of pre- and post-editing for years to come. But, the growing number of global corporations (such as Philips, Samsung, and HP) and international agencies and institutions (such as the UN and the European Commission) using the technology illustrates that machine translation--the first nonnumerical application of AI--is finally delivering practical solutions. Popular perception of MT has suffered from low-quality "gisting" translation that Web-based translation engines, such as Babelfish and other online services, generate. But MT engines designed for limited domains, and tailor-made systems that use controlled language, are already delivering services. The site makes available a wealth of information previously inaccessible to non-Japanese speakers. MT has also made it to the desktop. The system is self-learning--it improves over time as its associative memory grows.

Spectrum sensing is a fundamental problem in cognitive radio. We propose a function of covariance matrix based detection algorithm for spectrum sensing in cognitive radio network. Monotonically increasing property of function of matrix involving trace operation is utilized as the cornerstone for this algorithm. The advantage of proposed algorithm is it works under extremely low signal-to-noise ratio, like lower than -30 dB with limited sample data. Theoretical analysis of threshold setting for the algorithm is discussed. A performance comparison between the proposed algorithm and other state-of-the-art methods is provided, by the simulation on captured digital television (DTV) signal.

Cognitive radio is a breakthrough technology which is expected to have a profound impact on the way radio spectrum will be accessed, managed and shared in the future. In this paper I examine some of the implications of cognitive radio for future management of spectrum. Both a near-term view involving the opportunistic spectrum access model and a longer-term view involving a self-regulating dynamic spectrum access model within a society of cognitive radios are discussed.