Human behavioural analysis with self-organizing map for ambient assisted living

This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints

Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph

Police SWAT teams and Military Special Forces face mounting pressure and challenges from adversaries that can only be resolved by way of ever more sophisticated inputs into tactical operations. Lethal Autonomy provides constrained military/security forces with a viable option, but only if implementation has got proper empirically supported foundations. Autonomous weapon systems can be designed and developed to conduct ground, air and naval operations. This monograph offers some insights into the challenges of developing legal, reliable and ethical forms of autonomous weapons, that address the gap between Police or Law Enforcement and Military operations that is growing exponentially small. National adversaries are today in many instances hybrid threats, that manifest criminal and military traits, these often require deployment of hybrid-capability autonomous weapons imbued with the capability to taken on both Military and/or Security objectives. The Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that required military response and police investigations against a fighting cell of the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade

Combat Identification Using Multiple TUAV Swarm

In modern warfare, Tactical Unmanned Aerial Vehicles (TUAVs) are rapidly taking on a leading role in traditional and non-traditional ISR, to include Automatic Target Recognition (ATR). However, additional advancements in processors and sensors on TUAVs are still needed before they can be widely employed as a primary source for positive identification in the Combat Identification (CID) process. Cost is a driving factor for operating an ATR system using multiple TUAVs. The cost of high quality sensors appropriate for a single TUAV can be significantly higher than less sophisticated sensors suitable for deployment on a group, or swarm, of coordinated TUAVs. Employing two or more coordinated TUAVs with less complex sensors may lead to an equivalent or even better CID call than sending a single TUAV with more sophisticated sensors at a significantly higher cost. In addition, the coordinated TUAVs may be capable of reducing the time needed to correctly discriminate an object. Five measures of performance (accuracy, number of TUAVs shot down, TUAV preparation time, mean of decision time, mean of simulated mission time) from the simulation models are collected to compare the swarm system to the single TUAV system. Statistical comparisons are conducted using a paired t-test. The results illustrate improved performance of our swarm systems across most measures of performance

\u3cem\u3eGRASP News\u3c/em\u3e, Volume 8, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory. Edited by Thomas Lindsay

CREATING SPECIAL OPERATIONS FORCES' ORGANIC SMALL UNMANNED AIRCRAFT SYSTEM OF THE FUTURE

Emerging observations of the Ukrainian conflict reinforce standing assumptions that the future’s multidomain operational environment in which U.S. special operations forces (SOF) will deploy will be characterized by rapid, continuous advancements in intelligence, surveillance, and reconnaissance (ISR) technologies. The Department of Defense’s inability to develop and field innovative weapons and technical systems at the rate of its peer and near-peer competitors invites great risks to both its military members and its national security. One critical deficiency that U.S. SOF must address immediately is its use of artificial intelligence and machine learning in a small unmanned aircraft system (sUAS). We can no longer assume that we will achieve the air superiority or air parity that have enabled the persistent presence of theater-level assets to support military elements in contested and denied areas. This thesis focuses on enhancing U.S. SOF force protection and situational awareness by combining a cutting-edge sUAS with object recognition software to create an organic ISR capability. Following several field experiments in partnership with private industries to test and refine object recognition software when combined with a sUAS, our results strongly suggest that integrating object recognition capabilities into a SOF element’s organic sUAS can achieve the performance parameters necessary to fill the current gap in U.S. SOF force protection and ISR requirements.SOCOM S&TOffice of Naval ResearchMajor, United States ArmyApproved for public release. Distribution is unlimited

Girt by sea: understanding Australia’s maritime domains in a networked world

This study aims to provide the background, language and context necessary for an informed understanding of the challenges and dilemmas faced by those responsible for the efficacy of Australia’s maritime domain awareness system. Abstract Against a rapidly changing region dominated by the rise of China, India and, closer to home, Indonesia, Australia’s approaches to understanding its maritime domains will be influenced by strategic factors and diplomatic judgements as well as operational imperatives.  Australia’s alliance relationship with the United States and its relationships with regional neighbours may be expected to have a profound impact on the strength of the information sharing and interoperability regimes on which so much of Australia’s maritime domain awareness depends. The purpose of this paper is twofold.  First, it seeks to explain in plain English some of the principles, concepts and terms that maritime domain awareness practitioners grapple with on a daily basis.  Second, it points to a series of challenges that governments face in deciding how to spend scarce tax dollars to deliver a maritime domain awareness system that is necessary and sufficient for the protection and promotion of Australia’s national interests

The intelligence cycle is dead, long live the intelligence cycle: Rethinking intelligence fundamentals for a new intelligence doctrine

In the spring of 2009 the UK Ministry of Defence elected to undertake a review of the existing military Joint Intelligence Doctrine. The existing doctrine, Joint Warfare Doctrine 2-00 (JWP 2-00) Intelligence Support to Joint Operations had been promulgated in 2003 largely on the basis of coalition-oriented expeditionary and peace support operations in the Balkans, Middle East and Afghanistan. This had replaced an earlier, first edition of JWP 2-00 issued in 1999