Business models for industrial symbiosis: A guide for firms

Industrial symbiosis (IS) is a collaborative approach concerning physical exchange of materials, energy, and services among different firms: accordingly, wastes produced by a given firm are exploited as inputs by other firms. This approach is able to generate economic and environmental benefits at the same time, the former for the involved firms and the latter for the collectivity as a whole. For these reasons, the implementation of IS is largely recommended. However, despite its huge potentialities, the IS approach seems to be actually underdeveloped and not fully exploited. Firms without any prior experience of IS exchanges suffer from lack of awareness about how to integrate the IS practice into their current business models and how to gain economic benefits from IS. Since the willingness to obtain economic benefits is the main driver pushing firms to implement the IS practice, this issue constitutes an important barrier to the development of new IS relationships. In this paper, we contribute to this issue by identifying the different business models that each firm can adopt to implement the IS approach. In particular, we identify several business models for both firms producing waste and firms requiring waste. For each model, we highlight how firms can create and get economic value from IS. Moreover, from the interaction among firms, each of them implementing its own business model, several business scenarios at inter-firm level can arise. These scenarios are also presented: for each of them, strengths and weaknesses are identified and a short case study is discussed. The identified models can be useful at the company level since they provide indications about how to integrate the IS approach within their current business model

Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks

Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that transmits alerts when relevant video sequences are detected. Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort. To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.Comment: Presented at SPIE Security + Defence 2016 Proc. SPIE 9997, Target and Background Signatures I

KRATOS: An Open Source Hardware-Software Platform for Rapid Research in LPWANs

Long-range (LoRa) radio technologies have recently gained momentum in the IoT landscape, allowing low-power communications over distances up to several kilometers. As a result, more and more LoRa networks are being deployed. However, commercially available LoRa devices are expensive and propriety, creating a barrier to entry and possibly slowing down developments and deployments of novel applications. Using open-source hardware and software platforms would allow more developers to test and build intelligent devices resulting in a better overall development ecosystem, lower barriers to entry, and rapid growth in the number of IoT applications. Toward this goal, this paper presents the design, implementation, and evaluation of KRATOS, a low-cost LoRa platform running ContikiOS. Both, our hardware and software designs are released as an open- source to the research community.Comment: Accepted at WiMob 201

Poster Abstract: MagoNode++ - A Wake-Up-Radio-Enabled Wireless Sensor Mote for Energy-Neutral Applications

The combination of low-power design, energy harvesting and ultra-low-power wake-up radios is paving the way for perpetual operation of Wireless Sensor Networks (WSNs). In this work we present the MagoNode++, a novel WSN platform supporting energy harvesting and radio-triggered wake ups for energy- neutral applications. The MagoNode++ features an energy- harvesting subsystem composed by a light or thermoelectric harvester, a battery manager and a power manager module. It further integrates a state-of-the-art RF Wake-Up Receiver (WUR) that enables low-latency asynchronous communication, virtually eliminating idle listening at the main transceiver. Experimental results show that the MagoNode++ consumes only 2.8uA with the WUR in idle listening and the rest of the platform in sleep state, making it suitable for energy-constrained WSN scenarios and for energy-neutral applications

An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing

This paper presents an accurate and robust embedded motor-imagery brain-computer interface (MI-BCI). The proposed novel model, based on EEGNet, matches the requirements of memory footprint and computational resources of low-power microcontroller units (MCUs), such as the ARM Cortex-M family. Furthermore, the paper presents a set of methods, including temporal downsampling, channel selection, and narrowing of the classification window, to further scale down the model to relax memory requirements with negligible accuracy degradation. Experimental results on the Physionet EEG Motor Movement/Imagery Dataset show that standard EEGNet achieves 82.43%, 75.07%, and 65.07% classification accuracy on 2-, 3-, and 4-class MI tasks in global validation, outperforming the state-of-the-art (SoA) convolutional neural network (CNN) by 2.05%, 5.25%, and 5.48%. Our novel method further scales down the standard EEGNet at a negligible accuracy loss of 0.31% with 7.6x memory footprint reduction and a small accuracy loss of 2.51% with 15x reduction. The scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and consuming 4.28mJ per inference for operating the smallest model, and on a Cortex-M7 with 44ms and 18.1mJ per inference for the medium-sized model, enabling a fully autonomous, wearable, and accurate low-power BCI

