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International audience; In this paper, we study the effect of smartphone camera exposure on the performance of optical camera communications (OCC) link. The exposure parameters of image sensor sensitivity (ISO), aperture and shutter speed are included. A static OCC link with a 8 × 8 red, green and blue (RGB) LED array employed as the transmitter and a smartphone camera as the receiver is demonstrated to verify the study. Signal-to-noise ratio (SNR) analysis at different ISO values, the effect of aperture and shutter speed on communication link quality is performed. While SNRs of 20.6 dB and 16.9 dB are measured at 1 m and 2 m transmission distance, respectively for a ISO value of 100, they are decreased to 17.4 dB and 13.32 dB for a ISO of 800. The bit error rate (BER) of a 1 m long OCC link with a camera's shutter speed of 1/6000 s is 1.3 × 10 −3 (i.e., below the forward error correction BER limit of 3.8 × 10 −3) and is dropped to 0.0125 at a shutter speed of 1/20 s. This study provides insight of the basic smartphone settings and the exposure adjustment for further complex OCC links.

Multiple theories of working memory are described in the chapters of this book and often these theories are viewed as being mutually incompatible, yet each is associated with a supporting body of empirical evidence. This chapter argues that many of these differences reflect different research questions, different levels of explanation, and differences in how participants perform their assigned tasks in different laboratories, rather than fundamental theoretical adversity. It describes a version of a multiple component working memory in which a range of specialized cognitive functions (or mental tools) act in concert, giving the impression, at a different level of explanation, of a unified cognitive system. The chapter argues that more rapid and more substantial scientific progress on the understanding of the concept of working memory would be achieved through identifying the levels of explanation explored within each theoretical framework, and attempting to integrate theoretical frameworks rather than perpetuating debate with no clear resolution in sight.

International audience; Chemical synapses enable neurons to communicate rapidly, process and filter signals and to store information. However, studying their functional properties is difficult because synaptic connections typically consist of multiple synaptic contacts that release vesicles stochastically and exhibit time-dependent behavior. Moreover, most central synapses are small and inaccessible to direct measurements. Estimation of synaptic properties from responses recorded at the soma is complicated by the presence of nonuniform release probability and nonuniform quantal properties. The presence of multivesicular release and postsynaptic receptor saturation at some synapses can also complicate the interpretation of quantal parameters. Multiple-probability fluctuation analysis (MPFA; also known as variance-mean analysis) is a method that has been developed for estimating synaptic parameters from the variance and mean amplitude of synaptic responses recorded at different release probabilities. This statistical approach, which incorporates nonuniform synaptic properties, has become widely used for studying synaptic transmission. In this chapter, we describe the statistical models used to extract quantal parameters and discuss their interpretation when applying MPFA.

Part 2: Infrastructure Protection; International audience; State estimation is vital to the stability of control systems, especially in power systems, which rely heavily on measurement devices installed throughout wide-area power networks. Several researchers have analyzed the problems arising from bad data injection and topology errors, and have proposed protection and mitigation schemes. This chapter employs hierarchical state estimation based on the common weighted-least-squares formulation to study the propagation of faults in intermediate and top-level state estimates as a result of measurement reordering attacks on a single region in the bottom level. Although power grids are equipped with modern defense mechanisms such as those recommended by the ISO/IEC 62351 standard, reordering attacks are still possible. This chapter concentrates on how an inexpensive data swapping attack in one region in the lower level can influence the accuracy of other regions in the same level and upper levels, and force the system towards undesirable states. The results are validated using the IEEE 118-bus test case.

Aluminum based hybrid composites were produced from recycled AA7075 chips with the addition of TiC (d ≤ 3–5 micron), MoS2 and Al2O3 fiber. In the two groups of composites produced, the content of MoS2 and Al2O3 were fixed as 2 wt % and 3 wt % respectively, whereas TiC content was at two levels (5–10%). The combined method of powder metallurgy route, sintering followed by forging, was used to manufacture these composites. These composites are targeted for aeronautical and automotive industries for components subjected to static as well as cyclic and dynamic loading. In addition to mechanical properties, machinability of these composites is of importance hence, MoS2 was included in the formulation. Micro hardness, 3 point bending, low velocity impact and nanoindentation (creep and wear) tests were performed on samples manufactured by just sintering and sintering followed by forging. The results showed that, in generals, the samples that were forged after sintering yielded better properties. The microstructure analyses (matrix/interface) have been carried out by Scanning Electron Microscope (SEM).

