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Institution

DHA Suffa University

EducationKarachi, Pakistan
About: DHA Suffa University is a education organization based out in Karachi, Pakistan. It is known for research contribution in the topics: Wireless sensor network & Population. The organization has 117 authors who have published 151 publications receiving 2942 citations.

Papers published on a yearly basis

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Journal ArticleDOI
Daniel Conroy-Beam1, David M. Buss2, Kelly Asao2, Agnieszka Sorokowska3, Agnieszka Sorokowska4, Piotr Sorokowski3, Toivo Aavik5, Grace Akello6, Mohammad Madallh Alhabahba7, Charlotte Alm8, Naumana Amjad9, Afifa Anjum9, Chiemezie S. Atama10, Derya Atamtürk Duyar11, Richard Ayebare, Carlota Batres12, Mons Bendixen13, Aicha Bensafia14, Boris Bizumic15, Mahmoud Boussena14, Marina Butovskaya16, Marina Butovskaya17, Seda Can18, Katarzyna Cantarero19, Antonin Carrier20, Hakan Cetinkaya21, Ilona Croy4, Rosa María Cueto22, Marcin Czub3, Daria Dronova17, Seda Dural18, İzzet Duyar11, Berna Ertuğrul23, Agustín Espinosa22, Ignacio Estevan24, Carla Sofia Esteves25, Luxi Fang26, Tomasz Frackowiak3, Jorge Contreras Garduño27, Karina Ugalde González, Farida Guemaz, Petra Gyuris28, Mária Halamová29, Iskra Herak20, Marina Horvat30, Ivana Hromatko31, Chin Ming Hui26, Jas Laile Suzana Binti Jaafar32, Feng Jiang33, Konstantinos Kafetsios34, Tina Kavčič35, Leif Edward Ottesen Kennair13, Nicolas Kervyn20, Truong Thi Khanh Ha19, Imran Ahmed Khilji36, Nils C. Köbis37, Hoang Moc Lan19, András Láng28, Georgina R. Lennard15, Ernesto León22, Torun Lindholm8, Trinh Thi Linh19, Giulia Lopez38, Nguyen Van Luot19, Alvaro Mailhos24, Zoi Manesi39, Rocio Martinez40, Sarah L. McKerchar15, Norbert Meskó28, Girishwar Misra41, Conal Monaghan15, Emanuel C. Mora42, Alba Moya-Garófano40, Bojan Musil30, Jean Carlos Natividade43, Agnieszka Niemczyk3, George Nizharadze, Elisabeth Oberzaucher44, Anna Oleszkiewicz3, Anna Oleszkiewicz4, Mohd Sofian Omar-Fauzee45, Ike E. Onyishi10, Barış Özener11, Ariela Francesca Pagani38, Vilmante Pakalniskiene46, Miriam Parise38, Farid Pazhoohi47, Annette Pisanski42, Katarzyna Pisanski3, Katarzyna Pisanski48, Edna Lúcia Tinoco Ponciano, Camelia Popa49, Pavol Prokop50, Pavol Prokop51, Muhammad Rizwan, Mario Sainz52, Svjetlana Salkičević31, Ruta Sargautyte46, Ivan Sarmány-Schuller53, Susanne Schmehl44, Shivantika Sharad41, Razi Sultan Siddiqui54, Franco Simonetti55, Stanislava Stoyanova56, Meri Tadinac31, Marco Antonio Correa Varella57, Christin-Melanie Vauclair25, Luis Diego Vega, Dwi Ajeng Widarini, Gyesook Yoo58, Marta Zaťková29, Maja Zupančič59 
University of California, Santa Barbara1, University of Texas at Austin2, University of Wrocław3, Dresden University of Technology4, University of Tartu5, Gulu University6, Middle East University7, Stockholm University8, University of the Punjab9, University of Nigeria, Nsukka10, Istanbul University11, Franklin & Marshall College12, Norwegian University of Science and Technology13, University of Algiers14, Australian National University15, Russian State University for the Humanities16, Russian Academy of Sciences17, İzmir University of Economics18, University of Social Sciences and Humanities19, Université catholique de Louvain20, Ankara University21, Pontifical Catholic University of Peru22, Cumhuriyet University23, University of the Republic24, ISCTE – University Institute of Lisbon25, The Chinese University of Hong Kong26, National Autonomous University of Mexico27, University of Pécs28, University of Constantine the Philosopher29, University of Maribor30, University of Zagreb31, University of Malaya32, Central University of Finance and Economics33, University of Crete34, University of Primorska35, Institute of Molecular and Cell Biology36, University of Amsterdam37, Catholic University of the Sacred Heart38, VU University Amsterdam39, University of Granada40, University of Delhi41, University of Havana42, Pontifical Catholic University of Rio de Janeiro43, University of Vienna44, Universiti Utara Malaysia45, Vilnius University46, University of British Columbia47, University of Sussex48, Romanian Academy49, Slovak Academy of Sciences50, Comenius University in Bratislava51, University of Monterrey52, SAS Institute53, DHA Suffa University54, Pontifical Catholic University of Chile55, South-West University "Neofit Rilski"56, University of São Paulo57, Kyung Hee University58, University of Ljubljana59
TL;DR: This work combines this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets and finds that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.
Abstract: Humans express a wide array of ideal mate preferences. Around the world, people desire romantic partners who are intelligent, healthy, kind, physically attractive, wealthy, and more. In order for these ideal preferences to guide the choice of actual romantic partners, human mating psychology must possess a means to integrate information across these many preference dimensions into summaries of the overall mate value of their potential mates. Here we explore the computational design of this mate preference integration process using a large sample of n = 14,487 people from 45 countries around the world. We combine this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets. Across cultures, people higher in mate value appear to experience greater power of choice on the mating market in that they set higher ideal standards, better fulfill their preferences in choice, and pair with higher mate value partners. Furthermore, we find that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.

1,827 citations

Journal ArticleDOI
TL;DR: This paper presents the IoT technology from a bird's eye view covering its statistical/architectural trends, use cases, challenges and future prospects, and discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.
Abstract: The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc. have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird’s eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.

591 citations

Journal ArticleDOI
Kathryn V. Walter1, Daniel Conroy-Beam1, David M. Buss2, Kelly Asao2, Agnieszka Sorokowska3, Agnieszka Sorokowska4, Piotr Sorokowski5, Toivo Aavik6, Grace Akello7, Mohammad Madallh Alhabahba8, Charlotte Alm9, Naumana Amjad10, Afifa Anjum10, Chiemezie S. Atama11, Derya Atamtürk Duyar12, Richard Ayebare, Carlota Batres13, Mons Bendixen14, Aicha Bensafia15, Boris Bizumic16, Mahmoud Boussena15, Marina Butovskaya17, Marina Butovskaya18, Seda Can19, Katarzyna Cantarero20, Antonin Carrier21, Hakan Cetinkaya22, Ilona Croy3, Rosa María Cueto23, Marcin Czub4, Daria Dronova18, Seda Dural19, İzzet Duyar12, Berna Ertuğrul24, Agustín Espinosa23, Ignacio Estevan25, Carla Sofia Esteves26, Luxi Fang27, Tomasz Frackowiak4, Jorge Contreras Garduño28, Karina Ugalde González, Farida Guemaz, Petra Gyuris29, Mária Halamová, Iskra Herak21, Marina Horvat30, Ivana Hromatko31, Chin Ming Hui27, Jas Laile Suzana Binti Jaafar32, Feng Jiang33, Konstantinos Kafetsios34, Tina Kavčič35, Leif Edward Ottesen Kennair14, Nicolas Kervyn21, Truong Thi Khanh Ha20, Imran Ahmed Khilji, Nils C. Köbis36, Hoang Moc Lan20, András Láng29, Georgina R. Lennard16, Ernesto León23, Torun Lindholm9, Trinh Thi Linh20, Giulia Lopez37, Nguyen Van Luot20, Alvaro Mailhos25, Zoi Manesi38, Rocio Martinez39, Sarah L. McKerchar16, Norbert Meskó29, Girishwar Misra40, Conal Monaghan16, Emanuel C. Mora41, Alba Moya-Garófano39, Bojan Musil30, Jean Carlos Natividade42, Agnieszka Niemczyk4, George Nizharadze, Elisabeth Oberzaucher43, Anna Oleszkiewicz3, Anna Oleszkiewicz4, Mohd Sofian Omar-Fauzee44, Ike E. Onyishi11, Barış Özener12, Ariela Francesca Pagani37, Vilmante Pakalniskiene45, Miriam Parise37, Farid Pazhoohi46, Annette Pisanski41, Katarzyna Pisanski4, Katarzyna Pisanski47, Edna Lúcia Tinoco Ponciano, Camelia Popa48, Pavol Prokop49, Pavol Prokop50, Muhammad Rizwan, Mario Sainz51, Svjetlana Salkičević31, Ruta Sargautyte45, Ivan Sarmány-Schuller50, Susanne Schmehl43, Shivantika Sharad40, Razi Sultan Siddiqui52, Franco Simonetti53, Stanislava Stoyanova54, Meri Tadinac31, Marco Antonio Correa Varella55, Christin-Melanie Vauclair26, Luis Diego Vega, Dwi Ajeng Widarini, Gyesook Yoo56, Marta Zat’ková, Maja Zupančič57 
University of California, Santa Barbara1, University of Texas at Austin2, Dresden University of Technology3, University of Wrocław4, Opole University5, University of Tartu6, Gulu University7, Middle East University8, Stockholm University9, University of the Punjab10, University of Nigeria, Nsukka11, Istanbul University12, Franklin & Marshall College13, Norwegian University of Science and Technology14, University of Algiers15, Australian National University16, Russian State University for the Humanities17, Russian Academy of Sciences18, İzmir University of Economics19, University of Social Sciences and Humanities20, Université catholique de Louvain21, Ankara University22, Pontifical Catholic University of Peru23, Cumhuriyet University24, University of the Republic25, ISCTE – University Institute of Lisbon26, The Chinese University of Hong Kong27, National Autonomous University of Mexico28, University of Pécs29, University of Maribor30, University of Zagreb31, University of Malaya32, Central University of Finance and Economics33, University of Crete34, University of Primorska35, University of Amsterdam36, Catholic University of the Sacred Heart37, VU University Amsterdam38, University of Granada39, University of Delhi40, University of Havana41, Pontifical Catholic University of Rio de Janeiro42, University of Vienna43, Universiti Utara Malaysia44, Vilnius University45, University of British Columbia46, Centre national de la recherche scientifique47, Romanian Academy48, Comenius University in Bratislava49, Slovak Academy of Sciences50, University of Monterrey51, DHA Suffa University52, Pontifical Catholic University of Chile53, South-West University "Neofit Rilski"54, University of São Paulo55, Kyung Hee University56, University of Ljubljana57
TL;DR: Using a new 45-country sample (N = 14,399), this work attempted to replicate classic studies and test both the evolutionary and biosocial role perspectives, finding neither pathogen prevalence nor gender equality robustly predicted sex differences or preferences across countries.
Abstract: Considerable research has examined human mate preferences across cultures, finding universal sex differences in preferences for attractiveness and resources as well as sources of systematic cultural variation. Two competing perspectives-an evolutionary psychological perspective and a biosocial role perspective-offer alternative explanations for these findings. However, the original data on which each perspective relies are decades old, and the literature is fraught with conflicting methods, analyses, results, and conclusions. Using a new 45-country sample (N = 14,399), we attempted to replicate classic studies and test both the evolutionary and biosocial role perspectives. Support for universal sex differences in preferences remains robust: Men, more than women, prefer attractive, young mates, and women, more than men, prefer older mates with financial prospects. Cross-culturally, both sexes have mates closer to their own ages as gender equality increases. Beyond age of partner, neither pathogen prevalence nor gender equality robustly predicted sex differences or preferences across countries.

129 citations

Journal ArticleDOI
TL;DR: An energy harvester is designed, optimized, fabricated, and characterized for energy harvesting and IoT applications which simply recycles radio-frequency energy at 2.4 GHz, from nearby Wi-Fi/WLAN devices and converts them to useful dc power.
Abstract: Traditionally employed human-to-human and human-to-machine communication has recently been replaced by a new trend known as the Internet of things (IoT). IoT enables device-to-device communication without any human intervention, hence, offers many challenges. In this paradigm, machine’s self-sustainability due to limited energy capabilities presents a great challenge. Therefore, this paper proposed a low-cost energy harvesting device using rectenna to mitigate the problem in the areas where battery constraint issues arise. So, an energy harvester is designed, optimized, fabricated, and characterized for energy harvesting and IoT applications which simply recycles radio-frequency (RF) energy at 2.4 GHz, from nearby Wi-Fi/WLAN devices and converts them to useful dc power. The physical model comprises of antenna, filters, rectifier, and so on. A rectangular patch antenna is designed and optimized to resonate at 2.4 GHz using the well-known transmission-line model while the band-pass and low-pass filters are designed using lumped components. Schottky diode (HSMS-2820) is used for rectification. The circuit is designed and fabricated using the low-cost FR4 substrate ( ${h}$ = 16 mm and $\varepsilon _{r} = 4.6$ ) having the fabricated dimensions of 285 mm $\times \,\,90$ mm. Universal software radio peripheral and GNU Radio are employed to measure the received RF power, while similar measurements are carried out using R&S spectrum analyzer for validation. The received measured power is −64.4 dBm at the output port of the rectenna circuit. Hence, our design enables a pervasive deployment of self-operable next-generation IoT devices.

81 citations

Journal ArticleDOI
TL;DR: Quality assessment using both quantitative evaluations and user studies suggests that the presented algorithm produces tone-mapped images that are visually pleasant and preserve details of the original image better than the existing methods.
Abstract: High-dynamic-range (HDR) images require tone mapping to be displayed properly on lower dynamic range devices. In this paper, a tone-mapping algorithm that uses histogram of luminance to construct a lookup table (LUT) for tone mapping is presented. Characteristics of the human visual system (HVS) are used to give more importance to visually distinguishable intensities while constructing the histogram bins. The method begins with constructing a histogram of the luminance channel, using bins that are perceived to be uniformly spaced by the HVS. Next, a refinement step is used, which removes the pixels from the bins that are indistinguishable by the HVS. Finally, the available display levels are distributed among the bins proportionate to the pixels counts thus giving due consideration to the visual contribution of each bin in the image. Quality assessment using both quantitative evaluations and user studies suggests that the presented algorithm produces tone-mapped images that are visually pleasant and preserve details of the original image better than the existing methods. Finally, implementation details of the algorithm on GPU for parallel processing are presented, which could achieve a significant gain in speed over CPU-based implementation.

73 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202125
202018
201927
201818
201714
201611