Time and space for segmenting personal photo sets

Multimedia Tools and Applications - A personal collection of photos shows large variability in the depicted items, making difficult a fully automated solution to cope with sensory and semantic...     

A Low-Power Wakeup Radio for Application in WSN-Based Indoor Location Systems

This paper presents a low-power wakeup radio (WUR) for application in indoor location systems. The presented radio has a better performance than the state-of-the-art radios, since it has low-power consumption, only 10 μW and it is perfectly integrated into a wireless sensor network based on ZigBee, which is used for location purposes. This performance was achieved due to an optimised radio frequency design of the WUR, which was embedded with a low-cost/low-power processor, and due to an enhanced control algorithm. Moreover, a software defined radio approach has been used to implement the WUR protocol. The WUR was designed and integrated in an existing WSN-based indoor location system which was originally based on a periodic sleep-wake up duty-cycled protocol. In the WUR protocol the location sensor is kept in deep sleep mode until it receives an external wake-up order. According to estimates, with this scheme, the battery lifetime can be increased from 200 days (using conventional duty-cycle protocol) up to almost 8000 days (sensor waken-up few times per day).     

Scalable Data Processing for Community Sensing Applications

Participatory Sensing is a new computing paradigm that aims to turn personal mobile devices into advanced mobile sensing networks. For popular applications, we can expect a huge number of users to both contribute with sensor data and request information from the system. In such scenario, scalability of data processing becomes a major issue. In this paper, we present a system for supporting participatory sensing applications that leverages cluster or cloud infrastructures to provide a scalable data processing infrastructure. We propose and evaluate three strategies for data processing in this architecture.     

Maximizing the set of recurrent states of an MDP subject to convex constraints

This paper focuses on the design of time-homogeneous fully observed Markov decision processes (MDPs), with finite state and action spaces. The main objective is to obtain policies that generate the maximal set of recurrent states, subject to convex constraints on the set of invariant probability mass functions. We propose a design method that relies on a finitely parametrized convex program inspired on principles of entropy maximization. A numerical example is provided to illustrate these ideas.     

A survey on nonlinear analog-to-digital converters

This paper presents a survey on Nonlinear Analog-to-Digital converters (ADC). This class of converters is extremely relevant in applications where there is a need for non-uniform quantization characteristic, for example, some specific applications in the areas of light detection, hearing aid, nuclear physics, image acquisition, communication systems, etc. This survey outlines the state-of-the-art Nonlinear ADC topologies, such as, floating point, logarithmic, piecewise linear and oversampled nonlinear converters, and discusses their performance and advantages in terms of their applications.     

Check Your Biosignals Here: A new dataset for off-the-person ECG biometrics

The Check Your Biosignals Here initiative (CYBHi) was developed as a way of creating a dataset and consistently repeatable acquisition framework, to further extend research in electrocardiographic (ECG) biometrics. In particular, our work targets the novel trend towards off-the-person data acquisition, which opens a broad new set of challenges and opportunities both for research and industry. While datasets with ECG signals collected using medical grade equipment at the chest can be easily found, for off-the-person ECG data the solution is generally for each team to collect their own corpus at considerable expense of resources. In this paper we describe the context, experimental considerations, methods, and preliminary findings of two public datasets created by our team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers.     

A Quinacrine Analogue Selective Against Gastric Cancer Cells: Insight from Biochemical and Biophysical Studies

One of the earliest synthetic antimalarial drugs, quinacrine, was recently reported as interesting for the treatment of acute myeloid leukemia. Inspired by this and similar findings, we evaluated a set of quinacrine analogues against gastric (MKN‐28), colon (Caco‐2), and breast (MFC‐7) cancer cell lines and one normal human fibroblast cell line (HFF‐1). All the compounds, previously developed by us as dual‐stage antimalarial leads, displayed antiproliferative activity, and one of the set stood out as selective toward the gastric cancer cell line, MKN‐28. Interestingly, this compound was transported across an in vitro MKN‐28 model cell line in low amounts, and approximately 80 % was trapped inside those cells. Nuclear targeting of the same compound and its interactions with calf thymus DNA were assessed through combined fluorescence microscopy, spectroscopy, and calorimetry studies, which provided evidence for the compound's ability to reach the nucleus and to interact with DNA.