Democracy, Crime and Justice. Annals of the American Academy of Political and Social Sciences

As a growing number of nations embark on a path to democracy, criminologists have become increasingly interested and engaged in the challenges, concerns, and questions connecting democracy with both crime and criminal justice. Rising levels of violence and street crime, white collar crime and corruption both in countries where democracy is securely in place and where it is struggling, have fuelled a deepening skepticism as to the capacity of democracy to deliver on its promise of security and justice for all citizens. What role does crime and criminal justice play in the future of democracy and for democratic political development on a global level? The editors of this special volume of The Annals realized the importance of collecting research from a broad spectrum of countries and covering a range of problems that affect citizens, politicians, and criminal justice officials. The articles here represent a solid balance between mature democracies like the U.S. and U.K. as well as emerging democracies around the globe – specifically in Latin America, Africa and Eastern Europe. They are based on large and small cross-national samples, regional comparisons, and case studies. Each contribution addresses a seminal question for the future of democratic political development across the globe. What is the role of criminal justice in the process of building democracy and instilling confidence in its institutions? Is there a role for unions in democratizing police forces? What is the impact of widespread disenfranchisement of felons on democratic citizenship and the life of democratic institutions? Under what circumstances do mature democracies adopt punitive sentencing regimes? Addressing sensitive topics such as relations between police and the Muslim communities of Western Europe in the wake of terrorist attacks, this volume also sheds light on the effects of terrorism on mature democracies under increasing pressure to provide security for their citizens. By taking a broad vantage point, this collection of research delves into complex topics such as the relationship between the process of democratization and violent crime waves; the impact of rising crime rates on newly established as well as secure democracies; how crime may endanger the transition to democracy; and how existing practices of criminal justice in mature democracies affect their core values and institutions. The collection of these insightful articles not only begins to fill a gap in criminological research but also addresses issues of critical interest to political scientists as well as other social and behavioral scientists and scholars. Taking a fresh approach to the intersection of crime, criminal justice, and democracy, this volume of The Annals is a must-read for criminologists and political scientists and provides a solid foundation for further interdisciplinary research.

Towards the development of a gas sensor system for monitoring pollutant gases in the low troposphere using small unmanned aerial vehicles

The world is facing problems due to the effects of increased atmospheric pollution, climate change and global warming. Innovative technologies to identify, quantify and assess fluxes exchange of the pollutant gases between the Earth’s surface and atmosphere are required. This paper proposes the development of a gas sensor system for a small UAV to monitor pollutant gases, collect data and geo-locate where the sample was taken. The prototype has two principal systems: a light portable gas sensor and an optional electric–solar powered UAV. The prototype will be suitable to: operate in the lower troposphere (100-500m); collect samples; stamp time and geo-locate each sample. One of the limitations of a small UAV is the limited power available therefore a small and low power consumption payload is designed and built for this research. The specific gases targeted in this research are NO2, mostly produce by traffic, and NH3 from farming, with concentrations above 0.05 ppm and 35 ppm respectively which are harmful to human health. The developed prototype will be a useful tool for scientists to analyse the behaviour and tendencies of pollutant gases producing more realistic models of them.

Controlled emergency landing of an unpowered unmanned aerial system

The ability to perform autonomous emergency (forced) landings is one of the key technology enablers identified for UAS. This paper presents the flight test results of forced landings involving a UAS, in a controlled environment, and which was conducted to ascertain the performances of previously developed (and published) path planning and guidance algorithms. These novel 3-D nonlinear algorithms have been designed to control the vehicle in both the lateral and longitudinal planes of motion. These algorithms have hitherto been verified in simulation. A modified Boomerang 60 RC aircraft is used as the flight test platform, with associated onboard and ground support equipment sourced Off-the-Shelf or developed in-house at the Australian Research Centre for Aerospace Automation (ARCAA). HITL simulations were conducted prior to the flight tests and displayed good landing performance, however, due to certain identified interfacing errors, the flight results differed from that obtained in simulation. This paper details the lessons learnt and presents a plausible solution for the way forward.

Flight guardian: a common avionics architecture for collision avoidance and safe emergency landing for unmanned aerial systems

This paper presents an approach to derive requirements for an avionics architecture that provides onboard sense-and-avoid and autonomous emergency forced landing capabilities to a UAS. The approach is based on two design paradigms that (1) derive requirements analyzing the common functionality between these two functions to then derive requirements for sensors, computing capability, interfaces, etc. (2) consider the risk and safety mitigation associated with these functions to derive certification requirements for the system design. We propose to use the Aircraft Certification Matrix (ACM) approach to tailor the system Development Assurance Levels (DAL) and architecture requirements in accordance with acceptable risk criteria. This architecture is developed under the name “Flight Guardian”. Flight Guardian is an avionics architecture that integrates common sensory elements that are essential components of any UAS that is required to be dependable. The Flight Guardian concept is also applicable to conventionally piloted aircraft, where it will serve to reduce cockpit workload.

