A Multi-Modality Mobility Concept for a Small Package Delivery UAV

This paper will discuss a different approach to the typical notional small package delivery drone concept. Most delivery drone concepts employ a point-to-point aerial delivery CONOPS (Concept of Operations) from a warehouse directly to the front or back yards of a customers residence or a commercial office space. Instead, the proposed approach is somewhat analogous to current postal deliveries: a small aerial vehicle flies from a warehouse to designated neighborhood VTOL (Vertical Take-Off and Landing) landing spots where the aerial vehicle then converts to a "roadable" (ground-mobility) vehicle that then transits on sidewalks and/or bicycle paths till it arrives to the residence/office drop-off points. This concept and associated platform or vehicle will be referred in this paper as MICHAEL (Multimodal Intra-City Hauling and Aerial-Effected Logistics) concept. It is suggested that the MICHAEL concept potentially results in a more community friendly "delivery drone" approach

ModDrop: adaptive multi-modal gesture recognition

We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning. Each visual modality captures spatial information at a particular spatial scale (such as motion of the upper body or a hand), and the whole system operates at three temporal scales. Key to our technique is a training strategy which exploits: i) careful initialization of individual modalities; and ii) gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. We present experiments on the ChaLearn 2014 Looking at People Challenge gesture recognition track, in which we placed first out of 17 teams. Fusing multiple modalities at several spatial and temporal scales leads to a significant increase in recognition rates, allowing the model to compensate for errors of the individual classifiers as well as noise in the separate channels. Futhermore, the proposed ModDrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities. In addition, we demonstrate the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure

Cognitive Computing for Multimodal Sentiment Sensing and Emotion Recognition Fusion Based on Machine Learning Techniques Implemented by Computer Interface System

A multiple slot fractal antenna design has been determined communication efficiency and its multi-function activities.  High-speed small communication devices have been required for future smart chip applications, so that researchers have been employed new and creative antenna design. Antennas are key part in communication systems, those are used to improve communication parameters like gain, efficiency, and bandwidth. Consistently, modern antennas design with high bandwidth and gain balancing is very difficult, therefore an adaptive antenna array chip design is required. In this research work a coaxial fed antenna with fractal geometry design has been implemented for Wi-Fi and Radio altimeter application. The fractal geometry has been taken with multiple numbers of slots in the radiating structure for uncertain applications. The coaxial feeding location has been selected based on the good impedance matching condition (50 Ohms). The overall dimension mentioned for antenna are approximately 50X50X1.6 mm on FR4 substrate and performance characteristic analysis is performed with change in substrate material presented in this work. Dual-band resonant frequency is being emitted by the antenna with resonance at 3.1 and 4.3 GHz for FR4 substrate material and change in the resonant bands is obtained with change in substrate. The proposed Antenna is prototyped on Anritsu VNA tool and presented the comparative analysis like VSWR 12%, reflection coefficient 9.4%,3D-Gain 6.2% and surface current 9.3% had been improved

Digital platform e-Códex: a hybrid virtual space.

Mobile learning is the kind of learning that applies mainly to non-formal education actions from the perspective of continuing education. Within the scope of companies, the demand for educational actions is increasing, notably focused on distance education and training, aimed at both internal and external audiences. However, for the implementation of activities dedicated to mobile learning, it is necessary to develop a digital platform to host the virtual learning environment. Under these conditions, it should be structured in a flexible format and design to encourage interactivity between learners and teachers in the implementation of teaching and learning activities. The article presents and discusses a proposal for a digital mobile platform, called e-Códex, which aims to support educational actions, to promote collaboration and learning, as well as to meet the information dissemination and knowledge generation needs of the Brazilian agroforestry sector

Seeking multiple solutions:an updated survey on niching methods and their applications

Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving