Indoor-Guided Navigation for People Who Are Blind: Crowdsourcing for Route Mapping and Assistance

This paper presents an approach to enhance electronic traveling aids (ETAs) for people who are blind and severely visually impaired (BSVI) using indoor orientation and guided navigation by employing social outsourcing of indoor route mapping and assistance processes. This type of approach is necessary because GPS does not work well, and infrastructural investments are absent or too costly to install for indoor navigation. Our approach proposes the prior outsourcing of vision-based recordings of indoor routes from an online network of seeing volunteers, who gather and constantly update a web cloud database of indoor routes using specialized sensory equipment and web services. Computational intelligence-based algorithms process sensory data and prepare them for BSVI usage. In this way, people who are BSVI can obtain ready-to-use access to the indoor routes database. This type of service has not previously been offered in such a setting. Specialized wearable sensory ETA equipment, depth cameras, smartphones, computer vision algorithms, tactile and audio interfaces, and computational intelligence algorithms are employed for that matter. The integration of semantic data of points of interest (such as stairs, doors, WC, entrances/exits) and evacuation schemes could make the proposed approach even more attractive to BVSI users. Presented approach crowdsources volunteers’ real-time online help for complex navigational situations using a mobile app, a live video stream from BSVI wearable cameras, and digitalized maps of buildings’ evacuation schemes.

Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.

Cloud Server and Internet of Things Assisted System for Stress Monitoring

Currently, the Internet of Things (IoT) has gained attention for its capability for real-time monitoring. The advancement in sensor and wireless communication technology has led to the widespread adoption of IoT technology in distinct applications. The cloud server, in conjunction with the IoT, enables the visualization and analysis of real-time sensor data. The literature concludes that there is a lack of remote stress-monitoring devices available to assist doctors in observing the real-time stress status of patients in the hospital and in rehabilitation centers. To overcome this problem, we have proposed the use of the IoT and cloud-enabled stress devices to detect stress in a real-time environment. The IoT-enabled stress device establishes piconet communication with the master node to allow visualization of the sensory data on the cloud server. The threshold value (volt) for real-time stress detection by the stress device is identified by experimental analysis using MATLAB based on the results obtained from the performance of three different physical-stress generating tasks. In addition, the stress device is interfaced with the cloud server, and the sensor data are recorded on the cloud server. The sensor data logged into the cloud server can be utilized for future analysis.

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al., 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. Here we selected four image statistics (from single- to four-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. We interpreted the resulting psychometric data with an ideal observer model, finding a sharp decrease in sensitivity from two- to four-point correlations and a further decrease from four- to three-point. This ranking fully reproduces the trend we previously observed in humans, thus extending a direct demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

Karakteristik Fisikokimia dan Sensoris Kopi Arabika Menoreh dengan Teknik Seduhan Cold Brew

<p><em>Coffee is one of the most widely distributed and </em><em>consumed</em><em> </em><em>beverages</em><em> in the world. </em><em>In general, coffee is brewed using hot water, but</em><em> </em><em>a</em><em>s the coffee industry develops, cold water</em><em> also</em><em> </em><em>can be used for </em><em>the coffee brewing process</em><em>. This brewing technique is known as </em><em>cold bre</em><em>w.</em><em> There is</em><em> little</em><em> information </em><em>regarding</em><em> the characteristics of cold brew</em><em> coffee</em><em>. T</em><em>herefore</em><em> it is necessary to study the characteristics of cold brew </em><em>beverage</em><em>s, especially with local Indonesian coffee</em><em> as its main ingredient. This research used</em><em> Menoreh Arabica coffee</em><em> as its main research object</em><em>. Th</em><em>is study aimed</em><em> to determine the physicochemical and sensory characteristics of </em><em>M</em><em>enoreh </em><em>A</em><em>rabica coffee with cold brew brewing techniques.</em><em> This research</em><em> begins with roasting coffee into three types, </em><em>which</em><em> is light (T = 193</em><em> </em><em>^o</em><em>C, t = 5 minute), medium (T = 208</em><em> </em><em>^o</em><em>C, t = 7 minute), and dark (T = 223</em><em> </em><em>^o</em><em>C, t = 13 minute). Furthermore,</em><em> </em><em>the coffee is g</em><em>rinded</em><em> into two types</em><em> grind size</em><em> </em><em>(</em><em>medium and coarse</em><em>)</em><em>. </em><em>Samples</em><em> of cold brew formulation w</em><em>ere</em><em> made with an extraction time of 8 hours. </em><em>The </em><em>sensory analysis</em><em> is conducted</em><em> based on the SCA method. </em><em>Sensory data analysis was done to</em><em> determine the three best formulations</em><em> </em><em>according to roast </em><em>profiles,</em><em> continued with</em><em> the physicochemical analysis. The best cold brew sample obtained from this research was </em><em>medium-coarse</em><em> Arabica Menoreh coffee, </em><em>with the highest </em><em>sensory </em><em>parameters </em><em>values in </em><em>aroma, acidity, aftertaste, and sweetness</em><em>. </em><em>The value of pH, chlorogenic acid, and some organic acids affect acidity. Lactic acid affects</em><em> </em><em>body value,</em><em> and c</em><em>affeine levels were relatively stable in each sample. </em><em>This research result can be used as a reference for product diversification of Arabica Menoreh coffee.</em><em></em></p>

Hybrid Architectural Network Implementation to Realize a Fire Evacuation Path with 2.4 GHz Zigbee and LoRa

The Internet of Things (IoT) is playing a significant role in realizing real monitoring. In fire safety and evacuation, early fire event detection using IoT-enabled sensors may help to control and minimize further consequences of the fire accident. In this study, we propose a hybrid architecture based on 2.4 GHz Zigbee and long-range (LoRa) for real-time fire detection, monitoring, and assisting in the safe evacuation of the building. The architecture comprises five different components, namely: end device, evacuation path display controller, safety operation controller, vision node, and gateway. The end device and vision node provide real-time sensory data and visuals that provide details of fire occurrence. The evacuation path display controller and the safety operation controller based on the 2.4 GHz Zigbee receive data from the end device and make the decision accordingly. In addition, a Zigbee simulation is performed on the OPNET simulator to analyze the network parameters such as throughput, retransmission attempts, medium access (MAC) queue size and queue delay, and packet delivery ratio (PDR). The evaluation metrics of link budget and ToA of LoRa are also calculated by varying the code rate and spreading factor. To realize the proposed architecture, customization of hardware is carried out with the development of hardware prototypes. Dijkstra’s shortest path algorithm is implemented in the evacuation path display controller to provide the shortest evacuation path during a fire incident. The hardware of the system is implemented in real-time, and the system provides real-time sensor data along with the evacuation path.

Revealing nonlinear neural decoding by analyzing choices

Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding.

EPCT: An Efficient Privacy-Preserving and Collusion-Resisting Top- k Query Processing in WSNs

Data privacy threat arises during providing top- k query processing in the wireless sensor networks. This article presents an efficient privacy-preserving and collusion-resisting top- k (EPCT) query processing protocol. A minimized candidate encrypted dataset determination model is first designed, which is the foundation of EPCT. The model guides the idea of query processing and guarantees the correctness of the protocol. The symmetric encryption with different private key in each sensor is deployed to protect the privacy of sensory data even a few sensors in the networks have been colluding with adversaries. Based on the above model and security setting, two phases of interactions between the interested sensors and the sink are designed to implement the secure query processing protocol. The security analysis shows that the proposed protocol is capable of providing secure top- k queries in the manner of privacy protection and anticollusion, whereas the experimental result indicates that the protocol outperforms the existing works on communication overhead.