Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

About: Ghulam Ishaq Khan Institute of Engineering Sciences and Technology is a education organization based out in Topi, Pakistan. It is known for research contribution in the topics: Quantum efficiency & Diode. The organization has 618 authors who have published 940 publications receiving 10674 citations.

A flexible, printable, thin-film thermoelectric generator based on reduced graphene oxide–carbon nanotubes composites

Abstract: Thermoelectric energy harvesting is one of the keystones of modern green renewable energy generation. Unfortunately, most conventional state-of-the-art inorganic semiconductor thermoelectric generators are expensive, fragile, and not flexible. Considering these limitations, we developed a flexible printable thermoelectric generator (TEG) with both n-type and p-type organic composites of reduced graphene oxide, carbon nanotubes, poly(3,4-ethylenedixoythiphene)–polystyrene sulfate, and lead sulfide composite materials. We constructed a TEG of ten alternating n–p pairs as a prototype with an effective area of 1.4 cm2 each, which generated 13 mV thermovoltage at operating temperature difference of 77 °C. It demonstrates that its fabrication is scalable, printable, and relatively simple, and the resultant structure is flexible, conformal, and reconfigurable.

Refugees and Host Country Nexus: a Case Study of Pakistan

Abstract: The number of refugees in the world is on increase once again just like the 1990s. There is plentiful scholarly literature available on the nexus of refugees and host country. This particular study brings new insight into existing literature by focusing on Pakistan (the home of the second largest refugees by numbers) and employing the multi-criteria decision-making (MCDM) techniques such as analytic hierarchy process (AHP) and order of preference by similarity to ideal solution (TOPSIS) to determine how refugees influence host country, and how these influences vary among various refugee populations in the very same country while considering opinions of local communities and organizations dealing with these refugees. At the same time, using last 15 years’ data, the study forecasts the refugee number that may repatriate by 2018 using exponential smoothing technique. The study is useful for policy makers dealing with refugees, such as UNHCR or governments of refugee-hosting countries, and local populations of the host countries, and to the greater general readers having interest in this area.

Imagined character recognition through EEG signals using deep convolutional neural network

Abstract: Electroencephalography (EEG)-based brain computer interface (BCI) enables people to interact directly with computing devices through their brain signals. A BCI typically interprets EEG signals to reflect the user’s intent or other mental activity. Motor imagery (MI) is a commonly used technique in BCIs where a user is asked to imagine moving certain part of the body such as a hand or a foot. By correctly interpreting the signal, one can perform a multitude of tasks such as controlling wheel chair, playing computer games, or even typing text. However, the use of motor-imagery-based BCIs outside the laboratory environment is limited due to the lack of their reliability. This work focuses on another kind of mental imagery, namely, the visual imagery (VI). VI is the manipulation of visual information that comes from memory. This work presents a deep convolutional neural network (DCNN)–based system for the recognition of visual/mental imagination of English alphabets so as to enable typing directly via brain signals. The DCNN learns to extract the spatial features hidden in the EEG signal. As opposed to many deep neural networks that use raw EEG signals for classification, this work transforms the raw signals into band powers using Morlet wavelet transformation. The proposed approach is evaluated on two publicly available benchmark MI-EEG datasets and a visual imagery dataset specifically collected for this work. The obtained results demonstrate that the proposed model performs better than the existing state-of-the-art methods for MI-EEG classification and yields an average accuracy of 99.45% on the two public MI-EEG datasets. The model also achieves an average recognition rate of 95.2% for the 26 English-language alphabets. Overall working of the proposed solution for imagined character recognition through EEG signals

A grade point average assessment of analytical and numerical methods for parameter extraction of a practical PV device

Abstract: This paper presents a comprehensive evaluation and ranking mechanism of analytical and meta-heuristic algorithms for the extraction of parameters of a practical PV device. A total of 10 algorithms are analyzed on single and two diode PV device models to extract the series and shunt resistances R s and R s h , respectively. As a benchmark, the commercially available Mono-Crystalline Sanyo-HIT215 and Multi-Crystalline Kyocera-KC200GT PV module were selected. The resulting parameters have been characterized under different values of temperature and irradiance. The I–V curves obtained using these algorithms are retrofitted on the I–V curves provided by the manufacturer. The evaluation is further enhanced by calculating the total error, fitness value, convergence time, and standard deviation in terms of error and time. Furthermore, a cumulative grade point average mechanism is introduced to rank the studied algorithms.

Real-Time Vehicle Recognition and Improved Traffic Congestion Resolution

Abstract: An intelligent traffic management system (E-Traffic Warden) is proposed, using image processing techniques along with smart traffic control algorithm. Traffic recognition was achieved using cascade classifier for vehicle recognition utilizing Open CV and Visual Studio C/C++. The classifier was trained on 700 positive samples and 1140 negative samples. The results show that the accuracy of vehicle detection is approximately 93 percent. The count of vehicles at all approaches of intersection is used to estimate traffic. Traffic build up is then avoided or resolved by passing the extracted data to traffic control algorithm. The control algorithm shows approximately 86% improvement over Fixed-Delay controller in worst case scenarios.