The current industrial revolution is said to be driven by the digitization that exploits connected information across all aspects of manufacturing. Standards have been recognized as an important enabler. Ontology-based information standard may provide benefits not offered by current information standards. Although there have been ontologies developed in the industrial manufacturing domain, they have been fragmented and inconsistent, and little has received a standard status. With successes in developing coherent ontologies in the biological, biomedical, and financial domains, an effort called Industrial Ontologies Foundry (IOF) has been formed to pursue the same goal for the industrial manufacturing domain. However, developing a coherent ontology covering the entire industrial manufacturing domain has been known to be a mountainous challenge because of the multidisciplinary nature of manufacturing. To manage the scope and expectations, the IOF community kicked-off its effort with a proof-of-concept (POC) project. This paper describes the developments within the project. It also provides a brief update on the IOF organizational set up.

Abstract The frontal lobe plays a crucial role in human motor behavior. It is one of the last areas of the brain to mature, especially the prefrontal regions. After a brief historical perspective on the perceived dichotomy between the view of the brain as a static organ and that of a plastic, constantly changing structure, we discuss the stability/plasticity dilemma including examples of documented cortical reorganization taking place at multiple spatial and temporal scales. We pose that while plasticity is needed for motor learning, stability of the system is necessary for storage and maintenance of memorized skills. We discuss how this plasticity/stability dilemma is resolved along the life span and after a brain injury. We then examine the main challenges that clinicians have to overcome to promote recovery of function in patients with brain lesions, including attempts to use neurostimulation techniques as adjuvant to training-based customary neurorehabilitation.

We present the API for MUSICNTWRK, a python library for pitch class set and rhythmic sequences classification and manipulation, the generation of networks in generalized music and sound spaces, deep learning algorithms for timbre recognition, and the sonification of arbitrary data. The software is freely available under GPL 3.0 and can be downloaded at www.musicntwrk.com or installed as a PyPi project (pip install musicntwrk).

This study is aimed at understanding the implementation of Information Communication Technology (ICT) tool to electoral process and its challenges in Nigeria using actor-network theory (ANT) as a lens. Moment of translation of ANT was applied to gain an insight of the phenomenon. Empirical data was used for the analysis in the context of social behavior between human and non-human actors following inductive research approach. Case study methodology was carried out at Independent National Electoral Commission (INEC). The methods of data collection were through interview, participant observation and reviewing organizational documents. Challenges of the Smart Card Reader (SCR) in Nigeria’s electoral process are highlighted. Results of the study indicated that the challenges in implementation of the SCR emanated from the heterogeneous actors “human and non-human”, which lack synchrony during the process. Apart from the success of the card reader during accreditation, there was inadequate manpower training by INEC body and insufficient ICT infrastructure that weakened the ANT process.

In recent years, the learned local descriptors have outperformed handcrafted ones by a large margin, due to the powerful deep convolutional neural network architectures such as L2-Net [1] and triplet based metric learning [2]. However, there are two problems in the current methods, which hinders the overall performance. Firstly, the widely-used margin loss is sensitive to incorrect correspondences, which are prevalent in the existing local descriptor learning datasets. Second, the L2 distance ignores the fact that the feature vectors have been normalized to unit norm. To tackle these two problems and further boost the performance, we propose a robust angular loss which 1) uses cosine similarity instead of L2 distance to compare descriptors and 2) relies on a robust loss function that gives smaller penalty to triplets with negative relative similarity. The resulting descriptor shows robustness on different datasets, reaching the state-of-the-art result on Brown dataset , as well as demonstrating excellent generalization ability on the Hpatches dataset and a Wide Baseline Stereo dataset. Comment: Accepted by ACCV2018