Co-operative or coyote? Producers' choice between intermediary purchasers and Fairtrade and organic cooperatives in Chiapas

This study of organic and Fairtrade co-operatives in Mexico aims to find out why many coffee producers prefer not to join the certified co-operatives, despite their higher price offer. A study of costs of production of organic coffee concludes that it implies more work, but not necessarily higher yields. A main conclusion of the investigation is that the compulsory organic production methods deters many producers from entering the co-operatives, and that it is more attractive for producers with more free family labour, and less attractive for producers with very little coffee land. However, the study also shows that it is not only economic factors that influence the decisions of the producers on where to sell their coffee. Previous studies have shown that Fairtrade and organic certification can bring higher incomes and more security into the lives of marginalized farmers (Bray et al. 2002, Martinez-Torres 2006, Jaffeee 2007) hence it is important to understand more about how these systems can achieve their aims. This study shows that although the smallest farmers are less likely to become a part of these systems, the farmers who do are also very poor and vulnerable. Also, co-operatives need to be economically viable organisations and the organic requirements ensure a market with a higher price for the product, while at the same time keeping the organization at a manageable size. It is therefore recommended to keep the organic production requirements as a criteria for producers entering the co-operatives

Featureless visual processing for SLAM in changing outdoor environments

Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features

Low-Carbon Technologies in the Post-Bali Period: Accelerating their Development and Deployment. CEPS ECP Report No. 4, 4 December 2007

This report analyses the very broad issue of technology development, demonstration and diffusion with a view to identifying the key elements of a complementary global technology track in the post-2012 framework. It identifies a number of immediate and concrete steps that can be taken to provide content and a structure for such a track. The report features three sections dealing with innovation and technology, investment in developing countries and investment and finance, followed by an analysis of the various initiatives being taken on technology both within and outside the United Nations Framework Convention on Climate Change (UNFCCC). A final section presents ideas for the way forward followed by brief concluding remarks

Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost

Robotic and animal mapping systems share many challenges and characteristics: they must function in a wide variety of environmental conditions, enable the robot or animal to navigate effectively to find food or shelter, and be computationally tractable from both a speed and storage perspective. With regards to map storage, the mammalian brain appears to take a diametrically opposed approach to all current robotic mapping systems. Where robotic mapping systems attempt to solve the data association problem to minimise representational aliasing, neurons in the brain intentionally break data association by encoding large (potentially unlimited) numbers of places with a single neuron. In this paper, we propose a novel method based on supervised learning techniques that seeks out regularly repeating visual patterns in the environment with mutually complementary co-prime frequencies, and an encoding scheme that enables storage requirements to grow sub-linearly with the size of the environment being mapped. To improve robustness in challenging real-world environments while maintaining storage growth sub-linearity, we incorporate both multi-exemplar learning and data augmentation techniques. Using large benchmark robotic mapping datasets, we demonstrate the combined system achieving high-performance place recognition with sub-linear storage requirements, and characterize the performance-storage growth trade-off curve. The work serves as the first robotic mapping system with sub-linear storage scaling properties, as well as the first large-scale demonstration in real-world environments of one of the proposed memory benefits of these neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and Automation Letter

Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment

Many localization algorithms use a spatiotemporal window of sensory information in order to recognize spatial locations, and the length of this window is often a sensitive parameter that must be tuned to the specifics of the application. This letter presents a general method for environment-driven variation of the length of the spatiotemporal window based on searching for the most significant localization hypothesis, to use as much context as is appropriate but not more. We evaluate this approach on benchmark datasets using visual and Wi-Fi sensor modalities and a variety of sensory comparison front-ends under in-order and out-of-order traversals of the environment. Our results show that the system greatly reduces the maximum distance traveled without localization compared to a fixed-length approach while achieving competitive localization accuracy, and our proposed method achieves this performance without deployment-time tuning.Comment: Pre-print of article appearing in 2017 IEEE Robotics and Automation Letters. v2: incorporated reviewer feedbac

Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration

Convolutional Neural Networks (CNNs) have recently been shown to excel at performing visual place recognition under changing appearance and viewpoint. Previously, place recognition has been improved by intelligently selecting relevant spatial keypoints within a convolutional layer and also by selecting the optimal layer to use. Rather than extracting features out of a particular layer, or a particular set of spatial keypoints within a layer, we propose the extraction of features using a subset of the channel dimensionality within a layer. Each feature map learns to encode a different set of weights that activate for different visual features within the set of training images. We propose a method of calibrating a CNN-based visual place recognition system, which selects the subset of feature maps that best encodes the visual features that are consistent between two different appearances of the same location. Using just 50 calibration images, all collected at the beginning of the current environment, we demonstrate a significant and consistent recognition improvement across multiple layers for two different neural networks. We evaluate our proposal on three datasets with different types of appearance changes - afternoon to morning, winter to summer and night to day. Additionally, the dimensionality reduction approach improves the computational processing speed of the recognition system.Comment: Accepted to the Australasian Conference on Robotics and Automation 201