Ottoman Merchants and Venetian Notaries in the Early Modern Period

The aim of this paper is to study the presence of Ottoman merchants in Venice in the Modern Age. The Ottoman shipping partnership as well as the Venetian commenda were based on the mudarebe (classical Islamic shipping partnership) and several examples of this kind of partnerships may be found even between Christians and Muslims. Notarial deeds were above all proxies and often give information on merchants, their number and even their private life. Some of them were agents but there were also family companies and Ottoman grandees who were involved in international trade. Communities of merchants both in Istanbul and in Venice were also sometimes created. Last but not least, these sources contain also some examples of insurance made by Muslims to protect their goods. In general in the 16th and 17th centuries, the Ottoman merchants who traded in Venice were not alone, but they could refer to a real commercial network. Ancient historiographical theories say that in the Modern Age Ottoman Muslims were not interested in international trade and that they left it completely in Christian and Jewish hands, however documents tell us a completely different story, a story of contacts, exchanges, and even confidence and friendship

Wearable Vibration Based Computer Interaction and Communication System for Deaf

In individuals with impaired hearing, determining the direction of sound is a significant problem. The direction of sound was determined in this study, which allowed hearing impaired individuals to perceive where sounds originated. This study also determined whether something was being spoken loudly near the hearing impaired individual. In this manner, it was intended that they should be able to recognize panic conditions more quickly. The developed wearable system has four microphone inlets, two vibration motor outlets, and four Light Emitting Diode (LED) outlets. The vibration of motors placed on the right and left fingertips permits the indication of the direction of sound through specific vibration frequencies. This study applies the ReliefF feature selection method to evaluate every feature in comparison to other features and determine which features are more effective in the classification phase. This study primarily selects the best feature extraction and classification methods. Then, the prototype device has been tested using these selected methods on themselves. ReliefF feature selection methods are used in the studies; the success of K nearest neighborhood (Knn) classification had a 93% success rate and classification with Support Vector Machine (SVM) had a 94% success rate. At close range, SVM and two of the best feature methods were used and returned a 98% success rate. When testing our wearable devices on users in real time, we used a classification technique to detect the direction and our wearable devices responded in 0.68 s; this saves power in comparison to traditional direction detection methods. Meanwhile, if there was an echo in an indoor environment, the success rate increased; the echo canceller was disabled in environments without an echo to save power. We also compared our system with the localization algorithm based on the microphone array; the wearable device that we developed had a high success rate and it produced faster results at lower cost than other methods. This study provides a new idea for the benefit of deaf individuals that is preferable to a computer environment

Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People

Hearing-impaired people do not hear indoor and outdoor environment sounds, which are important for them both at home and outside. By means of a wearable device that we have developed, a hearing-impaired person will be informed of important sounds through vibrations, thereby understanding what kind of sound it is. Our system, which operates in real time, can achieve a success rate of 98% when estimating a door bell ringing sound, 99% success identifying an alarm sound, 99% success identifying a phone ringing, 91% success identifying honking, 93% success identifying brake sounds, 96% success identifying dog sounds, 97% success identifying human voice, and 96% success identifying other sounds using the audio fingerprint method. Audio fingerprint is a brief summary of an audio file, perceptively summarizing a piece of audio content. In this study, our wearable device is tested 100 times a day for 100 days on five deaf persons and 50 persons with normal hearing whose ears were covered by earphones that provided wind sounds. This study aims to improve the quality of life of deaf persons, and provide them a more prosperous life. In the questionnaire performed, deaf people rate the clarity of the system at 90%, usefulness at 97%, and the likelihood of using this device again at 100%

Memory Management Strategies for Parallel Volume Rendering

. The parallel implementation of the volume rendering technique offers the potential of solving this computationally complex problem in reasonable times. This paper discusses a virtual memory strategy which is used to cope with the very large distributed data sets associated with the ray casting algorithm used for volume rendering. Various caching techniques are examined to reduce communication latency associated with remote data fetches within a transputer network. Multi-threading tasks are also investigated in order to minimise the effect of data fetch delay and improve overall system performance for large multiprocessor systems. 1 Introduction Volume rendering is the conversion of a multi-dimensional volume data set which has been specified spatially, into a two-dimensional image. Volume rendering techniques in computer graphics are based on displaying surfaces by reducing the volume data to only the boundaries between materials. Significant computation is required to convert from t..

Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification

Retinal blood vessels have a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. For this reason, retinal vasculature extraction is important in order to help specialists for the diagnosis and treatment of systematic diseases. In this paper, a novel approach is developed to extract retinal blood vessel network. Our method comprises four stages: (1) preprocessing stage in order to prepare dataset for segmentation; (2) an enhancement procedure including Gabor, Frangi, and Gauss filters obtained separately before a top-hat transform; (3) a hard and soft clustering stage which includes K-means and Fuzzy C-means (FCM) in order to get binary vessel map; and (4) a postprocessing step which removes falsely segmented isolated regions. The method is tested on color retinal images obtained from STARE and DRIVE databases which are available online. As a result, Gabor filter followed by K-means clustering method achieves 95.94% and 95.71% of accuracy for STARE and DRIVE databases, respectively, which are acceptable for diagnosis systems

Dynamic Data Management for Parallel Volume Visualisation

The parallel implementation of volume visualisation offers the potential of solving this computationally complex problem in reasonable times. This paper discusses the preferred bias strategy for allocating tasks to processing elements. This strategy is able to exploit both spatial and temporal coherence within the problem domain between successive frames to improve the effectiveness of the distributed memory management system and thus increase overall system performance. Tree and torus configurations are also investigated to determine the effect configurations haveon the parallel implementation of volume visualisation on large multiprocessor systems

Improving bag-of-poses with semi-temporal pose descriptors for skeleton-based action recognition

International audienceOver the last few decades, human action recognition has become one of the most challenging tasks in the field of computer vision. Employing economical depth sensors such as Microsoft Kinect as well as recent successes of deep learning approaches in image understanding has led to effortless and accurate extraction of 3D skeleton information. In this study, we have introduced a novel bag-of-poses framework for action recognition by exploiting 3D skeleton data. Our assumption is that any action can be represented with a set of predefined spatiotemporal poses. The pose de-scriptor is composed of two parts, the first part is concatena-tion of the normalized coordinate of the skeleton joints. The second part consists of temporal displacement of the joints which is constructed with predefined temporal offset. In order to generate the key poses, we apply K-means clustering overall training pose descriptors of dataset. To classify an action pose, we train a SVM classifier with the generated key poses. Thereby, every action on dataset is encoded with key-poses histogram. We use ELM classifier to recognize the actions since it has been shown to be faster, accurate , and more reliable than other classifiers. The proposed framework is validated with four publicly available benchmark 3D action datasets. The results show that our frame-2 Saeid Agahian et al. work achieves state-of-the-art results on three of the datasets compared to the other methods and produces competitive result on the fourth

SNORAP: A Device for the Correction of Impaired Sleep Health by Using Tactile Stimulation for Individuals with Mild and Moderate Sleep Disordered Breathing

Sleep physiology and sleep hygiene play significant roles in maintaining the daily lives of individuals given that sleep is an important physiological need to protect the functions of the human brain. Sleep disordered breathing (SDB) is an important disease that disturbs this need. Snoring and Obstructive Sleep Apnea Syndrome (OSAS) are clinical conditions that affect all body organs and systems that intermittently, repeatedly, with at least 10 s or more breathing stops that decrease throughout the night and disturb sleep integrity. The aim of this study was to produce a new device for the treatment of patients especially with position and rapid eye movement (REM)-dependent mild and moderate OSAS. For this purpose, the main components of the device (the microphone (snore sensor), the heart rate sensor, and the vibration motor, which we named SNORAP) were applied to five volunteer patients (male, mean age: 33.2, body mass index mean: 29.3). After receiving the sound in real time with the microphone, the snoring sound was detected by using the Audio Fingerprint method with a success rate of 98.9%. According to the results obtained, the severity and the number of the snoring of the patients using SNORAP were found to be significantly lower than in the experimental conditions in the apnea hypopnea index (AHI), apnea index, hypopnea index, in supine position’s AHI, and REM position’s AHI before using SNORAP (Paired Sample Test, p < 0.05). REM sleep duration and nocturnal oxygen saturation were significantly higher when compared to the group not using the SNORAP (Paired Sample Test, p < 0.05)