Real-time power line extraction from unmanned aerial system video images

In this paper a real-time vision based power line extraction solution is investigated for active UAV guidance. The line extraction algorithm starts from ridge points detected by steerable filters. A collinear line segments fitting algorithm is followed up by considering global and local information together with multiple collinear measurements. GPU boosted algorithm implementation is also investigated in the experiment. The experimental result shows that the proposed algorithm outperforms two baseline line detection algorithms and is able to fitting long collinear line segments. The low computational cost of the algorithm make suitable for real-time applications.

Wind-energy based path planning for unmanned aerial vehicles using Markov decision processes

Exploiting wind-energy is one possible way to extend flight duration for Unmanned Arial Vehicles. Wind-energy can also be used to minimise energy consumption for a planned path. In this paper, we consider uncertain time-varying wind fields and plan a path through them. A Gaussian distribution is used to determine uncertainty in the Time-varying wind fields. We use Markov Decision Process to plan a path based upon the uncertainty of Gaussian distribution. Simulation results that compare the direct line of flight between start and target point and our planned path for energy consumption and time of travel are presented. The result is a robust path using the most visited cell while sampling the Gaussian distribution of the wind field in each cell.

Cross-entropy optimization for scaling factors of a fuzzy controller : a see-and-avoid approach for unmanned aerial systems

The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.

Faculty mentoring, evidence-based assessment, and student learning : an Australian exploration of American initiatives

There have been many improvements in Australian engineering education since the 1990s. However, given the recent drive for assuring the achievement of identified academic standards, more progress needs to be made, particularly in the area of evidence-based assessment. This paper reports on initiatives gathered from the literature and engineering academics in the USA, through an Australian National Teaching Fellowship program. The program aims to establish a process to help academics in designing and implementing evidence-based assessments that meet the needs of not only students and the staff that teach them, but also industry as well as accreditation bodies. The paper also examines the kinds and levels of support necessary for engineering academics, especially early career ones, to help meet the expectations of the current drive for assured quality and standards of both research and teaching. Academics are experiencing competing demands on their time and energy with very high expectations in research performance and increased teaching responsibilities, although many are researchers who have not had much pedagogic training. Based on the literature and investigation of relevant initiatives in the USA, we conducted interviews with several identified experts and change agents who have wrought effective academic cultural change within their institutions and beyond. These reveal that assuring the standards and quality of student learning outcomes through evidence-based assessments cannot be appropriately addressed without also addressing the issue of pedagogic training for academic staff. To be sustainable, such training needs to be complemented by a culture of on-going mentoring support from senior academics, formalised through the university administration, so that mentors are afforded resources, time, and appropriate recognition.

Hybrid game evolutionary algorithm for mission path planning of aerial survey tasks

The aim of this paper is to implement a Game-Theory based offline mission path planner for aerial inspection tasks of large linear infrastructures. Like most real-world optimisation problems, mission path planning involves a number of objectives which ideally should be minimised simultaneously. The goal of this work is then to develop a Multi-Objective (MO) optimisation tool able to provide a set of optimal solutions for the inspection task, given the environment data, the mission requirements and the definition of the objectives to minimise. Results indicate the robustness and capability of the method to find the trade-off between the Pareto-optimal solutions.

An evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles with tactical and kinematic constraints

This paper presents a novel evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles (UAVs) with tactical and kinematic constraints. A genetic algorithm (GA) is modified and extended for path planning. Two GAs are seeded at the initial and final positions with a common objective to minimise their distance apart under given UAV constraints. This is accomplished by the synchronous optimisation of subsequent control vectors. The proposed evolutionary computation approach is called synchronous genetic algorithm (SGA). The sequence of control vectors generated by the SGA constitutes to a near-optimal path plan. The resulting path plan exhibits no discontinuity when transitioning from curve to straight trajectories. Experiments and results show that the paths generated by the SGA are within 2% of the optimal solution. Such a path planner when implemented on a hardware accelerator, such as field programmable gate array chips, can be used in the UAV as on-board replanner, as well as in ground station systems for assisting in high precision planning and modelling of mission scenarios.

MAVwork : a framework for unified interfacing between micro aerial vehicles and visual controllers

Debugging control software for Micro Aerial Vehicles (MAV) can be risky out of the simulator, especially with professional drones that might harm people around or result in a high bill after a crash. We have designed a framework that enables a software application to communicate with multiple MAVs from a single unified interface. In this way, visual controllers can be first tested on a low-cost harmless MAV and, after safety is guaranteed, they can be moved to the production MAV at no additional cost. The framework is based on a distributed architecture over a network. This allows multiple configurations, like drone swarms or parallel processing of drones' video streams. Live tests have been performed and the results show comparatively low additional communication delays, while adding new functionalities and flexibility. This implementation is open-source and can be downloaded from github.com/uavster/mavwork