Measuring wage discrimination according to an expected utility approach

Following on from the seminal works by Blinder (1973) and Oaxaca (1973), many methods have been proposed to measure wage discrimination against women. Some of these methods focus on the entire distribution of the discrimination experienced by each woman, underlining a common aspect of poverty and discrimination analysis: the latter two are both based on an idea of deprivation which originates from a poverty line (in the case of poverty) and from the expected wage in the absence of discrimination (in the case of wage discrimination) (Jenkins, 1994; Del Río et al., 2011). These approaches hinge on conditional-to-individual-characteristics expected wages, lacking in any focus regarding the entire conditional wage distribution faced by each woman. In this paper we will discuss an expected utility approach to the study of wage discrimination. Adjusted and unadjusted for discrimination conditional-to-individual-characteristic wage distributions are evaluated for each woman by means of a utility function. And, in order to evaluate the presence and the discrimination intensity, these distributions will be compared on the basis of the respective certainty equivalent wages. As the choice of the utility function affects the results of the analysis, we will also evaluate the share of women for which the adjusted for discrimination conditional wage distribution second-order stochastically dominates the un-adjusted distribution. Finally, an empirical analysis will be performed for the Italian labour market

Dealing with a potential bias in estimating the share of discriminated women

The Blinder-Oaxaca [1, 6] decomposition neglects any distributional issues of discrimination. Instead, Jenkins [5] has argued the importance of a distributional approach in evaluating wage discrimination, focusing on the entire distribution of discrimination experienced by each woman. In their distributional approach, Del Río et al. [3] have adapted the Foster, Greer and Thorbecke (FGT) [4] poverty indices in studying wage discrimination. These discrimination indices depend on a parameter which can be interpreted as a measure of aversion to discrimination. When the aversion parameter is zero, the index measures the share of discriminated women. In this paper we will demonstrate that the naïve approach to the estimation of the share of discriminated women – similar to that used by Del Río et al. [3] – could be considerably biased. We propose testing the significance of the discrimination experienced by each woman, using appropriate statistical tests

Self-sustaining Ultra-wideband Positioning System for Event-driven Indoor Localization

Smart and unobtrusive mobile sensor nodes that accurately track their own position have the potential to augment data collection with location-based functions. To attain this vision of unobtrusiveness, the sensor nodes must have a compact form factor and operate over long periods without battery recharging or replacement. This paper presents a self-sustaining and accurate ultra-wideband-based indoor location system with conservative infrastructure overhead. An event-driven sensing approach allows for balancing the limited energy harvested in indoor conditions with the power consumption of ultra-wideband transceivers. The presented tag-centralized concept, which combines heterogeneous system design with embedded processing, minimizes idle consumption without sacrificing functionality. Despite modest infrastructure requirements, high localization accuracy is achieved with error-correcting double-sided two-way ranging and embedded optimal multilateration. Experimental results demonstrate the benefits of the proposed system: the node achieves a quiescent current of $47~nA$ and operates at $1.2~\mu A$ while performing energy harvesting and motion detection. The energy consumption for position updates, with an accuracy of $40~cm$ (2D) in realistic non-line-of-sight conditions, is $10.84~mJ$. In an asset tracking case study within a $200~m^2$ multi-room office space, the achieved accuracy level allows for identifying 36 different desk and storage locations with an accuracy of over $95~{\%}$. The system`s long-time self-sustainability has been analyzed over $700~days$ in multiple indoor lighting situations

A Fast and Accurate Optical Flow Camera for Resource-Constrained Edge Applications

Optical Flow (OF) is the movement pattern of pixels or edges that is caused in a visual scene by the relative motion between an agent and a scene. OF is used in a wide range of computer vision algorithms and robotics applications. While the calculation of OF is a resource-demanding task in terms of computational load and memory footprint, it needs to be executed at low latency, especially in robotics applications. Therefore, OF estimation is today performed on powerful CPUs or GPUs to satisfy the stringent requirements in terms of execution speed for control and actuation. On-sensor hardware acceleration is a promising approach to enable low latency OF calculations and fast execution even on resource-constrained devices such as nano drones and AR/VR glasses and headsets. This paper analyzes the achievable accuracy, frame rate, and power consumption when using a novel optical flow sensor consisting of a global shutter camera with an Application Specific Integrated Circuit (ASIC) for optical flow computation. The paper characterizes the optical flow sensor in high frame-rate, low-latency settings, with a frame rate of up to 88 fps at the full resolution of 1124 by 1364 pixels and up to 240 fps at a reduced camera resolution of 280 by 336, for both classical camera images and optical flow data.Comment: Accepted by IWASI 202

A Survey of Multi-Source Energy Harvesting Systems

Energy harvesting allows low-power embedded devices to be powered from naturally-ocurring or unwanted environmental energy (e.g. light, vibration, or temperature difference). While a number of systems incorporating energy harvesters are now available commercially, they are specific to certain types of energy source. Energy availability can be a temporal as well as spatial effect. To address this issue, ‘hybrid’ energy harvesting systems combine multiple harvesters on the same platform, but the design of these systems is not straightforward. This paper surveys their design, including trade-offs affecting their efficiency, applicability, and ease of deployment. This survey, and the taxonomy of multi-source energy harvesting systems that it presents, will be of benefit to designers of future systems. Furthermore, we identify and comment upon the current and future research directions in this field